tcsproject.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1NEhDDqc7x89YBp-FGDDkJI8BwSEnksAs",
      "authorship_tag": "ABX9TyOSdjPc8VS1uovzKuI2qtjY",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/josmyrose/Assignment1/blob/main/tcsproject.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7sOflD0U2Snw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "jnOhWgt3RE8I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ecb0cf6-2e46-41f9-8de1-6fe0b41f767c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/tcs project/MobileDataSets/MobileDataSets/MobileTrain.csv')"
      ],
      "metadata": {
        "id": "A1hoVTWL2dGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Information about dataset"
      ],
      "metadata": {
        "id": "4_yGiq8R2yOP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "ZNSYzJPM211d",
        "outputId": "b51d6dc1-a8c5-4531-fb25-a6a21f70b5b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
              "0            842     0          2.2         0   1       0           7    0.6   \n",
              "1           1021     1          0.5         1   0       1          53    0.7   \n",
              "2            563     1          0.5         1   2       1          41    0.9   \n",
              "3            615     1          2.5         0   0       0          10    0.8   \n",
              "4           1821     1          1.2         0  13       1          44    0.6   \n",
              "\n",
              "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
              "0        188        2  ...         20       756  2549     9     7         19   \n",
              "1        136        3  ...        905      1988  2631    17     3          7   \n",
              "2        145        5  ...       1263      1716  2603    11     2          9   \n",
              "3        131        6  ...       1216      1786  2769    16     8         11   \n",
              "4        141        2  ...       1208      1212  1411     8     2         15   \n",
              "\n",
              "   three_g  touch_screen  wifi  price_range  \n",
              "0        0             0     1            1  \n",
              "1        1             1     0            2  \n",
              "2        1             1     0            2  \n",
              "3        1             0     0            2  \n",
              "4        1             1     0            1  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-44206ab9-663b-435f-af64-2ddc308e32fe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>...</th>\n",
              "      <th>px_height</th>\n",
              "      <th>px_width</th>\n",
              "      <th>ram</th>\n",
              "      <th>sc_h</th>\n",
              "      <th>sc_w</th>\n",
              "      <th>talk_time</th>\n",
              "      <th>three_g</th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
              "      <td>0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>20</td>\n",
              "      <td>756</td>\n",
              "      <td>2549</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>905</td>\n",
              "      <td>1988</td>\n",
              "      <td>2631</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1263</td>\n",
              "      <td>1716</td>\n",
              "      <td>2603</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>1216</td>\n",
              "      <td>1786</td>\n",
              "      <td>2769</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1208</td>\n",
              "      <td>1212</td>\n",
              "      <td>1411</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44206ab9-663b-435f-af64-2ddc308e32fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-44206ab9-663b-435f-af64-2ddc308e32fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44206ab9-663b-435f-af64-2ddc308e32fe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-r5jHHsu29GR",
        "outputId": "4069caad-9414-46c1-d29d-7315edff2396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   battery_power  2000 non-null   int64  \n",
            " 1   blue           2000 non-null   int64  \n",
            " 2   clock_speed    2000 non-null   float64\n",
            " 3   dual_sim       2000 non-null   int64  \n",
            " 4   fc             2000 non-null   int64  \n",
            " 5   four_g         2000 non-null   int64  \n",
            " 6   int_memory     2000 non-null   int64  \n",
            " 7   m_dep          2000 non-null   float64\n",
            " 8   mobile_wt      2000 non-null   int64  \n",
            " 9   n_cores        2000 non-null   int64  \n",
            " 10  pc             2000 non-null   int64  \n",
            " 11  px_height      2000 non-null   int64  \n",
            " 12  px_width       2000 non-null   int64  \n",
            " 13  ram            2000 non-null   int64  \n",
            " 14  sc_h           2000 non-null   int64  \n",
            " 15  sc_w           2000 non-null   int64  \n",
            " 16  talk_time      2000 non-null   int64  \n",
            " 17  three_g        2000 non-null   int64  \n",
            " 18  touch_screen   2000 non-null   int64  \n",
            " 19  wifi           2000 non-null   int64  \n",
            " 20  price_range    2000 non-null   int64  \n",
            "dtypes: float64(2), int64(19)\n",
            "memory usage: 328.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ne6Fa2AW3Exq",
        "outputId": "864604a8-17b8-434d-bd2f-d55e5423a6bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHXBk5MD4FLA",
        "outputId": "8dade193-696a-4a95-ca1b-cda9c6dd595e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "battery_power    1094\n",
              "blue                2\n",
              "clock_speed        26\n",
              "dual_sim            2\n",
              "fc                 20\n",
              "four_g              2\n",
              "int_memory         63\n",
              "m_dep              10\n",
              "mobile_wt         121\n",
              "n_cores             8\n",
              "pc                 21\n",
              "px_height        1137\n",
              "px_width         1109\n",
              "ram              1562\n",
              "sc_h               15\n",
              "sc_w               19\n",
              "talk_time          19\n",
              "three_g             2\n",
              "touch_screen        2\n",
              "wifi                2\n",
              "price_range         4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()#There is no null value."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3j2wyEx3Nz9",
        "outputId": "1ee755a9-84ef-4934-8599-da746c1ebe44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "battery_power    0\n",
              "blue             0\n",
              "clock_speed      0\n",
              "dual_sim         0\n",
              "fc               0\n",
              "four_g           0\n",
              "int_memory       0\n",
              "m_dep            0\n",
              "mobile_wt        0\n",
              "n_cores          0\n",
              "pc               0\n",
              "px_height        0\n",
              "px_width         0\n",
              "ram              0\n",
              "sc_h             0\n",
              "sc_w             0\n",
              "talk_time        0\n",
              "three_g          0\n",
              "touch_screen     0\n",
              "wifi             0\n",
              "price_range      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis on the Dataset**"
      ],
      "metadata": {
        "id": "M-cphNxH7kKe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "sns.barplot(y=\"battery_power\", x=\"price_range\", data=df)"
      ],
      "metadata": {
        "id": "gyUGQxzD7nY_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "e93082d0-25de-41dc-8960-e40b6b6e889f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f27c6285e80>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Countplot of each column from the dataset"
      ],
      "metadata": {
        "id": "VSdCD6JyU7NS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df:\n",
        "    if (df[i].nunique())<=30:\n",
        "        sns.countplot(x=df[i])\n",
        "        plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "566BPqBHO0Ru",
        "outputId": "030cdb99-98cf-4d2e-e096-00e18d71b332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAO0UlEQVR4nO3dfayed13H8fdnK2MOHOvWkznaYhdZkMUn5hGKS4yhBLapdCIQUFzFJtU4eXBGqf7hDMYEIjoH6JKGjbVKkDnQVULEZTAJCpNTIGOskp1MR9t064GN8ZSJ1a9/nF/DTW37u7ud+6G736/kzvk9Xdf5NjnpJ9fDfV2pKiRJOpHTJl2AJGn6GRaSpC7DQpLUZVhIkroMC0lS16pJFzAKa9asqQ0bNky6DEk6pezZs+fLVTV3rLknZVhs2LCBhYWFSZchSaeUJA8cb87TUJKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1jSwsktyU5FCSewbGzk1ye5L72s/VbTxJ3pFkMcndSS4Z2GZLW39fki2jqleSdHyj/Ab3zcC7gF0DY9uBO6rqrUm2t/6bgcuBi9rnBcANwAuSnAtcC8wDBexJsruqHhlh3QD8+O/s6i/SzNnzJ1dNugRpIkZ2ZFFVHwcePmp4M7CztXcCVw6M76plnwLOSXIB8FLg9qp6uAXE7cBlo6pZknRs475mcX5VHWztB4HzW3stsG9g3f42drzx/yfJtiQLSRaWlpZWtmpJmnETu8Bdyy//XrEXgFfVjqqar6r5ubljPjRRkvQ4jfupsw8luaCqDrbTTIfa+AFg/cC6dW3sAPDTR43fOYY6pan1pbf88KRL0BR61h98fqT7H/eRxW7gyB1NW4DbBsavandFbQQebaerPgK8JMnqdufUS9qYJGmMRnZkkeR9LB8VrEmyn+W7mt4K3JJkK/AA8Kq2/MPAFcAi8C3gdQBV9XCSPwI+3da9paqOvmguSRqxkYVFVb3mOFObjrG2gKuPs5+bgJtWsDRJ0knyG9ySpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdU0kLJL8VpIvJLknyfuSnJnkwiR3JVlM8v4kZ7S1T239xTa/YRI1S9IsG3tYJFkLvAGYr6ofAk4HXg28Dbiuqp4NPAJsbZtsBR5p49e1dZKkMZrUaahVwPckWQWcBRwEXgTc2uZ3Ale29ubWp81vSpIx1ipJM2/sYVFVB4C3A19iOSQeBfYAX62qw23ZfmBta68F9rVtD7f15x293yTbkiwkWVhaWhrtP0KSZswkTkOtZvlo4ULgmcDTgMue6H6rakdVzVfV/Nzc3BPdnSRpwCROQ70Y+I+qWqqq/wY+CFwKnNNOSwGsAw609gFgPUCbfwbwlfGWLEmzbRJh8SVgY5Kz2rWHTcC9wMeAV7Q1W4DbWnt369PmP1pVNcZ6JWnmTeKaxV0sX6j+DPD5VsMO4M3ANUkWWb4mcWPb5EbgvDZ+DbB93DVL0qxb1V+y8qrqWuDao4bvB55/jLWPAa8cR12SpGPzG9ySpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdU0kLJKck+TWJP+eZG+SFyY5N8ntSe5rP1e3tUnyjiSLSe5OcskkapakWTapI4vrgX+sqh8EfhTYC2wH7qiqi4A7Wh/gcuCi9tkG3DD+ciVpto09LJI8A/gp4EaAqvp2VX0V2AzsbMt2Ale29mZgVy37FHBOkgvGXLYkzbRJHFlcCCwB70ny2STvTvI04PyqOtjWPAic39prgX0D2+9vY98lybYkC0kWlpaWRli+JM2eSYTFKuAS4Iaqeh7wTb5zygmAqiqgTmanVbWjquaran5ubm7FipUkTSYs9gP7q+qu1r+V5fB46MjppfbzUJs/AKwf2H5dG5MkjclQYZHkjmHGhlFVDwL7kjynDW0C7gV2A1va2BbgttbeDVzV7oraCDw6cLpKkjQGq040meRM4CxgTbuVNW3qbI5x3eAkvB54b5IzgPuB17EcXLck2Qo8ALyqrf0wcAWwCHyrrZUkjdEJwwL4NeBNwDOBPXwnLL4GvOvx/tKq+hwwf4ypTcdYW8DVj/d3SZKeuBOGRVVdD1yf5PVV9c4x1SRJmjK9IwsAquqdSX4S2DC4TVXtGlFdkqQpMlRYJPkr4AeAzwH/04YLMCwkaQYMFRYsX1+4uF0/kCTNmGG/Z3EP8H2jLESSNL2GPbJYA9yb5N+A/zoyWFUvG0lVkqSpMmxY/OEoi5AkTbdh74b651EXIkmaXsPeDfV1vvNgvzOApwDfrKqzR1WYJGl6DHtk8b1H2knC8jsmNo6qKEnSdDnpp862lxD9PfDSEdQjSZpCw56GevlA9zSWv3fx2EgqkiRNnWHvhvq5gfZh4D9ZPhUlSZoBw16z8LHgkjTDhn350bokf5fkUPt8IMm6URcnSZoOw17gfg/Lb6x7Zvv8QxuTJM2AYcNirqreU1WH2+dmYG6EdUmSpsiwYfGVJK9Ncnr7vBb4yigLkyRNj2HD4ldZfif2g8BB4BXAr4yoJknSlBn21tm3AFuq6hGAJOcCb2c5RCRJT3LDHln8yJGgAKiqh4HnjaYkSdK0GTYsTkuy+kinHVkMe1QiSTrFDfsf/p8Cn0zyt63/SuCPR1OSJGnaDPsN7l1JFoAXtaGXV9W9oytLkjRNhj6V1MLBgJCkGXTSjyiXJM0ew0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpa2Jh0R51/tkkH2r9C5PclWQxyfuTnNHGn9r6i21+w6RqlqRZNckjizcCewf6bwOuq6pnA48AW9v4VuCRNn5dWydJGqOJhEV7f/fPAO9u/bD8KJFb25KdwJWtvbn1afOb2npJ0phM6sjiz4HfBf639c8DvlpVh1t/P7C2tdcC+wDa/KNt/XdJsi3JQpKFpaWlUdYuSTNn7GGR5GeBQ1W1ZyX3W1U7qmq+qubn5nw9uCStpEm8k+JS4GVJrgDOBM4GrgfOSbKqHT2sAw609QeA9cD+JKuAZ+D7vyVprMZ+ZFFVv1dV66pqA/Bq4KNV9UvAx1h+tzfAFuC21t7d+rT5j1ZVjbFkSZp50/Q9izcD1yRZZPmaxI1t/EbgvDZ+DbB9QvVJ0sya6KtRq+pO4M7Wvh94/jHWPMbym/kkSRMyTUcWkqQpZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqSusYdFkvVJPpbk3iRfSPLGNn5uktuT3Nd+rm7jSfKOJItJ7k5yybhrlqRZN4kji8PAb1fVxcBG4OokFwPbgTuq6iLgjtYHuBy4qH22ATeMv2RJmm1jD4uqOlhVn2ntrwN7gbXAZmBnW7YTuLK1NwO7atmngHOSXDDmsiVppk30mkWSDcDzgLuA86vqYJt6EDi/tdcC+wY229/Gjt7XtiQLSRaWlpZGVrMkzaKJhUWSpwMfAN5UVV8bnKuqAupk9ldVO6pqvqrm5+bmVrBSSdJEwiLJU1gOivdW1Qfb8ENHTi+1n4fa+AFg/cDm69qYJGlMJnE3VIAbgb1V9WcDU7uBLa29BbhtYPyqdlfURuDRgdNVkqQxWDWB33kp8MvA55N8ro39PvBW4JYkW4EHgFe1uQ8DVwCLwLeA1423XEnS2MOiqj4B5DjTm46xvoCrR1qUJOmE/Aa3JKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2nTFgkuSzJF5MsJtk+6XokaZacEmGR5HTgL4DLgYuB1yS5eLJVSdLsOCXCAng+sFhV91fVt4G/ATZPuCZJmhmrJl3AkNYC+wb6+4EXDC5Isg3Y1rrfSPLFMdU2C9YAX550EdMgb98y6RL03fzbPOLarMRevv94E6dKWHRV1Q5gx6TreDJKslBV85OuQzqaf5vjc6qchjoArB/or2tjkqQxOFXC4tPARUkuTHIG8Gpg94RrkqSZcUqchqqqw0l+E/gIcDpwU1V9YcJlzRJP72la+bc5JqmqSdcgSZpyp8ppKEnSBBkWkqQuw0In5GNWNI2S3JTkUJJ7Jl3LrDAsdFw+ZkVT7GbgskkXMUsMC52Ij1nRVKqqjwMPT7qOWWJY6ESO9ZiVtROqRdIEGRaSpC7DQifiY1YkAYaFTszHrEgCDAudQFUdBo48ZmUvcIuPWdE0SPI+4JPAc5LsT7J10jU92fm4D0lSl0cWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiykFZJkw7GegprkziTzk6hJWimGhSSpy7CQVtaqJO9NsjfJrUnOGpxM8o2B9iuS3Nzac0k+kOTT7XPpmOuWTsiwkFbWc4C/rKrnAl8DfmPI7a4HrquqnwB+AXj3iOqTHpdVky5AepLZV1X/0tp/DbxhyO1eDFyc5Ej/7CRPr6pvnGAbaWwMC2llHf38nBP1zxxonwZsrKrHRlKV9AR5GkpaWc9K8sLW/kXgE0fNP5TkuUlOA35+YPyfgNcf6ST5sdGWKZ0cw0JaWV8Erk6yF1gN3HDU/HbgQ8C/AgcHxt8AzCe5O8m9wK+Po1hpWD51VpLU5ZGFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnq+j/URWwDK9zORwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVvklEQVR4nO3df5hdBX3n8fcHIir4AzBjjIQ2rKJPKY8rGilbf9QlrgJSgogsVm0EXKorKtqtgO5Tbfu4D1StWt2lSwWMiigCArJVoRS12y1ogqAB/IEKkmxIpv5e3ZVGv/vHOTlO40xyz2Xu3Enm/Xqeeeacc8/3nu/MnLmfe37eVBWSJAHsMe4GJEnzh6EgSeoYCpKkjqEgSeoYCpKkzqJxN/BALF68uJYvXz7uNiRpl7Ju3bp/qqqJ6R7bpUNh+fLlrF27dtxtSNIuJck9Mz3m7iNJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUmdkVzQnuQg4FthSVYe2094O/C5wP/BN4JSq+kH72DnAacDPgddW1WdG1Zs0W4694uLeNde+8JQRdCLNjlFuKXwAOGq7adcDh1bVk4CvA+cAJDkEOBn4zbbmvyXZc4S9SZKmMbJQqKrPA9/bbtp1VbW1Hb0JWNYOrwI+WlU/q6pvA3cBh4+qN0nS9MZ5TOFU4FPt8AHAvVMe29BO+xVJTk+yNsnaycnJEbcoSQvLWEIhyZuBrcAlfWur6oKqWlFVKyYmpr3zqyRpSHN+6+wkL6c5AL2yqqqdvBE4cMpsy9pp2s2d8ontDzvt3MUv+PQIOpEEc7ylkOQo4I3AcVX10ykPXQOcnOTBSQ4CDga+MJe9SZJGe0rqpcCzgcVJNgBvoTnb6MHA9UkAbqqqV1bV7UkuA+6g2a306qr6+ah6kyRNb2ShUFUvnmbyhTuY/23A20bVjyRp57yiWZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ05v/eRNJ88/8p39675HyecOYJOpPnBLQVJUsdQkCR13H0kSbuoLe/r91H2jz7jeTudxy0FSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHK5qBzef/l17zL3nVm0bUibRree0n7u1d85cvOHAEnWi2uKUgSeoYCpKkjqEgSeqMLBSSXJRkS5L1U6btn+T6JN9ov+/XTk+Sv0xyV5IvJ3nKqPqSJM1slAeaPwC8D/jglGlnAzdU1blJzm7HzwKOBg5uv34LOL/9PpDJ8z/cu7mJV720d40k7e5GtqVQVZ8Hvrfd5FXAmnZ4DXD8lOkfrMZNwL5Jlo6qN0nS9Ob6mMKSqtrUDt8HLGmHDwCmntu2oZ32K5KcnmRtkrWTk5Oj61SSFqCxHWiuqgJqiLoLqmpFVa2YmJgYQWeStHDN9cVrm5MsrapN7e6hLe30jcDUK1qWtdN2Cd9436pe8x98xtUj6kRSH+v/++beNYf+wZKdz7QLm+sthWuA1e3wauDqKdN/vz0L6Qjgh1N2M0mS5sjIthSSXAo8G1icZAPwFuBc4LIkpwH3ACe1s/8NcAxwF/BT4JRR9SVJmtnIQqGqXjzDQyunmbeAV4+qF0nSYLyiWZLUMRQkSR1DQZLU8fMUdgNXXXR0r/mPP/VTs7bs8z76vN41Z538mVlbvqTZ5ZaCJKljKEiSOu4+mgf+/q+P7TX/M//DtSPqRNJC55aCJKnjloKksVlzZf87Ha8+wRthjpJbCpKkjqEgSeq4+0jaha26vP81H1ef2P/aEi0cbilIkjpuKWiXdsxVZ/eu+Zvjzx1BJ9LuwVCQtMu64SP9z15a+XuevbQj7j6SJHUMBUlSx1CQJHUMBUlSx1CQJHU8+0hawF54xRd711zxwqeNoBPNF24pSJI6hoIkqePuI0kak83v7rf7bsmZo99155aCJKkzli2FJK8HXgEU8BXgFGAp8FHgUcA64GVVdf84+pOkQdz39nt61zzmj359BJ3MnjnfUkhyAPBaYEVVHQrsCZwMnAe8q6oeD3wfOG2ue5OkhW5cu48WAQ9NsgjYG9gEHAlc3j6+Bjh+TL1J0oI156FQVRuBdwDfoQmDH9LsLvpBVW1tZ9sAHDBdfZLTk6xNsnZysv8dEiVJMxvH7qP9gFXAQcBjgX2Aowatr6oLqmpFVa2YmPAWuJI0m8ZxoPk5wLerahIgyZXA04F9kyxqtxaWARvH0Js0p469/OO9a6498UUj6ERqjOOYwneAI5LsnSTASuAO4EbgxHae1cDVY+hNkha0cRxTuJnmgPItNKej7gFcAJwFvCHJXTSnpV44171J0kI3lusUquotwFu2m/wt4PAxtCNJanlFsySpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjoDhUKSGwaZJknate3w3kdJHkLzyWiL289BSPvQI5jhQ3AkSbuund0Q7w+AM2k+DGcdvwyFHwHvG2FfkqQx2GEoVNV7gPckeU1VvXeOepIkjclAt86uqvcm+W1g+dSaqvrgiPqSJI3BQKGQ5EPA44BbgZ+3kwswFCRpNzLoh+ysAA6pqhplM5Kk8Rr0OoX1wGNG2YgkafwG3VJYDNyR5AvAz7ZNrKrjRtKVJGksBg2Ft46yCUnS/DDo2UefG3UjkqTxG/Tsox/TnG0EsBfwIOAnVfWIUTUmSZp7g24pPHzbcJIAq4AjRtWUJGk8et8ltRpXAc8bQT+SpDEadPfRCVNG96C5buH/jaQjSdLYDHr20e9OGd4K3E2zC2koSfYF3g8cSnOs4lTga8DHaG6lcTdwUlV9f9hlSJL6G/SYwimzvNz3AJ+uqhOT7EVze+43ATdU1blJzgbOBs6a5eVKknZg0A/ZWZbkE0m2tF9XJFk2zAKTPBJ4FnAhQFXdX1U/oNnyWNPOtgY4fpjnlyQNb9ADzRcD19B8rsJjgU+204ZxEDAJXJzkS0nen2QfYElVbWrnuQ9YMl1xktOTrE2ydnJycsgWJEnTGTQUJqrq4qra2n59AJgYcpmLgKcA51fVYcBPaHYVddob7017872quqCqVlTViomJYVuQJE1n0APN303yUuDSdvzFwHeHXOYGYENV3dyOX04TCpuTLK2qTUmWAluGfH71dOEHn9tr/tN+/7oRdSJp3AbdUjgVOIlmt84m4ETg5cMssKruA+5N8sR20krgDprdU6vbaauBq4d5fknS8AbdUvhTYPW2U0ST7A+8gyYshvEa4JL2zKNvAafQBNRlSU4D7qEJIUnSHBo0FJ409ZqBqvpeksOGXWhV3UpzAdz2Vg77nJKkB27Q3Ud7JNlv20i7pTBooEiSdhGDvrC/E/jHJB9vx18EvG00LUmSxmXQK5o/mGQtcGQ76YSqumN0bUmSxmHgXUBtCBgEkrQb633rbEnS7stQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUmdsoZBkzyRfSnJtO35QkpuT3JXkY0n2GldvkrRQjXNL4XXAnVPGzwPeVVWPB74PnDaWriRpARtLKCRZBjwfeH87HuBI4PJ2ljXA8ePoTZIWsnFtKbwbeCPwi3b8UcAPqmprO74BOGC6wiSnJ1mbZO3k5OToO5WkBWTOQyHJscCWqlo3TH1VXVBVK6pqxcTExCx3J0kL26IxLPPpwHFJjgEeAjwCeA+wb5JF7dbCMmDjGHqTpAVtzrcUquqcqlpWVcuBk4G/q6qXADcCJ7azrQaunuveJGmhm0/XKZwFvCHJXTTHGC4ccz+StOCMY/dRp6o+C3y2Hf4WcPg4+5GkhW4+bSlIksbMUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVJnzkMhyYFJbkxyR5Lbk7yunb5/kuuTfKP9vt9c9yZJC904thS2An9YVYcARwCvTnIIcDZwQ1UdDNzQjkuS5tCch0JVbaqqW9rhHwN3AgcAq4A17WxrgOPnujdJWujGekwhyXLgMOBmYElVbWofug9YMkPN6UnWJlk7OTk5J31K0kIxtlBI8jDgCuDMqvrR1MeqqoCarq6qLqiqFVW1YmJiYg46laSFYyyhkORBNIFwSVVd2U7enGRp+/hSYMs4epOkhWwcZx8FuBC4s6r+YspD1wCr2+HVwNVz3ZskLXSLxrDMpwMvA76S5NZ22puAc4HLkpwG3AOcNIbeJGlBm/NQqKr/CWSGh1fOZS+SpH/JK5olSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSZ15FwpJjkrytSR3JTl73P1I0kIyr0IhyZ7AfwWOBg4BXpzkkPF2JUkLx7wKBeBw4K6q+lZV3Q98FFg15p4kacFIVY27h06SE4GjquoV7fjLgN+qqjOmzHM6cHo7+kTgazt4ysXAPz2Alqy3flet35V7t3709b9eVRPTPbDoASx0LKrqAuCCQeZNsraqVgy7LOut31Xrd+XerR9v/XzbfbQROHDK+LJ2miRpDsy3UPgicHCSg5LsBZwMXDPmniRpwZhXu4+qamuSM4DPAHsCF1XV7Q/gKQfazWS99bth/a7cu/VjrJ9XB5olSeM133YfSZLGyFCQJHV221B4ILfLSHJRki1J1g+57AOT3JjkjiS3J3ldz/qHJPlCktva+j8Zooc9k3wpybV9a9v6u5N8JcmtSdb2rN03yeVJvprkziT/pkftE9tlbvv6UZIzey7/9e3vbX2SS5M8pGf969ra2wdZ9nTrS5L9k1yf5Bvt9/161r+oXf4vkuzw1MIZ6t/e/v6/nOQTSfbtWf9nbe2tSa5L8tg+9VMe+8MklWRxz+W/NcnGKevBMX2Xn+Q17e/g9iR/3nP5H5uy7LuT3Nqz/slJbtr2/5Pk8J71/zrJP7b/g59M8ogZaqd9remz/v2KqtrtvmgOUn8T+FfAXsBtwCE96p8FPAVYP+TylwJPaYcfDny95/IDPKwdfhBwM3BEzx7eAHwEuHbIn+FuYPGQtWuAV7TDewH7PoC/4300F9oMWnMA8G3goe34ZcDLe9QfCqwH9qY5EeNvgcf3XV+APwfObofPBs7rWf8bNBdnfhZYMcTynwssaofPG2L5j5gy/Frgr/rUt9MPpDlp5J4drUszLP+twH8a8G82Xf2/bf92D27HH923/ymPvxP4457Lvw44uh0+Bvhsz/ovAr/TDp8K/NkMtdO+1vRZ/7b/2l23FB7Q7TKq6vPA94ZdeFVtqqpb2uEfA3fSvFgNWl9V9X/a0Qe1XwOfEZBkGfB84P0DNz1LkjySZiW/EKCq7q+qHwz5dCuBb1bVPT3rFgEPTbKI5sX9f/eo/Q3g5qr6aVVtBT4HnLCjghnWl1U04Uj7/fg+9VV1Z1Xt6Gr9ndVf1/YPcBPNNT996n80ZXQfdrD+7eD/5V3AG3dUu5P6gcxQ/yrg3Kr6WTvPlmGWnyTAScClPesL2Pbu/pHsYB2cof4JwOfb4euBF85QO9NrzcDr3/Z211A4ALh3yvgGerwoz6Yky4HDaN7t96nbs91k3QJcX1V96t9N88/4iz7L3E4B1yVZl+bWIoM6CJgELm53X70/yT5D9nAyO/hnnE5VbQTeAXwH2AT8sKqu6/EU64FnJnlUkr1p3uUduJOa6Sypqk3t8H3AkiGeY7acCnyqb1GStyW5F3gJ8Mc9a1cBG6vqtr7LneKMdhfWRb12fzSeQPN3vDnJ55I8bcgenglsrqpv9Kw7E3h7+/t7B3BOz/rb+eUb2RcxwDq43WvN0Ovf7hoK80KShwFXAGdu985rp6rq51X1ZJp3eIcnOXTAZR4LbKmqdb0b/peeUVVPoblj7auTPGvAukU0m8LnV9VhwE9oNl97SXPx4nHAx3vW7Ufzz3QQ8FhgnyQvHbS+qu6k2d1yHfBp4Fbg5316mOY5ix5berMpyZuBrcAlfWur6s1VdWBbe8bO5p+yzL2BN9EzSLZzPvA44Mk04f7OnvWLgP2BI4A/Ai5r3/X39WJ6vjFpvQp4ffv7ez3tlnMPpwL/Mck6mt1C9+9o5h291vRd/3bXUBj77TKSPIjmj3RJVV057PO0u15uBI4asOTpwHFJ7qbZbXZkkg8PsdyN7fctwCdodskNYgOwYcqWzeU0IdHX0cAtVbW5Z91zgG9X1WRV/TNwJfDbfZ6gqi6sqqdW1bOA79Psp+1rc5KlAO33GXdfjEqSlwPHAi9pXxiGdQkz7L6YweNoQvm2dj1cBtyS5DGDPkFVbW7fGP0C+GsGX/+22QBc2e6K/QLNVvOMB7un0+5+PAH4WM9lA6ymWfegeWPTq/+q+mpVPbeqnkoTSt/cQZ/TvdYMvf7trqEw1ttltO9ILgTurKq/GKJ+YtvZIkkeCvw74KuD1FbVOVW1rKqW0/zcf1dVA79Tbpe5T5KHbxumOWg50JlYVXUfcG+SJ7aTVgJ39Fl+a9h3aN8Bjkiyd/t3WEmzn3VgSR7dfv81mheFjwzRxzU0Lwy0368e4jmGluQoml2Ix1XVT4eoP3jK6CoGXP8AquorVfXoqlrerocbaA6G3tdj+UunjL6AAde/Ka6iOdhMkifQnPDQ966jzwG+WlUbetZBcwzhd9rhI4Feu5+mrIN7AP8Z+KsZ5pvptWb49W/QI9K72hfNvuCv0yTsm3vWXkqzyfrPNCv0aT3rn0GzufZlmt0PtwLH9Kh/EvCltn49OzjzYSfP82yGOPuI5qyt29qv24f4/T0ZWNv2fxWwX8/6fYDvAo8c8uf+E5oXsfXAh2jPQOlR//c0QXYbsHKY9QV4FHADzYvB3wL796x/QTv8M2