01_Pandas_Titanic_DataSet_Analysis.ipynb

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      "name": "01_Pandas_Titanic_DataSet_Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM2cUnPuUNCEhq7CrtLCnXw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohd-faizy/Lib_Pandas/blob/master/01_Pandas_Titanic_DataSet_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvqcNYQrH453",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YMwsbSVMGgh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LMv7pLjMakV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic_df = pd.read_excel('/content/drive/My Drive/Colab Notebooks/08_DataScience_Libraries /01_Pandas_Lib/titanic3.xls',\n",
        "                           index_col=None, na_values=['NA'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsHKbshEM7bT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "90c29084-7ecc-40d1-fd3e-efe4f7a8edfc"
      },
      "source": [
        "titanic_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</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",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>B5</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Louis, MO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>male</td>\n",
              "      <td>0.9167</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pclass  survived  ...   body                        home.dest\n",
              "0       1         1  ...    NaN                     St Louis, MO\n",
              "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
              "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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          "height": 287
        },
        "outputId": "b6ae4aeb-b87a-497b-d4cf-c4f0c1e2ac6d"
      },
      "source": [
        "titanic_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1046.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1308.000000</td>\n",
              "      <td>121.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.294882</td>\n",
              "      <td>0.381971</td>\n",
              "      <td>29.881135</td>\n",
              "      <td>0.498854</td>\n",
              "      <td>0.385027</td>\n",
              "      <td>33.295479</td>\n",
              "      <td>160.809917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.837836</td>\n",
              "      <td>0.486055</td>\n",
              "      <td>14.413500</td>\n",
              "      <td>1.041658</td>\n",
              "      <td>0.865560</td>\n",
              "      <td>51.758668</td>\n",
              "      <td>97.696922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166700</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>155.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.275000</td>\n",
              "      <td>256.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>328.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            pclass     survived  ...         fare        body\n",
              "count  1309.000000  1309.000000  ...  1308.000000  121.000000\n",
              "mean      2.294882     0.381971  ...    33.295479  160.809917\n",
              "std       0.837836     0.486055  ...    51.758668   97.696922\n",
              "min       1.000000     0.000000  ...     0.000000    1.000000\n",
              "25%       2.000000     0.000000  ...     7.895800   72.000000\n",
              "50%       3.000000     0.000000  ...    14.454200  155.000000\n",
              "75%       3.000000     1.000000  ...    31.275000  256.000000\n",
              "max       3.000000     1.000000  ...   512.329200  328.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNp-sMBeNGTa",
        "colab_type": "code",
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          "height": 414
        },
        "outputId": "9016985d-a2c6-4448-e2c0-50b66a3b53ce"
      },
      "source": [
        "titanic_df.drop(['ticket', 'cabin', 'boat', 'body'], axis=1).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>Allison, Master. Hudson Trevor</td>\n",
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              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>female</td>\n",
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              "      <td>151.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>S</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pclass  survived  ... embarked                        home.dest\n",
              "0       1         1  ...        S                     St Louis, MO\n",
              "1       1         1  ...        S  Montreal, PQ / Chesterville, ON\n",
              "2       1         0  ...        S  Montreal, PQ / Chesterville, ON\n",
              "3       1         0  ...        S  Montreal, PQ / Chesterville, ON\n",
              "4       1         0  ...        S  Montreal, PQ / Chesterville, ON\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHxn88qqNiFA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "8f54d1bc-ad17-4fe7-a484-8049462be04c"
      },
      "source": [
        "titanic_df.isnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <th>2</th>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1304</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1309 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      pclass  survived   name    sex  ...  embarked   boat   body  home.dest\n",
              "0      False     False  False  False  ...     False  False   True      False\n",
              "1      False     False  False  False  ...     False  False   True      False\n",
              "2      False     False  False  False  ...     False   True   True      False\n",
              "3      False     False  False  False  ...     False   True  False      False\n",
              "4      False     False  False  False  ...     False   True   True      False\n",
              "...      ...       ...    ...    ...  ...       ...    ...    ...        ...\n",
              "1304   False     False  False  False  ...     False   True  False       True\n",
              "1305   False     False  False  False  ...     False   True   True       True\n",
              "1306   False     False  False  False  ...     False   True  False       True\n",
              "1307   False     False  False  False  ...     False   True   True       True\n",
              "1308   False     False  False  False  ...     False   True   True       True\n",
              "\n",
              "[1309 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNT18-YLNyEv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "28376dea-a4f9-4c6c-be26-656d90145910"
      },
      "source": [
        "titanic_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pclass          0\n",
              "survived        0\n",
              "name            0\n",
              "sex             0\n",
              "age           263\n",
              "sibsp           0\n",
              "parch           0\n",
              "ticket          0\n",
              "fare            1\n",
              "cabin        1014\n",
              "embarked        2\n",
              "boat          823\n",
              "body         1188\n",
              "home.dest     564\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wS7e4_R8Wt2X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "5d6ae9e1-9fab-4617-f1fd-eed17d5706aa"
      },
      "source": [
        "pd.value_counts(titanic_df['survived']).plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ffab1691c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "ZLbYZMKPb74n",
