Improved_Titanic_Disaster_Analysis.ipynb

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      "authorship_tag": "ABX9TyPXEkVGkYic+Sysz7uMQ/B2",
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      "name": "python3",
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tunahanayaz/Improved-Titanic-Disaster-Analysis/blob/main/Improved_Titanic_Disaster_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "4WIcheDAECny"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = sns.load_dataset(\"titanic\")\n",
        "pd.set_option(\"display.max_columns\",None)\n",
        "pd.set_option(\"display.width\", 500)"
      ],
      "metadata": {
        "id": "r5iYHJk-EKyu"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_df(dataframe, head = 5):\n",
        "  print(\"############## Shape #############\")\n",
        "  print(dataframe.shape)\n",
        "  print(\"############## Types #############\")\n",
        "  print(dataframe.dtypes)\n",
        "  print(\"############## Head ##############\")\n",
        "  print(dataframe.head())\n",
        "  print(\"############## Tail ##############\")\n",
        "  print(dataframe.tail())\n",
        "  print(\"############## NA ##############\")\n",
        "  print(dataframe.isnull().sum())\n",
        "  print(\"############## Quantiles ##############\")\n",
        "  print(dataframe.describe().T)"
      ],
      "metadata": {
        "id": "VbeIq0J-GeLU"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQL1rZWJH6Nt",
        "outputId": "fe1cd781-7435-41a7-9945-b946901eec23"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "############## Shape #############\n",
            "(891, 15)\n",
            "############## Types #############\n",
            "survived          int64\n",
            "pclass            int64\n",
            "sex              object\n",
            "age             float64\n",
            "sibsp             int64\n",
            "parch             int64\n",
            "fare            float64\n",
            "embarked         object\n",
            "class          category\n",
            "who              object\n",
            "adult_male         bool\n",
            "deck           category\n",
            "embark_town      object\n",
            "alive            object\n",
            "alone              bool\n",
            "dtype: object\n",
            "############## Head ##############\n",
            "   survived  pclass     sex   age  sibsp  parch     fare embarked  class    who  adult_male deck  embark_town alive  alone\n",
            "0         0       3    male  22.0      1      0   7.2500        S  Third    man        True  NaN  Southampton    no  False\n",
            "1         1       1  female  38.0      1      0  71.2833        C  First  woman       False    C    Cherbourg   yes  False\n",
            "2         1       3  female  26.0      0      0   7.9250        S  Third  woman       False  NaN  Southampton   yes   True\n",
            "3         1       1  female  35.0      1      0  53.1000        S  First  woman       False    C  Southampton   yes  False\n",
            "4         0       3    male  35.0      0      0   8.0500        S  Third    man        True  NaN  Southampton    no   True\n",
            "############## Tail ##############\n",
            "     survived  pclass     sex   age  sibsp  parch   fare embarked   class    who  adult_male deck  embark_town alive  alone\n",
            "886         0       2    male  27.0      0      0  13.00        S  Second    man        True  NaN  Southampton    no   True\n",
            "887         1       1  female  19.0      0      0  30.00        S   First  woman       False    B  Southampton   yes   True\n",
            "888         0       3  female   NaN      1      2  23.45        S   Third  woman       False  NaN  Southampton    no  False\n",
            "889         1       1    male  26.0      0      0  30.00        C   First    man        True    C    Cherbourg   yes   True\n",
            "890         0       3    male  32.0      0      0   7.75        Q   Third    man        True  NaN   Queenstown    no   True\n",
            "############## NA ##############\n",
            "survived         0\n",
            "pclass           0\n",
            "sex              0\n",
            "age            177\n",
            "sibsp            0\n",
            "parch            0\n",
            "fare             0\n",
            "embarked         2\n",
            "class            0\n",
            "who              0\n",
            "adult_male       0\n",
            "deck           688\n",
            "embark_town      2\n",
            "alive            0\n",
            "alone            0\n",
            "dtype: int64\n",
            "############## Quantiles ##############\n",
            "          count       mean        std   min      25%      50%   75%       max\n",
            "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
            "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
            "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
            "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
            "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
            "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#EDA"
      ],
      "metadata": {
        "id": "uGYqLyTMJ62Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"embarked\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7SkdEZoJab8",
        "outputId": "3f80998e-75bb-45a2-80da-ed1a4d023fef"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"sex\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WMyTW2BKbHS",
        "outputId": "c6b34585-e2d7-402d-87ec-710f5f47cf32"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['male', 'female'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"sex\"].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGICa2RMKbEt",
        "outputId": "b2d2b779-e7c2-4af2-b6ea-420d75523789"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = [col for col in df.columns if str(df[col].dtypes) in [\"category\",\"object\",\"bool\"]]"
      ],
      "metadata": {
        "id": "ENFyPImwKbCX"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHpj3soPKbAA",
        "outputId": "56adf2b8-a316-47ab-82a8-be21af3f60c8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex',\n",
              " 'embarked',\n",
              " 'class',\n",
              " 'who',\n",
              " 'adult_male',\n",
              " 'deck',\n",
              " 'embark_town',\n",
              " 'alive',\n",
              " 'alone']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_but_cat = [col for col in df.columns if (df[col].nunique() < 10) & ((df[col].dtype) in [\"int64\",\"float64\"])]"
      ],
      "metadata": {
        "id": "yjCC3T7sKa3I"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_but_cat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3M6K9upU03f",
        "outputId": "cd36d5ea-6b8b-4e34-f8fa-68bd5f25c016"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['survived', 'pclass', 'sibsp', 'parch']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_but_car = [col for col in df.columns if df[col].nunique() > 20 and str(df[col].dtypes) in [\"category\",\"object\"]]"
      ],
      "metadata": {
        "id": "GBMZVnnbVfJA"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_but_car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "h_aUcY4DWAzy",
        "outputId": "d255c0d6-0bfb-43d4-8dc8-991d4d22c000"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = cat_cols + num_but_cat"
      ],
      "metadata": {
        "id": "YFJjBvBUWrKB"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = [col for col in cat_cols if col not in cat_but_car]"
      ],
      "metadata": {
        "id": "M-FeW7FUWw3w"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRJzucA6XAn9",
        "outputId": "7f65ff71-f137-41b0-9c8a-ba6bc430ea81"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex',\n",
              " 'embarked',\n",
              " 'class',\n",
