Stem_and_Leaf_Display_&_Cross_Tabulation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM4cbQKcLr4sRIpjDX1tx3O",
      "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/mvharsh/Applied-Statistics-with-Python/blob/main/Stem_and_Leaf_Display_%26_Cross_Tabulation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Stem-and-Leaf Display**"
      ],
      "metadata": {
        "id": "Ci2KibQ39n1B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWou-sSe9dJ1",
        "outputId": "2be3b1b8-b9a2-420a-b163-786efebdc196"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting stemgraphic\n",
            "  Downloading stemgraphic-0.9.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 626 kB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from stemgraphic) (3.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from stemgraphic) (1.3.5)\n",
            "Collecting docopt\n",
            "  Downloading docopt-0.6.2.tar.gz (25 kB)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.8/dist-packages (from stemgraphic) (0.11.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->stemgraphic) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib->stemgraphic) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->stemgraphic) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->stemgraphic) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->stemgraphic) (1.4.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib->stemgraphic) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->stemgraphic) (2022.6)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from seaborn->stemgraphic) (1.7.3)\n",
            "Building wheels for collected packages: docopt\n",
            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13723 sha256=9d5a5fe98499b38d1cc20c1b8b8304e6d8eefe6ead55f2ce761cd100a1ad7048\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/ea/58/ead137b087d9e326852a851351d1debf4ada529b6ac0ec4e8c\n",
            "Successfully built docopt\n",
            "Installing collected packages: docopt, stemgraphic\n",
            "Successfully installed docopt-0.6.2 stemgraphic-0.9.1\n"
          ]
        }
      ],
      "source": [
        "!pip install stemgraphic"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import stemgraphic\n",
        "\n",
        "da=[112,72,69,97,107,73,92,76,86,73,126,128,118,127,124,82,104,132,134,83,92,\n",
        "      108,96,100,92,115,76,91,102,81,95,141,81,80,106,84,119,113,98,75,68,98,\n",
        "      115,106,95,100,85,94,106,119]\n",
        "\n",
        "stemgraphic.stem_graphic(da, scale = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "KH1bpCy09sHw",
        "outputId": "db1d8222-b951-4959-bb32-e70f827fef80"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 540x216 with 1 Axes>,\n",
              " <matplotlib.axes._axes.Axes at 0x7fe2ac5c1e80>)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Cross Tabulation**"
      ],
      "metadata": {
        "id": "n_U4hDaF9tEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# creating some data\n",
        "\n",
        "a = np.array([\"Bad\", \"Good\", \"Very Good\", \"Excellent\", \"Excellent\", \n",
        "                 \"Very Good\", \"Good\", \"Bad\",\"Bad\", \"Good\", \"Very Good\", \n",
        "                 \"Excellent\", \"Excellent\", \"Very Good\", \"Good\", \"Bad\",\n",
        "                 \"Bad\", \"Good\", \"Very Good\", \"Excellent\", \"Excellent\", \n",
        "                 \"Very Good\", \"Good\", \"Bad\",\"Bad\", \"Good\", \"Very Good\", \n",
        "                 \"Excellent\", \"Excellent\", \"Very Good\", \"Good\", \"Bad\"],\n",
        "                dtype=object)\n",
        "\n",
        "b = np.array([\"0 - 100\", \"100 - 250\", \"250 - 500\", \"500 - 1000\", \n",
        "                 \"1000 - 2500\", \"2500 - 5000\", \"5000 - 7500\", \"7500 - 10000\", \n",
        "                 \"10000 - 15000\", \"15000 - 20000\", \"20000 - 30000\", \n",
        "                 \"Above 30000\", \"0 - 100\", \"100 - 250\", \"250 - 500\", \n",
        "                 \"500 -1000\", \"1000 - 2500\", \"2500 - 5000\", \"5000 - 7500\", \n",
        "                 \"7500 - 10000\", \"10000 - 15000\", \"15000 - 20000\", \n",
        "                 \"20000 - 30000\", \"Above 30000\",\"0 - 100\", \"100 - 250\", \n",
        "                 \"250 - 500\", \"500 -1000\", \"1000 - 2500\", \"2500 - 5000\", \n",
        "                 \"5000 - 7500\", \"7500 - 10000\"],dtype=object)\n",
        "\n",
        "# form the cross tab\n",
        "\n",
        "pd.crosstab(b, a, rownames=['Cost (in Rs.)'], colnames=['Rating'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "p5b8Z92v90X9",
        "outputId": "184c7ea2-8bc2-45a9-a678-bcdd3b7cd4f2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rating         Bad  Excellent  Good  Very Good\n",
              "Cost (in Rs.)                                 \n",
              "0 - 100          2          1     0          0\n",
              "100 - 250        0          0     2          1\n",
              "1000 - 2500      1          2     0          0\n",
              "10000 - 15000    1          1     0          0\n",
              "15000 - 20000    0          0     1          1\n",
              "20000 - 30000    0          0     1          1\n",
              "250 - 500        0          0     1          2\n",
              "2500 - 5000      0          0     1          2\n",
              "500 - 1000       0          1     0          0\n",
              "500 -1000        1          1     0          0\n",
              "5000 - 7500      0          0     2          1\n",
              "7500 - 10000     2          1     0          0\n",
              "Above 30000      1          1     0          0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e37e46e1-8103-4c97-bc36-322690752487\">\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>Rating</th>\n",
              "      <th>Bad</th>\n",
              "      <th>Excellent</th>\n",
              "      <th>Good</th>\n",
              "      <th>Very Good</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cost (in Rs.)</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0 - 100</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100 - 250</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1000 - 2500</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10000 - 15000</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15000 - 20000</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20000 - 30000</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250 - 500</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2500 - 5000</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500 - 1000</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500 -1000</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5000 - 7500</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7500 - 10000</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Above 30000</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e37e46e1-8103-4c97-bc36-322690752487')\"\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",
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              "  </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-e37e46e1-8103-4c97-bc36-322690752487 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-e37e46e1-8103-4c97-bc36-322690752487');\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": 3
        }
      ]
    }
  ]
}