Az8Jme9XfRHFfbtv7t6Oyh6eqvaH9/XwY+CRww7P8LOzmTbYblfwj4Srv8a4ClPev3Aj7c/gy3AEf27R/4APDKIf/+zwDWtevQzcBTe9a/jub16+vAubR3n5imdtrXmj7r3/Zf3uZCktTZXXcfSZKGYChIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIsyDJ8vbe/Zek+QyJy9urqp+W5H+l+WyML2y7Ulyar7x4TZoF7R0qv01zI8F/SHIRzVXVrwT+fVV9sf2glG235JbmJbcUpNlzb1X9Qzv8YeB5wKaq+iI0n1FgIGi+MxSk2bP9Znev26VL84GhIM2eX8svP4/692g+8Wzptg94SfLw9nbM0rzlMQVpFrTHFD5Nc3fYp9LcZfVlwG8C7wUeCvxf4Dn1y49aleYdQ0GaBW0oXFtVA31CnjRfuftIktRxS0GS1HFLQZLUMRQkSR1DQZLUMRQkSR1DQZLU+f9KcqaYNnUe8wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARAklEQVR4nO3de7CdVX3G8e8jUfGCgnCkmEDjKGqZWhEiYnHUglKkrWEsWmyVVFLjtHhh7NjS/tFWR6c6tlLE1pERNVhveIU6jsogarUCJojcIjW1WpJBE7mK10J//WOvLLdwApsk79k553w/M3v2Wutd+92/2ZPkyXtPVSFJEsD9pl2AJGn3YShIkjpDQZLUGQqSpM5QkCR1S6ZdwM7Yb7/9avny5dMuQ5LmlfXr1/+gqmZmWzavQ2H58uWsW7du2mVI0ryS5LvbW+buI0lSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqRs0FJJ8J8lVSa5Isq6NPSLJhUm+1d73aeNJ8rYkG5NcmeSwIWuTJN3dXGwp/FZVHVpVK1r/dOCiqjoYuKj1AZ4LHNxea4B3zEFtkqQx09h9tBJY29prgRPGxs+tkUuAvZMcMIX6JGnRGvqK5gI+l6SAd1bV2cD+VXVDW/49YP/WXgpcP/bZTW3shrExkqxhtCXBQQcddI9ffvhrz93Z+heM9W85eafX8T+vf+IuqGRhOOhvrtrpdRx11lG7oJKF4Suv/MpOr+OLz3jmLqhkYXjml764w58dOhSeXlWbkzwSuDDJN8cXVlW1wJhYC5azAVasWOFj4yRpFxp091FVbW7vW4BPAEcA39+2W6i9b2nTNwMHjn18WRuTJM2RwUIhyUOS7LWtDRwLXA1cAKxq01YB57f2BcDJ7SykI4Fbx3YzSZLmwJC7j/YHPpFk2/d8oKo+k+RrwHlJVgPfBV7Y5n8aOB7YCPwYeOmAtUmSZjFYKFTVt4EnzTJ+I3DMLOMFnDpUPZKke+cVzZKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1A0eCkn2SPL1JJ9q/UcnuTTJxiQfTvKANv7A1t/Yli8fujZJ0i+biy2FVwMbxvpvBs6oqscCNwOr2/hq4OY2fkabJ0maQ4OGQpJlwO8A72r9AEcDH21T1gIntPbK1qctP6bNlyTNkaG3FP4J+Avg/1p/X+CWqrqj9TcBS1t7KXA9QFt+a5v/S5KsSbIuybqtW7cOWbskLTqDhUKS3wW2VNX6Xbneqjq7qlZU1YqZmZlduWpJWvSWDLjuo4DnJTke2BN4GHAmsHeSJW1rYBmwuc3fDBwIbEqyBHg4cOOA9UmS7mKwLYWq+quqWlZVy4GTgM9X1R8BFwMntmmrgPNb+4LWpy3/fFXVUPVJku5uGtcp/CXwmiQbGR0zOKeNnwPs28ZfA5w+hdokaVEbcvdRV1VfAL7Q2t8Gjphlzk+BF8xFPZKk2XlFsySpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYOFQpI9k1yW5BtJrknyujb+6CSXJtmY5MNJHtDGH9j6G9vy5UPVJkma3ZBbCj8Djq6qJwGHAsclORJ4M3BGVT0WuBlY3eavBm5u42e0eZKkOTRYKNTI7a17//Yq4Gjgo218LXBCa69sfdryY5JkqPokSXc36DGFJHskuQLYAlwI/BdwS1Xd0aZsApa29lLgeoC2/FZg3yHrkyT9skFDoarurKpDgWXAEcATdnadSdYkWZdk3datW3e6RknSL8zJ2UdVdQtwMfA0YO8kS9qiZcDm1t4MHAjQlj8cuHGWdZ1dVSuqasXMzMzgtUvSYjLk2UczSfZu7QcBzwE2MAqHE9u0VcD5rX1B69OWf76qaqj6JEl3t+Tep+ywA4C1SfZgFD7nVdWnklwLfCjJG4CvA+e0+ecA70uyEbgJOGnA2iRJs5goFJJcVFXH3NvYuKq6EnjyLOPfZnR84a7jPwVeMEk9kqRh3GMoJNkTeDCwX5J9gG2niD6MX5w1JElaIO5tS+HlwGnAo4D1/CIUbgPePmBdkqQpuMdQqKozgTOTvLKqzpqjmiRJUzLRMYWqOivJbwLLxz9TVecOVJckaQomPdD8PuAxwBXAnW24AENBkhaQSU9JXQEc4nUDkrSwTXrx2tXArwxZiCRp+ibdUtgPuDbJZYxuiQ1AVT1vkKokSVMxaSj83ZBFSJJ2D5OeffTFoQuRJE3fpGcf/ZDR2UYAD2D0wJwfVdXDhipMkjT3Jt1S2Gtbuz0NbSVw5FBFSZKm4z7fOrs9ZvOTwG8PUI8kaYom3X30/LHu/Rhdt/DTQSqSJE3NpGcf/d5Y+w7gO4x2IUmSFpBJjym8dOhCJEnTN9ExhSTLknwiyZb2+liSZUMXJ0maW5MeaH4Po2coP6q9/q2NSZIWkElDYaaq3lNVd7TXe4GZAeuSJE3BpKFwY5IXJ9mjvV4M3DhkYZKkuTdpKJwCvBD4HnADcCLwxwPVJEmakklPSX09sKqqbgZI8gjgHxiFhSRpgZh0S+E3tgUCQFXdBDx5mJIkSdMyaSjcL8k+2zptS2HSrQxJ0jwx6T/s/wh8NclHWv8FwBuHKUmSNC2TXtF8bpJ1wNFt6PlVde1wZUmSpmHiXUAtBAwCSVrA7vOtsyVJC5ehIEnqDAVJUmcoSJI6Q0GS1BkKkqRusFBIcmCSi5Ncm+SaJK9u449IcmGSb7X3fdp4krwtycYkVyY5bKjaJEmzG3JL4Q7gz6vqEOBI4NQkhwCnAxdV1cHARa0P8Fzg4PZaA7xjwNokSbMYLBSq6oaqury1fwhsAJYCK4G1bdpa4ITWXgmcWyOXAHsnOWCo+iRJdzcnxxSSLGd0V9VLgf2r6oa26HvA/q29FLh+7GOb2thd17Umybok67Zu3TpYzZK0GA0eCkkeCnwMOK2qbhtfVlUF1H1ZX1WdXVUrqmrFzIxPBJWkXWnQUEhyf0aB8P6q+ngb/v623ULtfUsb3wwcOPbxZW1MkjRHhjz7KMA5wIaqeuvYoguAVa29Cjh/bPzkdhbSkcCtY7uZJElzYMgH5RwFvAS4KskVbeyvgTcB5yVZDXyX0bOfAT4NHA9sBH4MvHTA2iRJsxgsFKrqy0C2s/iYWeYXcOpQ9UiS7p1XNEuSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUjdYKCR5d5ItSa4eG3tEkguTfKu979PGk+RtSTYmuTLJYUPVJUnaviG3FN4LHHeXsdOBi6rqYOCi1gd4LnBwe60B3jFgXZKk7RgsFKrqS8BNdxleCaxt7bXACWPj59bIJcDeSQ4YqjZJ0uzm+pjC/lV1Q2t/D9i/tZcC14/N29TG7ibJmiTrkqzbunXrcJVK0iI0tQPNVVVA7cDnzq6qFVW1YmZmZoDKJGnxmutQ+P623ULtfUsb3wwcODZvWRuTJM2huQ6FC4BVrb0KOH9s/OR2FtKRwK1ju5kkSXNkyVArTvJB4FnAfkk2AX8LvAk4L8lq4LvAC9v0TwPHAxuBHwMvHaouSdL2DRYKVfWi7Sw6Zpa5BZw6VC2SpMl4RbMkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSp261CIclxSa5LsjHJ6dOuR5IWm90mFJLsAfwz8FzgEOBFSQ6ZblWStLjsNqEAHAFsrKpvV9XPgQ8BK6dckyQtKqmqadcAQJITgeOq6k9a/yXAU6vqFXeZtwZY07qPB66b00J3zH7AD6ZdxALi77nr+FvuWvPl9/zVqpqZbcGSua5kZ1XV2cDZ067jvkiyrqpWTLuOhcLfc9fxt9y1FsLvuTvtPtoMHDjWX9bGJElzZHcKha8BByd5dJIHACcBF0y5JklaVHab3UdVdUeSVwCfBfYA3l1V10y5rF1lXu3umgf8PXcdf8tda97/nrvNgWZJ0vTtTruPJElTZihIkjpDYUDetmPXSvLuJFuSXD3tWua7JAcmuTjJtUmuSfLqadc0nyXZM8llSb7Rfs/XTbumHeUxhYG023b8J/AcYBOjs6teVFXXTrWweSzJM4DbgXOr6tenXc98luQA4ICqujzJXsB64AT/fO6YJAEeUlW3J7k/8GXg1VV1yZRLu8/cUhiOt+3YxarqS8BN065jIaiqG6rq8tb+IbABWDrdquavGrm9de/fXvPyf9yGwnCWAteP9TfhXzrthpIsB54MXDrdSua3JHskuQLYAlxYVfPy9zQUpEUsyUOBjwGnVdVt065nPquqO6vqUEZ3YzgiybzcxWkoDMfbdmi31vZ9fwx4f1V9fNr1LBRVdQtwMXDctGvZEYbCcLxth3Zb7cDoOcCGqnrrtOuZ75LMJNm7tR/E6ASTb063qh1jKAykqu4Att22YwNw3gK6bcdUJPkg8FXg8Uk2JVk97ZrmsaOAlwBHJ7mivY6fdlHz2AHAxUmuZPQfwgur6lNTrmmHeEqqJKlzS0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBWlMktcnefa065CmxesUpCbJHlV150DrXtIuaJR2a24paFFIsjzJN5O8P8mGJB9N8uAk30ny5iSXAy9I8t4kJ7bPPCXJf7QHp1yWZK92J8y3JPlakiuTvPwevvNZSf49yQXAtW3sk0nWtwexrBmbe3uSN7bvuiTJ/m38Ma1/VZI3JLl97DOvHatj3j7URbsXQ0GLyeOBf6mqXwNuA/6sjd9YVYdV1Ye2TWz3q/owowelPAl4NvATYDVwa1U9BXgK8LIkj76H7zysreNxrX9KVR0OrABelWTfNv4Q4JL2XV8CXtbGzwTOrKonMrr9+rb6jgUOZvTcjkOBw9tDiKSdYihoMbm+qr7S2v8KPL21PzzL3McDN1TV1wCq6ra2++dY4OR23/xLgX0Z/eO8PZdV1X+P9V+V5BvAJYzuorvtsz8Htt0rZz2wvLWfBnyktT8wtp5j2+vrwOXAE+6lDmkiS6ZdgDSH7noAbVv/R/dhHQFeWVWfnXB+X3eSZzHa4nhaVf04yReAPdvi/61fHOC7k3v/uxng76vqnZMWLk3CLQUtJgcleVpr/yGj5+huz3XAAUmeAtCOJyxhdNfbP23PIiDJ45I8ZMLvfzhwcwuEJwBHTvCZS4Dfb+2TxsY/C5zSHpJDkqVJHjlhHdJ2GQpaTK4DTk2yAdgHeMf2Jrbnav8BcFbb3XMho//Vv4vRQePLk1wNvJPJt7g/Ayxp3/8mRv/g35vTgNe0WzI/Fri11fc5RruTvprkKuCjwF4T1iFtl6ekalFozyH+VFXNq0ckJnkw8JOqqiQnAS+qqpXTrksLl8cUpN3b4cDb25PSbgFOmXI9WuDcUpB2UpInAu+7y/DPquqp06hH2hmGgiSp80CzJKkzFCRJnaEgSeoMBUlS9/8xcWb9dQRMLwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Distplot for various columns\n",
        "plt.figure(figsize = (30,10))\n",
        "plt.subplot(331)\n",
        "sns.distplot(df['battery_power'])\n",
        "plt.subplot(332)\n",
        "sns.distplot(df['clock_speed'])\n",
        "plt.subplot(333)\n",
        "sns.distplot(df['int_memory'])\n",
        "plt.subplot(334)\n",
        "sns.distplot(df['m_dep'])\n",
        "plt.subplot(335)\n",
        "sns.distplot(df['mobile_wt'])\n",
        "plt.subplot(336)\n",
        "sns.distplot(df['px_height'])\n",
        "plt.subplot(337)\n",
        "sns.distplot(df['px_width'])\n",
        "plt.subplot(338)\n",
        "sns.distplot(df['ram'])\n",
        "plt.subplot(339)\n",
        "sns.distplot(df['talk_time'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "klzVrIjfYS-K",
        "outputId": "3ffbfd92-a25f-4011-b048-4515f7f294d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x720 with 9 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bluetooth devices\n",
        "df[\"is_bluetooth\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['blue'][i]==0:\n",
        "        df['is_bluetooth'][i]='No'\n",
        "    else:\n",
        "        df['is_bluetooth'][i]='Yes'\n",
        "px.pie(data_frame = df, names = 'is_bluetooth', title = 'Percentage of devices having bluetooth', hole= 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "DAGSSfJ9YZ4K",
        "outputId": "01c703c4-d717-4380-b262-d906f493a76a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-5768d01b48fb>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['is_bluetooth'][i]='No'\n",
            "<ipython-input-17-5768d01b48fb>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['is_bluetooth'][i]='Yes'\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"e6bbc395-9be9-44f1-8a7f-fb1d9132d3ed\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e6bbc395-9be9-44f1-8a7f-fb1d9132d3ed\")) {                    Plotly.newPlot(                        \"e6bbc395-9be9-44f1-8a7f-fb1d9132d3ed\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"is_bluetooth=%{label}<extra></extra>\",\"labels\":[\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having bluetooth\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('e6bbc395-9be9-44f1-8a7f-fb1d9132d3ed');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dual sim\n",
        "df[\"is_DualSim\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['dual_sim'][i]==0:\n",
        "        df['is_DualSim'][i]='No'\n",
        "    else:\n",
        "        df['is_DualSim'][i]='Yes'\n",
        "px.pie(data_frame = df, names = 'is_DualSim', title = 'Percentage of devices having dual sim', hole= 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "o7uGpPnmYp6R",
        "outputId": "a68523dc-a280-4fcc-c9d9-6fcfd11b4b25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-cb526bbfef0d>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-18-cb526bbfef0d>:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"4f2da282-e8bb-4bdc-a8d0-be8e926acefd\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"4f2da282-e8bb-4bdc-a8d0-be8e926acefd\")) {                    Plotly.newPlot(                        \"4f2da282-e8bb-4bdc-a8d0-be8e926acefd\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"is_DualSim=%{label}<extra></extra>\",\"labels\":[\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having dual sim\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('4f2da282-e8bb-4bdc-a8d0-be8e926acefd');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4G\n",
        "df[\"is_4G\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['four_g'][i]==0:\n",
        "        df['is_4G'][i]='No'\n",
        "    else:\n",
        "        df['is_4G'][i]='Yes'\n",
        "px.pie(data_frame = df, names = 'is_4G', title = 'Percentage of devices having 4G connection', hole= 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "N6n0ZPOQY1vg",
        "outputId": "4eb1ae23-417f-4004-c6a4-4de844372a99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-b33c46f3c760>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-19-b33c46f3c760>:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"45e31e0f-9568-41f0-aabe-558c8f19cb3f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"45e31e0f-9568-41f0-aabe-558c8f19cb3f\")) {                    Plotly.newPlot(                        \"45e31e0f-9568-41f0-aabe-558c8f19cb3f\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"is_4G=%{label}<extra></extra>\",\"labels\":[\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having 4G connection\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('45e31e0f-9568-41f0-aabe-558c8f19cb3f');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3G\n",
        "df[\"is_3G\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['three_g'][i]==0:\n",
        "        df['is_3G'][i]='No'\n",
        "    else:\n",
        "        df['is_3G'][i]='Yes'\n",
        "px.pie(data_frame = df, names = 'is_3G', title = 'Percentage of devices having 3G connection', hole= 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "duKF-eoHY_XF",
        "outputId": "e89882be-db9f-4852-d7d4-2f6abce864fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-c066b4749f68>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-20-c066b4749f68>:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"7c16e81d-fb08-49e2-8be7-0dbc3ffea878\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"7c16e81d-fb08-49e2-8be7-0dbc3ffea878\")) {                    Plotly.newPlot(                        \"7c16e81d-fb08-49e2-8be7-0dbc3ffea878\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"is_3G=%{label}<extra></extra>\",\"labels\":[\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having 3G connection\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('7c16e81d-fb08-49e2-8be7-0dbc3ffea878');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Touch Screen\n",
        "df[\"is_touchscreen\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['touch_screen'][i]==0:\n",
        "        df['is_touchscreen'][i]='No'\n",
        "    else:\n",
        "        df['is_touchscreen'][i]='Yes'\n",
        "px.pie(data_frame = df, names = 'is_touchscreen', title = 'Percentage of devices having touch screen', hole= 0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "kXWo-bqgZIs6",
        "outputId": "0ddb7a69-6c0f-482b-d44a-d4f30e1f2012"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-406982a8bb2b>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-21-406982a8bb2b>:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"81b0cdcf-5718-4c52-9377-3d80eba3e892\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"81b0cdcf-5718-4c52-9377-3d80eba3e892\")) {                    Plotly.newPlot(                        \"81b0cdcf-5718-4c52-9377-3d80eba3e892\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"is_touchscreen=%{label}<extra></extra>\",\"labels\":[\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having touch screen\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('81b0cdcf-5718-4c52-9377-3d80eba3e892');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Wifi\n",
        "df[\"is_wifi\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['wifi'][i]==0:\n",
        "        df['is_wifi'][i]='No'\n",
        "    else:\n",
        "        df['is_wifi'][i]='Yes'\n",
        "px.pie(data_frame = df, names = 'is_wifi', title = 'Percentage of devices having Wifi', hole= 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "4p3IqBlaZSVR",
        "outputId": "276fecfc-aebf-4606-9cb2-3471c91eddb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-099b832797b2>:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-22-099b832797b2>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"192b076d-194c-402d-96e4-5a2a56181a82\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"192b076d-194c-402d-96e4-5a2a56181a82\")) {                    Plotly.newPlot(                        \"192b076d-194c-402d-96e4-5a2a56181a82\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"is_wifi=%{label}<extra></extra>\",\"labels\":[\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"Yes\",\"No\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"No\",\"No\",\"Yes\",\"Yes\",\"No\",\"No\",\"Yes\",\"No\",\"Yes\",\"Yes\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having Wifi\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('192b076d-194c-402d-96e4-5a2a56181a82');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Processors\n",
        "df[\"cores\"]=''\n",
        "for i in range(len(df)):\n",
        "    if df['n_cores'][i]==1:\n",
        "        df['cores'][i]='single core'\n",
        "    elif df['n_cores'][i]==2:\n",
        "        df['cores'][i]='dual core'\n",
        "    elif df['n_cores'][i]==3:\n",
        "        df['cores'][i]='triple core'\n",
        "    elif df['n_cores'][i]==4:\n",
        "        df['cores'][i]='quad core'\n",
        "    elif df['n_cores'][i]==5:\n",
        "        df['cores'][i]='penta core'\n",
        "    elif df['n_cores'][i]==6:\n",
        "        df['cores'][i]='hexa core'\n",
        "    elif df['n_cores'][i]==7:\n",
        "        df['cores'][i]='hepta core'\n",
        "    else:\n",
        "        df['cores'][i]='octa core'\n",
        "px.pie(data_frame = df, names = 'cores', title = 'Percentage of devices having different types of cores', hole= 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "q0VCUx5eZdht",
        "outputId": "4fb7123d-8efd-4889-99a2-9aa2f0675c70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-a50a2db02370>:7: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:9: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:13: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:15: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:5: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:19: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:11: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-23-a50a2db02370>:17: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"6a8397da-0eb6-4112-9d51-5db7a1f321ec\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6a8397da-0eb6-4112-9d51-5db7a1f321ec\")) {                    Plotly.newPlot(                        \"6a8397da-0eb6-4112-9d51-5db7a1f321ec\",                        [{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hole\":0.2,\"hovertemplate\":\"cores=%{label}<extra></extra>\",\"labels\":[\"dual core\",\"triple core\",\"penta core\",\"hexa core\",\"dual core\",\"single core\",\"octa core\",\"quad core\",\"hepta core\",\"penta core\",\"penta core\",\"octa core\",\"quad core\",\"quad core\",\"single core\",\"dual core\",\"octa core\",\"triple core\",\"penta core\",\"quad core\",\"hepta core\",\"dual core\",\"quad core\",\"triple core\",\"triple core\",\"octa core\",\"quad core\",\"hepta core\",\"dual core\",\"penta core\",\"hepta core\",\"octa core\",\"single core\",\"quad core\",\"octa core\",\"octa core\",\"quad core\",\"dual core\",\"hexa core\",\"dual core\",\"single core\",\"dual core\",\"dual core\",\"triple core\",\"dual core\",\"triple core\",\"octa core\",\"hepta core\",\"octa core\",\"octa core\",\"triple core\",\"octa core\",\"dual core\",\"triple core\",\"hepta core\",\"triple core\",\"octa core\",\"triple core\",\"hexa core\",\"penta core\",\"triple core\",\"octa core\",\"dual core\",\"dual core\",\"triple core\",\"triple core\",\"quad core\",\"triple core\",\"dual core\",\"quad core\",\"hepta core\",\"quad core\",\"triple core\",\"single core\",\"quad core\",\"hexa core\",\"triple core\",\"single core\",\"quad core\",\"hexa core\",\"hepta core\",\"quad core\",\"penta core\",\"triple core\",\"single core\",\"octa core\",\"penta core\",\"triple core\",\"penta core\",\"quad core\",\"triple core\",\"hepta core\",\"hepta core\",\"hexa core\",\"triple core\",\"triple core\",\"penta core\",\"hexa core\",\"dual core\",\"hepta core\",\"hexa core\",\"octa core\",\"penta core\",\"quad core\",\"hepta core\",\"quad core\",\"penta core\",\"penta core\",\"single core\",\"single core\",\"triple core\",\"hepta core\",\"hexa core\",\"penta core\",\"hepta core\",\"hepta core\",\"octa core\",\"triple core\",\"single core\",\"triple core\",\"octa core\",\"quad core\",\"triple core\",\"dual core\",\"penta core\",\"single core\",\"quad core\",\"hexa core\",\"triple core\",\"hexa core\",\"hepta core\",\"single core\",\"hepta core\",\"octa core\",\"quad core\",\"penta core\",\"hexa core\",\"octa core\",\"hepta core\",\"octa core\",\"single core\",\"triple core\",\"single core\",\"hepta core\",\"triple core\",\"dual core\",\"dual core\",\"hepta core\",\"triple core\",\"single core\",\"penta core\",\"hepta core\",\"hexa core\",\"dual core\",\"dual core\",\"single core\",\"quad core\",\"hexa core\",\"hexa core\",\"single core\",\"penta core\",\"hexa core\",\"penta core\",\"single core\",\"octa core\",\"quad core\",\"octa core\",\"hexa core\",\"hepta core\",\"quad core\",\"hepta core\",\"penta core\",\"quad core\",\"penta core\",\"penta core\",\"hexa core\",\"hepta core\",\"penta core\",\"dual core\",\"hepta core\",\"single core\",\"hexa core\",\"octa core\",\"quad core\",\"penta core\",\"triple core\",\"hexa core\",\"single core\",\"single core\",\"penta core\",\"single core\",\"penta core\",\"octa core\",\"dual core\",\"quad core\",\"hepta core\",\"triple core\",\"triple core\",\"quad core\",\"triple core\",\"single core\",\"hepta core\",\"triple core\",\"hepta core\",\"hexa core\",\"octa core\",\"hexa core\",\"penta core\",\"hepta core\",\"hexa core\",\"triple core\",\"octa core\",\"penta core\",\"penta core\",\"single core\",\"triple core\",\"octa core\",\"penta core\",\"triple core\",\"penta core\",\"penta core\",\"dual core\",\"quad core\",\"hexa core\",\"quad core\",\"dual core\",\"single core\",\"octa core\",\"quad core\",\"triple core\",\"penta core\",\"single core\",\"octa core\",\"triple