              " 'who',\n",
              " 'adult_male',\n",
              " 'deck',\n",
              " 'embark_town',\n",
              " 'alive',\n",
              " 'alone',\n",
              " 'survived',\n",
              " 'pclass',\n",
              " 'sibsp',\n",
              " 'parch']"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[cat_cols]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "iMFEg04xXTIv",
        "outputId": "62a7972e-c3b9-41bb-d698-82a921fe68ee"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sex embarked   class    who  adult_male deck  embark_town alive  alone  survived  pclass  sibsp  parch\n",
              "0      male        S   Third    man        True  NaN  Southampton    no  False         0       3      1      0\n",
              "1    female        C   First  woman       False    C    Cherbourg   yes  False         1       1      1      0\n",
              "2    female        S   Third  woman       False  NaN  Southampton   yes   True         1       3      0      0\n",
              "3    female        S   First  woman       False    C  Southampton   yes  False         1       1      1      0\n",
              "4      male        S   Third    man        True  NaN  Southampton    no   True         0       3      0      0\n",
              "..      ...      ...     ...    ...         ...  ...          ...   ...    ...       ...     ...    ...    ...\n",
              "886    male        S  Second    man        True  NaN  Southampton    no   True         0       2      0      0\n",
              "887  female        S   First  woman       False    B  Southampton   yes   True         1       1      0      0\n",
              "888  female        S   Third  woman       False  NaN  Southampton    no  False         0       3      1      2\n",
              "889    male        C   First    man        True    C    Cherbourg   yes   True         1       1      0      0\n",
              "890    male        Q   Third    man        True  NaN   Queenstown    no   True         0       3      0      0\n",
              "\n",
              "[891 rows x 13 columns]"
            ],
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              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18eca501-0548-4bf4-866b-cfa23b58553a')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-18eca501-0548-4bf4-866b-cfa23b58553a 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-18eca501-0548-4bf4-866b-cfa23b58553a');\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",
              "\n",
              "\n",
              "<div id=\"df-9db89b8e-0d96-4cb8-8c98-9765f1c1287c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9db89b8e-0d96-4cb8-8c98-9765f1c1287c')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9db89b8e-0d96-4cb8-8c98-9765f1c1287c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[cat_cols].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mEzJbTEXfeV",
        "outputId": "bd1bb4d3-6c5c-421b-9714-84526b6fd4c0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex            2\n",
              "embarked       3\n",
              "class          3\n",
              "who            3\n",
              "adult_male     2\n",
              "deck           7\n",
              "embark_town    3\n",
              "alive          2\n",
              "alone          2\n",
              "survived       2\n",
              "pclass         3\n",
              "sibsp          7\n",
              "parch          7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name):\n",
        "  print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                      \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "  print(\"######################################\")"
      ],
      "metadata": {
        "id": "G6O1x8KRX4-p"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary(df,\"sex\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qivpOfGKX48I",
        "outputId": "31cbab0d-f68e-4ce5-a9d1-b83ba2293b78"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "  cat_summary(df,col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9TEVOKgX43Q",
        "outputId": "ebf70212-5bcd-430c-e5ae-1f775d6cf96d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n",
            "   embarked      Ratio\n",
            "S       644  72.278339\n",
            "C       168  18.855219\n",
            "Q        77   8.641975\n",
            "######################################\n",
            "        class      Ratio\n",
            "Third     491  55.106622\n",
            "First     216  24.242424\n",
            "Second    184  20.650954\n",
            "######################################\n",
            "       who      Ratio\n",
            "man    537  60.269360\n",
            "woman  271  30.415264\n",
            "child   83   9.315376\n",
            "######################################\n",
            "       adult_male     Ratio\n",
            "True          537  60.26936\n",
            "False         354  39.73064\n",
            "######################################\n",
            "   deck     Ratio\n",
            "C    59  6.621773\n",
            "B    47  5.274972\n",
            "D    33  3.703704\n",
            "E    32  3.591470\n",
            "A    15  1.683502\n",
            "F    13  1.459035\n",
            "G     4  0.448934\n",
            "######################################\n",
            "             embark_town      Ratio\n",
            "Southampton          644  72.278339\n",
            "Cherbourg            168  18.855219\n",
            "Queenstown            77   8.641975\n",
            "######################################\n",
            "     alive      Ratio\n",
            "no     549  61.616162\n",
            "yes    342  38.383838\n",
            "######################################\n",
            "       alone     Ratio\n",
            "True     537  60.26936\n",
            "False    354  39.73064\n",
            "######################################\n",
            "   survived      Ratio\n",
            "0       549  61.616162\n",
            "1       342  38.383838\n",
            "######################################\n",
            "   pclass      Ratio\n",
            "3     491  55.106622\n",
            "1     216  24.242424\n",
            "2     184  20.650954\n",
            "######################################\n",
            "   sibsp      Ratio\n",
            "0    608  68.237935\n",
            "1    209  23.456790\n",
            "2     28   3.142536\n",
            "4     18   2.020202\n",
            "3     16   1.795735\n",
            "8      7   0.785634\n",
            "5      5   0.561167\n",
            "######################################\n",
            "   parch      Ratio\n",
            "0    678  76.094276\n",
            "1    118  13.243547\n",
            "2     80   8.978676\n",
            "5      5   0.561167\n",
            "3      5   0.561167\n",
            "4      4   0.448934\n",
            "6      1   0.112233\n",
            "######################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name, plot = False):\n",
        "  print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                      \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "  print(\"######################################\")\n",
        "\n",
        "  if plot:\n",
        "    sns.countplot(x=dataframe[col_name],data = dataframe)\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "HrIJjrCjTW1w"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary(df,\"sex\",plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "IZYkP2rhorRJ",
        "outputId": "835f5e3c-439c-40d7-be59-6b0fb9ca1e67"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "  if df[col].dtypes ==\"bool\":\n",
        "    df[col] = df[col].astype(int)\n",
        "    cat_summary(df,col,plot=True)\n",
        "\n",
        "  else:\n",
        "    cat_summary(df,col, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "t2vUQ0sQo0Yg",