        "colab_type": "text"
      },
      "source": [
        "$0$ $\\rightarrow$ $Died$\n",
        "\n",
        "$1$ $\\rightarrow$ $Survived$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8kJNUFapZxmk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c4086387-cec7-483f-a25d-d220bcdd9f1e"
      },
      "source": [
        "titanic_df['survived'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3819709702062643"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmxVGi8wZ5Ct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "40d3af3d-7732-4aa4-c991-99881c668626"
      },
      "source": [
        "titanic_df.groupby(['sex']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>body</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>2.154506</td>\n",
              "      <td>0.727468</td>\n",
              "      <td>28.687071</td>\n",
              "      <td>0.652361</td>\n",
              "      <td>0.633047</td>\n",
              "      <td>46.198097</td>\n",
              "      <td>166.62500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>2.372479</td>\n",
              "      <td>0.190985</td>\n",
              "      <td>30.585233</td>\n",
              "      <td>0.413998</td>\n",
              "      <td>0.247924</td>\n",
              "      <td>26.154601</td>\n",
              "      <td>160.39823</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          pclass  survived        age     sibsp     parch       fare       body\n",
              "sex                                                                            \n",
              "female  2.154506  0.727468  28.687071  0.652361  0.633047  46.198097  166.62500\n",
              "male    2.372479  0.190985  30.585233  0.413998  0.247924  26.154601  160.39823"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLgOooamaZOy",
        "colab_type": "code",
        "colab": {
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          "height": 257
        },
        "outputId": "4b3273f2-35ed-40d2-e258-56f5e870bf2c"
      },
      "source": [
        "titanic_df.groupby(['sex', 'pclass']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
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              "      <th>sex</th>\n",
              "      <th>pclass</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
              "      <th>1</th>\n",
              "      <td>0.965278</td>\n",
              "      <td>37.037594</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.472222</td>\n",
              "      <td>109.412385</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.886792</td>\n",
              "      <td>27.499191</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.650943</td>\n",
              "      <td>23.234827</td>\n",
              "      <td>52.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.490741</td>\n",
              "      <td>22.185307</td>\n",
              "      <td>0.791667</td>\n",
              "      <td>0.731481</td>\n",
              "      <td>15.324250</td>\n",
              "      <td>183.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
              "      <th>1</th>\n",
              "      <td>0.340782</td>\n",
              "      <td>41.029250</td>\n",
              "      <td>0.340782</td>\n",
              "      <td>0.279330</td>\n",
              "      <td>69.888385</td>\n",
              "      <td>162.828571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.146199</td>\n",
              "      <td>30.815401</td>\n",
              "      <td>0.327485</td>\n",
              "      <td>0.192982</td>\n",
              "      <td>19.904946</td>\n",
              "      <td>171.233333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.152130</td>\n",
              "      <td>25.962273</td>\n",
              "      <td>0.470588</td>\n",
              "      <td>0.255578</td>\n",
              "      <td>12.415462</td>\n",
              "      <td>151.854167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               survived        age     sibsp     parch        fare        body\n",
              "sex    pclass                                                                 \n",
              "female 1       0.965278  37.037594  0.555556  0.472222  109.412385         NaN\n",
              "       2       0.886792  27.499191  0.500000  0.650943   23.234827   52.000000\n",
              "       3       0.490741  22.185307  0.791667  0.731481   15.324250  183.000000\n",
              "male   1       0.340782  41.029250  0.340782  0.279330   69.888385  162.828571\n",
              "       2       0.146199  30.815401  0.327485  0.192982   19.904946  171.233333\n",
              "       3       0.152130  25.962273  0.470588  0.255578   12.415462  151.854167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mjrqXvHa4Ng",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "22c2d3e7-1caf-46b9-fbf9-e6fc600b7058"
      },
      "source": [
        "titanic_df[titanic_df['age']<18].groupby(['sex', 'pclass']).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
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              "      <th>survived</th>\n",
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              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>pclass</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
              "      <th>1</th>\n",
              "      <td>0.875000</td>\n",
              "      <td>14.125000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>104.083337</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.273150</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>27.998844</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.543478</td>\n",
              "      <td>8.416667</td>\n",
              "      <td>1.456522</td>\n",
              "      <td>1.043478</td>\n",
              "      <td>18.284148</td>\n",
              "      <td>328.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
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              "      <td>9.845243</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>1.714286</td>\n",
              "      <td>129.752371</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.733333</td>\n",
              "      <td>6.222220</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>31.750280</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.233333</td>\n",
              "      <td>9.838888</td>\n",
              "      <td>1.966667</td>\n",
              "      <td>1.016667</td>\n",
              "      <td>21.677570</td>\n",
              "      <td>65.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               survived        age     sibsp     parch        fare   body\n",
              "sex    pclass                                                            \n",
              "female 1       0.875000  14.125000  0.500000  0.875000  104.083337    NaN\n",
              "       2       1.000000   8.273150  0.666667  1.166667   27.998844    NaN\n",
              "       3       0.543478   8.416667  1.456522  1.043478   18.284148  328.0\n",
              "male   1       0.857143   9.845243  0.571429  1.714286  129.752371    NaN\n",
              "       2       0.733333   6.222220  0.600000  0.933333   31.750280    NaN\n",
              "       3       0.233333   9.838888  1.966667  1.016667   21.677570   65.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    }
  ]
}