Titanic.ipynb

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        "<a href=\"https://colab.research.google.com/github/kdmwangi/CODSOFT/blob/main/Titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Titanic Survival Prediction Model\n",
        "<img src=\"https://esq.h-cdn.co/assets/16/16/1600x798/gallery-1460987104-titanic.JPG\"/>"
      ],
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      "source": [
        "The dataset is pulled from kaggle and the overall tasks is to use it to build a model that predicts whether a passenger on the titanic survived or not.\n",
        "\n",
        "The data includes information about individual passengers; age, gender, ticket class, fare, cabin and whether or not they survived or not."
      ],
      "metadata": {
        "id": "o7DHbQov98R6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration and Cleaning"
      ],
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      "source": [
        "# first step would be to import the data set to google colab\n",
        "# import the necessary libraries, pandas, numpy, matplotlib, sklearn, plotly\n",
        "# check for NaN values in the dataframe\n",
        "import pandas as pd\n",
        "\n",
        "import numpy\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n"
      ],
      "metadata": {
        "id": "f_o0mXCO_TS-"
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      "outputs": []
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      "source": [
        "# read the data first\n",
        "# use the pandas read_csv method\n",
        "titanic_df = pd.read_csv('Titanic-Dataset.csv')"
      ],
      "metadata": {
        "id": "R0g_mRcNR0nS"
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "# display the data frame head and tail\n",
        "# check the number of rows and column\n",
        "# check the column head\n",
        "# give a brief overview of data using the info() method\n",
        "# shape property\n",
        "\n",
        "\n",
        "titanic_df.head()"
      ],
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        "outputId": "4774dd73-b3d6-4fb8-ba69-a5615ce38fac"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
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        "titanic_df.tail()"
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              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.0      0      0      112053  30.00   B42        S  \n",
              "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
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              "      <td>30.00</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-627aed03-acbe-4fb2-8fa5-8ef42dbfdde0');\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  @keyframes spin {\n",
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              "\n",
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              "    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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.shape\n",
        "print(type(titanic_df.shape))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQxZtlY1YSNJ",
        "outputId": "5e224ed8-2342-4ea5-a8a8-116b3820c75e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# describe the data frame rows and columns\n",
        "print(f'Titanic dataset has {titanic_df.shape[0]} rows and {titanic_df.shape[1]} columns')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RylOjxIlYkCR",
        "outputId": "a7d3faff-4d2a-45c6-c81d-1e5c64522758"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titanic dataset has 891 rows and 12 columns\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# what are the columns names present in the dataset\n",
        "titanic_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXX0ES7NhKAJ",
        "outputId": "6ba662f1-5fc1-43b0-9070-4e413823c2c1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# brief info about the data\n",
        "titanic_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aD88iHmibDFC",
        "outputId": "f0e9c82c-ddfd-4f1a-ba13-205e3f6211ec"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PPAARfsBbGO2",
        "outputId": "4831f8d3-b489-4d3d-86a6-df0dd3e6452e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0b1c6c6-15e0-424c-ab9c-0b96774db5e5\" 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",
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              "\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <th>Fare</th>\n",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
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              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
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              "      <td>14.526497</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
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              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a0b1c6c6-15e0-424c-ab9c-0b96774db5e5 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-a0b1c6c6-15e0-424c-ab9c-0b96774db5e5');\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",
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              "  }\n",
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              "  }\n",
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              "  @keyframes spin {\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check the data type of every column in the dataset\n",
        "# loop of the tuple when accessing the columns\n",
        "# access the individual columns and use index to access the first element\n",
        "for col in titanic_df.columns:\n",
        "    print(col, type(titanic_df[col][0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMKpK3YSjcyG",
        "outputId": "8ac6562a-3dbd-4fc4-acdd-9d71a85438ec"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId <class 'numpy.int64'>\n",