        "outputId": "2a9de3c7-1f6e-46ba-e8c0-c5b9eabd00df"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   embarked      Ratio\n",
            "S       644  72.278339\n",
            "C       168  18.855219\n",
            "Q        77   8.641975\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        class      Ratio\n",
            "Third     491  55.106622\n",
            "First     216  24.242424\n",
            "Second    184  20.650954\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       who      Ratio\n",
            "man    537  60.269360\n",
            "woman  271  30.415264\n",
            "child   83   9.315376\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   adult_male     Ratio\n",
            "1         537  60.26936\n",
            "0         354  39.73064\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   deck     Ratio\n",
            "C    59  6.621773\n",
            "B    47  5.274972\n",
            "D    33  3.703704\n",
            "E    32  3.591470\n",
            "A    15  1.683502\n",
            "F    13  1.459035\n",
            "G     4  0.448934\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             embark_town      Ratio\n",
            "Southampton          644  72.278339\n",
            "Cherbourg            168  18.855219\n",
            "Queenstown            77   8.641975\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     alive      Ratio\n",
            "no     549  61.616162\n",
            "yes    342  38.383838\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   alone     Ratio\n",
            "1    537  60.26936\n",
            "0    354  39.73064\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   survived      Ratio\n",
            "0       549  61.616162\n",
            "1       342  38.383838\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   pclass      Ratio\n",
            "3     491  55.106622\n",
            "1     216  24.242424\n",
            "2     184  20.650954\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   sibsp      Ratio\n",
            "0    608  68.237935\n",
            "1    209  23.456790\n",
            "2     28   3.142536\n",
            "4     18   2.020202\n",
            "3     16   1.795735\n",
            "8      7   0.785634\n",
            "5      5   0.561167\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   parch      Ratio\n",
            "0    678  76.094276\n",
            "1    118  13.243547\n",
            "2     80   8.978676\n",
            "5      5   0.561167\n",
            "3      5   0.561167\n",
            "4      4   0.448934\n",
            "6      1   0.112233\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name, plot = False):\n",
        "  if dataframe[col_name].dtypes == \"bool\":\n",
        "    dataframe[col_name] = dataframe[col_name].astype(int)\n",
        "\n",
        "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "    print(\"######################################\")\n",
        "\n",
        "    if plot:\n",
        "      sns.countplot(x=dataframe[col_name],data = dataframe)\n",
        "      plt.show(block=True)\n",
        "  else:\n",
        "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "    print(\"######################################\")\n",
        "    if plot:\n",
        "      sns.countplot(x=dataframe[col_name],data = dataframe)\n",
        "      plt.show(block=True)"
      ],
      "metadata": {
        "id": "KMhSlQUvqP2K"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary(df, \"adult_male\", plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "aoFsIIg3stOD",
        "outputId": "ae534d40-97ea-4048-e1e0-a0929643781d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   adult_male     Ratio\n",
            "1         537  60.26936\n",
            "0         354  39.73064\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"age\",\"fare\"]].describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "LOPosm8xs6rV",
        "outputId": "2f80392c-a504-46f3-e02b-bb1b5a53880d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      count       mean        std   min      25%      50%   75%       max\n",
              "age   714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
              "fare  891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
            ],
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              "      <th>age</th>\n",
              "      <td>714.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>0.42</td>\n",
              "      <td>20.1250</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>38.0</td>\n",
              "      <td>80.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fare</th>\n",
              "      <td>891.0</td>\n",
              "      <td>32.204208</td>\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee81f127-6114-489e-939b-35ba0756017a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = [col for col in df.columns if df[col].dtypes in [\"int\",\"float\"]]"
      ],
      "metadata": {
        "id": "YTInum_T3fWG"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = [col for col in df.columns if col not in cat_cols]"
      ],
      "metadata": {
        "id": "4oFQxhJz3xj_"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VamqqlF4CSe",
        "outputId": "f4742e6a-be69-48c7-9acc-62e74d56fbbe"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age', 'fare']"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def num_summary(dataframe, numerical_col):\n",
        "  quantiles = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95]\n",
        "  print(dataframe[numerical_col].describe(quantiles).T)"
      ],
      "metadata": {
        "id": "KHh4XsJC4DTo"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_summary(df, \"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n-OPeNCg41lW",
        "outputId": "eb98961a-7a90-48ab-cb31-fe155a2335d0"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "5%         4.000000\n",
            "10%       14.000000\n",
            "20%       19.000000\n",
            "30%       22.000000\n",
            "40%       25.000000\n",
            "50%       28.000000\n",
            "60%       31.800000\n",
            "70%       36.000000\n",
            "80%       41.000000\n",
            "90%       50.000000\n",
            "95%       56.000000\n",
            "max       80.000000\n",
            "Name: age, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  num_summary(df, col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUDrijTc45zC",
        "outputId": "80367015-6cb9-4dce-8a78-73aa0e493c33"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "5%         4.000000\n",
            "10%       14.000000\n",
            "20%       19.000000\n",
            "30%       22.000000\n",
            "40%       25.000000\n",
            "50%       28.000000\n",
            "60%       31.800000\n",
            "70%       36.000000\n",
            "80%       41.000000\n",
            "90%       50.000000\n",
            "95%       56.000000\n",
            "max       80.000000\n",
            "Name: age, dtype: float64\n",
            "count    891.000000\n",
            "mean      32.204208\n",
            "std       49.693429\n",
            "min        0.000000\n",
            "5%         7.225000\n",
            "10%        7.550000\n",
            "20%        7.854200\n",
            "30%        8.050000\n",
            "40%       10.500000\n",
            "50%       14.454200\n",
            "60%       21.679200\n",
            "70%       27.000000\n",
            "80%       39.687500\n",
            "90%       77.958300\n",
            "95%      112.079150\n",
            "max      512.329200\n",
            "Name: fare, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def num_summary(dataframe, numerical_col, plot = False):\n",
        "  quantiles = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95]\n",
        "  print(dataframe[numerical_col].describe(quantiles).T)\n",
        "  if plot:\n",
        "    dataframe[numerical_col].hist()\n",