core\",\"dual core\",\"single core\",\"penta core\",\"penta core\",\"hepta core\",\"quad core\",\"octa core\",\"hepta core\",\"hexa core\",\"penta core\",\"hepta core\",\"hepta core\",\"dual core\",\"quad core\",\"penta core\",\"dual core\",\"dual core\",\"penta core\",\"octa core\",\"quad core\",\"single core\",\"hepta core\",\"quad core\",\"hepta core\",\"triple core\",\"hexa core\",\"hepta core\",\"quad core\",\"single core\",\"quad core\",\"dual core\",\"dual core\",\"hexa core\",\"hexa core\",\"penta core\",\"single core\",\"hepta core\",\"octa core\",\"single core\",\"triple core\",\"dual core\",\"quad core\",\"quad core\",\"quad core\",\"hepta core\",\"octa core\",\"hexa core\",\"triple core\",\"hepta core\",\"triple core\",\"octa core\",\"single core\",\"hepta core\",\"octa core\",\"single core\",\"hexa core\",\"dual core\",\"hepta core\",\"hexa core\",\"penta core\",\"penta core\",\"hepta core\",\"octa core\",\"hexa core\",\"octa core\",\"hepta core\",\"single core\",\"hexa core\",\"single core\",\"dual core\",\"dual core\",\"triple core\",\"triple core\",\"hexa core\",\"hexa core\",\"quad core\",\"dual core\",\"hepta core\",\"triple core\",\"octa core\",\"dual core\",\"octa core\",\"quad core\",\"dual core\",\"single core\",\"triple core\",\"dual core\",\"single core\",\"single core\",\"single core\",\"hexa core\",\"hepta core\",\"single core\",\"single core\",\"single core\",\"penta core\",\"single core\",\"hexa core\",\"quad core\",\"triple core\",\"dual core\",\"quad core\",\"triple core\",\"single core\",\"single core\",\"hepta core\",\"triple core\",\"hexa core\",\"octa core\",\"hepta core\",\"single core\",\"quad core\",\"penta core\",\"hexa core\",\"single core\",\"triple core\",\"single core\",\"penta core\",\"octa core\",\"octa core\",\"hepta core\",\"quad core\",\"single core\",\"hepta core\",\"penta core\",\"hepta core\",\"hepta core\",\"dual core\",\"dual core\",\"quad core\",\"quad core\",\"octa core\",\"hexa core\",\"octa core\",\"single core\",\"hexa core\",\"dual core\",\"hepta core\",\"penta core\",\"hepta core\",\"penta core\",\"hepta core\",\"dual core\",\"hexa core\",\"quad core\",\"dual core\",\"hepta core\",\"hepta core\",\"triple core\",\"dual core\",\"single core\",\"triple core\",\"octa core\",\"octa core\",\"hepta core\",\"hepta core\",\"hexa core\",\"hepta core\",\"quad core\",\"hepta core\",\"quad core\",\"dual core\",\"octa core\",\"dual core\",\"quad core\",\"single core\",\"triple core\",\"hepta core\",\"triple core\",\"quad core\",\"hexa core\",\"quad core\",\"penta core\",\"penta core\",\"octa core\",\"quad core\",\"penta core\",\"hexa core\",\"penta core\",\"penta core\",\"triple core\",\"quad core\",\"octa core\",\"penta core\",\"hepta core\",\"dual core\",\"penta core\",\"triple core\",\"quad core\",\"dual core\",\"quad core\",\"triple core\",\"hexa core\",\"hepta core\",\"hexa core\",\"dual core\",\"octa core\",\"octa core\",\"single core\",\"hexa core\",\"quad core\",\"dual core\",\"hepta core\",\"hexa core\",\"dual core\",\"hepta core\",\"octa core\",\"hepta core\",\"hepta core\",\"triple core\",\"hepta core\",\"dual core\",\"dual core\",\"penta core\",\"single core\",\"octa core\",\"dual core\",\"triple core\",\"triple core\",\"hexa core\",\"hepta core\",\"triple core\",\"triple core\",\"penta core\",\"penta core\",\"quad core\",\"hepta core\",\"hepta core\",\"octa core\",\"dual core\",\"octa core\",\"triple core\",\"quad core\",\"octa core\",\"hepta core\",\"single core\",\"hepta core\",\"penta core\",\"triple core\",\"quad core\",\"triple core\",\"quad core\",\"single core\",\"quad core\",\"quad core\",\"quad core\",\"hexa core\",\"single core\",\"penta core\",\"dual core\",\"hexa core\",\"quad core\",\"penta core\",\"quad core\",\"penta core\",\"triple core\",\"hexa core\",\"single core\",\"hepta core\",\"octa core\",\"penta core\",\"penta core\",\"triple core\",\"dual core\",\"triple core\",\"dual core\",\"octa core\",\"hexa core\",\"hepta core\",\"hepta core\",\"dual core\",\"single core\",\"hepta core\",\"triple core\",\"penta core\",\"triple core\",\"quad core\",\"single core\",\"single core\",\"quad core\",\"hexa core\",\"hepta core\",\"octa core\",\"triple core\",\"quad core\",\"triple core\",\"single core\",\"octa core\",\"triple core\",\"penta core\",\"dual core\",\"octa core\",\"octa core\",\"quad core\",\"dual core\",\"single core\",\"hexa core\",\"penta core\",\"penta core\",\"quad core\",\"hepta core\",\"octa core\",\"hexa core\",\"quad core\",\"hexa core\",\"octa core\",\"single core\",\"quad core\",\"octa core\",\"penta core\",\"dual core\",\"hexa core\",\"octa core\",\"dual core\",\"single core\",\"dual core\",\"penta core\",\"octa core\",\"octa core\",\"octa core\",\"quad core\",\"penta core\",\"octa core\",\"single core\",\"single core\",\"single core\",\"octa core\",\"dual core\",\"octa core\",\"hexa core\",\"hexa core\",\"hepta core\",\"octa core\",\"dual core\",\"dual core\",\"dual core\",\"penta core\",\"hexa core\",\"dual core\",\"single core\",\"hepta core\",\"penta core\",\"single core\",\"penta core\",\"quad core\",\"single core\",\"octa core\",\"triple core\",\"penta core\",\"triple core\",\"triple core\",\"triple core\",\"single core\",\"quad core\",\"octa core\",\"penta core\",\"penta core\",\"single core\",\"penta core\",\"triple core\",\"quad core\",\"triple core\",\"dual core\",\"octa core\",\"penta core\",\"hepta core\",\"hexa core\",\"quad core\",\"octa core\",\"hepta core\",\"dual core\",\"penta core\",\"hepta core\",\"hepta core\",\"octa core\",\"quad core\",\"triple core\",\"dual core\",\"hepta core\",\"triple core\",\"hepta core\",\"octa core\",\"single core\",\"single core\",\"penta core\",\"dual core\",\"quad core\",\"hexa core\",\"hepta core\",\"single core\",\"quad core\",\"quad core\",\"triple core\",\"triple core\",\"hexa core\",\"octa core\",\"hepta core\",\"penta core\",\"octa core\",\"triple core\",\"hexa core\",\"triple core\",\"hepta core\",\"octa core\",\"penta core\",\"octa core\",\"penta core\",\"hepta core\",\"hexa core\",\"triple core\",\"penta core\",\"octa core\",\"penta core\",\"single core\",\"quad core\",\"single core\",\"hepta core\",\"octa core\",\"hepta core\",\"hepta core\",\"triple core\",\"octa core\",\"hepta core\",\"quad core\",\"hexa core\",\"quad core\",\"triple core\",\"octa core\",\"triple core\",\"hepta core\",\"quad core\",\"quad core\",\"octa core\",\"triple core\",\"hexa core\",\"quad core\",\"quad core\",\"hepta core\",\"hepta core\",\"penta core\",\"triple core\",\"single core\",\"dual core\",\"hexa core\",\"hexa core\",\"penta core\",\"hepta core\",\"hepta core\",\"single core\",\"hepta core\",\"triple core\",\"hepta core\",\"penta core\",\"dual core\",\"quad core\",\"hexa core\",\"penta core\",\"quad core\",\"penta core\",\"octa core\",\"single core\",\"triple core\",\"penta core\",\"hexa core\",\"penta core\",\"triple core\",\"triple core\",\"single core\",\"single core\",\"penta core\",\"hepta core\",\"hepta core\",\"penta core\",\"hexa core\",\"dual core\",\"hepta core\",\"dual core\",\"octa core\",\"dual core\",\"hepta core\",\"dual core\",\"triple core\",\"quad core\",\"triple core\",\"penta core\",\"hepta core\",\"dual core\",\"quad core\",\"hepta core\",\"octa core\",\"triple core\",\"hexa core\",\"hepta core\",\"triple core\",\"triple core\",\"single core\",\"hexa core\",\"single core\",\"hepta core\",\"hepta core\",\"hepta core\",\"quad core\",\"hexa core\",\"hepta core\",\"hepta core\",\"triple core\",\"hexa core\",\"dual core\",\"hexa core\",\"single core\",\"single core\",\"octa core\",\"single core\",\"dual core\",\"triple core\",\"triple core\",\"dual core\",\"dual core\",\"hexa core\",\"quad core\",\"single core\",\"penta core\",\"triple core\",\"hexa core\",\"triple core\",\"hexa core\",\"quad core\",\"penta core\",\"hepta core\",\"octa core\",\"penta core\",\"hepta core\",\"dual core\",\"hexa core\",\"hexa core\",\"dual core\",\"octa core\",\"dual core\",\"dual core\",\"single core\",\"hepta core\",\"triple core\",\"dual core\",\"hepta core\",\"single core\",\"octa core\",\"penta core\",\"hepta core\",\"dual core\",\"single core\",\"quad core\",\"octa core\",\"triple core\",\"dual core\",\"single core\",\"triple core\",\"penta core\",\"penta core\",\"hexa core\",\"hexa core\",\"octa core\",\"penta core\",\"hepta core\",\"hepta core\",\"hepta core\",\"triple core\",\"single core\",\"single core\",\"triple core\",\"quad core\",\"hexa core\",\"penta core\",\"triple core\",\"dual core\",\"single core\",\"hepta core\",\"quad core\",\"penta core\",\"single core\",\"single core\",\"hexa core\",\"single core\",\"quad core\",\"hepta core\",\"penta core\",\"single core\",\"triple core\",\"dual core\",\"hepta core\",\"octa core\",\"hexa core\",\"triple core\",\"hexa core\",\"hexa core\",\"hepta core\",\"single core\",\"triple core\",\"dual core\",\"hexa core\",\"hepta core\",\"octa core\",\"single core\",\"penta core\",\"single core\",\"octa core\",\"single core\",\"quad core\",\"triple core\",\"dual core\",\"octa core\",\"triple core\",\"hepta core\",\"single core\",\"hexa core\",\"triple core\",\"dual core\",\"hepta core\",\"quad core\",\"dual core\",\"hexa core\",\"quad core\",\"octa core\",\"triple core\",\"dual core\",\"octa core\",\"triple core\",\"penta core\",\"hepta core\",\"dual core\",\"octa core\",\"hepta core\",\"dual core\",\"hepta core\",\"hepta core\",\"triple core\",\"quad core\",\"triple core\",\"dual core\",\"triple core\",\"hexa core\",\"dual core\",\"single core\",\"hexa core\",\"penta core\",\"penta core\",\"dual core\",\"penta core\",\"hexa core\",\"triple core\",\"single core\",\"hexa core\",\"hepta core\",\"quad core\",\"octa core\",\"single core\",\"penta core\",\"single core\",\"hepta core\",\"penta core\",\"dual core\",\"triple core\",\"triple core\",\"single core\",\"penta core\",\"quad core\",\"quad core\",\"octa core\",\"hexa core\",\"dual core\",\"quad core\",\"triple core\",\"triple core\",\"single core\",\"single core\",\"triple core\",\"quad core\",\"quad core\",\"dual core\",\"dual core\",\"quad core\",\"quad core\",\"single core\",\"triple core\",\"triple core\",\"hepta core\",\"hexa core\",\"octa core\",\"triple core\",\"single core\",\"penta core\",\"single core\",\"single core\",\"quad core\",\"quad core\",\"hepta core\",\"dual core\",\"single core\",\"triple core\",\"quad core\",\"triple core\",\"triple core\",\"single core\",\"hexa core\",\"octa core\",\"triple core\",\"triple core\",\"octa core\",\"dual core\",\"hepta core\",\"quad core\",\"triple core\",\"hexa core\",\"octa core\",\"single core\",\"dual core\",\"quad core\",\"dual core\",\"hexa core\",\"hepta core\",\"penta core\",\"octa core\",\"triple core\",\"octa core\",\"quad core\",\"single core\",\"single core\",\"dual core\",\"hexa core\",\"single core\",\"hepta core\",\"penta core\",\"octa core\",\"dual core\",\"octa core\",\"single core\",\"triple core\",\"octa core\",\"dual core\",\"quad core\",\"single core\",\"quad core\",\"single core\",\"hexa core\",\"penta core\",\"dual core\",\"penta core\",\"single core\",\"octa core\",\"triple core\",\"hepta core\",\"quad core\",\"quad core\",\"hepta core\",\"hepta core\",\"penta core\",\"penta core\",\"hepta core\",\"hepta core\",\"quad core\",\"hexa core\",\"hexa core\",\"triple core\",\"dual core\",\"octa core\",\"hexa core\",\"hexa core\",\"penta core\",\"dual core\",\"triple core\",\"hepta core\",\"octa core\",\"penta core\",\"single core\",\"dual core\",\"octa core\",\"penta core\",\"quad core\",\"dual core\",\"triple core\",\"hexa core\",\"dual core\",\"quad core\",\"hepta core\",\"single core\",\"octa core\",\"quad core\",\"quad core\",\"quad core\",\"triple core\",\"triple core\",\"penta core\",\"triple core\",\"quad core\",\"dual core\",\"dual core\",\"penta core\",\"single core\",\"hexa core\",\"hepta core\",\"quad core\",\"hepta core\",\"triple core\",\"hepta core\",\"dual core\",\"triple core\",\"penta core\",\"hepta core\",\"hepta core\",\"triple core\",\"dual core\",\"hepta core\",\"hepta core\",\"penta core\",\"octa core\",\"hepta core\",\"single core\",\"single core\",\"triple core\",\"penta core\",\"octa core\",\"penta core\",\"dual core\",\"quad core\",\"hexa core\",\"octa core\",\"octa core\",\"quad core\",\"single core\",\"single core\",\"hexa core\",\"hexa core\",\"triple core\",\"hexa core\",\"hexa core\",\"single core\",\"quad core\",\"hepta core\",\"hepta core\",\"hepta core\",\"triple core\",\"hexa core\",\"single core\",\"penta core\",\"triple core\",\"hexa core\",\"octa core\",\"penta core\",\"hexa core\",\"quad core\",\"hepta core\",\"penta core\",\"penta core\",\"single core\",\"triple core\",\"triple core\",\"dual core\",\"penta core\",\"dual core\",\"quad core\",\"quad core\",\"triple core\",\"hepta core\",\"penta core\",\"dual core\",\"hepta core\",\"quad core\",\"penta core\",\"single core\",\"triple core\",\"single core\",\"single core\",\"dual core\",\"hepta core\",\"hepta core\",\"single core\",\"dual core\",\"octa core\",\"hexa core\",\"dual core\",\"octa core\",\"dual core\",\"hexa core\",\"dual core\",\"octa core\",\"dual core\",\"quad core\",\"octa core\",\"penta core\",\"octa core\",\"single core\",\"triple core\",\"triple core\",\"penta core\",\"single core\",\"octa core\",\"hepta core\",\"single core\",\"octa core\",\"quad core\",\"penta core\",\"octa core\",\"single core\",\"dual core\",\"dual core\",\"hexa core\",\"hexa core\",\"penta core\",\"quad core\",\"triple core\",\"triple core\",\"hepta core\",\"quad core\",\"quad core\",\"hexa core\",\"hepta core\",\"hexa core\",\"single core\",\"triple core\",\"octa core\",\"penta core\",\"hepta core\",\"quad core\",\"penta core\",\"octa core\",\"hepta core\",\"triple core\",\"octa core\",\"hepta core\",\"triple core\",\"octa core\",\"triple core\",\"hexa core\",\"quad core\",\"dual core\",\"hepta core\",\"penta core\",\"octa core\",\"hexa core\",\"triple core\",\"dual core\",\"dual core\",\"hepta core\",\"quad core\",\"dual core\",\"triple core\",\"quad core\",\"hexa core\",\"single core\",\"octa core\",\"single core\",\"hepta core\",\"hexa core\",\"octa core\",\"hexa core\",\"hepta core\",\"dual core\",\"triple core\",\"hepta core\",\"single core\",\"single core\",\"octa core\",\"hepta core\",\"dual core\",\"dual core\",\"hepta core\",\"dual core\",\"hexa core\",\"triple core\",\"penta core\",\"triple core\",\"quad core\",\"octa core\",\"penta core\",\"triple core\",\"triple core\",\"single core\",\"triple core\",\"octa core\",\"quad core\",\"quad core\",\"triple core\",\"hepta core\",\"dual core\",\"octa core\",\"hepta core\",\"triple core\",\"quad core\",\"hexa core\",\"octa core\",\"single core\",\"triple core\",\"quad core\",\"triple core\",\"single core\",\"single core\",\"quad core\",\"dual core\",\"single core\",\"hexa core\",\"single core\",\"penta core\",\"hepta core\",\"hepta core\",\"penta core\",\"single core\",\"dual core\",\"octa core\",\"hepta core\",\"octa core\",\"quad core\",\"hexa core\",\"octa core\",\"hepta core\",\"dual core\",\"penta core\",\"dual core\",\"single core\",\"penta core\",\"hexa core\",\"hexa core\",\"dual core\",\"hexa core\",\"hexa core\",\"hexa core\",\"hepta core\",\"quad core\",\"single core\",\"octa core\",\"hepta core\",\"single core\",\"single core\",\"single core\",\"triple core\",\"single core\",\"dual core\",\"triple core\",\"hexa core\",\"quad core\",\"dual core\",\"hepta core\",\"single core\",\"single core\",\"octa core\",\"octa core\",\"hepta core\",\"single core\",\"penta core\",\"hexa core\",\"hexa core\",\"hexa core\",\"penta core\",\"dual core\",\"triple core\",\"hexa core\",\"penta core\",\"penta core\",\"quad core\",\"hepta core\",\"quad core\",\"penta core\",\"octa core\",\"single core\",\"octa core\",\"hepta core\",\"hepta core\",\"triple core\",\"dual core\",\"dual core\",\"penta core\",\"dual core\",\"hepta core\",\"octa core\",\"triple core\",\"penta core\",\"triple core\",\"penta core\",\"quad core\",\"single core\",\"quad core\",\"penta core\",\"dual core\",\"triple core\",\"hexa core\",\"quad core\",\"single core\",\"hepta core\",\"hepta core\",\"penta core\",\"hexa core\",\"dual core\",\"penta core\",\"triple core\",\"octa core\",\"penta core\",\"single core\",\"triple core\",\"octa core\",\"quad core\",\"quad core\",\"hepta core\",\"octa core\",\"octa core\",\"quad core\",\"quad core\",\"dual core\",\"octa core\",\"hepta core\",\"penta core\",\"hexa core\",\"penta core\",\"penta core\",\"dual core\",\"octa core\",\"hepta core\",\"quad core\",\"dual core\",\"single core\",\"quad core\",\"dual core\",\"octa core\",\"octa core\",\"octa core\",\"dual core\",\"dual core\",\"quad core\",\"penta core\",\"hexa core\",\"penta core\",\"single core\",\"single core\",\"quad core\",\"dual core\",\"quad core\",\"octa core\",\"octa core\",\"penta core\",\"dual core\",\"quad core\",\"hepta core\",\"penta core\",\"dual core\",\"penta core\",\"quad core\",\"quad core\",\"dual core\",\"single core\",\"octa core\",\"quad core\",\"octa core\",\"triple core\",\"penta core\",\"quad core\",\"quad core\",\"quad core\",\"dual core\",\"hexa core\",\"penta core\",\"triple core\",\"penta core\",\"single core\",\"octa core\",\"quad core\",\"hexa core\",\"octa core\",\"hepta core\",\"octa core\",\"quad core\",\"octa core\",\"quad core\",\"triple core\",\"dual core\",\"octa core\",\"quad core\",\"dual core\",\"penta core\",\"dual core\",\"single core\",\"quad core\",\"dual core\",\"octa core\",\"hepta core\",\"quad core\",\"penta core\",\"octa core\",\"penta core\",\"quad core\",\"hexa core\",\"hexa core\",\"single core\",\"hepta core\",\"hexa core\",\"hexa core\",\"octa core\",\"triple core\",\"octa core\",\"quad core\",\"octa core\",\"quad core\",\"octa core\",\"octa core\",\"hexa core\",\"penta core\",\"dual core\",\"triple core\",\"triple core\",\"quad core\",\"quad core\",\"octa core\",\"penta core\",\"hexa core\",\"quad core\",\"octa core\",\"octa core\",\"hexa core\",\"quad core\",\"octa core\",\"triple core\",\"quad core\",\"penta core\",\"single core\",\"single core\",\"quad core\",\"single core\",\"hexa core\",\"single core\",\"octa core\",\"dual core\",\"hexa core\",\"dual core\",\"hexa core\",\"quad core\",\"hexa core\",\"hexa core\",\"octa core\",\"triple core\",\"quad core\",\"triple core\",\"triple core\",\"single core\",\"quad core\",\"hexa core\",\"single core\",\"single core\",\"octa core\",\"single core\",\"single core\",\"hexa core\",\"quad core\",\"hexa core\",\"dual core\",\"penta core\",\"octa core\",\"triple core\",\"hepta core\",\"penta core\",\"hepta core\",\"triple core\",\"single core\",\"single core\",\"octa core\",\"hexa core\",\"dual core\",\"octa core\",\"hexa core\",\"triple core\",\"triple core\",\"dual core\",\"dual core\",\"octa core\",\"quad core\",\"hexa core\",\"penta core\",\"quad core\",\"hepta core\",\"hexa core\",\"octa core\",\"hexa core\",\"quad core\",\"penta core\",\"octa core\",\"single core\",\"dual core\",\"hepta core\",\"quad core\",\"hepta core\",\"dual core\",\"single core\",\"octa core\",\"dual core\",\"single core\",\"quad core\",\"quad core\",\"triple core\",\"octa core\",\"single core\",\"octa core\",\"hexa core\",\"hexa core\",\"hepta core\",\"penta core\",\"octa core\",\"dual core\",\"triple core\",\"quad core\",\"hepta core\",\"octa core\",\"dual core\",\"penta core\",\"dual core\",\"octa core\",\"single core\",\"octa core\",\"hexa core\",\"single core\",\"triple core\",\"dual core\",\"hepta core\",\"hepta core\",\"octa core\",\"hexa core\",\"dual core\",\"penta core\",\"octa core\",\"dual core\",\"quad core\",\"hexa core\",\"quad core\",\"penta core\",\"single core\",\"dual core\",\"hexa core\",\"quad core\",\"dual core\",\"quad core\",\"hexa core\",\"hepta core\",\"dual core\",\"quad core\",\"octa core\",\"octa core\",\"dual core\",\"octa core\",\"octa core\",\"single core\",\"penta core\",\"hexa core\",\"dual core\",\"dual core\",\"hepta core\",\"octa core\",\"quad core\",\"single