타이타닉.ipynb

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  "metadata": {
    "colab": {
      "name": "타이타닉.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNkAJUc+djj/K5jjwn+92hP",
      "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/yujungha/Data_Analysis/blob/main/%ED%83%80%EC%9D%B4%ED%83%80%EB%8B%89.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj-0BQjuYsK3"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "PF0qR5-XcSNJ",
        "outputId": "0aaae3fd-7053-4f45-c4e9-f9983d0efaee"
      },
      "source": [
        "df = pd.read_csv('train.csv')\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</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",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
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              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
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              "      <td>Q</td>\n",
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              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500   NaN         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250   NaN         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500   NaN         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lo8QufulcZGc",
        "outputId": "6050282e-6e52-4c76-e50c-c946e2818732"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jbFSzBi9dIpj",
        "outputId": "e48f3235-57e7-4733-bfca-5bde3ddbd3b0"
      },
      "source": [
        "print(\"평균 :\", df['Fare'].mean(), \"표준편차 :\", df['Fare'].std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "평균 : 32.2042079685746 표준편차 : 49.693428597180905\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8u7It2f0Szl",
        "outputId": "6ce897a7-952c-47d5-8478-70e3edae3ba1"
      },
      "source": [
        "df['Fare'][df['Survived'] == 1].mean(), df['Fare'][df['Survived'] == 1].std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48.39540760233917, 66.59699811829472)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFwc0QRNl4Sd",
        "outputId": "9f25a901-f7ec-4234-afd8-99bcb49e126b"
      },
      "source": [
        "len(df[(df['Fare'] >= 0) & (df['Fare'] <= 49) & (df['Survived'] == 1)]) / len(df[(df['Fare'] >= 0) & (df['Fare'] <= 49)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3177441540577717"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6CuZoMvv8eU",
        "outputId": "3ecd5834-e99a-48fa-a057-a8edf3dfced3"
      },
      "source": [
        "fares = [[0,29],[30,59],[60,99],[100,139],[140,199],[200,299],[300,599]]\n",
        "arr = []\n",
        "\n",
        "for fare in fares:\n",
        "  a = len(df[(df['Fare'] >= fare[0]) & (df['Fare'] <= fare[1]) & (df['Survived'] == 1)]) / len(df[(df['Fare'] >= fare[0]) & (df['Fare'] <= fare[1])])\n",
        "  print(a)\n",
        "  arr.append(a)\n",
        "print(arr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.3110419906687403\n",
            "0.49572649572649574\n",
            "0.6231884057971014\n",
            "0.7727272727272727\n",
            "0.7272727272727273\n",
            "0.6470588235294118\n",
            "1.0\n",
            "[0.3110419906687403, 0.49572649572649574, 0.6231884057971014, 0.7727272727272727, 0.7272727272727273, 0.6470588235294118, 1.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7U5qq0J3qLPN",
        "outputId": "6a03c9ba-11d6-4c6c-83ff-a2bc224da36d"
      },
      "source": [
        "print(fares, arr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0, 29], [30, 59], [60, 99], [100, 139], [140, 199], [200, 299], [300, 599]] [0.3110419906687403, 0.49572649572649574, 0.6231884057971014, 0.7727272727272727, 0.7272727272727273, 0.6470588235294118, 1.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "RlCjLItp5Icu",
        "outputId": "377e167d-6cb1-43a3-d926-bbee05c85730"
      },
      "source": [
        "plt.plot(fares, arr)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gdzs9Zcm8DPQ",
        "outputId": "9c8835f9-73b3-43f4-fb05-a65757492908"
      },
      "source": [
        "l = []\n",
        "for i in range(1, 4):\n",
        "  l.append(len(df[(df['Pclass'] == i) & (df['Survived'] == 1)]) / len(df[df['Pclass'] == i]))\n",
        "l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6296296296296297, 0.47282608695652173, 0.24236252545824846]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9TQqoPPS4TAJ",
        "outputId": "5699ed1e-0077-4fc0-e772-1959fa0147f8"
      },
      "source": [
        "plt.bar([i for i in range(1,4)], l)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Acyx9+mIB+Brwc5vT6Wg6zOte4BeTXAP+Ezh0vf9n6nkT8DPA53rnOgF+A7gVJnuf0W1uk7jfbgEeztIfELoBeLSqPpHx3wplo3WZ18i56O0HJKlB1+MFVUnSGhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUH/Cyr1BgzYVW9JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_t5uC0dL6We-",
        "outputId": "f9af2a4f-325a-4af4-b9b5-83a67bc8dc6e"
      },
      "source": [
        "len(df[(df['Name'].str.contains('Mrs')) & (df['Survived'] == 1)]) / len(df[(df['Name'].str.contains('Mrs'))])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7906976744186046"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9QvWpAmBm6Lh",
        "outputId": "07cb5ded-aeba-4ada-fca1-dc2b11a70f30"
      },
      "source": [
        "status = ['Master', 'Mr', 'Miss', 'Rev', 'Lady', 'Dr', 'Mrs', 'Ms']\n",
        "arr = []\n",
        "\n",
        "for i in status:\n",
        "  arr.append(len(df[(df['Name'].str.contains(i)) & (df['Survived'] == 1)]) / len(df[(df['Name'].str.contains(i))]))\n",
        "print(arr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.575, 0.28438948995363217, 0.6978021978021978, 0.0, 1.0, 0.5454545454545454, 0.7906976744186046, 1.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "xjpwmetUs4uR",
        "outputId": "63a99789-2264-48b0-efce-3ad1da5c9dfe"
      },
      "source": [
        "plt.plot(status, arr)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQkIntxyESbB",
        "outputId": "5b35fce1-e84b-46e1-a32f-d17f15674fc6"
      },
      "source": [
        "len(df[df['Name'].str.contains('Rev')])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    }
  ]
}