        "    plt.xlabel(numerical_col)\n",
        "    plt.title(numerical_col)\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "arggblhl5EzL"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_summary(df,\"age\", plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "aOOFroep5e8w",
        "outputId": "e1057269-5294-4565-ce09-b8d603bcee27"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "5%         4.000000\n",
            "10%       14.000000\n",
            "20%       19.000000\n",
            "30%       22.000000\n",
            "40%       25.000000\n",
            "50%       28.000000\n",
            "60%       31.800000\n",
            "70%       36.000000\n",
            "80%       41.000000\n",
            "90%       50.000000\n",
            "95%       56.000000\n",
            "max       80.000000\n",
            "Name: age, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  num_summary(df,col,plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dHEtZOkC5j68",
        "outputId": "2e905cd3-e6af-49fc-e28d-21c56cda76cd"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "5%         4.000000\n",
            "10%       14.000000\n",
            "20%       19.000000\n",
            "30%       22.000000\n",
            "40%       25.000000\n",
            "50%       28.000000\n",
            "60%       31.800000\n",
            "70%       36.000000\n",
            "80%       41.000000\n",
            "90%       50.000000\n",
            "95%       56.000000\n",
            "max       80.000000\n",
            "Name: age, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    891.000000\n",
            "mean      32.204208\n",
            "std       49.693429\n",
            "min        0.000000\n",
            "5%         7.225000\n",
            "10%        7.550000\n",
            "20%        7.854200\n",
            "30%        8.050000\n",
            "40%       10.500000\n",
            "50%       14.454200\n",
            "60%       21.679200\n",
            "70%       27.000000\n",
            "80%       39.687500\n",
            "90%       77.958300\n",
            "95%      112.079150\n",
            "max      512.329200\n",
            "Name: fare, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Capturing Variables and Generalizing Operations"
      ],
      "metadata": {
        "id": "4hThq40x50k7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "SKSp6qUl5sUN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e919f0c-6cc9-4056-eb6b-d6eb3529a972"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype   \n",
            "---  ------       --------------  -----   \n",
            " 0   survived     891 non-null    int64   \n",
            " 1   pclass       891 non-null    int64   \n",
            " 2   sex          891 non-null    object  \n",
            " 3   age          714 non-null    float64 \n",
            " 4   sibsp        891 non-null    int64   \n",
            " 5   parch        891 non-null    int64   \n",
            " 6   fare         891 non-null    float64 \n",
            " 7   embarked     889 non-null    object  \n",
            " 8   class        891 non-null    category\n",
            " 9   who          891 non-null    object  \n",
            " 10  adult_male   891 non-null    int64   \n",
            " 11  deck         203 non-null    category\n",
            " 12  embark_town  889 non-null    object  \n",
            " 13  alive        891 non-null    object  \n",
            " 14  alone        891 non-null    int64   \n",
            "dtypes: category(2), float64(2), int64(6), object(5)\n",
            "memory usage: 92.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
        "  \"\"\"\n",
        "  docstring oluşturma\n",
        "  Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir...\n",
        "\n",
        "  Parameters\n",
        "  --------\n",
        "  dataframe: dataframe\n",
        "    değişken isimleri alınmak istenen dataframe'dir\n",
        "  cat_th: int, float\n",
        "    numerik fakat ketogorik olan değişkenler için sınıf eşik değeri\n",
        "  car_th: int, float\n",
        "    kategorik fakat kardinal değişkenler için sınıf eşik değeri\n",
        "\n",
        "  Returns\n",
        "  -----\n",
        "  cat_cols: list\n",
        "    Kategorik değişken listesi\n",
        "  num_cols: list\n",
        "    Numerik değişken listesi\n",
        "  cat_but_car: list\n",
        "    Kategorik görünümlü kardinal değişken listesi\n",
        "\n",
        "  NOTES\n",
        "  --------\n",
        "  cat_cols + num_cols + cat_but_car = toplam değişken sayısı\n",
        "    num_but_cat cat_cols'un içerisinde.\n",
        "  \"\"\"\n",
        "  cat_cols = [col for col in df.columns if str(df[col].dtypes) in [\"category\",\"object\",\"bool\"]]\n",
        "\n",
        "  num_but_cat = [col for col in df.columns if df[col].nunique() < 10 and df[col].dtypes in [\"int\",\"float\"]]\n",
        "\n",
        "  cat_but_car = [col for col in df.columns if\n",
        "                 df[col].nunique() > 20 and str(df[col].dtypes) in [\"category\", \"object\"]]\n",
        "\n",
        "  cat_cols = cat_cols + num_but_cat\n",
        "  cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
        "\n",
        "  print(f\"Observations: {dataframe.shape[0]}\")\n",
        "  print(f\"Variables: {dataframe.shape[1]}\")\n",
        "  print(f\"cat_cols: {len(cat_cols)}\")\n",
        "  print(f\"num_cols: {len(num_cols)}\")\n",
        "  print(f\"cat_but_car: {len(cat_but_car)}\")\n",
        "  print(f\"num_but_cat: {len(num_but_cat)}\")\n",
        "\n",
        "  return cat_cols, num_cols, cat_but_car"
      ],
      "metadata": {
        "id": "6sn873_TpD8G"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "help(grab_col_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "foOtbBJgrE-h",
        "outputId": "39f20b2d-858a-4f1c-d1ff-d7a543c1c93a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function grab_col_names in module __main__:\n",
            "\n",
            "grab_col_names(dataframe, cat_th=10, car_th=20)\n",
            "    docstring oluşturma\n",
            "    Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir...\n",
            "    \n",
            "    Parameters\n",
            "    --------\n",
            "    dataframe: dataframe\n",
            "      değişken isimleri alınmak istenen dataframe'dir\n",
            "    cat_th: int, float\n",
            "      numerik fakat ketogorik olan değişkenler için sınıf eşik değeri\n",
            "    car_th: int, float\n",
            "      kategorik fakat kardinal değişkenler için sınıf eşik değeri\n",
            "    \n",
            "    Returns\n",
            "    -----\n",
            "    cat_cols: list\n",
            "      Kategorik değişken listesi\n",
            "    num_cols: list\n",
            "      Numerik değişken listesi\n",
            "    cat_but_car: list\n",
            "      Kategorik görünümlü kardinal değişken listesi\n",
            "    \n",
            "    NOTES\n",
            "    --------\n",
            "    cat_cols + num_cols + cat_but_car = toplam değişken sayısı\n",
            "      num_but_cat cat_cols'un içerisinde.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MRNc5DqrHaP",
        "outputId": "32137373-c1e8-4932-a822-b3ef5041153c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 891\n",
            "Variables: 15\n",
            "cat_cols: 13\n",
            "num_cols: 2\n",
            "cat_but_car: 0\n",
            "num_but_cat: 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uupL0LtXumFg",
        "outputId": "f34aa2a3-8b74-41ef-cda3-e88767b624e8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex',\n",
              " 'embarked',\n",
              " 'class',\n",
              " 'who',\n",
              " 'deck',\n",
              " 'embark_town',\n",
              " 'alive',\n",
              " 'survived',\n",
              " 'pclass',\n",
              " 'sibsp',\n",
              " 'parch',\n",