core\",\"penta core\",\"dual core\",\"quad core\",\"dual core\",\"octa core\",\"hexa core\",\"quad core\",\"single core\",\"hepta core\",\"triple core\",\"penta core\",\"hepta core\",\"hexa core\",\"single core\",\"quad core\",\"dual core\",\"single core\",\"octa core\",\"penta core\",\"octa core\",\"hexa core\",\"quad core\",\"dual core\",\"penta core\",\"penta core\",\"penta core\",\"hexa core\",\"single core\",\"triple core\",\"octa core\",\"hepta core\",\"single core\",\"hexa core\",\"octa core\",\"triple core\",\"octa core\",\"quad core\",\"single core\",\"dual core\",\"penta core\",\"penta core\",\"quad core\",\"quad core\",\"hexa core\",\"dual core\",\"octa core\",\"hexa core\",\"dual core\",\"quad core\",\"triple core\",\"dual core\",\"penta core\",\"single core\",\"hepta core\",\"octa core\",\"single core\",\"triple core\",\"hexa core\",\"dual core\",\"quad core\",\"dual core\",\"triple core\",\"quad core\",\"hepta core\",\"single core\",\"quad core\",\"dual core\",\"penta core\",\"hexa core\",\"octa core\",\"single core\",\"single core\",\"hepta core\",\"quad core\",\"octa core\",\"quad core\",\"octa core\",\"quad core\",\"hexa core\",\"octa core\",\"hexa core\",\"single core\",\"single core\",\"dual core\",\"quad core\",\"penta core\",\"octa core\",\"quad core\",\"hexa core\",\"hexa core\",\"single core\",\"hepta core\",\"quad core\",\"single core\",\"octa core\",\"quad core\",\"penta core\",\"penta core\",\"dual core\",\"single core\",\"quad core\",\"octa core\",\"hexa core\",\"hexa core\",\"penta core\",\"dual core\",\"single core\",\"dual core\",\"single core\",\"hepta core\",\"quad core\",\"dual core\",\"hexa core\",\"hepta core\",\"triple core\",\"triple core\",\"penta core\",\"quad core\",\"quad core\",\"quad core\",\"single core\",\"quad core\",\"hepta core\",\"octa core\",\"quad core\",\"quad core\",\"octa core\",\"penta core\",\"octa core\",\"octa core\",\"hepta core\",\"quad core\",\"quad core\",\"quad core\",\"triple core\",\"quad core\",\"hepta core\",\"single core\",\"penta core\",\"dual core\",\"dual core\",\"hexa core\",\"penta core\",\"hepta core\",\"dual core\",\"penta core\",\"penta core\",\"dual core\",\"penta core\",\"octa core\",\"single core\",\"triple core\",\"triple core\",\"hexa core\",\"octa core\",\"hexa core\",\"octa core\",\"penta core\",\"octa core\",\"hepta core\",\"dual core\",\"triple core\",\"dual core\",\"penta core\",\"quad core\",\"penta core\",\"dual core\",\"quad core\",\"hexa core\",\"single core\",\"hexa core\",\"quad core\",\"dual core\",\"quad core\",\"triple core\",\"octa core\",\"octa core\",\"dual core\",\"dual core\",\"octa core\",\"hepta core\",\"hexa core\",\"hexa core\",\"hepta core\",\"octa core\",\"hepta core\",\"triple core\",\"triple core\",\"octa core\",\"dual core\",\"octa core\",\"hepta core\",\"triple core\",\"triple core\",\"triple core\",\"triple core\",\"penta core\",\"hexa core\",\"dual core\",\"quad core\",\"quad core\",\"single core\",\"quad core\",\"octa core\",\"penta core\",\"dual core\",\"quad core\",\"hexa core\",\"dual core\",\"single core\",\"quad core\",\"hepta core\",\"hexa core\",\"penta core\",\"triple core\",\"hexa core\",\"hexa core\",\"dual core\",\"dual core\",\"penta core\",\"hexa core\",\"single core\",\"octa core\",\"hepta core\",\"penta core\",\"hepta core\",\"penta core\",\"quad core\",\"dual core\",\"penta core\",\"quad core\",\"quad core\",\"penta core\",\"penta core\",\"penta core\",\"dual core\",\"hepta core\",\"penta core\",\"hexa core\",\"triple core\",\"single core\",\"single core\",\"triple core\",\"hexa core\",\"single core\",\"hepta core\",\"single core\",\"triple core\",\"hepta core\",\"quad core\",\"octa core\",\"quad core\",\"quad core\",\"dual core\",\"dual core\",\"hepta core\",\"triple core\",\"single core\",\"hepta core\",\"single core\",\"dual core\",\"hepta core\",\"hepta core\",\"triple core\",\"hepta core\",\"quad core\",\"quad core\",\"hexa core\",\"hepta core\",\"hexa core\",\"quad core\",\"penta core\",\"single core\",\"hepta core\",\"dual core\",\"dual core\",\"octa core\",\"triple core\",\"hepta core\",\"quad core\",\"triple core\",\"hepta core\",\"octa core\",\"triple core\",\"hexa core\",\"hepta core\",\"triple core\",\"penta core\",\"octa core\",\"quad core\",\"hexa core\",\"hexa core\",\"quad core\",\"single core\",\"penta core\",\"penta core\",\"hepta core\",\"hexa core\",\"single core\",\"single core\",\"quad core\",\"triple core\",\"hepta core\",\"octa core\",\"single core\",\"dual core\",\"dual core\",\"penta core\",\"quad core\",\"hexa core\",\"triple core\",\"single core\",\"penta core\",\"quad core\",\"single core\",\"octa core\",\"quad core\",\"penta core\",\"penta core\",\"hepta core\",\"octa core\",\"penta core\",\"penta core\",\"quad core\",\"triple core\",\"dual core\",\"octa core\",\"quad core\",\"dual core\",\"triple core\",\"dual core\",\"hepta core\",\"hepta core\",\"dual core\",\"hexa core\",\"single core\",\"octa core\",\"triple core\",\"penta core\",\"hexa core\",\"hepta core\",\"hepta core\",\"triple core\",\"hepta core\",\"octa core\",\"single core\",\"triple core\",\"octa core\",\"quad core\",\"dual core\",\"penta core\",\"quad core\",\"single core\",\"penta core\",\"quad core\",\"hexa core\",\"hexa core\",\"hexa core\",\"penta core\",\"octa core\",\"hexa core\",\"hexa core\",\"hepta core\",\"hepta core\",\"triple core\",\"hepta core\",\"octa core\",\"single core\",\"quad core\",\"penta core\",\"penta core\",\"penta core\",\"quad core\",\"hexa core\",\"hexa core\",\"penta core\",\"hepta core\",\"triple core\",\"quad core\",\"octa core\",\"dual core\",\"hexa core\",\"dual core\",\"octa core\",\"penta core\",\"hepta core\",\"hexa core\",\"single core\",\"dual core\",\"octa core\",\"hepta core\",\"dual core\",\"penta core\",\"triple core\",\"octa core\",\"triple core\",\"hexa core\",\"penta core\",\"hepta core\",\"penta core\",\"dual core\",\"quad core\",\"single core\",\"triple core\",\"single core\",\"octa core\",\"triple core\",\"penta core\",\"single core\",\"hexa core\",\"quad core\",\"octa core\",\"penta core\",\"hexa core\"],\"legendgroup\":\"\",\"name\":\"\",\"showlegend\":true,\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Percentage of devices having different types of cores\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('6a8397da-0eb6-4112-9d51-5db7a1f321ec');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training set\n",
        "df1 = df.loc[:,['battery_power','blue','dual_sim','fc','four_g','int_memory','m_dep','mobile_wt','n_cores','pc','px_height','px_width','ram','sc_h','sc_w','talk_time','three_g','wifi','price_range']]\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "k4wxuEloZgAu",
        "outputId": "8f891434-7547-4155-bae8-e4ab81f170f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      battery_power  blue  dual_sim  fc  four_g  int_memory  m_dep  mobile_wt  \\\n",
              "0               842     0         0   1       0           7    0.6        188   \n",
              "1              1021     1         1   0       1          53    0.7        136   \n",
              "2               563     1         1   2       1          41    0.9        145   \n",
              "3               615     1         0   0       0          10    0.8        131   \n",
              "4              1821     1         0  13       1          44    0.6        141   \n",
              "...             ...   ...       ...  ..     ...         ...    ...        ...   \n",
              "1995            794     1         1   0       1           2    0.8        106   \n",
              "1996           1965     1         1   0       0          39    0.2        187   \n",
              "1997           1911     0         1   1       1          36    0.7        108   \n",
              "1998           1512     0         0   4       1          46    0.1        145   \n",
              "1999            510     1         1   5       1          45    0.9        168   \n",
              "\n",
              "      n_cores  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  three_g  \\\n",
              "0           2   2         20       756  2549     9     7         19        0   \n",
              "1           3   6        905      1988  2631    17     3          7        1   \n",
              "2           5   6       1263      1716  2603    11     2          9        1   \n",
              "3           6   9       1216      1786  2769    16     8         11        1   \n",
              "4           2  14       1208      1212  1411     8     2         15        1   \n",
              "...       ...  ..        ...       ...   ...   ...   ...        ...      ...   \n",
              "1995        6  14       1222      1890   668    13     4         19        1   \n",
              "1996        4   3        915      1965  2032    11    10         16        1   \n",
              "1997        8   3        868      1632  3057     9     1          5        1   \n",
              "1998        5   5        336       670   869    18    10         19        1   \n",
              "1999        6  16        483       754  3919    19     4          2        1   \n",
              "\n",
              "      wifi  price_range  \n",
              "0        1            1  \n",
              "1        0            2  \n",
              "2        0            2  \n",
              "3        0            2  \n",
              "4        0            1  \n",
              "...    ...          ...  \n",
              "1995     0            0  \n",
              "1996     1            2  \n",
              "1997     0            3  \n",
              "1998     1            0  \n",
              "1999     1            3  \n",
              "\n",
              "[2000 rows x 19 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb715032-59d9-4136-b65c-41e05d5a6853\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>pc</th>\n",
              "      <th>px_height</th>\n",
              "      <th>px_width</th>\n",
              "      <th>ram</th>\n",
              "      <th>sc_h</th>\n",
              "      <th>sc_w</th>\n",
              "      <th>talk_time</th>\n",
              "      <th>three_g</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>756</td>\n",
              "      <td>2549</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>905</td>\n",
              "      <td>1988</td>\n",
              "      <td>2631</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1263</td>\n",
              "      <td>1716</td>\n",
              "      <td>2603</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>1216</td>\n",
              "      <td>1786</td>\n",
              "      <td>2769</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1208</td>\n",
              "      <td>1212</td>\n",
              "      <td>1411</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>794</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>106</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>1222</td>\n",
              "      <td>1890</td>\n",
              "      <td>668</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>1965</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>0.2</td>\n",
              "      <td>187</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>915</td>\n",
              "      <td>1965</td>\n",
              "      <td>2032</td>\n",
              "      <td>11</td>\n",
              "      <td>10</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>1911</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>0.7</td>\n",
              "      <td>108</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>868</td>\n",
              "      <td>1632</td>\n",
              "      <td>3057</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>1512</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>0.1</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>336</td>\n",
              "      <td>670</td>\n",
              "      <td>869</td>\n",
              "      <td>18</td>\n",
              "      <td>10</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>510</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>0.9</td>\n",
              "      <td>168</td>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>483</td>\n",
              "      <td>754</td>\n",
              "      <td>3919</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb715032-59d9-4136-b65c-41e05d5a6853')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb715032-59d9-4136-b65c-41e05d5a6853 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb715032-59d9-4136-b65c-41e05d5a6853');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.loc[:,['touch_screen','clock_speed']]\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vmYADcicZuqX",
        "outputId": "0f21ead4-71b0-4a47-bf98-e4217f1abd53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      touch_screen  clock_speed\n",
              "0                0          2.2\n",
              "1                1          0.5\n",
              "2                1          0.5\n",
              "3                0          2.5\n",
              "4                1          1.2\n",
              "...            ...          ...\n",
              "1995             1          0.5\n",
              "1996             1          2.6\n",
              "1997             1          0.9\n",
              "1998             1          0.9\n",
              "1999             1          2.0\n",
              "\n",
              "[2000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6aa8a6a6-f6f4-4bab-b3db-7368ace78f19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>clock_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>1</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>1</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6aa8a6a6-f6f4-4bab-b3db-7368ace78f19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6aa8a6a6-f6f4-4bab-b3db-7368ace78f19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6aa8a6a6-f6f4-4bab-b3db-7368ace78f19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merging Dataset"
      ],
      "metadata": {
        "id": "5BTcKtGiZ1BK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.concat([df1, df2])\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "qXcZ4zyzZ3an",
        "outputId": "243e0e66-3b42-48be-ee8a-a07a87278ca1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      battery_power  blue  dual_sim    fc  four_g  int_memory  m_dep  \\\n",
              "0             842.0   0.0       0.0   1.0     0.0         7.0    0.6   \n",
              "1            1021.0   1.0       1.0   0.0     1.0        53.0    0.7   \n",
              "2             563.0   1.0       1.0   2.0     1.0        41.0    0.9   \n",
              "3             615.0   1.0       0.0   0.0     0.0        10.0    0.8   \n",
              "4            1821.0   1.0       0.0  13.0     1.0        44.0    0.6   \n",
              "...             ...   ...       ...   ...     ...         ...    ...   \n",
              "1995            NaN   NaN       NaN   NaN     NaN         NaN    NaN   \n",
              "1996            NaN   NaN       NaN   NaN     NaN         NaN    NaN   \n",
              "1997            NaN   NaN       NaN   NaN     NaN         NaN    NaN   \n",
              "1998            NaN   NaN       NaN   NaN     NaN         NaN    NaN   \n",
              "1999            NaN   NaN       NaN   NaN     NaN         NaN    NaN   \n",
              "\n",
              "      mobile_wt  n_cores    pc  ...  px_width     ram  sc_h  sc_w  talk_time  \\\n",
              "0         188.0      2.0   2.0  ...     756.0  2549.0   9.0   7.0       19.0   \n",
              "1         136.0      3.0   6.0  ...    1988.0  2631.0  17.0   3.0        7.0   \n",
              "2         145.0      5.0   6.0  ...    1716.0  2603.0  11.0   2.0        9.0   \n",
              "3         131.0      6.0   9.0  ...    1786.0  2769.0  16.0   8.0       11.0   \n",
              "4         141.0      2.0  14.0  ...    1212.0  1411.0   8.0   2.0       15.0   \n",
              "...         ...      ...   ...  ...       ...     ...   ...   ...        ...   \n",
              "1995        NaN      NaN   NaN  ...       NaN     NaN   NaN   NaN        NaN   \n",
              "1996        NaN      NaN   NaN  ...       NaN     NaN   NaN   NaN        NaN   \n",
              "1997        NaN      NaN   NaN  ...       NaN     NaN   NaN   NaN        NaN   \n",
              "1998        NaN      NaN   NaN  ...       NaN     NaN   NaN   NaN        NaN   \n",
              "1999        NaN      NaN   NaN  ...       NaN     NaN   NaN   NaN        NaN   \n",
              "\n",
              "      three_g  wifi  price_range  touch_screen  clock_speed  \n",
              "0         0.0   1.0          1.0           NaN          NaN  \n",
              "1         1.0   0.0          2.0           NaN          NaN  \n",
              "2         1.0   0.0          2.0           NaN          NaN  \n",
              "3         1.0   0.0          2.0           NaN          NaN  \n",
              "4         1.0   0.0          1.0           NaN          NaN  \n",
              "...       ...   ...          ...           ...          ...  \n",
              "1995      NaN   NaN          NaN           1.0          0.5  \n",
              "1996      NaN   NaN          NaN           1.0          2.6  \n",
              "1997      NaN   NaN          NaN           1.0          0.9  \n",
              "1998      NaN   NaN          NaN           1.0          0.9  \n",
              "1999      NaN   NaN          NaN           1.0          2.0  \n",
              "\n",
              "[4000 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f834b57-ad21-44a4-8520-cef2aa7d0c96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>pc</th>\n",
              "      <th>...</th>\n",
              "      <th>px_width</th>\n",
              "      <th>ram</th>\n",
              "      <th>sc_h</th>\n",
              "      <th>sc_w</th>\n",
              "      <th>talk_time</th>\n",
              "      <th>three_g</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>clock_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>...</td>\n",
              "      <td>756.0</td>\n",
              "      <td>2549.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>2631.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1716.0</td>\n",
              "      <td>2603.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1786.0</td>\n",
              "      <td>2769.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1212.0</td>\n",
              "      <td>1411.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4000 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f834b57-ad21-44a4-8520-cef2aa7d0c96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7f834b57-ad21-44a4-8520-cef2aa7d0c96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7f834b57-ad21-44a4-8520-cef2aa7d0c96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ranking According to Price Range"
      ],
      "metadata": {
        "id": "cq_ktqBwaRWo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"rank_by_price\"] = df[\"price_range\"].rank()\n",
        "dt1 = df\n",
        "dt1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "Zk1Po6BeaSUQ",
        "outputId": "0fcbb7b6-5b7e-439a-a8a9-86eeb6e7014a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
              "0            842     0          2.2         0   1       0           7    0.6   \n",
              "1           1021     1          0.5         1   0       1          53    0.7   \n",
              "2            563     1          0.5         1   2       1          41    0.9   \n",