            "Survived <class 'numpy.int64'>\n",
            "Pclass <class 'numpy.int64'>\n",
            "Name <class 'str'>\n",
            "Sex <class 'str'>\n",
            "Age <class 'numpy.float64'>\n",
            "SibSp <class 'numpy.int64'>\n",
            "Parch <class 'numpy.int64'>\n",
            "Ticket <class 'str'>\n",
            "Fare <class 'numpy.float64'>\n",
            "Cabin <class 'float'>\n",
            "Embarked <class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you can use pandas dtypes to check the type of the dataframe columns\n",
        "titanic_df.dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNBg99zHDIEw",
        "outputId": "9e9e09ee-f2d7-4eeb-8e40-d0740d49edd5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for NaN values in the dataset\n",
        "# isna method return NaN values in the dataset\n",
        "titanic_df.isna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oLE7DIUplJjr",
        "outputId": "033f2699-0ead-44eb-8487-57771a28d3a6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
              "0          False     False   False  False  False  False  False  False   False   \n",
              "1          False     False   False  False  False  False  False  False   False   \n",
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              "4          False     False   False  False  False  False  False  False   False   \n",
              "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
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              "890        False     False   False  False  False  False  False  False   False   \n",
              "\n",
              "      Fare  Cabin  Embarked  \n",
              "0    False   True     False  \n",
              "1    False  False     False  \n",
              "2    False   True     False  \n",
              "3    False  False     False  \n",
              "4    False   True     False  \n",
              "..     ...    ...       ...  \n",
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              "\n",
              "[891 rows x 12 columns]"
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# a better descriptive way to check for NaN values would be to use the any()\n",
        "#  its similar to all() python method that returns True if  all elements are\n",
        "# True and False otherwise.\n",
        "\n",
        "titanic_df.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azMDPRnGit87",
        "outputId": "179b56be-f225-44eb-ccec-f07a165fa468"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Survived       False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age             True\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare           False\n",
              "Cabin           True\n",
              "Embarked        True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from the above data we can tell that Age, Cabin, Embarked contain NaN values\n",
        "# next step will be to clean the data to remove the values using dropna method\n",
        "clean_titanic_df = titanic_df.dropna()\n",
        "# use the reset_index(drop=True) to rearrange elements after drop the NaN elements\n",
        "clean_titanic_df.reset_index(drop=True,inplace=True)"
      ],
      "metadata": {
        "id": "9uzmv4HIlRum"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check if the cleaning was successful\n",
        "clean_titanic_df.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycurCW3OjJK2",
        "outputId": "5df1fe7a-7ef8-452a-b117-87962e1eaecc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Survived       False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age            False\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare           False\n",
              "Cabin          False\n",
              "Embarked       False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the next stage would be to check if there are any duplicates in dataset\n",
        "# for this we are also going to call the any method that checks whether there are\n",
        "# duplicate values if any it return true\n",
        "clean_titanic_df.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRniF8Xhoivh",
        "outputId": "50264a9b-07b1-42e4-a382-741286e93110"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df.iloc[1]\n",
        "titanic_dup = clean_titanic_df+clean_titanic_df.iloc[0]\n"
      ],
      "metadata": {
        "id": "Hv5fVoFE3_lJ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVRbPg0O5LVH",
        "outputId": "31d5859c-f684-44eb-8001-bd24aa373a78"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3HC8ZsI54e2",
        "outputId": "ea265792-f968-4b4f-d66a-02a3362cf629"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(183, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dup.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3J78DBp7rZY",
        "outputId": "3877a0b5-fd6b-4a90-e1de-f7c98f0a6608"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(183, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dup.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXei4Tza7vCe",
        "outputId": "e1f2b2eb-a04a-4147-9585-ab4392b86e1c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UA4ROYRb6CZJ",
        "outputId": "cdbd939e-c3ec-4add-b7b1-eacfc7cb067b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# you can always use the sum() method together with duplicated to find the\n",
        "#  number of duplicate values in the dataset\n",
        "# from the above example I created another dataframe and append to it the\n",
        "# row at index 1 using iloc that take index as attribute\n",
        "# then performing duplicated method just as a brief example\n",
        "titanic_dup.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_FLYUqm8nVV",