              " 'adult_male',\n",
              " 'alone']"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dIVKBPFvFdy",
        "outputId": "7bc8c3f0-cddf-4b08-89ae-c392c4e68b33"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age', 'fare']"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_but_car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ncu2lsJCvG7s",
        "outputId": "7b424e33-fc4f-4907-cbdc-cd8eb6157895"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name):\n",
        "  print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                      \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "  print(\"######################################\")"
      ],
      "metadata": {
        "id": "Gg-T6K9qvIPQ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_summary(df,\"sex\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJg6hvkavfWF",
        "outputId": "d47c82ed-f14e-4a03-9037-ffe3a8a54a16"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "  cat_summary(df,col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drwPDyIBvitK",
        "outputId": "02da4422-e345-4a93-e502-e79a3a6d921b"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n",
            "   embarked      Ratio\n",
            "S       644  72.278339\n",
            "C       168  18.855219\n",
            "Q        77   8.641975\n",
            "######################################\n",
            "        class      Ratio\n",
            "Third     491  55.106622\n",
            "First     216  24.242424\n",
            "Second    184  20.650954\n",
            "######################################\n",
            "       who      Ratio\n",
            "man    537  60.269360\n",
            "woman  271  30.415264\n",
            "child   83   9.315376\n",
            "######################################\n",
            "   deck     Ratio\n",
            "C    59  6.621773\n",
            "B    47  5.274972\n",
            "D    33  3.703704\n",
            "E    32  3.591470\n",
            "A    15  1.683502\n",
            "F    13  1.459035\n",
            "G     4  0.448934\n",
            "######################################\n",
            "             embark_town      Ratio\n",
            "Southampton          644  72.278339\n",
            "Cherbourg            168  18.855219\n",
            "Queenstown            77   8.641975\n",
            "######################################\n",
            "     alive      Ratio\n",
            "no     549  61.616162\n",
            "yes    342  38.383838\n",
            "######################################\n",
            "   survived      Ratio\n",
            "0       549  61.616162\n",
            "1       342  38.383838\n",
            "######################################\n",
            "   pclass      Ratio\n",
            "3     491  55.106622\n",
            "1     216  24.242424\n",
            "2     184  20.650954\n",
            "######################################\n",
            "   sibsp      Ratio\n",
            "0    608  68.237935\n",
            "1    209  23.456790\n",
            "2     28   3.142536\n",
            "4     18   2.020202\n",
            "3     16   1.795735\n",
            "8      7   0.785634\n",
            "5      5   0.561167\n",
            "######################################\n",
            "   parch      Ratio\n",
            "0    678  76.094276\n",
            "1    118  13.243547\n",
            "2     80   8.978676\n",
            "5      5   0.561167\n",
            "3      5   0.561167\n",
            "4      4   0.448934\n",
            "6      1   0.112233\n",
            "######################################\n",
            "   adult_male     Ratio\n",
            "1         537  60.26936\n",
            "0         354  39.73064\n",
            "######################################\n",
            "   alone     Ratio\n",
            "1    537  60.26936\n",
            "0    354  39.73064\n",
            "######################################\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def num_summary(dataframe, numerical_col, plot = False):\n",
        "  quantiles = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95]\n",
        "  print(dataframe[numerical_col].describe(quantiles).T)\n",
        "  if plot:\n",
        "    dataframe[numerical_col].hist()\n",
        "    plt.xlabel(numerical_col)\n",
        "    plt.title(numerical_col)\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "GeVkIcPEvnWE"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  num_summary(df, col, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rcHDJ1ocv15d",
        "outputId": "534b1808-f922-48c7-cf4e-9a351d1b14ba"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "5%         4.000000\n",
            "10%       14.000000\n",
            "20%       19.000000\n",
            "30%       22.000000\n",
            "40%       25.000000\n",
            "50%       28.000000\n",
            "60%       31.800000\n",
            "70%       36.000000\n",
            "80%       41.000000\n",
            "90%       50.000000\n",
            "95%       56.000000\n",
            "max       80.000000\n",
            "Name: age, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    891.000000\n",
            "mean      32.204208\n",
            "std       49.693429\n",
            "min        0.000000\n",
            "5%         7.225000\n",
            "10%        7.550000\n",
            "20%        7.854200\n",
            "30%        8.050000\n",
            "40%       10.500000\n",
            "50%       14.454200\n",
            "60%       21.679200\n",
            "70%       27.000000\n",
            "80%       39.687500\n",
            "90%       77.958300\n",
            "95%      112.079150\n",
            "max      512.329200\n",
            "Name: fare, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g00ViIrfwWaD",
        "outputId": "ed9bf6f5-e803-4ea9-dfc3-54adeca76e4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex',\n",
              " 'embarked',\n",
              " 'class',\n",
              " 'who',\n",
              " 'deck',\n",
              " 'embark_town',\n",
              " 'alive',\n",
              " 'survived',\n",
              " 'pclass',\n",
              " 'sibsp',\n",
              " 'parch',\n",
              " 'adult_male',\n",
              " 'alone']"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cat_summary(dataframe, col_name, plot = False):\n",
        "  print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
        "                      \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
        "  print(\"######################################\")\n",
        "\n",
        "  if plot:\n",
        "    sns.countplot(x=dataframe[col_name],data = dataframe)\n",
        "    plt.show(block=True)"
      ],
      "metadata": {
        "id": "egVl6NSSwfW6"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "  cat_summary(df, col, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "69gjaYAlw0QK",
        "outputId": "3a57506a-ebdf-4283-b517-1032691858db"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        sex      Ratio\n",
            "male    577  64.758698\n",
            "female  314  35.241302\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   embarked      Ratio\n",
            "S       644  72.278339\n",
            "C       168  18.855219\n",
            "Q        77   8.641975\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        class      Ratio\n",
            "Third     491  55.106622\n",
            "First     216  24.242424\n",
            "Second    184  20.650954\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       who      Ratio\n",
            "man    537  60.269360\n",
            "woman  271  30.415264\n",
            "child   83   9.315376\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   deck     Ratio\n",
            "C    59  6.621773\n",
            "B    47  5.274972\n",
            "D    33  3.703704\n",
            "E    32  3.591470\n",