              "3            615     1          2.5         0   0       0          10    0.8   \n",
              "4           1821     1          1.2         0  13       1          44    0.6   \n",
              "\n",
              "   mobile_wt  n_cores  ...  wifi  price_range  is_bluetooth  is_DualSim  \\\n",
              "0        188        2  ...     1            1            No          No   \n",
              "1        136        3  ...     0            2           Yes         Yes   \n",
              "2        145        5  ...     0            2           Yes         Yes   \n",
              "3        131        6  ...     0            2           Yes          No   \n",
              "4        141        2  ...     0            1           Yes          No   \n",
              "\n",
              "   is_4G  is_3G  is_touchscreen  is_wifi        cores  rank_by_price  \n",
              "0     No     No              No      Yes    dual core          750.5  \n",
              "1    Yes    Yes             Yes       No  triple core         1250.5  \n",
              "2    Yes    Yes             Yes       No   penta core         1250.5  \n",
              "3     No    Yes              No       No    hexa core         1250.5  \n",
              "4    Yes    Yes             Yes       No    dual core          750.5  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9061100d-d268-4e3c-b0d2-a7981e3edf0d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>...</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "      <th>is_bluetooth</th>\n",
              "      <th>is_DualSim</th>\n",
              "      <th>is_4G</th>\n",
              "      <th>is_3G</th>\n",
              "      <th>is_touchscreen</th>\n",
              "      <th>is_wifi</th>\n",
              "      <th>cores</th>\n",
              "      <th>rank_by_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
              "      <td>0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>dual core</td>\n",
              "      <td>750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>triple core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>penta core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>hexa core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>dual core</td>\n",
              "      <td>750.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9061100d-d268-4e3c-b0d2-a7981e3edf0d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9061100d-d268-4e3c-b0d2-a7981e3edf0d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9061100d-d268-4e3c-b0d2-a7981e3edf0d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt1[\"rank_by_price\"] = dt1[\"rank_by_price\"].sort_values()\n",
        "dt1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "_ENd_EvEaVt_",
        "outputId": "a7246900-5b0f-4042-d258-3cea4058bfa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
              "0               842     0          2.2         0   1       0           7   \n",
              "1              1021     1          0.5         1   0       1          53   \n",
              "2               563     1          0.5         1   2       1          41   \n",
              "3               615     1          2.5         0   0       0          10   \n",
              "4              1821     1          1.2         0  13       1          44   \n",
              "...             ...   ...          ...       ...  ..     ...         ...   \n",
              "1995            794     1          0.5         1   0       1           2   \n",
              "1996           1965     1          2.6         1   0       0          39   \n",
              "1997           1911     0          0.9         1   1       1          36   \n",
              "1998           1512     0          0.9         0   4       1          46   \n",
              "1999            510     1          2.0         1   5       1          45   \n",
              "\n",
              "      m_dep  mobile_wt  n_cores  ...  wifi  price_range  is_bluetooth  \\\n",
              "0       0.6        188        2  ...     1            1            No   \n",
              "1       0.7        136        3  ...     0            2           Yes   \n",
              "2       0.9        145        5  ...     0            2           Yes   \n",
              "3       0.8        131        6  ...     0            2           Yes   \n",
              "4       0.6        141        2  ...     0            1           Yes   \n",
              "...     ...        ...      ...  ...   ...          ...           ...   \n",
              "1995    0.8        106        6  ...     0            0           Yes   \n",
              "1996    0.2        187        4  ...     1            2           Yes   \n",
              "1997    0.7        108        8  ...     0            3            No   \n",
              "1998    0.1        145        5  ...     1            0            No   \n",
              "1999    0.9        168        6  ...     1            3           Yes   \n",
              "\n",
              "      is_DualSim  is_4G  is_3G  is_touchscreen  is_wifi        cores  \\\n",
              "0             No     No     No              No      Yes    dual core   \n",
              "1            Yes    Yes    Yes             Yes       No  triple core   \n",
              "2            Yes    Yes    Yes             Yes       No   penta core   \n",
              "3             No     No    Yes              No       No    hexa core   \n",
              "4             No    Yes    Yes             Yes       No    dual core   \n",
              "...          ...    ...    ...             ...      ...          ...   \n",
              "1995         Yes    Yes    Yes             Yes       No    hexa core   \n",
              "1996         Yes     No    Yes             Yes      Yes    quad core   \n",
              "1997         Yes    Yes    Yes             Yes       No    octa core   \n",
              "1998          No    Yes    Yes             Yes      Yes   penta core   \n",
              "1999         Yes    Yes    Yes             Yes      Yes    hexa core   \n",
              "\n",
              "      rank_by_price  \n",
              "0             750.5  \n",
              "1            1250.5  \n",
              "2            1250.5  \n",
              "3            1250.5  \n",
              "4             750.5  \n",
              "...             ...  \n",
              "1995          250.5  \n",
              "1996         1250.5  \n",
              "1997         1750.5  \n",
              "1998          250.5  \n",
              "1999         1750.5  \n",
              "\n",
              "[2000 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cba5df5e-b71b-453f-8a4f-51360e0329e6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>...</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "      <th>is_bluetooth</th>\n",
              "      <th>is_DualSim</th>\n",
              "      <th>is_4G</th>\n",
              "      <th>is_3G</th>\n",
              "      <th>is_touchscreen</th>\n",
              "      <th>is_wifi</th>\n",
              "      <th>cores</th>\n",
              "      <th>rank_by_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
              "      <td>0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>dual core</td>\n",
              "      <td>750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>triple core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>penta core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>hexa core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>dual core</td>\n",
              "      <td>750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>794</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>106</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>hexa core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>1965</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>0.2</td>\n",
              "      <td>187</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>quad core</td>\n",
              "      <td>1250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>1911</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>0.7</td>\n",
              "      <td>108</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>octa core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>1512</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>0.1</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>penta core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>510</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>0.9</td>\n",
              "      <td>168</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>hexa core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cba5df5e-b71b-453f-8a4f-51360e0329e6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cba5df5e-b71b-453f-8a4f-51360e0329e6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cba5df5e-b71b-453f-8a4f-51360e0329e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt1.sort_values(by=[\"rank_by_price\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "7p0XWifSaYmX",
        "outputId": "99dc9f22-6d17-4255-feda-4dc9f7739bb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
              "1838            720     1          0.9         1  12       1          14   \n",
              "1762            808     1          0.5         1   3       0          46   \n",
              "419             914     1          0.9         0   2       0           4   \n",
              "846            1489     0          2.4         1   8       0          32   \n",
              "845            1907     0          2.0         0  13       0          55   \n",
              "...             ...   ...          ...       ...  ..     ...         ...   \n",
              "981            1046     1          2.8         1   0       1          58   \n",
              "982            1035     0          0.6         1   2       1          44   \n",
              "987            1688     0          2.5         0   0       1          21   \n",
              "956             862     0          2.3         0   9       1           6   \n",
              "1999            510     1          2.0         1   5       1          45   \n",
              "\n",
              "      m_dep  mobile_wt  n_cores  ...  wifi  price_range  is_bluetooth  \\\n",
              "1838    0.8        165        4  ...     0            0           Yes   \n",
              "1762    0.5        105        8  ...     1            0           Yes   \n",
              "419     0.8        100        2  ...     0            0           Yes   \n",
              "846     0.6        200        1  ...     0            0            No   \n",
              "845     0.9        105        7  ...     1            0            No   \n",
              "...     ...        ...      ...  ...   ...          ...           ...   \n",
              "981     0.2        100        8  ...     1            3           Yes   \n",
              "982     0.5         99        3  ...     0            3            No   \n",
              "987     0.2        170        7  ...     1            3            No   \n",
              "956     0.1        107        8  ...     1            3            No   \n",
              "1999    0.9        168        6  ...     1            3           Yes   \n",
              "\n",
              "      is_DualSim  is_4G  is_3G  is_touchscreen  is_wifi        cores  \\\n",
              "1838         Yes    Yes    Yes             Yes       No    quad core   \n",
              "1762         Yes     No    Yes              No      Yes    octa core   \n",
              "419           No     No    Yes             Yes       No    dual core   \n",
              "846          Yes     No     No              No       No  single core   \n",
              "845           No     No    Yes              No      Yes   hepta core   \n",
              "...          ...    ...    ...             ...      ...          ...   \n",
              "981          Yes    Yes    Yes             Yes      Yes    octa core   \n",
              "982          Yes    Yes    Yes             Yes       No  triple core   \n",
              "987           No    Yes    Yes             Yes      Yes   hepta core   \n",
              "956           No    Yes    Yes             Yes      Yes    octa core   \n",
              "1999         Yes    Yes    Yes             Yes      Yes    hexa core   \n",
              "\n",
              "      rank_by_price  \n",
              "1838          250.5  \n",
              "1762          250.5  \n",
              "419           250.5  \n",
              "846           250.5  \n",
              "845           250.5  \n",
              "...             ...  \n",
              "981          1750.5  \n",
              "982          1750.5  \n",
              "987          1750.5  \n",
              "956          1750.5  \n",
              "1999         1750.5  \n",
              "\n",
              "[2000 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-366cf158-cbf6-47f7-9355-5ae78ed23150\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>...</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "      <th>is_bluetooth</th>\n",
              "      <th>is_DualSim</th>\n",
              "      <th>is_4G</th>\n",
              "      <th>is_3G</th>\n",
              "      <th>is_touchscreen</th>\n",
              "      <th>is_wifi</th>\n",
              "      <th>cores</th>\n",
              "      <th>rank_by_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1838</th>\n",
              "      <td>720</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>0.8</td>\n",
              "      <td>165</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>quad core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1762</th>\n",
              "      <td>808</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>0.5</td>\n",
              "      <td>105</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>octa core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>914</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.8</td>\n",
              "      <td>100</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>dual core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>1489</td>\n",
              "      <td>0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0.6</td>\n",
              "      <td>200</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>single core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>845</th>\n",
              "      <td>1907</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0.9</td>\n",
              "      <td>105</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>hepta core</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>981</th>\n",
              "      <td>1046</td>\n",
              "      <td>1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>58</td>\n",
              "      <td>0.2</td>\n",
              "      <td>100</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>octa core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>982</th>\n",
              "      <td>1035</td>\n",
              "      <td>0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.5</td>\n",
              "      <td>99</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>triple core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>987</th>\n",
              "      <td>1688</td>\n",
              "      <td>0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>0.2</td>\n",
              "      <td>170</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>hepta core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>956</th>\n",
              "      <td>862</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>107</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>octa core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>510</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>0.9</td>\n",
              "      <td>168</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>hexa core</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-366cf158-cbf6-47f7-9355-5ae78ed23150')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-366cf158-cbf6-47f7-9355-5ae78ed23150 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-366cf158-cbf6-47f7-9355-5ae78ed23150');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ranking on all the features using rank()"
      ],
      "metadata": {
        "id": "g4oMYySeagxH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dt2 = pd.read_csv('/content/drive/MyDrive/tcs project/MobileDataSets/MobileDataSets/MobileTrain.csv')\n",
        "RankedDataset1 = dt2.rank()\n",
        "RankedDataset1.sort_values(by=\"price_range\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "4yI6OL5VacZX",
        "outputId": "d4374a68-1427-4c3e-a064-8f6bc25ab19a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      battery_power    blue  clock_speed  dual_sim      fc  four_g  \\\n",
              "1838          320.5  1505.5        638.5    1491.0  1853.0  1479.0   \n",
              "1762          435.5  1505.5        207.0    1491.0   993.5   479.0   \n",
              "419           589.0  1505.5        638.5     491.0   814.0   479.0   \n",
              "846          1323.5   505.5       1612.5    1491.0  1601.0   479.0   \n",
              "845          1875.0   505.5       1337.0     491.0  1895.5   479.0   \n",
              "...             ...     ...          ...       ...     ...     ...   \n",
              "981           753.0  1505.5       1868.0    1491.0   237.5  1479.0   \n",
              "982           737.5   505.5        450.5    1491.0   814.0  1479.0   \n",
              "987          1583.0   505.5       1678.5     491.0   237.5  1479.0   \n",
              "956           515.5   505.5       1544.5     491.0  1678.5  1479.0   \n",
              "1999           21.0  1505.5       1337.0    1491.0  1281.0  1479.0   \n",
              "\n",
              "      int_memory   m_dep  mobile_wt  n_cores  ...  px_height  px_width  \\\n",
              "1838       432.0  1595.5     1414.5    872.5  ...      657.5     978.0   \n",
              "1762      1451.5  1003.0      436.0   1872.5  ...      189.0      45.0   \n",
              "419         77.5  1595.5      325.5    366.0  ...      329.5    1341.0   \n",
              "846       1011.0  1198.5     1991.0    121.5  ...      288.0     408.0   \n",
              "845       1727.0  1797.0      436.0   1615.0  ...     1231.5    1363.0   \n",
              "...          ...     ...        ...      ...  ...        ...       ...   \n",
              "981       1828.5   427.0      325.5   1872.5  ...      455.0     728.0   \n",
              "982       1382.0  1003.0      309.0    612.5  ...      419.5    1937.0   \n",
              "987        669.5   427.0     1496.0   1615.0  ...     1134.0     481.5   \n",
              "956        142.0   160.5      472.5   1872.5  ...     1371.0    1966.0   \n",
              "1999      1418.0  1797.0     1457.5   1370.5  ...      868.0     331.5   \n",
              "\n",
              "         ram    sc_h    sc_w  talk_time  three_g  touch_screen    wifi  \\\n",
              "1838   445.5  1214.0  1284.5      146.5   1239.0        1497.5   493.5   \n",
              "1762   442.0  1353.0  1008.0      901.0   1239.0         497.5  1493.5   \n",
              "419    306.0   791.5  1284.5      363.0   1239.0        1497.5   493.5   \n",
              "846    292.0   287.0  1153.5      696.5    239.0         497.5   493.5   \n",
              "845     16.0  1214.0   836.5     1636.5   1239.0         497.5  1493.5   \n",