        "outputId": "58b3271d-e6ca-4837-d53b-fd278964649d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqqI-S3Z6MfD",
        "outputId": "32c96216-5759-4263-96a2-81c2b5ebfb97"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Survived       False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age             True\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare           False\n",
              "Cabin           True\n",
              "Embarked        True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the challenge with the dataset is that when we drop all NaN values we remain\n",
        "# with a very small dataset to analyze, to my thinking we can maybe drop the\n",
        "# column all of which is dependable with the prediction or the model in question\n",
        "# we should understand that the aim of the dataset is to build a strong\n",
        "#  classification model to predict if passenger survives or not\n",
        "# take a good look at the strong variable or dependables\n",
        "# check how to trim the data to eliminate the columns that you probably do not\n",
        "#  want to analyze\n",
        "# convert the different columns type e.g age to int\n",
        "\n",
        "titanic_df.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p84hplV36_ey",
        "outputId": "095c3893-66fc-48d7-c4b3-76f5723d9a18"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Survived       False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age             True\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare           False\n",
              "Cabin           True\n",
              "Embarked        True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlWTQU6MBQ8S",
        "outputId": "2277f84e-c5a8-47bf-fed7-914b3f5213d8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trim the data to use only the columns that do not have NaN values\n",
        "# check NaN values in Age column\n",
        "titanic_df['Embarked'].isna().sum()"
      ],
      "metadata": {
        "id": "E4RpRFgODe-q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8180552b-370d-41a7-db5d-ec2b09de81fe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop the NaN values in Age\n",
        "clean_df = titanic_df['Age'].dropna()\n",
        "titanic_df['Age'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mDxUkhXdtjj",
        "outputId": "5641ecee-8c00-49e5-d0ab-8fd90ca85cd1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLHhPOiheTyn",
        "outputId": "ab6adc18-1788-48e3-86e3-027116ddbb6b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Cabin          0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df.shape"
      ],
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        "colab": {
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        "id": "Rn6zjR64iDCP",
        "outputId": "1dff6523-1fe4-4455-9cfb-a7e714455c9d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(183, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.Cabin.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BueI2u2iwVs",
        "outputId": "e2b33302-4007-4d21-f4da-22b22535c065"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "687"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "create a model by dropping all the NaN values\n",
        "the next sample drop the NaN values in Age only and omit the Cabin column since it has most NaN values."
      ],
      "metadata": {
        "id": "fZp0P9y-iUK0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the sample data set by dropping all the NaN values\n",
        "clean_titanic_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LKkgFydbiHly",
        "outputId": "d0538c1d-ab3c-46e2-a1cc-0599d6617c8a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            2         1       1   \n",
              "1            4         1       1   \n",
              "2            7         0       1   \n",
              "3           11         1       3   \n",
              "4           12         1       1   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "2                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
              "3                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
              "4                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
              "\n",
              "   Parch    Ticket     Fare Cabin Embarked  \n",
              "0      0  PC 17599  71.2833   C85        C  \n",
              "1      0    113803  53.1000  C123        S  \n",
              "2      0     17463  51.8625   E46        S  \n",
              "3      1   PP 9549  16.7000    G6        S  \n",
              "4      0    113783  26.5500  C103        S  "
            ],
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              "        document.querySelector('#df-42a03edf-8572-499c-8d65-84e982a6741a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-42a03edf-8572-499c-8d65-84e982a6741a');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# average fare of the titanic\n",
        "clean_titanic_df['Fare'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dY69QwJKNGc",
        "outputId": "71b31c5e-c503-4311-fc70-5799bad8bec9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78.68246885245901"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df[clean_titanic_df['Fare'] > 10].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lX2rm3qTL_B7",
        "outputId": "683efbe6-0e2c-410e-b667-ef2e1ddadf42"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId                                2\n",
              "Survived                                   0\n",
              "Pclass                                     1\n",
              "Name           Allen, Miss. Elisabeth Walton\n",
              "Sex                                   female\n",
              "Age                                     0.92\n",