            "A    15  1.683502\n",
            "F    13  1.459035\n",
            "G     4  0.448934\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             embark_town      Ratio\n",
            "Southampton          644  72.278339\n",
            "Cherbourg            168  18.855219\n",
            "Queenstown            77   8.641975\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     alive      Ratio\n",
            "no     549  61.616162\n",
            "yes    342  38.383838\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   survived      Ratio\n",
            "0       549  61.616162\n",
            "1       342  38.383838\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   pclass      Ratio\n",
            "3     491  55.106622\n",
            "1     216  24.242424\n",
            "2     184  20.650954\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   sibsp      Ratio\n",
            "0    608  68.237935\n",
            "1    209  23.456790\n",
            "2     28   3.142536\n",
            "4     18   2.020202\n",
            "3     16   1.795735\n",
            "8      7   0.785634\n",
            "5      5   0.561167\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   parch      Ratio\n",
            "0    678  76.094276\n",
            "1    118  13.243547\n",
            "2     80   8.978676\n",
            "5      5   0.561167\n",
            "3      5   0.561167\n",
            "4      4   0.448934\n",
            "6      1   0.112233\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   adult_male     Ratio\n",
            "1         537  60.26936\n",
            "0         354  39.73064\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   alone     Ratio\n",
            "1    537  60.26936\n",
            "0    354  39.73064\n",
            "######################################\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  num_summary(df, col, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R-NRREoBw71m",
        "outputId": "f981a806-713f-432b-985b-e33cf5d2b9a5"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    714.000000\n",
            "mean      29.699118\n",
            "std       14.526497\n",
            "min        0.420000\n",
            "5%         4.000000\n",
            "10%       14.000000\n",
            "20%       19.000000\n",
            "30%       22.000000\n",
            "40%       25.000000\n",
            "50%       28.000000\n",
            "60%       31.800000\n",
            "70%       36.000000\n",
            "80%       41.000000\n",
            "90%       50.000000\n",
            "95%       56.000000\n",
            "max       80.000000\n",
            "Name: age, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    891.000000\n",
            "mean      32.204208\n",
            "std       49.693429\n",
            "min        0.000000\n",
            "5%         7.225000\n",
            "10%        7.550000\n",
            "20%        7.854200\n",
            "30%        8.050000\n",
            "40%       10.500000\n",
            "50%       14.454200\n",
            "60%       21.679200\n",
            "70%       27.000000\n",
            "80%       39.687500\n",
            "90%       77.958300\n",
            "95%      112.079150\n",
            "max      512.329200\n",
            "Name: fare, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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gHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGiSqgzJs3Tw6HI+Jr+PDh4f3Hjx9XVVWVBgwYoH79+qmiokKtra0Rc7S0tKi8vFwZGRnKycnRzJkzdeLEidisBgAA9Aop0T7hs5/9rF555ZX/myDl/6aYMWOGXnrpJa1YsUKZmZmaOnWqJkyYoN/97neSpK6uLpWXl8vj8WjDhg06cOCAvvKVr8jpdOr++++PwXIAAEBvEHVASUlJkcfj6bH90KFDWrx4sZYtW6Zrr71WkrRkyRJdcskl2rhxo4qLi9XQ0KBdu3bplVdekdvt1ujRo7VgwQJ9//vf17x585SamnrmKwIAAAkv6oCyZ88e5ebmKj09XV6vV3V1dRo8eLCam5sVDAZVUlISHjt8+HANHjxYTU1NKi4uVlNTk0aOHCm32x0eU1ZWpilTpmjnzp0aM2bMSY8ZCAQUCATCjzs6OiRJwWBQwWAw2iV8ou750pKsmM5rt1j3wS7ddSZKvYmG/tqL/tqL/trLhP5Gc+yoAkpRUZGWLl2qiy++WAcOHFBtba0+//nPa8eOHfL7/UpNTVVWVlbEc9xut/x+vyTJ7/dHhJPu/d37Pk5dXZ1qa2t7bG9oaFBGRkY0SzhlC8aGbJnXLqtXr453CVHx+XzxLqFXo7/2or/2or/2imd/jx07dspjowoo48ePD38/atQoFRUVaciQIfrVr36lPn36RDNVVGpqalRdXR1+3NHRoby8PJWWlsrlcsX0WMFgUD6fT7O3JikQcsR0bjvtmFcW7xJOSXd/x40bJ6fTGe9yeh36ay/6ay/6ay8T+tt9BeRURH2J58OysrJ00UUX6e2339a4cePU2dmp9vb2iLMora2t4deseDwebd68OWKO7rt8Tva6lm5paWlKS0vrsd3pdNrW5EDIoUBX4gSURPtjtvNnB/prN/prL/prr3j2N5rjntH7oBw5ckTvvPOOBg0apMLCQjmdTjU2Nob37969Wy0tLfJ6vZIkr9er7du3q62tLTzG5/PJ5XKpoKDgTEoBAAC9SFRnUL773e/qxhtv1JAhQ7R//37NnTtXycnJuuOOO5SZmalJkyapurpa2dnZcrlcmjZtmrxer4qLiyVJpaWlKigo0J133qmFCxfK7/dr1qxZqqqqOukZEgAA8OkUVUD5f//v/+mOO+7Q+++/r3PPPVdXXXWVNm7cqHPPPVeStGjRIiUlJamiokKBQEBlZWV67LHHws9PTk7WqlWrNGXKFHm9XvXt21eVlZWaP39+bFcFAAASWlQBZfny5Z+4Pz09XfX19aqvr//YMUOGDEm4O04AAMDZxWfxAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADDOGQWUBx54QA6HQ9OnTw9vO378uKqqqjRgwAD169dPFRUVam1tjXheS0uLysvLlZGRoZycHM2cOVMnTpw4k1IAAEAvctoBZcuWLfrpT3+qUaNGRWyfMWOGXnzxRa1YsULr1q3T/v37NWHChPD+rq4ulZeXq7OzUxs2bNCzzz6rpUuXas6cOae/CgAA0KucVkA5cuSIJk6cqKeeekrnnHNOePuhQ4e0ePFiPfTQQ7r22mtVWFioJUuWaMOGDdq4caMkqaGhQbt27dJzzz2n0aNHa/z48VqwYIHq6+vV2dkZm1UBAICElnI6T6qqqlJ5eblKSkp07733hrc3NzcrGAyqpKQkvG348OEaPHiwmpqaVFxcrKamJo0cOVJutzs8pqysTFOmTNHOnTs1ZsyYHscLBAIKBALhxx0dHZKkYDCoYDB4Okv4WN3zpSVZMZ3XbrHug12660yUehMN/bUX/bUX/bWXCf2N5thRB5Tly5frzTff1JYtW3rs8/v9Sk1NVVZWVsR2t9stv98fHvPhcNK9v3vfydTV1am2trbH9oaGBmVkZES7hFOyYGzIlnntsnr16niXEBWfzxfvEno1+msv+msv+muvePb32LFjpzw2qoDy7rvv6tvf/rZ8Pp/S09OjLux01dTUqLq6Ovy4o6NDeXl5Ki0tlcvliumxgsGgfD6fZm9NUiDkiOncdtoxryzeJZyS7v6OGzdOTqcz3uX0OvTXXvTXXvTXXib0t/sKyKmIKqA0Nzerra1Nn/vc58Lburq6tH79ev3kJz/Ryy+/rM7OTrW3t0ecRWltbZXH45EkeTwebd68OWLe7rt8usd8VFpamtLS0npsdzqdtjU5EHIo0JU4ASXR/pjt/NmB/tqN/tqL/tornv2N5rhRvUj2uuuu0/bt27Vt27bw19ixYzVx4sTw906nU42NjeHn7N69Wy0tLfJ6vZIkr9er7du3q62tLTzG5/PJ5XKpoKAgmnIAAEAvFdUZlP79+2vEiBER2/r27asBAwaEt0+aNEnV1dXKzs6Wy+XStGnT5PV6VVxcLEkqLS1VQUGB7rzzTi1cuFB+v1+zZs1SVVXVSc+SAACAT5/TuovnkyxatEhJSUmqqKhQIBBQWVmZHnvssfD+5ORkrVq1SlOmTJHX61Xfvn1VWVmp+fPnx7oUAACQoM44oLz++usRj9PT01VfX6/6+vqPfc6QIUMS7q4TAABw9vBZPAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACME1VAefzxxzVq1Ci5XC65XC55vV6tWbMmvP/48eOqqqrSgAED1K9fP1VUVKi1tTVijpaWFpWXlysjI0M5OTmaOXOmTpw4EZvVAACAXiGqgHL++efrgQceUHNzs7Zu3aprr71WN910k3bu3ClJmjFjhl588UWtWLFC69at0/79+zVhwoTw87u6ulReXq7Ozk5t2LBBzz77rJYuXao5c+bEdlUAACChpUQz+MYbb4x4fN999+nxxx/Xxo0bdf7552vx4sVatmyZrr32WknSkiVLdMkll2jjxo0qLi5WQ0ODdu3apVdeeUVut1ujR4/WggUL9P3vf1/z5s1Tampq7FYGAAASVlQB5cO6urq0YsUKHT16VF6vV83NzQoGgyopKQmPGT58uAYPHqympiYVFxerqalJI0eOlNvtDo8pKyvTlClTtHPnTo0ZM+akxwoEAgoEAuHHHR0dkqRgMKhgMHi6Szip7vnSkqyYzmu3WPfBLt11Jkq9iYb+2ov+2ov+2suE/kZz7KgDyvbt2+X1enX8+HH169dPK1euVEFBgbZt26bU1FRlZWVFjHe73fL7/ZIkv98fEU6693fv+zh1dXWqra3tsb2hoUEZGRnRLuGULBgbsmVeu6xevTreJUTF5/PFu4Rejf7ai/7ai/7aK579PXbs2CmPjTqgXHzxxdq2bZsOHTqkX//616qsrNS6deuinSYqNTU1qq6uDj/u6OhQXl6eSktL5XK5YnqsYDAon8+n2VuTFAg5Yjq3nXbMK4t3Caeku7/jxo2T0+mMdzm9Dv21F/21F/21lwn97b4CciqiDiipqam68MILJUmFhYXasmWLfvzjH+u2225TZ2en2tvbI86itLa2yuPxSJI8Ho82b94cMV/3XT7dY04mLS1NaWlpPbY7nU7bmhwIORToSpyAkmh/zHb+7EB/7UZ/7UV/7RXP/kZz3DN+H5RQKKRAIKDCwkI5nU41NjaG9+3evVstLS3yer2SJK/Xq+3bt6utrS08xufzyeVyqaCg4ExLAQAAvURUZ1Bqamo0fvx4DR48WIcPH9ayZcv0+uuv6+WXX1ZmZqYmTZqk6upqZWdny+Vyadq0afJ6vSouLpYklZaWqqCgQHfeeacWLlwov9+vWbNmqaqq6qRnSAAAwKdTVAGlra1NX/nKV3TgwAFlZmZq1KhRevnllzVu3DhJ0qJFi5SUlKSKigoFAgGVlZXpscceCz8/OTlZq1at0pQpU+T1etW3b19VVlZq/vz5sV0VAABIaFEFlMWLF3/i/vT0dNXX16u+vv5jxwwZMiTh7jgBAABnF5/FAwAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4UQWUuro6XXbZZerfv79ycnJ08803a/fu3RFjjh8/rqqqKg0YMED9+vVTRUWFWltbI8a0tLSovLxcGRkZysnJ0cyZM3XixIkzXw0AAOgVogoo69atU1VVlTZu3Cifz6dgMKjS0lIdPXo0PGbGjBl68cUXtWLFCq1bt0779+/XhAkTwvu7urpUXl6uzs5ObdiwQc8++6yWLl2qOXPmxG5VAAAgoaVEM3jt2rURj5cuXaqcnBw1NzfrC1/4gg4dOqTFixdr2bJluvbaayVJS5Ys0SWXXKKNGzequLhYDQ0N2rVrl1555RW53W6NHj1aCxYs0Pe//33NmzdPqampsVsdAABISGf0GpRDhw5JkrKzsyVJzc3NCgaDKikpCY8ZPny4Bg8erKamJklSU1OTRo4cKbfbHR5TVlamjo4O7dy580zKAQAAvURUZ1A+LBQKafr06bryyis1YsQISZLf71dqaqqysrIixrrdbvn9/vCYD4eT7v3d+04mEAgoEAiEH3d0dEiSgsGggsHg6S7hpLrnS0uyYjqv3WLdB7t015ko9SYa+msv+msv+msvE/obzbFPO6BUVVVpx44deuONN053ilNWV1en2traHtsbGhqUkZFhyzEXjA3ZMq9dVq9eHe8SouLz+eJdQq9Gf+1Ff+1Ff+0Vz/4eO3bslMeeVkCZOnWqVq1apfXr1+v8888Pb/d4POrs7FR7e3vEWZTW1lZ5PJ7wmM2bN0fM132XT/eYj6qpqVF1dXX4cUdHh/Ly8lRaWiqXy3U6S/hYwWBQPp9Ps7cmKRByxHRuO+2YVxbvEk5Jd3/HjRsnp9MZ73J6HfprL/prL/prLxP6230F5FREFVAsy9K0adO0cuVKvf7668rPz4/YX1hYKKfTqcbGRlVUVEiSdu/erZaWFnm9XkmS1+vVfffdp7a2NuXk5Ej6IM25XC4VFBSc9LhpaWlKS0vrsd3pdNrW5EDIoUBX4gSURPtjtvNnB/prN/prL/prr3j2N5rjRhVQqqqqtGzZMv3mN79R//79w68ZyczMVJ8+fZSZmalJkyapurpa2dnZcrlcmjZtmrxer4qLiyVJpaWlKigo0J133qmFCxfK7/dr1qxZqqqqOmkIAQAAnz5RBZTHH39cknTNNddEbF+yZInuuusuSdKiRYuUlJSkiooKBQIBlZWV6bHHHguPTU5O1qpVqzRlyhR5vV717dtXlZWVmj9//pmtBAAA9BpRX+L5Z9LT01VfX6/6+vqPHTNkyJCEe1EnAAA4e/gsHgAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGiTqgrF+/XjfeeKNyc3PlcDj0wgsvROy3LEtz5szRoEGD1KdPH5WUlGjPnj0RYw4ePKiJEyfK5XIpKytLkyZN0pEjR85oIQAAoPeIOqAcPXpUl156qerr60+6f+HChXrkkUf0xBNPaNOmTerbt6/Kysp0/Pjx8JiJEydq586d8vl8WrVqldavX6/Jkyef/ioAAECvkhLtE8aPH6/x48efdJ9lWXr44Yc1a9Ys3XTTTZKkn/3sZ3K73XrhhRd0++236w9/+IPWrl2rLVu2aOzYsZKkRx99VF/84hf1ox/9SLm5uWewnE+vofe8FO8STklasqWFl0sj5r2s3ffdEO9yAACGiulrUPbu3Su/36+SkpLwtszMTBUVFampqUmS1NTUpKysrHA4kaSSkhIlJSVp06ZNsSwHAAAkqKjPoHwSv98vSXK73RHb3W53eJ/f71dOTk5kESkpys7ODo/5qEAgoEAgEH7c0dEhSQoGgwoGgzGrv3tOSUpLsmI6Lz7Q3de0JCvmPzv83+8vvbUH/bUX/bWXCf2N5tgxDSh2qaurU21tbY/tDQ0NysjIsOWYC8aGbJkXH1gwNqTVq1fHu4xey+fzxbuEXo3+2ov+2iue/T127Ngpj41pQPF4PJKk1tZWDRo0KLy9tbVVo0ePDo9pa2uLeN6JEyd08ODB8PM/qqamRtXV1eHHHR0dysvLU2lpqVwuVyyXoGAwKJ/Pp9lbkxQIOWI6Nz44c7JgbEiztyapec718S6n1+n+/R03bpycTme8y+l16K+96K+9TOhv9xWQUxHTgJKfny+Px6PGxsZwIOno6NCmTZs0ZcoUSZLX61V7e7uam5tVWFgoSXr11VcVCoVUVFR00nnT0tKUlpbWY7vT6bStyYGQQ4EuAopdAiEH/wDZyM6/DdBfu9Ffe8Wzv9EcN+qAcuTIEb399tvhx3v37tW2bduUnZ2twYMHa/r06br33ns1bNgw5efna/bs2crNzdXNN98sSbrkkkt0/fXX6+6779YTTzyhYDCoqVOn6vbbb+cOHgAAIOk0AsrWrVv1L//yL+HH3ZdeKisrtXTpUn3ve9/T0aNHNXnyZLW3t+uqq67S2rVrlZ6eHn7Oz3/+c02dOlXXXXedkpKSVFFRoUceeSQGywEAAL1B1AHlmmuukWV9/B0uDodD8+fP1/z58z92THZ2tpYtWxbtoQEAwKcEn8UDAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjpMS7AHx6Db3npXiXELV9D5THuwQA+FTgDAoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBzeqA3o5XhDPACJKK5nUOrr6zV06FClp6erqKhImzdvjmc5AADAEHELKL/85S9VXV2tuXPn6s0339Sll16qsrIytbW1xaskAABgiLhd4nnooYd0991366tf/aok6YknntBLL72kZ555Rvfcc0+8ygJgAFMvS6UlW1p4uTRi3ssKdDki9nFZCoituASUzs5ONTc3q6amJrwtKSlJJSUlampqikdJAADYwpTA/UkB+2TiHbrjElDee+89dXV1ye12R2x3u9364x//2GN8IBBQIBAIPz506JAk6eDBgwoGgzGtLRgM6tixY0oJJqkr9M9/gIhOSsjSsWOhhO3vhd/9VbxL+ERpSZZmjQlp9H88r8D/9pdXwsfOJ/3+vv/++3Gqqvfo/vf3/fffl9PpjHc5MZNy4mi8S5AU/b+/dvxOHz58WJJkWdY/HZsQ/3bV1dWptra2x/b8/Pw4VIMz9a/xLqCXo7/2+rj+DnzwrJYBnJZo/n2w83f68OHDyszM/MQxcQkoAwcOVHJyslpbWyO2t7a2yuPx9BhfU1Oj6urq8ONQKKSDBw9qwIABcjhi+3/hHR0dysvL07vvviuXyxXTuUF/7UZ/7UV/7UV/7WVCfy3L0uHDh5Wbm/tPx8YloKSmpqqwsFCNjY26+eabJX0QOhobGzV16tQe49PS0pSWlhaxLSsry9YaXS4XfyA2or/2or/2or/2or/2ind//9mZk25xu8RTXV2tyspKjR07VpdffrkefvhhHT16NHxXDwAA+PSKW0C57bbb9Le//U1z5syR3+/X6NGjtXbt2h4vnAUAAJ8+cX2R7NSpU096SSee0tLSNHfu3B6XlBAb9Nde9Nde9Nde9NdeidZfh3Uq9/oAAACcRXyaMQAAMA4BBQAAGIeAAgAAjENAAQAAxiGgfEh9fb2GDh2q9PR0FRUVafPmzfEuKSGsX79eN954o3Jzc+VwOPTCCy9E7LcsS3PmzNGgQYPUp08flZSUaM+ePRFjDh48qIkTJ8rlcikrK0uTJk3SkSNHzuIqzFVXV6fLLrtM/fv3V05Ojm6++Wbt3r07Yszx48dVVVWlAQMGqF+/fqqoqOjxTs0tLS0qLy9XRkaGcnJyNHPmTJ04ceJsLsVIjz/+uEaNGhV+8yqv16s1a9aE99Pb2HnggQfkcDg0ffr08Db6e2bmzZsnh8MR8TV8+PDw/oTurwXLsixr+fLlVmpqqvXMM89YO3futO6++24rKyvLam1tjXdpxlu9erX1H//xH9bzzz9vSbJWrlwZsf+BBx6wMjMzrRdeeMH6n//5H+tLX/qSlZ+fb/3jH/8Ij7n++uutSy+91Nq4caP13//939aFF15o3XHHHWd5JWYqKyuzlixZYu3YscPatm2b9cUvftEaPHiwdeTIkfCYb3zjG1ZeXp7V2Nhobd261SouLrauuOKK8P4TJ05YI0aMsEpKSqy33nrLWr16tTVw4ECrpqYmHksyym9/+1vrpZdesv70pz9Zu3fvtn7wgx9YTqfT2rFjh2VZ9DZWNm/ebA0dOtQaNWqU9e1vfzu8nf6emblz51qf/exnrQMHDoS//va3v4X3J3J/CSj/6/LLL7eqqqrCj7u6uqzc3Fyrrq4ujlUlno8GlFAoZHk8HuuHP/xheFt7e7uVlpZm/eIXv7Asy7J27dplSbK2bNkSHrNmzRrL4XBYf/3rX89a7Ymira3NkmStW7fOsqwP+ul0Oq0VK1aEx/zhD3+wJFlNTU2WZX0QIpOSkiy/3x8e8/jjj1sul8sKBAJndwEJ4JxzzrGefvppehsjhw8ftoYNG2b5fD7r6quvDgcU+nvm5s6da1166aUn3Zfo/eUSj6TOzk41NzerpKQkvC0pKUklJSVqamqKY2WJb+/evfL7/RG9zczMVFFRUbi3TU1NysrK0tixY8NjSkpKlJSUpE2bNp31mk136NAhSVJ2drYkqbm5WcFgMKLHw4cP1+DBgyN6PHLkyIh3ai4rK1NHR4d27tx5Fqs3W1dXl5YvX66jR4/K6/XS2xipqqpSeXl5RB8lfndjZc+ePcrNzdUFF1ygiRMnqqWlRVLi9zeu7yRrivfee09dXV093mbf7Xbrj3/8Y5yq6h38fr8knbS33fv8fr9ycnIi9qekpCg7Ozs8Bh8IhUKaPn26rrzySo0YMULSB/1LTU3t8QGaH+3xyX4G3fs+7bZv3y6v16vjx4+rX79+WrlypQoKCrRt2zZ6e4aWL1+uN998U1u2bOmxj9/dM1dUVKSlS5fq4osv1oEDB1RbW6vPf/7z2rFjR8L3l4ACJJCqqirt2LFDb7zxRrxL6VUuvvhibdu2TYcOHdKvf/1rVVZWat26dfEuK+G9++67+va3vy2fz6f09PR4l9MrjR8/Pvz9qFGjVFRUpCFDhuhXv/qV+vTpE8fKzhyXeCQNHDhQycnJPV7Z3NraKo/HE6eqeofu/n1Sbz0ej9ra2iL2nzhxQgcPHqT/HzJ16lStWrVKr732ms4///zwdo/Ho87OTrW3t0eM/2iPT/Yz6N73aZeamqoLL7xQhYWFqqur06WXXqof//jH9PYMNTc3q62tTZ/73OeUkpKilJQUrVu3To888ohSUlLkdrvpb4xlZWXpoosu0ttvv53wv78EFH3wj1NhYaEaGxvD20KhkBobG+X1euNYWeLLz8+Xx+OJ6G1HR4c2bdoU7q3X61V7e7uam5vDY1599VWFQiEVFRWd9ZpNY1mWpk6dqpUrV+rVV19Vfn5+xP7CwkI5nc6IHu/evVstLS0RPd6+fXtEEPT5fHK5XCooKDg7C0kgoVBIgUCA3p6h6667Ttu3b9e2bdvCX2PHjtXEiRPD39Pf2Dpy5IjeeecdDRo0KPF/f+P6El2DLF++3EpLS7OWLl1q7dq1y5o8ebKVlZUV8cpmnNzhw4ett956y3rrrbcsSdZDDz1kvfXWW9Zf/vIXy7I+uM04KyvL+s1vfmP9/ve/t2666aaT3mY8ZswYa9OmTdYbb7xhDRs2jNuM/9eUKVOszMxM6/XXX4+4lfDYsWPhMd/4xjeswYMHW6+++qq1detWy+v1Wl6vN7y/+1bC0tJSa9u2bdbatWutc88914hbCePtnnvusdatW2ft3bvX+v3vf2/dc889lsPhsBoaGizLorex9uG7eCyL/p6p73znO9brr79u7d271/rd735nlZSUWAMHDrTa2tosy0rs/hJQPuTRRx+1Bg8ebKWmplqXX365tXHjxniXlBBee+01S1KPr8rKSsuyPrjVePbs2Zbb7bbS0tKs6667ztq9e3fEHO+//751xx13WP369bNcLpf11a9+1Tp8+HAcVmOek/VWkrVkyZLwmH/84x/WN7/5Teucc86xMjIyrC9/+cvWgQMHIubZt2+fNX78eKtPnz7WwIEDre985ztWMBg8y6sxz9e+9jVryJAhVmpqqnXuueda1113XTicWBa9jbWPBhT6e2Zuu+02a9CgQVZqaqp13nnnWbfddpv19ttvh/cncn8dlmVZ8Tl3AwAAcHK8BgUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgDbWZalyZMnKzs7Ww6HQ9u2bYt3SQAMxxu1AbDdmjVrdNNNN+n111/XBRdcoIEDByolhQ9TB/Dx+BcCgO26P7zsiiuuOO05gsGgnE5nDKsCYDIu8QCw1V133aVp06appaVFDodDQ4cO1dq1a3XVVVcpKytLAwYM0A033KB33nkn/Jx9+/bJ4XDol7/8pa6++mqlp6fr5z//uSTp6aef1iWXXKL09HQNHz5cjz32WLyWBsBGXOIBYKtDhw7pkUce0ZNPPqktW7YoOTlZ69evl8Ph0KhRo3TkyBHNmTNH+/bt07Zt25SUlKR9+/YpPz9fQ4cO1YMPPqgxY8YoPT1dr776qmbOnKmf/OQnGjNmjN566y3dfffdeuihh1RZWRnvpQKIIS7xALBVZmam+vfvr+TkZHk8HklSRUVFxJhnnnlG5557rnbt2qURI0aEt0+fPl0TJkwIP547d64efPDB8Lb8/Hzt2rVLP/3pTwkoQC/DJR4AZ92ePXt0xx136IILLpDL5dLQoUMlSS0tLRHjxo4dG/7+6NGjeueddzRp0iT169cv/HXvvfdGXB4C0DtwBgXAWXfjjTdqyJAheuqpp5Sbm6tQKKQRI0aos7MzYlzfvn3D3x85ckSS9NRTT6moqChiXHJysv1FAzirCCgAzqr3339fu3fv1lNPPaXPf/7zkqQ33njjnz7P7XYrNzdXf/7znzVx4kS7ywQQZwQUAGfVOeecowEDBujJJ5/UoEGD1NLSonvuueeUnltbW6tvfetbyszM1PXXX69AIKCtW7fq73//u6qrq22uHMDZxGtQAJxVSUlJWr58uZqbmzVixAjNmDFDP/zhD0/puV//+tf19NNPa8mSJRo5cqSuvvpqLV26VPn5+TZXDeBs4zZjAABgHM6gAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGCc/w/aVfFZjDl0CwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis of Target Variable"
      ],
      "metadata": {
        "id": "8WSzTOzvxQJ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  if df[col].dtypes == \"bool\":\n",
        "    df[col] = df[col].astype(int)"
      ],
      "metadata": {
        "id": "gT__sK9HH0La"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols, num_cols, cat_but_car = grab_col_names(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWYl30inIg1P",
        "outputId": "36c7df4a-a639-4ad4-8af1-137f8d7d319d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observations: 891\n",
            "Variables: 15\n",
            "cat_cols: 13\n",
            "num_cols: 2\n",
            "cat_but_car: 0\n",
            "num_but_cat: 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jRvYD_4zIl9b",
        "outputId": "220e0bb4-6222-4bce-fd7e-a1d092189c9e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class    who  adult_male deck  embark_town alive  alone\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third    man           1  NaN  Southampton    no      0\n",
              "1         1       1  female  38.0      1      0  71.2833        C  First  woman           0    C    Cherbourg   yes      0\n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third  woman           0  NaN  Southampton   yes      1\n",