              "...      ...     ...     ...        ...      ...           ...     ...   \n",
              "981   1919.0  1660.0  1797.5      798.5   1239.0        1497.5  1493.5   \n",
              "982   1732.0  1214.0  1008.0     1106.0   1239.0        1497.5   493.5   \n",
              "987   1994.0  1660.0  1897.0      696.5   1239.0        1497.5  1493.5   \n",
              "956   1703.5   541.5   836.5      798.5   1239.0        1497.5  1493.5   \n",
              "1999  1959.0  1938.5   836.5       50.0   1239.0        1497.5  1493.5   \n",
              "\n",
              "      price_range  \n",
              "1838        250.5  \n",
              "1762        250.5  \n",
              "419         250.5  \n",
              "846         250.5  \n",
              "845         250.5  \n",
              "...           ...  \n",
              "981        1750.5  \n",
              "982        1750.5  \n",
              "987        1750.5  \n",
              "956        1750.5  \n",
              "1999       1750.5  \n",
              "\n",
              "[2000 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38e159c0-4487-45ef-8e0f-2a6fe92166a5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>...</th>\n",
              "      <th>px_height</th>\n",
              "      <th>px_width</th>\n",
              "      <th>ram</th>\n",
              "      <th>sc_h</th>\n",
              "      <th>sc_w</th>\n",
              "      <th>talk_time</th>\n",
              "      <th>three_g</th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1838</th>\n",
              "      <td>320.5</td>\n",
              "      <td>1505.5</td>\n",
              "      <td>638.5</td>\n",
              "      <td>1491.0</td>\n",
              "      <td>1853.0</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>432.0</td>\n",
              "      <td>1595.5</td>\n",
              "      <td>1414.5</td>\n",
              "      <td>872.5</td>\n",
              "      <td>...</td>\n",
              "      <td>657.5</td>\n",
              "      <td>978.0</td>\n",
              "      <td>445.5</td>\n",
              "      <td>1214.0</td>\n",
              "      <td>1284.5</td>\n",
              "      <td>146.5</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>493.5</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1762</th>\n",
              "      <td>435.5</td>\n",
              "      <td>1505.5</td>\n",
              "      <td>207.0</td>\n",
              "      <td>1491.0</td>\n",
              "      <td>993.5</td>\n",
              "      <td>479.0</td>\n",
              "      <td>1451.5</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>436.0</td>\n",
              "      <td>1872.5</td>\n",
              "      <td>...</td>\n",
              "      <td>189.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>442.0</td>\n",
              "      <td>1353.0</td>\n",
              "      <td>1008.0</td>\n",
              "      <td>901.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>497.5</td>\n",
              "      <td>1493.5</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>589.0</td>\n",
              "      <td>1505.5</td>\n",
              "      <td>638.5</td>\n",
              "      <td>491.0</td>\n",
              "      <td>814.0</td>\n",
              "      <td>479.0</td>\n",
              "      <td>77.5</td>\n",
              "      <td>1595.5</td>\n",
              "      <td>325.5</td>\n",
              "      <td>366.0</td>\n",
              "      <td>...</td>\n",
              "      <td>329.5</td>\n",
              "      <td>1341.0</td>\n",
              "      <td>306.0</td>\n",
              "      <td>791.5</td>\n",
              "      <td>1284.5</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>493.5</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>1323.5</td>\n",
              "      <td>505.5</td>\n",
              "      <td>1612.5</td>\n",
              "      <td>1491.0</td>\n",
              "      <td>1601.0</td>\n",
              "      <td>479.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>1198.5</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>121.5</td>\n",
              "      <td>...</td>\n",
              "      <td>288.0</td>\n",
              "      <td>408.0</td>\n",
              "      <td>292.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>1153.5</td>\n",
              "      <td>696.5</td>\n",
              "      <td>239.0</td>\n",
              "      <td>497.5</td>\n",
              "      <td>493.5</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>845</th>\n",
              "      <td>1875.0</td>\n",
              "      <td>505.5</td>\n",
              "      <td>1337.0</td>\n",
              "      <td>491.0</td>\n",
              "      <td>1895.5</td>\n",
              "      <td>479.0</td>\n",
              "      <td>1727.0</td>\n",
              "      <td>1797.0</td>\n",
              "      <td>436.0</td>\n",
              "      <td>1615.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1231.5</td>\n",
              "      <td>1363.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1214.0</td>\n",
              "      <td>836.5</td>\n",
              "      <td>1636.5</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>497.5</td>\n",
              "      <td>1493.5</td>\n",
              "      <td>250.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>981</th>\n",
              "      <td>753.0</td>\n",
              "      <td>1505.5</td>\n",
              "      <td>1868.0</td>\n",
              "      <td>1491.0</td>\n",
              "      <td>237.5</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>1828.5</td>\n",
              "      <td>427.0</td>\n",
              "      <td>325.5</td>\n",
              "      <td>1872.5</td>\n",
              "      <td>...</td>\n",
              "      <td>455.0</td>\n",
              "      <td>728.0</td>\n",
              "      <td>1919.0</td>\n",
              "      <td>1660.0</td>\n",
              "      <td>1797.5</td>\n",
              "      <td>798.5</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>1493.5</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>982</th>\n",
              "      <td>737.5</td>\n",
              "      <td>505.5</td>\n",
              "      <td>450.5</td>\n",
              "      <td>1491.0</td>\n",
              "      <td>814.0</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>1382.0</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>309.0</td>\n",
              "      <td>612.5</td>\n",
              "      <td>...</td>\n",
              "      <td>419.5</td>\n",
              "      <td>1937.0</td>\n",
              "      <td>1732.0</td>\n",
              "      <td>1214.0</td>\n",
              "      <td>1008.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>493.5</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>987</th>\n",
              "      <td>1583.0</td>\n",
              "      <td>505.5</td>\n",
              "      <td>1678.5</td>\n",
              "      <td>491.0</td>\n",
              "      <td>237.5</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>669.5</td>\n",
              "      <td>427.0</td>\n",
              "      <td>1496.0</td>\n",
              "      <td>1615.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1134.0</td>\n",
              "      <td>481.5</td>\n",
              "      <td>1994.0</td>\n",
              "      <td>1660.0</td>\n",
              "      <td>1897.0</td>\n",
              "      <td>696.5</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>1493.5</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>956</th>\n",
              "      <td>515.5</td>\n",
              "      <td>505.5</td>\n",
              "      <td>1544.5</td>\n",
              "      <td>491.0</td>\n",
              "      <td>1678.5</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>160.5</td>\n",
              "      <td>472.5</td>\n",
              "      <td>1872.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1371.0</td>\n",
              "      <td>1966.0</td>\n",
              "      <td>1703.5</td>\n",
              "      <td>541.5</td>\n",
              "      <td>836.5</td>\n",
              "      <td>798.5</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>1493.5</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1505.5</td>\n",
              "      <td>1337.0</td>\n",
              "      <td>1491.0</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>1418.0</td>\n",
              "      <td>1797.0</td>\n",
              "      <td>1457.5</td>\n",
              "      <td>1370.5</td>\n",
              "      <td>...</td>\n",
              "      <td>868.0</td>\n",
              "      <td>331.5</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>1938.5</td>\n",
              "      <td>836.5</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>1497.5</td>\n",
              "      <td>1493.5</td>\n",
              "      <td>1750.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38e159c0-4487-45ef-8e0f-2a6fe92166a5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38e159c0-4487-45ef-8e0f-2a6fe92166a5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-38e159c0-4487-45ef-8e0f-2a6fe92166a5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ranking all the features separately to correct output"
      ],
      "metadata": {
        "id": "NraeiaHybAUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# because not all features are good when values are high and not all features are good when values are low\n",
        "# It depends on each and every feature\n",
        "b = dt2\n",
        "b[\"rank_by_price\"] = b[\"price_range\"].rank()\n",
        "b[\"rank_by_battery\"] = b[\"battery_power\"].rank(ascending=False)\n",
        "b[\"rank_by_blueooth\"] = b[\"blue\"].rank(ascending=False)\n",
        "b[\"rank_by_clockspeed\"] = b[\"clock_speed\"].rank(ascending=False)\n",
        "b[\"rank_by_DualSIM\"] = b[\"dual_sim\"].rank(ascending=False)\n",
        "b[\"rank_by_fc\"] = b[\"fc\"].rank(ascending=False)\n",
        "b[\"rank_by_4G\"] = b[\"four_g\"].rank(ascending=False)\n",
        "b[\"rank_by_InternalMemory\"] = b[\"int_memory\"].rank(ascending=False)\n",
        "b[\"rank_by_mdep\"] = b[\"m_dep\"].rank(ascending=False)\n",
        "b[\"rank_by_weight\"] = b[\"mobile_wt\"].rank(ascending=True)\n",
        "b[\"rank_by_ncores\"] = b[\"n_cores\"].rank(ascending=False)\n",
        "b[\"rank_by_pc\"] = b[\"pc\"].rank(ascending=False)\n",
        "b[\"rank_by_height\"] = b[\"px_height\"].rank(ascending=False)\n",
        "b[\"rank_by_width\"] = b[\"px_width\"].rank(ascending=False)\n",
        "b[\"rank_by_ram\"] = b[\"ram\"].rank(ascending=False)\n",
        "b[\"rank_by_sch\"] = b[\"sc_h\"].rank(ascending=False)\n",
        "b[\"rank_by_scw\"] = b[\"sc_w\"].rank(ascending=False)\n",
        "b[\"rank_by_talktime\"] = b[\"talk_time\"].rank(ascending=False)\n",
        "b[\"rank_by_3G\"] = b[\"three_g\"].rank(ascending=False)\n",
        "b[\"rank_by_touchscreen\"] = b[\"touch_screen\"].rank(ascending=False)\n",
        "b[\"rank_by_wifi\"] = b[\"wifi\"].rank(ascending=False)\n",
        "b.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "E_kXNW8Ha9Y1",
        "outputId": "93c07a1c-4756-45a9-cda8-f07f640928d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
              "0            842     0          2.2         0   1       0           7    0.6   \n",
              "1           1021     1          0.5         1   0       1          53    0.7   \n",
              "2            563     1          0.5         1   2       1          41    0.9   \n",
              "3            615     1          2.5         0   0       0          10    0.8   \n",
              "4           1821     1          1.2         0  13       1          44    0.6   \n",
              "\n",
              "   mobile_wt  n_cores  ...  rank_by_pc  rank_by_height  rank_by_width  \\\n",
              "0        188        2  ...      1746.0          1972.5         1664.5   \n",
              "1        136        3  ...      1402.0           545.5           17.0   \n",
              "2        145        5  ...      1402.0           212.0          386.5   \n",
              "3        131        6  ...      1090.5           250.0          287.0   \n",
              "4        141        2  ...       606.5           261.5         1054.0   \n",
              "\n",
              "   rank_by_ram  rank_by_sch  rank_by_scw  rank_by_talktime  rank_by_3G  \\\n",
              "0        762.5       1459.5        716.5             159.0      1762.0   \n",
              "1        714.0        341.0       1355.0            1418.5       762.0   \n",
              "2        732.0       1209.5       1532.5            1202.5       762.0   \n",
              "3        639.0        509.0        588.0             996.0       762.0   \n",
              "4       1375.0       1580.0       1532.5             587.0       762.0   \n",
              "\n",
              "   rank_by_touchscreen  rank_by_wifi  \n",
              "0               1503.5         507.5  \n",
              "1                503.5        1507.5  \n",
              "2                503.5        1507.5  \n",
              "3               1503.5        1507.5  \n",
              "4                503.5        1507.5  \n",
              "\n",
              "[5 rows x 42 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4ca25e9-435d-4d53-9412-fb5e5654a2ef\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>...</th>\n",
              "      <th>rank_by_pc</th>\n",
              "      <th>rank_by_height</th>\n",
              "      <th>rank_by_width</th>\n",
              "      <th>rank_by_ram</th>\n",
              "      <th>rank_by_sch</th>\n",
              "      <th>rank_by_scw</th>\n",
              "      <th>rank_by_talktime</th>\n",
              "      <th>rank_by_3G</th>\n",
              "      <th>rank_by_touchscreen</th>\n",
              "      <th>rank_by_wifi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
              "      <td>0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1746.0</td>\n",
              "      <td>1972.5</td>\n",
              "      <td>1664.5</td>\n",
              "      <td>762.5</td>\n",
              "      <td>1459.5</td>\n",
              "      <td>716.5</td>\n",
              "      <td>159.0</td>\n",
              "      <td>1762.0</td>\n",
              "      <td>1503.5</td>\n",
              "      <td>507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>1402.0</td>\n",
              "      <td>545.5</td>\n",
              "      <td>17.0</td>\n",
              "      <td>714.0</td>\n",
              "      <td>341.0</td>\n",
              "      <td>1355.0</td>\n",
              "      <td>1418.5</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1402.0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>386.5</td>\n",
              "      <td>732.0</td>\n",
              "      <td>1209.5</td>\n",
              "      <td>1532.5</td>\n",
              "      <td>1202.5</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>1090.5</td>\n",
              "      <td>250.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>639.0</td>\n",
              "      <td>509.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>996.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>1503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>606.5</td>\n",
              "      <td>261.5</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>1580.0</td>\n",
              "      <td>1532.5</td>\n",
              "      <td>587.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 42 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4ca25e9-435d-4d53-9412-fb5e5654a2ef')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a4ca25e9-435d-4d53-9412-fb5e5654a2ef button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4ca25e9-435d-4d53-9412-fb5e5654a2ef');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RankedDataset2 = b.iloc[:,21:]\n",
        "RankedDataset2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "udkXFH3DbU1E",
        "outputId": "69ccab2d-e81c-4b70-e266-34d32728c440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      rank_by_price  rank_by_battery  rank_by_blueooth  rank_by_clockspeed  \\\n",
              "0             750.5           1512.5            1495.5               525.0   \n",
              "1            1250.5           1278.5             495.5              1794.0   \n",
              "2            1250.5           1909.5             495.5              1794.0   \n",
              "3            1250.5           1830.0             495.5               322.5   \n",
              "4             750.5            242.0             495.5              1193.5   \n",
              "...             ...              ...               ...                 ...   \n",
              "1995          250.5           1587.5             495.5              1794.0   \n",
              "1996         1250.5             58.0             495.5               258.0   \n",
              "1997         1750.5            121.5            1495.5              1362.5   \n",
              "1998          250.5            648.0            1495.5              1362.5   \n",
              "1999         1750.5           1980.0             495.5               664.0   \n",
              "\n",
              "      rank_by_DualSIM  rank_by_fc  rank_by_4G  rank_by_InternalMemory  \\\n",
              "0              1510.0      1404.0      1522.0                  1820.5   \n",
              "1               510.0      1763.5       522.0                   334.5   \n",
              "2               510.0      1187.0       522.0                   720.0   \n",
              "3              1510.0      1763.5      1522.0                  1710.5   \n",
              "4              1510.0       105.5       522.0                   619.0   \n",
              "...               ...         ...         ...                     ...   \n",
              "1995            510.0      1763.5       522.0                  1979.5   \n",
              "1996            510.0      1763.5      1522.0                   784.5   \n",
              "1997            510.0      1404.0       522.0                   860.0   \n",
              "1998           1510.0       856.0       522.0                   549.5   \n",
              "1999            510.0       720.0       522.0                   583.0   \n",
              "\n",
              "      rank_by_mdep  rank_by_weight  ...  rank_by_pc  rank_by_height  \\\n",
              "0            802.5          1783.5  ...      1746.0          1972.5   \n",
              "1            609.5           932.5  ...      1402.0           545.5   \n",
              "2            204.0          1069.0  ...      1402.0           212.0   \n",
              "3            405.5           846.0  ...      1090.5           250.0   \n",
              "4            802.5          1006.0  ...       606.5           261.5   \n",
              "...            ...             ...  ...         ...             ...   \n",
              "1995         405.5           456.0  ...       606.5           244.5   \n",
              "1996        1574.0          1766.5  ...      1650.0           536.0   \n",
              "1997         609.5           486.5  ...      1650.0           583.5   \n",
              "1998        1840.5          1069.0  ...      1479.0          1395.5   \n",
              "1999         204.0          1457.5  ...       418.5          1133.0   \n",
              "\n",
              "      rank_by_width  rank_by_ram  rank_by_sch  rank_by_scw  rank_by_talktime  \\\n",