              "SibSp                                      0\n",
              "Parch                                      0\n",
              "Ticket                                110152\n",
              "Fare                                 10.4625\n",
              "Cabin                                    A10\n",
              "Embarked                                   C\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_titanic = clean_titanic_df.groupby(by='Pclass').count()"
      ],
      "metadata": {
        "id": "o_KfAaikjzzD"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_titanic.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "tiehTcAvsosL",
        "outputId": "0fe8453b-b615-4179-dd5d-3d8bad4b323a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PassengerId  Survived  Name  Sex  Age  SibSp  Parch  Ticket  Fare  \\\n",
              "Pclass                                                                      \n",
              "1               158       158   158  158  158    158    158     158   158   \n",
              "2                15        15    15   15   15     15     15      15    15   \n",
              "3                10        10    10   10   10     10     10      10    10   \n",
              "\n",
              "        Cabin  Embarked  \n",
              "Pclass                   \n",
              "1         158       158  \n",
              "2          15        15  \n",
              "3          10        10  "
            ],
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0a94cea-127c-4a06-a8e4-528a4751b3fe')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-f0a94cea-127c-4a06-a8e4-528a4751b3fe 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-f0a94cea-127c-4a06-a8e4-528a4751b3fe');\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-749e53de-681a-4d1f-bd55-5963604f53ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-749e53de-681a-4d1f-bd55-5963604f53ad')\"\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-749e53de-681a-4d1f-bd55-5963604f53ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "there is a challenge with the data since it contains float numbers which indicate the there were children under 1yrs"
      ],
      "metadata": {
        "id": "CWatwXJBFHXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df['Age'].astype('int')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-z43Vxssyn_",
        "outputId": "e6288703-ae7c-47da-88cf-791b1c9515f1"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      38\n",
              "1      35\n",
              "2      54\n",
              "3       4\n",
              "4      58\n",
              "       ..\n",
              "178    47\n",
              "179    33\n",
              "180    56\n",
              "181    19\n",
              "182    26\n",
              "Name: Age, Length: 183, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Am-YtvrOvbkW",
        "outputId": "1127fc05-2773-4451-bbb4-ffc27542ce07"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The oldest person to Survive on the titanic"
      ],
      "metadata": {
        "id": "_uhkoAyDz7Fu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_grp = clean_titanic_df.groupby(by='Survived').count()"
      ],
      "metadata": {
        "id": "ScWbmyJMvg_P"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_grp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "tiQf8HEBzzxq",
        "outputId": "91d59b68-efeb-417d-d6c9-fd6233e6bef2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          PassengerId  Pclass  Name  Sex  Age  SibSp  Parch  Ticket  Fare  \\\n",
              "Survived                                                                    \n",
              "0                  60      60    60   60   60     60     60      60    60   \n",
              "1                 123     123   123  123  123    123    123     123   123   \n",
              "\n",
              "          Cabin  Embarked  \n",
              "Survived                   \n",
              "0            60        60  \n",
              "1           123       123  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b7d257f-5d9e-4acd-927b-a03b55d007dc\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>123</td>\n",
              "      <td>123</td>\n",
              "      <td>123</td>\n",
              "      <td>123</td>\n",
              "      <td>123</td>\n",
              "      <td>123</td>\n",
              "      <td>123</td>\n",
              "      <td>123</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-4b7d257f-5d9e-4acd-927b-a03b55d007dc')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-4b7d257f-5d9e-4acd-927b-a03b55d007dc 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-4b7d257f-5d9e-4acd-927b-a03b55d007dc');\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-8dc90ed0-9f4d-4539-bea0-8583582d8a80\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8dc90ed0-9f4d-4539-bea0-8583582d8a80')\"\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",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-8dc90ed0-9f4d-4539-bea0-8583582d8a80 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of male vs female in dataset\n",
        "# value_counts is a combination of groupby().count()\n",
        "# you should specify the column ie it works on a Series not a Dataframe\n",
        "titanic_df['Sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hL4mHaqU5RdW",
        "outputId": "4e32c67f-6d89-40b3-fe0a-fdfb141aabd3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "male_female = titanic_df.groupby(by='Sex').count()"
      ],
      "metadata": {
        "id": "MENJppm05-tM"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "survived_perished = titanic_df.groupby(by='Survived').count()\n",
        "survived_perished"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ArVG3SZiNkBV",