              "3         1       1  female  35.0      1      0  53.1000        S  First  woman           0    C  Southampton   yes      0\n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third    man           1  NaN  Southampton    no      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8bea3b7-d5c6-44cd-8559-0d37ebb9b81c\" 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>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>0</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>0</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8bea3b7-d5c6-44cd-8559-0d37ebb9b81c')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a8bea3b7-d5c6-44cd-8559-0d37ebb9b81c 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-a8bea3b7-d5c6-44cd-8559-0d37ebb9b81c');\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",
              "\n",
              "\n",
              "<div id=\"df-724e1853-a6b0-46f5-a385-2e777587a2fa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-724e1853-a6b0-46f5-a385-2e777587a2fa')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-724e1853-a6b0-46f5-a385-2e777587a2fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"sex\")[\"survived\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwNkhO2RI8Ay",
        "outputId": "61032f7c-3420-42cf-e782-925f6e579662"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex\n",
              "female    0.742038\n",
              "male      0.188908\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def target_summary_with_cat(dataframe, target, categorical_col):\n",
        "  print(pd.DataFrame({\"TARGET_MEAN\": dataframe.groupby(categorical_col)[target].mean()}))"
      ],
      "metadata": {
        "id": "CywddUiuJ6Fg"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_summary_with_cat(df, \"survived\",\"sex\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UTFLXyvrKdKx",
        "outputId": "50c3e1b9-b8c9-4066-ec53-86f03920ac86"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        TARGET_MEAN\n",
            "sex                \n",
            "female     0.742038\n",
            "male       0.188908\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_summary_with_cat(df, \"survived\",\"pclass\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLDVeb-UKoJ9",
        "outputId": "d378c3c4-a1a8-4123-acfb-c71a10f5d2b7"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        TARGET_MEAN\n",
            "pclass             \n",
            "1          0.629630\n",
            "2          0.472826\n",
            "3          0.242363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in cat_cols:\n",
        "  target_summary_with_cat(df, \"survived\", col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLKpribjKvLp",
        "outputId": "c13703ae-aaa3-473d-afdb-861b353b5a97"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        TARGET_MEAN\n",
            "sex                \n",
            "female     0.742038\n",
            "male       0.188908\n",
            "          TARGET_MEAN\n",
            "embarked             \n",
            "C            0.553571\n",
            "Q            0.389610\n",
            "S            0.336957\n",
            "        TARGET_MEAN\n",
            "class              \n",
            "First      0.629630\n",
            "Second     0.472826\n",
            "Third      0.242363\n",
            "       TARGET_MEAN\n",
            "who               \n",
            "child     0.590361\n",
            "man       0.163873\n",
            "woman     0.756458\n",
            "      TARGET_MEAN\n",
            "deck             \n",
            "A        0.466667\n",
            "B        0.744681\n",
            "C        0.593220\n",
            "D        0.757576\n",
            "E        0.750000\n",
            "F        0.615385\n",
            "G        0.500000\n",
            "             TARGET_MEAN\n",
            "embark_town             \n",
            "Cherbourg       0.553571\n",
            "Queenstown      0.389610\n",
            "Southampton     0.336957\n",
            "       TARGET_MEAN\n",
            "alive             \n",
            "no             0.0\n",
            "yes            1.0\n",
            "          TARGET_MEAN\n",
            "survived             \n",
            "0                 0.0\n",
            "1                 1.0\n",
            "        TARGET_MEAN\n",
            "pclass             \n",
            "1          0.629630\n",
            "2          0.472826\n",
            "3          0.242363\n",
            "       TARGET_MEAN\n",
            "sibsp             \n",
            "0         0.345395\n",
            "1         0.535885\n",
            "2         0.464286\n",
            "3         0.250000\n",
            "4         0.166667\n",
            "5         0.000000\n",
            "8         0.000000\n",
            "       TARGET_MEAN\n",
            "parch             \n",
            "0         0.343658\n",
            "1         0.550847\n",
            "2         0.500000\n",
            "3         0.600000\n",
            "4         0.000000\n",
            "5         0.200000\n",
            "6         0.000000\n",
            "            TARGET_MEAN\n",
            "adult_male             \n",
            "0              0.717514\n",
            "1              0.163873\n",
            "       TARGET_MEAN\n",
            "alone             \n",
            "0         0.505650\n",
            "1         0.303538\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"survived\")[\"age\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIABGHQnLBRP",
        "outputId": "00f027b6-8010-4d78-9f4a-f509a882d391"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived\n",
              "0    30.626179\n",
              "1    28.343690\n",
              "Name: age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"survived\").agg({\"age\":\"mean\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "4-kktmF9MRua",
        "outputId": "d3819656-b6a1-4fb3-eb38-96432f2ebc9d"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                age\n",
              "survived           \n",
              "0         30.626179\n",
              "1         28.343690"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b9551d4e-4f1d-47f5-8ba4-4f2f73756877\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b9551d4e-4f1d-47f5-8ba4-4f2f73756877 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-b9551d4e-4f1d-47f5-8ba4-4f2f73756877');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c4ce567c-1e32-439f-8989-b255d48222df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c4ce567c-1e32-439f-8989-b255d48222df')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c4ce567c-1e32-439f-8989-b255d48222df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def target_summary_with_num(dataframe, target, numerical_col):\n",
        "  print(dataframe.groupby(target).agg({numerical_col: \"mean\"}), end= \"\\n\\n\\n\")"
      ],
      "metadata": {
        "id": "4qE2CsSTMqXL"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_summary_with_num(df, \"survived\", \"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qeYGRx4DNJ73",
        "outputId": "d3a5658e-558a-4ec0-fdf4-81ab6b97cf2d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                age\n",
            "survived           \n",
            "0         30.626179\n",
            "1         28.343690\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in num_cols:\n",
        "  target_summary_with_num(df, \"survived\", col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "McF48Tu-NUuT",
        "outputId": "d533c8a0-ff98-4459-ef3f-73643c1279df"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                age\n",
            "survived           \n",
            "0         30.626179\n",
            "1         28.343690\n",
            "\n",
            "\n",
            "               fare\n",
            "survived           \n",
            "0         22.117887\n",
            "1         48.395408\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jIkg3_P1oXh4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}