              "0            1664.5        762.5       1459.5        716.5             159.0   \n",
              "1              17.0        714.0        341.0       1355.0            1418.5   \n",
              "2             386.5        732.0       1209.5       1532.5            1202.5   \n",
              "3             287.0        639.0        509.0        588.0             996.0   \n",
              "4            1054.0       1375.0       1580.0       1532.5             587.0   \n",
              "...             ...          ...          ...          ...               ...   \n",
              "1995          163.0       1767.0        924.0       1164.5             159.0   \n",
              "1996           54.5       1062.0       1209.5        375.0             471.5   \n",
              "1997          502.0        504.0       1459.5       1715.5            1638.0   \n",
              "1998         1792.5       1664.5        184.5        375.0             159.0   \n",
              "1999         1669.5         42.0         62.5       1164.5            1951.0   \n",
              "\n",
              "      rank_by_3G  rank_by_touchscreen  rank_by_wifi  \n",
              "0         1762.0               1503.5         507.5  \n",
              "1          762.0                503.5        1507.5  \n",
              "2          762.0                503.5        1507.5  \n",
              "3          762.0               1503.5        1507.5  \n",
              "4          762.0                503.5        1507.5  \n",
              "...          ...                  ...           ...  \n",
              "1995       762.0                503.5        1507.5  \n",
              "1996       762.0                503.5         507.5  \n",
              "1997       762.0                503.5        1507.5  \n",
              "1998       762.0                503.5         507.5  \n",
              "1999       762.0                503.5         507.5  \n",
              "\n",
              "[2000 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85421772-07ab-4b7b-b823-820ebaf8bd9b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank_by_price</th>\n",
              "      <th>rank_by_battery</th>\n",
              "      <th>rank_by_blueooth</th>\n",
              "      <th>rank_by_clockspeed</th>\n",
              "      <th>rank_by_DualSIM</th>\n",
              "      <th>rank_by_fc</th>\n",
              "      <th>rank_by_4G</th>\n",
              "      <th>rank_by_InternalMemory</th>\n",
              "      <th>rank_by_mdep</th>\n",
              "      <th>rank_by_weight</th>\n",
              "      <th>...</th>\n",
              "      <th>rank_by_pc</th>\n",
              "      <th>rank_by_height</th>\n",
              "      <th>rank_by_width</th>\n",
              "      <th>rank_by_ram</th>\n",
              "      <th>rank_by_sch</th>\n",
              "      <th>rank_by_scw</th>\n",
              "      <th>rank_by_talktime</th>\n",
              "      <th>rank_by_3G</th>\n",
              "      <th>rank_by_touchscreen</th>\n",
              "      <th>rank_by_wifi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>750.5</td>\n",
              "      <td>1512.5</td>\n",
              "      <td>1495.5</td>\n",
              "      <td>525.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>1522.0</td>\n",
              "      <td>1820.5</td>\n",
              "      <td>802.5</td>\n",
              "      <td>1783.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1746.0</td>\n",
              "      <td>1972.5</td>\n",
              "      <td>1664.5</td>\n",
              "      <td>762.5</td>\n",
              "      <td>1459.5</td>\n",
              "      <td>716.5</td>\n",
              "      <td>159.0</td>\n",
              "      <td>1762.0</td>\n",
              "      <td>1503.5</td>\n",
              "      <td>507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1250.5</td>\n",
              "      <td>1278.5</td>\n",
              "      <td>495.5</td>\n",
              "      <td>1794.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>1763.5</td>\n",
              "      <td>522.0</td>\n",
              "      <td>334.5</td>\n",
              "      <td>609.5</td>\n",
              "      <td>932.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1402.0</td>\n",
              "      <td>545.5</td>\n",
              "      <td>17.0</td>\n",
              "      <td>714.0</td>\n",
              "      <td>341.0</td>\n",
              "      <td>1355.0</td>\n",
              "      <td>1418.5</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1250.5</td>\n",
              "      <td>1909.5</td>\n",
              "      <td>495.5</td>\n",
              "      <td>1794.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>1187.0</td>\n",
              "      <td>522.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1069.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1402.0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>386.5</td>\n",
              "      <td>732.0</td>\n",
              "      <td>1209.5</td>\n",
              "      <td>1532.5</td>\n",
              "      <td>1202.5</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1250.5</td>\n",
              "      <td>1830.0</td>\n",
              "      <td>495.5</td>\n",
              "      <td>322.5</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>1763.5</td>\n",
              "      <td>1522.0</td>\n",
              "      <td>1710.5</td>\n",
              "      <td>405.5</td>\n",
              "      <td>846.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1090.5</td>\n",
              "      <td>250.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>639.0</td>\n",
              "      <td>509.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>996.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>1503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>750.5</td>\n",
              "      <td>242.0</td>\n",
              "      <td>495.5</td>\n",
              "      <td>1193.5</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>105.5</td>\n",
              "      <td>522.0</td>\n",
              "      <td>619.0</td>\n",
              "      <td>802.5</td>\n",
              "      <td>1006.0</td>\n",
              "      <td>...</td>\n",
              "      <td>606.5</td>\n",
              "      <td>261.5</td>\n",
              "      <td>1054.0</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>1580.0</td>\n",
              "      <td>1532.5</td>\n",
              "      <td>587.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>250.5</td>\n",
              "      <td>1587.5</td>\n",
              "      <td>495.5</td>\n",
              "      <td>1794.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>1763.5</td>\n",
              "      <td>522.0</td>\n",
              "      <td>1979.5</td>\n",
              "      <td>405.5</td>\n",
              "      <td>456.0</td>\n",
              "      <td>...</td>\n",
              "      <td>606.5</td>\n",
              "      <td>244.5</td>\n",
              "      <td>163.0</td>\n",
              "      <td>1767.0</td>\n",
              "      <td>924.0</td>\n",
              "      <td>1164.5</td>\n",
              "      <td>159.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>1250.5</td>\n",
              "      <td>58.0</td>\n",
              "      <td>495.5</td>\n",
              "      <td>258.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>1763.5</td>\n",
              "      <td>1522.0</td>\n",
              "      <td>784.5</td>\n",
              "      <td>1574.0</td>\n",
              "      <td>1766.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>536.0</td>\n",
              "      <td>54.5</td>\n",
              "      <td>1062.0</td>\n",
              "      <td>1209.5</td>\n",
              "      <td>375.0</td>\n",
              "      <td>471.5</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>1750.5</td>\n",
              "      <td>121.5</td>\n",
              "      <td>1495.5</td>\n",
              "      <td>1362.5</td>\n",
              "      <td>510.0</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>522.0</td>\n",
              "      <td>860.0</td>\n",
              "      <td>609.5</td>\n",
              "      <td>486.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>583.5</td>\n",
              "      <td>502.0</td>\n",
              "      <td>504.0</td>\n",
              "      <td>1459.5</td>\n",
              "      <td>1715.5</td>\n",
              "      <td>1638.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>1507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>250.5</td>\n",
              "      <td>648.0</td>\n",
              "      <td>1495.5</td>\n",
              "      <td>1362.5</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>522.0</td>\n",
              "      <td>549.5</td>\n",
              "      <td>1840.5</td>\n",
              "      <td>1069.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>1395.5</td>\n",
              "      <td>1792.5</td>\n",
              "      <td>1664.5</td>\n",
              "      <td>184.5</td>\n",
              "      <td>375.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>507.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>1750.5</td>\n",
              "      <td>1980.0</td>\n",
              "      <td>495.5</td>\n",
              "      <td>664.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>522.0</td>\n",
              "      <td>583.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1457.5</td>\n",
              "      <td>...</td>\n",
              "      <td>418.5</td>\n",
              "      <td>1133.0</td>\n",
              "      <td>1669.5</td>\n",
              "      <td>42.0</td>\n",
              "      <td>62.5</td>\n",
              "      <td>1164.5</td>\n",
              "      <td>1951.0</td>\n",
              "      <td>762.0</td>\n",
              "      <td>503.5</td>\n",
              "      <td>507.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85421772-07ab-4b7b-b823-820ebaf8bd9b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85421772-07ab-4b7b-b823-820ebaf8bd9b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85421772-07ab-4b7b-b823-820ebaf8bd9b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Classification"
      ],
      "metadata": {
        "id": "MGAE9neWbgUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "lamOf0cfbih4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/tcs project/MobileDataSets/MobileDataSets/MobileTrain.csv')"
      ],
      "metadata": {
        "id": "R7DoFp_Tbjn4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "x = sc_x.fit_transform(x)\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25, random_state=0)"
      ],
      "metadata": {
        "id": "1CETle83bzdS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#logistic Regression\n",
        "log=LogisticRegression()\n",
        "log.fit(x_train,y_train)\n",
        "print(\"Training score of LogisticRegression is: {}\".format(log.score(x_train,y_train)*100))\n",
        "y_predlog=log.predict(x_test)\n",
        "print(y_predlog)\n",
        "acrr=accuracy_score(y_test,y_predlog)*100\n",
        "print(\"Accuracy of Logistic Regression Classifier is: {}%\".format(acrr))\n",
        "print(\"Confusion matrix Logistic Regression Classifier is: \\n{}\".format(confusion_matrix(y_test,y_predlog)))\n",
        "print(\"{}\".format(classification_report(y_test,y_predlog)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzpOkU-Mb1r5",
        "outputId": "d22bd13e-47c5-4c42-c791-18978ea001e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score of LogisticRegression is: 97.93333333333332\n",
            "[3 0 2 2 3 0 0 3 3 1 1 3 0 2 3 0 3 2 2 1 0 0 3 1 2 2 3 1 3 1 1 0 2 0 1 3 0\n",
            " 0 3 3 3 1 3 3 1 3 0 1 3 1 1 3 0 3 0 2 2 2 0 3 3 1 3 2 1 2 3 2 2 2 3 2 1 0\n",
            " 1 3 2 2 1 2 3 3 3 0 0 0 2 1 2 3 1 2 2 1 0 3 3 3 0 3 1 1 2 1 3 2 2 3 2 3 3\n",
            " 0 0 1 3 3 0 0 1 0 0 3 2 2 1 2 1 1 0 2 1 3 3 3 3 3 3 2 0 1 1 2 1 3 0 3 0 0\n",
            " 2 0 1 1 1 1 3 0 0 3 1 3 2 1 3 1 2 3 3 2 1 0 3 1 2 3 3 0 2 2 3 1 2 1 0 1 2\n",
            " 1 2 0 3 3 1 1 0 2 3 0 1 2 2 0 3 3 3 1 2 3 3 3 0 0 0 2 3 3 0 0 1 3 2 3 3 3\n",
            " 0 0 2 3 3 1 0 2 0 0 0 3 2 1 2 2 1 1 0 2 3 3 0 0 1 3 3 1 3 0 3 1 1 0 2 3 3\n",
            " 2 0 0 1 2 3 2 2 3 1 1 0 3 3 2 1 3 2 2 2 1 0 2 2 1 0 0 2 2 2 3 0 1 3 0 2 2\n",
            " 3 0 2 0 1 1 3 0 0 2 3 1 2 0 2 0 3 0 3 3 2 3 1 2 2 1 1 2 0 1 0 3 1 0 3 0 0\n",
            " 1 3 0 3 1 1 0 1 3 0 2 1 1 2 1 1 0 2 0 0 3 1 2 3 2 2 0 3 2 2 1 3 2 3 3 3 0\n",
            " 2 0 3 0 1 1 2 3 1 3 1 2 0 1 2 3 0 0 1 3 0 3 0 2 2 1 1 0 2 0 0 1 3 0 3 3 0\n",
            " 2 1 3 1 1 3 2 0 3 2 2 0 0 3 0 1 1 1 3 2 3 2 0 3 0 0 1 3 0 0 3 2 2 2 3 0 0\n",
            " 1 2 1 2 0 3 3 0 3 3 0 2 2 1 0 2 2 1 3 2 2 0 2 0 3 3 2 1 0 3 1 2 0 0 1 3 0\n",
            " 3 0 0 1 2 0 1 3 0 3 2 1 2 0 3 0 2 3 2]\n",
            "Accuracy of Logistic Regression Classifier is: 96.2%\n",
            "Confusion matrix Logistic Regression Classifier is: \n",
            "[[122   2   0   0]\n",
            " [  3 103   4   0]\n",
            " [  0   5 116   4]\n",
            " [  0   0   1 140]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       124\n",
            "           1       0.94      0.94      0.94       110\n",
            "           2       0.96      0.93      0.94       125\n",
            "           3       0.97      0.99      0.98       141\n",
            "\n",
            "    accuracy                           0.96       500\n",
            "   macro avg       0.96      0.96      0.96       500\n",
            "weighted avg       0.96      0.96      0.96       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gussain NB\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "nb=GaussianNB()\n",
        "nb.fit(x_train,y_train)\n",
        "print(\"Training score of GaussianNB is {}\".format(nb.score(x_train,y_train)*100))\n",
        "y_pred=nb.predict(x_test)\n",
        "ac_nb=accuracy_score(y_test,y_pred)*100\n",
        "print(\"Accuracy of Naive Bayes Classifier is: {}\".format(ac_nb))\n",
        "print(\"Confusion matrix of Naive Bayes Classifier is:{}\".format(confusion_matrix(y_test,y_pred)))\n",
        "print(\"{}\".format(classification_report(y_test,y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Am2dwSNPb_x2",
        "outputId": "67770e69-31ee-4fc9-dde1-848e728a3f9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score of GaussianNB is 81.39999999999999\n",
            "Accuracy of Naive Bayes Classifier is: 84.39999999999999\n",
            "Confusion matrix of Naive Bayes Classifier is:[[117   7   0   0]\n",
            " [  6  83  21   0]\n",
            " [  0  22  92  11]\n",
            " [  0   0  11 130]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.94      0.95       124\n",
            "           1       0.74      0.75      0.75       110\n",
            "           2       0.74      0.74      0.74       125\n",
            "           3       0.92      0.92      0.92       141\n",
            "\n",
            "    accuracy                           0.84       500\n",
            "   macro avg       0.84      0.84      0.84       500\n",
            "weighted avg       0.84      0.84      0.84       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SVM\n",
        "from sklearn.svm import SVC\n",
        "svm=SVC(kernel='rbf')\n",
        "svm.fit(x_train,y_train)\n",
        "print(\"Training score of SVM is: {}\".format(svm.score(x_train,y_train)*100))\n",
        "y_pred_svm=svm.predict(x_test)\n",
        "ac_svm=accuracy_score(y_test,y_pred_svm)*100\n",
        "print(\"Accuracy of SVM is: {}\".format(ac_svm))\n",
        "print(\"Confusion matrix of SVM is: {}\".format(confusion_matrix(y_test,y_pred_svm)))\n",
        "print(\"{}\".format(classification_report(y_test,y_pred_svm)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIzJutl_cD1P",
        "outputId": "a4042bdd-b02d-4112-feba-d0c8278a6c2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score of SVM is: 98.06666666666666\n",
            "Accuracy of SVM is: 89.0\n",
            "Confusion matrix of SVM is: [[118   6   0   0]\n",
            " [  6  96   8   0]\n",
            " [  0  15 106   4]\n",
            " [  0   0  16 125]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95       124\n",
            "           1       0.82      0.87      0.85       110\n",
            "           2       0.82      0.85      0.83       125\n",
            "           3       0.97      0.89      0.93       141\n",
            "\n",
            "    accuracy                           0.89       500\n",
            "   macro avg       0.89      0.89      0.89       500\n",
            "weighted avg       0.89      0.89      0.89       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Decision Tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "DT=DecisionTreeClassifier(criterion = \"entropy\")\n",
        "DT.fit(x_train,y_train)\n",
        "print(\"Training score of DecisionTreeClassifier is: {}\".format(DT.score(x_train,y_train)*100))\n",
        "y_pred_DT=DT.predict(x_test)\n",
        "ac_DT=accuracy_score(y_test,y_pred_DT)*100\n",
        "print(\"Accuracy of Decision Tree Classifier is: {}\".format(ac_DT))\n",
        "print(\"Confusion matrix of Decision Tree Classifier is: {}\".format(confusion_matrix(y_test,y_pred_DT)))\n",
        "print(\"{}\".format(classification_report(y_test,y_pred_DT)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKxPdjqGcJXf",
        "outputId": "8a353043-f8c6-4ff0-84e3-df4020992829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score of DecisionTreeClassifier is: 100.0\n",
            "Accuracy of Decision Tree Classifier is: 86.4\n",
            "Confusion matrix of Decision Tree Classifier is: [[111  13   0   0]\n",
            " [ 11  92   7   0]\n",
            " [  0  12 103  10]\n",
            " [  0   0  15 126]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.90      0.90       124\n",
            "           1       0.79      0.84      0.81       110\n",
            "           2       0.82      0.82      0.82       125\n",
            "           3       0.93      0.89      0.91       141\n",
            "\n",
            "    accuracy                           0.86       500\n",
            "   macro avg       0.86      0.86      0.86       500\n",
            "weighted avg       0.87      0.86      0.86       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "RF=RandomForestClassifier(n_estimators=300)\n",
        "RF.fit(x_train,y_train)\n",
        "print(\"Training score of Random Forest Classifier is: {}\".format(RF.score(x_train,y_train)*100))\n",
        "y_pred_RF=RF.predict(x_test)\n",
        "ac_RF=accuracy_score(y_test,y_pred_RF)*100\n",
        "print(\"Accuracy of Random Forest Classifier is: {}\".format(ac_RF))\n",
        "print(\"Confusion matrix of Random Forest Classifier is: {}\".format(confusion_matrix(y_test,y_pred_RF)))\n",
        "print(\"{}\".format(classification_report(y_test,y_pred_RF)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Bcv69x5c90X",
        "outputId": "4060b95e-c416-4b7f-a960-7de88b84056c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training score of Random Forest Classifier is: 100.0\n",
            "Accuracy of Random Forest Classifier is: 88.2\n",
            "Confusion matrix of Random Forest Classifier is: [[123   1   0   0]\n",
            " [  7  88  15   0]\n",
            " [  0  18  98   9]\n",
            " [  0   0   9 132]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97       124\n",
            "           1       0.82      0.80      0.81       110\n",
            "           2       0.80      0.78      0.79       125\n",
            "           3       0.94      0.94      0.94       141\n",
            "\n",
            "    accuracy                           0.88       500\n",
            "   macro avg       0.88      0.88      0.88       500\n",
            "weighted avg       0.88      0.88      0.88       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Classifiers\n",
        "classifiers=[\"LogisticRegression\",\"GaussianNB\",\"SVM\",\"DecisionTreeClassifier\",\"RandomForestClassifier\"]\n",
        "accuracy_=[acrr,ac_nb,ac_svm,ac_DT,ac_RF]\n",
        "df_ac=pd.DataFrame({'model':classifiers,\"accuracy\":accuracy_})\n",
        "px.histogram(data_frame=df_ac,x=\"model\",y=\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "iOhPML2ZdD8w",
        "outputId": "00e6e6a9-a1d4-48c0-963c-ca0becf12152"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"e3d7b06b-d69e-4ea3-b33c-2c4166de996a\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e3d7b06b-d69e-4ea3-b33c-2c4166de996a\")) {                    Plotly.newPlot(                        \"e3d7b06b-d69e-4ea3-b33c-2c4166de996a\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"sum\",\"hovertemplate\":\"model=%{x}<br>sum of accuracy=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[\"LogisticRegression\",\"GaussianNB\",\"SVM\",\"DecisionTreeClassifier\",\"RandomForestClassifier\"],\"xaxis\":\"x\",\"y\":[96.2,84.39999999999999,89.0,86.4,88.2],\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"model\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"sum of accuracy\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('e3d7b06b-d69e-4ea3-b33c-2c4166de996a');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "train_accuracy=[]\n",
        "test_accuracy=[]\n",
        "for i in range(100,600,100):\n",
        "    c=RandomForestRegressor(n_estimators=i)\n",
        "    c.fit(x_train,y_train)\n",
        "    train_accuracy.append(c.score(x_train,y_train))\n",
        "    test_accuracy.append(c.score(x_test,y_test))\n",
        "frame=pd.DataFrame({\"n_neighbors\":range(100,600,100),\"train_accuracy\":train_accuracy,\"test_accuracy\":test_accuracy})\n",
        "frame\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(range(100,600,100),frame[\"train_accuracy\"],label=\"Train\")\n",
        "plt.plot(range(100,600,100),frame[\"test_accuracy\"],label=\"Test\")\n",
        "plt.title(\"Optimal Number of Estimators\")\n",
        "plt.xlabel(\"Estimators\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "print(frame)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "__PDf_-XdJIw",
        "outputId": "31216ea3-eae3-4115-a5b3-69dc3688bef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAG5CAYAAADLbpPTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dfZhdZX3v//d3ZpIMmEA0iQoECahVYsWAIypt5UE9YhV50B7hiNWfvWpR0fZYilB/bZEeio+10nrqsS2lVisoLYqPqAgHzkGFifKMaFQqAcUYTQAhyTx8zx977cmanT2TPcnsmTsz79d1DbPWve619vfea5j55F5r7x2ZiSRJksrQM9sFSJIkaTvDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSxkTEkyLi4Yjo7cKxz4uIj0/3cXdVRGREPGWWHvtpEXFzRDwUEW/r4uN07XxK6h7DmbQHi4jXR8RtEfFIRPw0Iv4+IpZOYf97IuJFzfXM/HFmLs7Mke5UPGEdx1Rh6X+2tP+fiHj9TNYyQ84GrsnMJZl5UevGiLg2IrZUwar59bmdHXQmz2dpYVuaSwxn0h4qIv4YeA/wJ8C+wPOAg4CvRsTC2axtF/0KeG1ErJrlOqYkIvp2YbeDgDt20ufMKlg1v07Yhccp1i4+b9K8YDiT9kARsQ/wLuCtmfnlzBzKzHuA/wqsAk6v+p0XEZdHxGXVJbRvR8Szqm3/CjwJ+Fw1M3N2RKyqZrD6qj7XRsT/iIgbmrM3EbEsIj4REQ9GxE31MBURH4qIe6ttayPit6YwrE3AJcBfTDDmcTM1u1tr5bcj4ocR8fOIeF9E9NSO/4aIuCsifhkRV0XEQbVtGRFviYjvA9+foN5XRMQdEbGpqu3Qqv3rwLHA31V1/toUniMiYnlEfL467i8i4vqI6JnJ8xkRxwN/Cry6Os4tVfv+EXFlVde6iPj9lvN3eUR8PCIeBF4fEUdGxGB1/Aci4q+n8lxIc5XhTNozHQX0A/9Rb8zMh4EvAi+uNZ8IfBp4HPBvwGciYkFmvhb4MXBCNTPz3gke61TgtcABwJOBbwD/XB3vLsaHqZuANbXH+nRE9E9hXBcAr4yIp01hn12tFeBkYAA4gsbz9AaAiDiRRvg4BVgBXA98smXfk4DnAqtbi6gC1yeBP6r2/yKN0LQwM4+rjtecGfveFMf4x8D66rhPqOrMmTyfmfll4K+Ay6rHelbV/9Kqtv2BVwF/FRHH1Y53InA5sBT4BPAh4EOZuU9Vy6em+FxIc5LhTNozLQd+npnDbbb9pNretDYzL8/MIeCvaYS6503hsf45M3+QmZuBLwE/yMyvVY/9aeDwZsfM/HhmbszM4cz8ALAI6DhoZeZPgY8A50+hvl2qtfKezPxFZv4Y+BvgtKr9DODCzLyr2vevgDX12bNq+y8y89E2dbwa+EJmfrV63t8P7EUjVHfqomp2rPn1l1X7ELAfcFA1Y3p9Tu1DkrtyPiPiQOA3gHdk5pbMvBn4R+B3a92+kZmfyczR6nkbAp4SEcsz8+HM/OYUxiHNWYYzac/0c2D5BPft7Fdtb7q3uZCZo2yf2ejUA7XlR9usL26uRMRZ1aXAzRGxica9cPWg2In3AC9pXn6doo5rrdxbW/5Ptj8vBwEfagYj4BdA0Jhtardvq/2r4wFjz/u9LfvvzNsyc2nt68+q9vcB64CvVJdkz5nCMaF753N/4BeZ+VCt7T+Z/Dn7PeDXgO9Wl1RfPrWhSHOT4UzaM30D2ErjstuYiFgMvBS4utZ8YG17D7ASuL9qmsqMy6Sq+5HOpnHf22MzcymwmUao6VhmbqQxi/WXLZt+BexdW3/irlc75sDa8pPY/rzcC/xBSzjaKzNvqJc6yXHvpxHwAIiIqB7rvt0tODMfysw/zsxDgFcAb4+IF3ZQ05R0cD5bH+t+4HERsaTW9iTGj3ncPpn5/cw8DXg8jVB+eUQ8ZrrGIO2pDGfSHqi6JPUu4G8j4viIWFDdyP0pGjNj/1rr/uyIOKWaZfsjGqGuefnoAeCQaSprCTAMbAD6IuLPgX128Vh/TeMS4KG1tpuBF0Tjvbv2Bc7dnWIrfxIRj60uyf0hcFnV/hHg3Ih4BkBE7BsRvzOF434KeFlEvDAiFtC4T2wrcMPku+1cRLw8Ip5SBb7NwAgwWm2eyfP5ALCq+SKKzLyXxvgujIj+iDiMxszYhG+3ERGnR8SKamZxU9U8OlF/ab4wnEl7qOqG7z+lcT/Tg8C3aMz4vDAzt9a6fpbGPVC/pHEj+CnVfVAAFwL/f3X57qzdLOkq4MvA92hcztrC5Jf+JpSZDwLvpXEjerPtqzTC063AWuDzu1kvNJ6btTSC3xeAf6oe6woaMzmXVq8svJ3GjGSn9d9N4xWzf0vjEvMJNG7U3zaF2pqv5mx+ra3anwp8DXiYxgzq/8zMa6ptM3k+P1193xgR366WT6PxauH7gSuAv8jMr03yGMcDd0TEwzReHHDqBPfwSfNKTO0+Ukl7kog4D3hKZp4+27VIkjrjzJkkSVJBDGeSJEkF8bKmJElSQZw5kyRJKsic+eDZ5cuX56pVq2a7DEmSpJ1au3btzzNzRbttcyacrVq1isHBwdkuQ5Ikaaci4j8n2uZlTUmSpIIYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqiOFMkiSpIIYzSZKkghjOJEmSCmI4kyRJKojhTJIkqSCGM0mSpIIYziRJkgrSN9sFSJIk7Y7MJBNGMhmtlkczGRlNRrOxfdxyNpZHR1v3S0ZGYUFvcMiKxbM2HsOZ1Ebzf+ShkWTb8CjbRkYBiGhsDyAiqu/QWGpsaKw3tm/v2+jT3J+Wthhrqx2z3lnSbmn+8QbIan37crN9ex+q9tHqD3mObl9u/vFv/LGfIAhkMlrtM9oMArU//mPt4/rU11v2HR1/nNHRlj6t6+361ZZHmnXvsF+jvpzgsUeqMYyO1pZr4892y7n9eWk9/g79xraNP3675zpbxjKdnvL4xXzt7UdP70GnwHCmWdEIPo3QMzTc/J5sGxll2/AoQyOjY9sb61X/Zt/6flWAGqrtu63W1vzaOrZe6z9S3y/HjrltZHTa/2ffHfXA1wxvUIXCYIe21v7Vhu1tE4TMZkyMYIfguUPobB5jkpBJ6z4t/WnpH9F+HNTG0W5sY3VO9Fy0jKP1Mds/F9URgtrPwo5/4JubMqkt51hbtdf25WxZH/vPju0THZ+qfeLjb6+hXR31OmtD26F9ouOPG+Okx98+uNa6suVYrXV0cnyq53Jnx9d2vT1BT/WPv56A3gh6qt8JjW1BRNDbAz1tt7Xfp3nM+vF7e4IFY9sb7e2O39NT37b9+D31PkHVb3vf+n4d9xu3T72G8cdZ0j+78chwNgeNjiZDo5OFmmTbyAjbhrMl0IwPLuPD0ei4WaSh4fr28cfZHo5Gxh6/NRyNjE7/b80FvcGC3h4W9vU0vvf2sKA3xtab2/Za0Ms+/X1j6wtr2xb09rCgL1jY3H9s3+1BoPlLf8c/YBP/UR7bh5btO/xrvt0foGy7/7i21j/oSds/as3HnOzxmn9QG2OcfBz1P6StIWKsrfUYbcJJ/THHjaN2vHG1tz4XuWONjdpG24aRsbYJnovWcewQBNsFyGpjuxDZ6DtBqAaipxkeo+3xtx++s+PTUtcOIbt1trfD49NS12THbw369X6tgbj29E18/JY6Wv/RMNHxmeD5n+j4rePpnTAgdBoYGsu9PY3HbBcGenuaYWnHwNBbhZmeloDUesyxwNQzPjw1+/W0jFFlM5xNUWaOCzztQs0Osz/VjNDQDtvbHWf7ttbjDA0nW1uO05jpGX+c4S4En76eGAspY4GmHoL6elhYbVvcDD5jgSfGwtGiWlAaC0FtjjMuLI3bL8avV8df2NvjLx5J0pxgOJuCI/7yq/ziV9um/bgRjIWU8bM4scPszr4LF1R9Y9xs0MLe8f3Hh6OWUDUWgiY6zo4zTr09Bh9JkmaC4WwKXvf8VYxkVrM27UPNwtpMzoJx7RPPBhl8JElSk+FsCv7wRU+d7RIkSdIc19U3oY2I4yPi7ohYFxHntNl+UERcHRG3RsS1EbGytu09EXF79fXqbtYpSZJUiq6Fs4joBT4MvBRYDZwWEatbur0f+FhmHgacD1xY7fsy4AhgDfBc4KyI2KdbtUqSJJWimzNnRwLrMvOHmbkNuBQ4saXPauDr1fI1te2rgesyczgzfwXcChzfxVolSZKK0M1wdgBwb219fdVWdwtwSrV8MrAkIpZV7cdHxN4RsRw4Fjiw9QEi4o0RMRgRgxs2bJj2AUiSJM202f7g87OAoyPiO8DRwH3ASGZ+BfgicAPwSeAbwEjrzpn50cwcyMyBFStWzGDZkiRJ3dHNcHYf42e7VlZtYzLz/sw8JTMPB95ZtW2qvl+QmWsy88U03sj5e12sVZIkqQjdDGc3AU+NiIMjYiFwKnBlvUNELI+IZg3nAhdX7b3V5U0i4jDgMOArXaxVkiSpCF17n7PMHI6IM4GrgF7g4sy8IyLOBwYz80rgGODCiEjgOuAt1e4LgOurj+N5EDg9M4e7VaskSVIpovlBwnu6gYGBHBwcnO0yJEmSdioi1mbmQLtts/2CAEmSJNUYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqiOFMkiSpIIYzSZKkghjOJEmSCmI4kyRJKojhTJIkqSCGM0mSpIIYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqiOFMkiSpIIYzSZKkghjOJEmSCmI4kyRJKojhTJIkqSCGM0mSpIIYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqiOFMkiSpIIYzSZKkghjOJEmSCmI4kyRJKojhTJIkqSCGM0mSpIIYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqiOFMkiSpIIYzSZKkghjOJEmSCmI4kyRJKojhTJIkqSCGM0mSpIIYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqiOFMkiSpIIYzSZKkgnQ1nEXE8RFxd0Ssi4hz2mw/KCKujohbI+LaiFhZ2/beiLgjIu6KiIsiIrpZqyRJUgm6Fs4iohf4MPBSYDVwWkSsbun2fuBjmXkYcD5wYbXvUcBvAIcBvw48Bzi6W7VKkiSVopszZ0cC6zLzh5m5DbgUOLGlz2rg69XyNbXtCfQDC4FFwALggS7WKkmSVIRuhrMDgHtr6+urtrpbgFOq5ZOBJRGxLDO/QSOs/aT6uioz7+pirZIkSUWY7RcEnAUcHRHfoXHZ8j5gJCKeAhwKrKQR6I6LiN9q3Tki3hgRgxExuGHDhpmsW5IkqSu6Gc7uAw6sra+s2sZk5v2ZeUpmHg68s2rbRGMW7ZuZ+XBmPgx8CXh+6wNk5kczcyAzB1asWNGtcUiSJM2Yboazm4CnRsTBEbEQOBW4st4hIpZHRLOGc4GLq+Uf05hR64uIBTRm1bysKUmS5ryuhbPMHAbOBK6iEaw+lZl3RMT5EfGKqtsxwN0R8T3gCcAFVfvlwA+A22jcl3ZLZn6uW7VKkiSVIjJztmuYFgMDAzk4ODjbZUiSJO1URKzNzIF222b7BQGSJEmqMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQXpajiLiOMj4u6IWBcR57TZflBEXB0Rt0bEtRGxsmo/NiJurn1tiYiTulmrJElSCboWziKiF/gw8FJgNXBaRKxu6fZ+4GOZeRhwPnAhQGZek5lrMnMNcBzwCPCVbtUqSZJUim7OnB0JrMvMH2bmNuBS4MSWPquBr1fL17TZDvAq4EuZ+UjXKpUkSSpEN8PZAcC9tfX1VVvdLcAp1fLJwJKIWNbS51Tgk12pUJIkqTCz/YKAs4CjI+I7wNHAfcBIc2NE7Ac8E7iq3c4R8caIGIyIwQ0bNsxEvZIkSV3VzXB2H3BgbX1l1TYmM+/PzFMy83DgnVXbplqX/wpckZlD7R4gMz+amQOZObBixYrprV6SJGkWdDOc3QQ8NSIOjoiFNC5PXlnvEBHLI6JZw7nAxS3HOA0vaUqSpHmka+EsM4eBM2lckrwL+FRm3hER50fEK6puxwB3R8T3gCcAFzT3j4hVNGbe/ne3apQkSSpNZOZs1zAtBgYGcnBwcLbLkCRJ2qmIWJuZA+22zfYLAiRJklRjOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKstNwFhEn1N6LTJIkSV3USeh6NfD9iHhvRDy92wVJkiTNZzsNZ5l5OnA48APgkoj4RvWZlku6Xp0kSdI809Hlysx8ELgcuBTYDzgZ+HZEvLWLtUmSJM07fTvrUH3U0v8HPAX4GHBkZv4sIvYG7gT+trslSpKkuWRoaIj169ezZcuW2S6l6/r7+1m5ciULFizoeJ+dhjPglcAHM/O6emNmPhIRvzfFGiVJ0jy3fv16lixZwqpVq4iI2S6nazKTjRs3sn79eg4++OCO9+vksuZ5wI3NlYjYq/pQcjLz6qmVKUmS5rstW7awbNmyOR3MACKCZcuWTXmGsJNw9mlgtLY+UrVJkiTtkrkezJp2ZZydhLO+zNzWXKmWF075kSRJkrRTnYSzDdWLAgCIiBOBn3evJEmSpO7ZuHEja9asYc2aNTzxiU/kgAMOGFvftm3bpPsODg7ytre9rav1dfKCgDOAT0TE3wEB3Av8blerkiRJ6pJly5Zx8803A3DeeeexePFizjrrrLHtw8PD9PW1j0gDAwMMDAx0tb5O3oT2B5n5PGA1cGhmHpWZ67palSRJ0gx6/etfzxlnnMFzn/tczj77bG688Uae//znc/jhh3PUUUdx9913A3Dttdfy8pe/HGgEuze84Q0cc8wxHHLIIVx00UXTUksnM2dExMuAZwD9zRvbMvP8aalAkiTNW+/63B3cef+D03rM1fvvw1+c8Iwp77d+/XpuuOEGent7efDBB7n++uvp6+vja1/7Gn/6p3/Kv//7v++wz3e/+12uueYaHnroIZ72tKfxpje9aUrvadZOJ29C+xFgb+BY4B+BV1F7aw1JkqS54Hd+53fo7e0FYPPmzbzuda/j+9//PhHB0NBQ231e9rKXsWjRIhYtWsTjH/94HnjgAVauXLlbdXQyc3ZUZh4WEbdm5rsi4gPAl3brUSVJkmCXZri65TGPeczY8p/92Z9x7LHHcsUVV3DPPfdwzDHHtN1n0aJFY8u9vb0MDw/vdh2dvFqz+c5pj0TE/sAQjc/XlCRJmpM2b97MAQccAMAll1wyo4/dSTj7XEQsBd4HfBu4B/i3bhYlSZI0m84++2zOPfdcDj/88GmZDZuKyMyJN0b0AM/LzBuq9UVAf2ZunqH6OjYwMJCDg4OzXYYkSdqJu+66i0MPPXS2y5gx7cYbEWszs+17ckw6c5aZo8CHa+tbSwxmkiRJc0UnlzWvjohXxnz5ECxJkqRZ1Ek4+wMaH3S+NSIejIiHImJ635BEkiRJQAdvpZGZS2aiEEmSJHX2JrQvaNeemddNfzmSJEnzWydvQvsnteV+4EhgLXBcVyqSJEmaxzq5rHlCfT0iDgT+pmsVSZIkddHGjRt54QtfCMBPf/pTent7WbFiBQA33ngjCxcunHT/a6+9loULF3LUUUd1pb6OPvi8xXpg/rw5iSRJmlOWLVvGzTffDMB5553H4sWLOeusszre/9prr2Xx4sVdC2c7fbVmRPxtRFxUff0dcD2NTwqQJEmaE9auXcvRRx/Ns5/9bF7ykpfwk5/8BICLLrqI1atXc9hhh3Hqqadyzz338JGPfIQPfvCDrFmzhuuvv37aa+lk5qz+tvvDwCcz8/9OeyWSJGn++dI58NPbpveYT3wmvPTdHXfPTN761rfy2c9+lhUrVnDZZZfxzne+k4svvph3v/vd/OhHP2LRokVs2rSJpUuXcsYZZ0x5tm0qOglnlwNbMnMEICJ6I2LvzHykKxVJkiTNoK1bt3L77bfz4he/GICRkRH2228/AA477DBe85rXcNJJJ3HSSSfNSD2dhLOrgRcBD1frewFfAbpzoVWSJM0fU5jh6pbM5BnPeAbf+MY3dtj2hS98geuuu47Pfe5zXHDBBdx22zTP8rXRyScE9GdmM5hRLe/dvZIkSZJmzqJFi9iwYcNYOBsaGuKOO+5gdHSUe++9l2OPPZb3vOc9bN68mYcffpglS5bw0EMPda2eTsLZryLiiOZKRDwbeLRrFUmSJM2gnp4eLr/8ct7xjnfwrGc9izVr1nDDDTcwMjLC6aefzjOf+UwOP/xw3va2t7F06VJOOOEErrjiiq69ICAyc/IOEc8BLgXuBwJ4IvDqzFw77dXshoGBgRwcHNx5R0mSNKvuuusuDj10/rwrV7vxRsTazBxo17+TN6G9KSKeDjytaro7M4d2u1JJkiTtoJP3OXsL8JjMvD0zbwcWR8Sbu1+aJEnS/NPJPWe/n5mbmiuZ+Uvg97tXkiRJmut2dlvVXLEr4+wknPVGRDRXIqIXmPxDpyRJkibQ39/Pxo0b53xAy0w2btxIf3//lPbr5H3OvgxcFhH/q1r/A+BLU6xPkiQJgJUrV7J+/Xo2bNgw26V0XX9/PytXrpzSPp2Es3cAbwTOqNZvpfGKTUmSpClbsGABBx988GyXUaydXtbMzFHgW8A9wJHAccBd3S1LkiRpfppw5iwifg04rfr6OXAZQGYeOzOlSZIkzT+TXdb8LnA98PLMXAcQEf99RqqSJEmapya7rHkK8BPgmoj4h4h4IY1PCJAkSVKXTBjOMvMzmXkq8HTgGuCPgMdHxN9HxH+ZqQIlSZLmk05eEPCrzPy3zDwBWAl8h8YrOCVJkjTNOnkT2jGZ+cvM/GhmvrBbBUmSJM1nUwpnkiRJ6i7DmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkF6Wo4i4jjI+LuiFgXEee02X5QRFwdEbdGxLURsbK27UkR8ZWIuCsi7oyIVd2sVZIkqQRdC2cR0Qt8GHgpsBo4LSJWt3R7P/CxzDwMOB+4sLbtY8D7MvNQ4EjgZ92qVZIkqRTdnDk7EliXmT/MzG3ApcCJLX1WA1+vlq9pbq9CXF9mfhUgMx/OzEe6WKskSVIRuhnODgDura2vr9rqbgFOqZZPBpZExDLg14BNEfEfEfGdiHhfNRM3TkS8MSIGI2Jww4YNXRiCJEnSzJrtFwScBRwdEd8BjgbuA0aAPuC3qu3PAQ4BXt+6c/U5nwOZObBixYoZK1qSJKlbuhnO7gMOrK2vrNrGZOb9mXlKZh4OvLNq20Rjlu3m6pLoMPAZ4Igu1ipJklSEboazm4CnRsTBEbEQOBW4st4hIpZHRLOGc4GLa/sujYjmdNhxwJ1drFWSJKkIXQtn1YzXmcBVwF3ApzLzjog4PyJeUXU7Brg7Ir4HPAG4oNp3hMYlzasj4jYggH/oVq2SJEmliMyc7RqmxcDAQA4ODs52GZIkSTsVEWszc6Ddttl+QYAkSZJqDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkG6Gs4i4viIuDsi1kXEOW22HxQRV0fErRFxbUSsrG0biYibq68ru1mnJElSKfq6deCI6AU+DLwYWA/cFBFXZuadtW7vBz6Wmf8SEccBFwKvrbY9mplrulWfJElSibo5c3YksC4zf5iZ24BLgRNb+qwGvl4tX9NmuyRJ0rzSzXB2AHBvbX191VZ3C3BKtXwysCQillXr/RExGBHfjIiT2j1ARLyx6jO4YcOG6axdkiRpVsz2CwLOAo6OiO8ARwP3ASPVtoMycwD4b8DfRMSTW3fOzI9m5kBmDqxYsWLGipYkSeqWrt1zRiNoHVhbX1m1jcnM+6lmziJiMfDKzNxUbbuv+v7DiLgWOBz4QRfrlSRJmnXdnDm7CXhqRBwcEQuBU4Fxr7qMiOUR0azhXODiqv2xEbGo2Qf4DaD+QgJJkqQ5qWvhLDOHgTOBq4C7gE9l5h0RcX5EvKLqdgxwd0R8D3gCcEHVfigwGBG30HihwLtbXuUpSZI0J0VmznYN02JgYCAHBwdnuwxJkqSdioi11b31O5jtFwRIkiSpxnAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSVz3NYAAAA8DSURBVAUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFaRvtguQpK7JhNERGNnW+Bod3r48MtxZ++gw9PQ1vnoXQM+Cxvex5T7oXbi9vadaH1teUG3vg4jZfkYk7QEMZ5I6kwkjQzA6VIWXoepr2wy3dxiqmssl6emrQtzCRqjrJNDtbiBsu3/L8oT7tKnTgCl1neFMmg2jo+1DyGhLINnt9mkMSaNDXXxCYnu4GAsJteXW9oWPgd7H7rx/T5v9p9re01ubfRuqgl/9eR4eHxzrz/1oy/ex5eGd7z86DMNbYOtDHe4zQ0G0p/7cThQYJwihOwTClkA5pX06fcyWwNnTa8BU8QxnEsDQo/DoJtiyCbZs3r78aLW+ZXPjD+VuhafabE6OdG8s0buTwNHStqB/10JLR+0Ld5zlaRe2/IO5+5qXcOvhblzQ7iAQjs067ixQth5/kn22PTK1ENt1sZNw1/L/yIJ+6Nur9r366utvs60fFuxdbdtrgu97Nx5LmkRXf0Ii4njgQ0Av8I+Z+e6W7QcBFwMrgF8Ap2fm+tr2fYA7gc9k5pndrFV7uEzY9nCbULVpktBVaxvZOvnxFy5u/GKth42xf5Ev3B44Fj6mTftE/TsILR211wNRFXQ0/0RUP1N9jSCwJ8psCXodBMJJA2Vr+JsoUE4QGIe3NsLlIxsb/4Ab2gLDte85umvj7OnbMdQ1A9yCvdpsaw2HE22bIBz2Lpje86Su61o4i4he4MPAi4H1wE0RcWVm3lnr9n7gY5n5LxFxHHAh8Nra9r8ErutWjSrM6AhsfXDygDVZ26SzUQH9+8JeS6F/aeP7Pvvv2Na/tKXtsbBoH/+lK82EiO3/ECld8x7MZlgbeqQxuz706MTfJ9s2XB1jLAxu2bHPrs64R+/4mbt6GGz93jYc9rfZf5JwuCecv8J18y/OkcC6zPwhQERcCpxIYyasaTXw9mr5GuAzzQ0R8WzgCcCXgYEu1qnpNDI0wQxVa5hqncna3Ahm5MTH7llQhaZ9G8Fp78fB4w4Z3zYuaNXaFi6BHt85RtI0iYC+hY2v/n1n5jFHhhoBrnUGr/59h5DYQZ9Hf9nS99FpDIOtl4D3ah8Kx4XDnVwWbg2FvQvm3G0R3QxnBwD31tbXA89t6XMLcAqNS58nA0siYhnwS+ADwOnAiyZ6gIh4I/BGgCc96UnTVvi8N3b/1RRmrZrLQ7+a/Nh9e40PTvvsD49f3VnAWrD3nPsfUJI61rsAeved4TA4ySzfpCHx0Ym3bdnUvs/o8K7VGT1tLutOdlm4g3C411I44NnT+3xOwWxfqzkL+LuIeD2Ny5f3ASPAm4EvZub6mOSPcWZ+FPgowMDAwCRTLvNM6/1Xk91r1S5g7fT+qyXjLwE+7uBJLgu2tPUtmpnnQJK0e8YuMe8zM483MtwS5Dq8FNzuEnD9UvKWzS0hsdo22QtQlj8NzrxxZsbdRjfD2X3AgbX1lVXbmMy8n8bMGRGxGHhlZm6KiOcDvxURbwYWAwsj4uHMPKeL9ZZldBS2ThKgJgtdWzbv5F8gLfdf9e8L++zXQcCq2r3/SpI03Xr7oHcJLFoyM483MlwLcy2zez2z+3eum49+E/DUiDiYRig7Ffhv9Q4RsRz4RWaOAufSeOUmmfmaWp/XAwN7ZDDr+P6r1kuFndx/1Tc+OO39uB1nsNpdKuzft3GDu/dfSZLms94+6F0MixbPdiU76Fo4y8zhiDgTuIrGW2lcnJl3RMT5wGBmXgkcA1wYEUnjsuZbulXPtPjxtxo3TnZ6qXCn91/1jw9O++wPjz905wHL+68kSZqzInNu3Ko1MDCQg4OD3X2QCw+sZrRqFi7p4O0Y2rT179u4QVGSJM07EbE2M9u+G4U3D03FaZ+svdrQ+68kSdL0M1lMxarfnO0KJEnSHOdd4ZIkSQUxnEmSJBXEcCZJklQQw5kkSVJBDGeSJEkFMZxJkiQVxHAmSZJUEMOZJElSQQxnkiRJBTGcSZIkFcRwJkmSVBDDmSRJUkEMZ5IkSQUxnEmSJBXEcCZJklSQyMzZrmFaRMQG4D9n4KGWAz+fgccp0XweO8zv8Tv2+Ws+j38+jx3m9/hnYuwHZeaKdhvmTDibKRExmJkDs13HbJjPY4f5PX7HPj/HDvN7/PN57DC/xz/bY/eypiRJUkEMZ5IkSQUxnE3dR2e7gFk0n8cO83v8jn3+ms/jn89jh/k9/lkdu/ecSZIkFcSZM0mSpIIYziRJkgpiOGsRERdHxM8i4vZa2+Mi4qsR8f3q+2Or9oiIiyJiXUTcGhFHzF7lu2+CsZ8XEfdFxM3V12/Xtp1bjf3uiHjJ7FQ9PSLiwIi4JiLujIg7IuIPq/Y5f+4nGft8Off9EXFjRNxSjf9dVfvBEfGtapyXRcTCqn1Rtb6u2r5qNuvfHZOM/ZKI+FHt3K+p2ufMz31TRPRGxHci4vPV+pw/73Vtxj8vzn1E3BMRt1VjHKzayvl9n5l+1b6AFwBHALfX2t4LnFMtnwO8p1r+beBLQADPA7412/V3YeznAWe16bsauAVYBBwM/ADone0x7MbY9wOOqJaXAN+rxjjnz/0kY58v5z6AxdXyAuBb1Tn9FHBq1f4R4E3V8puBj1TLpwKXzfYYujD2S4BXtek/Z37ua2N6O/BvwOer9Tl/3ncy/nlx7oF7gOUtbcX8vnfmrEVmXgf8oqX5ROBfquV/AU6qtX8sG74JLI2I/Wam0uk3wdgnciJwaWZuzcwfAeuAI7tWXJdl5k8y89vV8kPAXcABzINzP8nYJzLXzn1m5sPV6oLqK4HjgMur9tZz3/yZuBx4YUTEDJU7rSYZ+0TmzM89QESsBF4G/GO1HsyD897UOv6dmFPnfgLF/L43nHXmCZn5k2r5p8ATquUDgHtr/dYz+R+1PdWZ1VTuxc1pXubw2KvLFYfTmEWYV+e+ZewwT859dWnnZuBnwFdpzAZuyszhqkt9jGPjr7ZvBpbNbMXTp3Xsmdk89xdU5/6DEbGoaptr5/5vgLOB0Wp9GfPkvFdax980H859Al+JiLUR8caqrZjf94azKcrGHOd8ev+RvweeDKwBfgJ8YHbL6a6IWAz8O/BHmflgfdtcP/dtxj5vzn1mjmTmGmAljVnAp89ySTOmdewR8evAuTSeg+cAjwPeMYsldkVEvBz4WWaune1aZsMk45/z577ym5l5BPBS4C0R8YL6xtn+fW8468wDzSnM6vvPqvb7gANr/VZWbXNGZj5Q/fIeBf6B7Zev5tzYI2IBjXDyicz8j6p5Xpz7dmOfT+e+KTM3AdcAz6dx6aKv2lQf49j4q+37AhtnuNRpVxv78dWl7szMrcA/MzfP/W8Ar4iIe4BLaVzO/BDz57zvMP6I+Pg8Ofdk5n3V958BV9AYZzG/7w1nnbkSeF21/Drgs7X2361eyfE8YHNtSnROaLmufjLQfCXnlcCp1SuYDgaeCtw40/VNl+rekX8C7srMv65tmvPnfqKxz6NzvyIillbLewEvpnHf3TXAq6puree++TPxKuDr1b+y9zgTjP27tT9QQeO+m/q5nxM/95l5bmauzMxVNG7w/3pmvoZ5cN5hwvGfPh/OfUQ8JiKWNJeB/0JjnOX8vt+dVxPMxS/gkzQu4QzRuK78ezTuK7ga+D7wNeBxVd8APkzj/pTbgIHZrr8LY//Xamy3Vj+g+9X6v7Ma+93AS2e7/t0c+2/SmMK+Fbi5+vrt+XDuJxn7fDn3hwHfqcZ5O/DnVfshNELnOuDTwKKqvb9aX1dtP2S2x9CFsX+9Ove3Ax9n+ys658zPfcvzcAzbX60458/7TsY/5899dY5vqb7uAN5ZtRfz+96Pb5IkSSqIlzUlSZIKYjiTJEkqiOFMkiSpIIYzSZKkghjOJEmSCmI4k7THioiRiLi59nXOJH1PiojVtfXzI+JF01DD0oh48+4eR5KafCsNSXusiHg4Mxd32PcSGu/ldPnO+k6xhlXVcX99Cvv05fbPb5SkcZw5kzTnRMS7I+LO6sOb3x8RRwGvAN5XzbA9OSIuiYhXVf3viYgLq22DEXFERFwVET+IiDOqPosj4uqI+HZE3BYRJ1YP927gydW+76veRfx9EXF71e/V1f7HRMT1EXElcGf1LuVfiIhbqr6vnoWnSlKB+nbeRZKKtVdE3Fxbv5DGO3ufDDw9MzMilmbmpioUjc2cNT6dZpwfZ+aaiPggcAmNzx7sp/FO6R8BtgAnZ+aDEbEc+GZ1zHOAX8/Gh4cTEa+k8WHxzwKWAzdFxHXVYxxR9f1R1e/+zHxZtd++0/i8SNqDGc4k7ckebYaipupDqbcA/xQRnwc+3+Gxrqy+30bjI2seAh6KiK3V50/+CviriHgBMAocADyhzXF+E/hkZo7Q+CDl/w08B3gQuDEzf1R7nA9ExHtohMbrO6xT0hznZU1Jc0p1L9eRwOXAy4Evd7jr1ur7aG25ud4HvAZYATy7CoQP0JhZm4pf1er8Ho2ZtNuA/xERfz7FY0maowxnkuaUiFgM7JuZXwT+O43LiwAPAUt249D7Aj/LzKGIOBY4aILjXg+8OiJ6I2IF8AIaH5TdWuf+wCOZ+XHgfTSCmiR5WVPSHq31nrMvAx8CPhsR/UAAb6+2XQr8Q0S8DXjVLjzWJ4DPRcRtwCDwXYDM3BgR/zcibge+BJwNPB+4BUjg7Mz8aUQ8veV4z6TxAoVRYAh40y7UJGkO8q00JEmSCuJlTUmSpIIYziRJkgpiOJMkSSqI4UySJKkghjNJkqSCGM4kSZIKYjiTJEkqyP8DhhDbAmF9es8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   n_neighbors  train_accuracy  test_accuracy\n",
            "0          100        0.990865       0.939113\n",
            "1          200        0.991336       0.940372\n",
            "2          300        0.991275       0.940797\n",
            "3          400        0.991328       0.940404\n",
            "4          500        0.991500       0.939719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "kfold = KFold(n_splits = 10)\n",
        "xyz = []\n",
        "accuracy = []\n",
        "\n",
        "classifiers=[\"SVM\",\"Logistic Regression\",\"Decision Tree\",\"Random Forest\",\"GaussianNB\"]\n",
        "models=[SVC(kernel='rbf'),LogisticRegression(),DecisionTreeClassifier(),RandomForestClassifier(n_estimators=300,random_state=0),GaussianNB()]\n",
        "\n",
        "for i in models:\n",
        "    model = i\n",
        "    cv_result=cross_val_score(model,x_train,y_train,cv=kfold,scoring=\"accuracy\")\n",
        "    cv_result = cv_result\n",
        "    xyz.append(cv_result.mean())\n",
        "    accuracy.append(cv_result)\n",
        "    \n",
        "cv_models_datafeame= pd.DataFrame(xyz, index = classifiers)\n",
        "cv_models_datafeame.columns = ['CV Mean']\n",
        "cv_models_datafeame\n",
        "cv_models_datafeame.sort_values(['CV Mean'], ascending =[0])\n",
        "\n",
        "box = pd.DataFrame(accuracy, index = [classifiers])\n",
        "boxT = box.T\n",
        "plt.figure(figsize = (10,7))\n",
        "ax = sns.boxplot(data = boxT, orient = \"h\", palette = \"Set2\", width = 0.6)\n",
        "ax.set_yticklabels(classifiers)\n",
        "ax.set_title('Cross Validation accuracy with different classifiers')\n",
        "ax.set_xlabel('Accuracy')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "xk-2YNdkdUHG",
        "outputId": "56c6bd1f-370e-4382-dddc-f478ad7a9eb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}