        "outputId": "1195a04a-839f-4d87-db17-9e5eb0f7d5d5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          PassengerId  Pclass  Name  Sex  Age  SibSp  Parch  Ticket  Fare  \\\n",
              "Survived                                                                    \n",
              "0                 549     549   549  549  424    549    549     549   549   \n",
              "1                 342     342   342  342  290    342    342     342   342   \n",
              "\n",
              "          Cabin  Embarked  \n",
              "Survived                   \n",
              "0            68       549  \n",
              "1           136       340  "
            ],
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              "      <td>290</td>\n",
              "      <td>342</td>\n",
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              "      <td>342</td>\n",
              "      <td>342</td>\n",
              "      <td>136</td>\n",
              "      <td>340</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-0b7aba60-bbc1-44e4-b69f-1f4e74de0247')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0b7aba60-bbc1-44e4-b69f-1f4e74de0247 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-0b7aba60-bbc1-44e4-b69f-1f4e74de0247');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "    </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",
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              "      --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",
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              "    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-f3cfbdbf-f9e8-404e-ae16-8d634f523d7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8d69312a-540d-49ab-abba-a1c2f36e7560\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('survived_perished')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8d69312a-540d-49ab-abba-a1c2f36e7560 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('survived_perished');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(survived_perished, x=survived_perished.index,y='PassengerId',color=survived_perished.index,\n",
        "             color_discrete_map={'0':'Red', '1':'Blue'}, title='Number of survivors and perished ')\n",
        "fig.update_coloraxes(showscale=False)\n",
        "fig.update_xaxes(type='category')\n",
        "fig.update_layout(xaxis_title='Survived',yaxis_title='Passengers')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "lf4ywDGrPo9y",
        "outputId": "3cc049a1-48e3-4ae8-ad49-63adafec3857"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
      "cell_type": "markdown",
      "source": [
        "the percentage of male and female in the titanic aboard"
      ],
      "metadata": {
        "id": "yazHGvVm9lfa"
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.pie(male_female,names=male_female.index, values='PassengerId',\n",
        "             title=\"Percentage of male vs female onboard\",hole=0.4,\n",
        "             labels=male_female.index)\n",
        "fig.update_traces(textposition='inside',textinfo='percent+label')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "zEhlCKCy60e4",
        "outputId": "6c7cfcb2-d0d3-48fb-dd5f-96b3d2c1dd6d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:137: FutureWarning:\n",
            "\n",
            "Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
            "\n"
          ]
        },
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Oldest and youngest passengers"
      ],
      "metadata": {
        "id": "PyqNc21mA57S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clean_titanic_df['Age'].idxmax()\n",
        "oldest = clean_titanic_df.iloc[clean_titanic_df['Age'].idxmax()]\n",
        "youngest = clean_titanic_df.iloc[clean_titanic_df['Age'].idxmin()]\n",
        "print(f'the oldest passenger was {oldest[\"Name\"]}, {int(oldest[\"Age\"])} years old')\n",
        "print(f'the youngest passenger was {youngest[\"Name\"]}, {int(youngest[\"Age\"] * 12)} months old')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vC_VVZTC7wIo",
        "outputId": "ead04cfd-cdfd-4ee3-c41f-e6071720aa44"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the oldest passenger was Barkworth, Mr. Algernon Henry Wilson, 80 years old\n",
            "the youngest passenger was Allison, Master. Hudson Trevor, 11 months old\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df['Age'].idxmax()\n",
        "oldest = titanic_df.iloc[titanic_df['Age'].idxmax()]\n",
        "youngest = titanic_df.iloc[titanic_df['Age'].idxmin()]\n",
        "print(f'the oldest passenger was {oldest[\"Name\"]}, {int(oldest[\"Age\"])} years old')\n",
        "print(f'the youngest passenger was {youngest[\"Name\"]}, {int(youngest[\"Age\"] * 12)} months old')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WVvv_8jAtL8",
        "outputId": "5d476968-6233-4a5d-cb9e-077d05373978"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the oldest passenger was Barkworth, Mr. Algernon Henry Wilson, 80 years old\n",
            "the youngest passenger was Thomas, Master. Assad Alexander, 5 months old\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **group the data and check how many men to women survived**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KXl62_QSXV9M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pv = titanic_df.pivot(columns = 'Sex', values='Survived')\n",
        "pv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PANsBhX88pjD",
        "outputId": "32e39c6d-c3b6-4807-aaf9-d55def01b3a8"
      },
      "execution_count": 112,
      "outputs": [
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              "    </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-985900aa-65b5-412b-9289-fb658a05d616 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8654f2cf-8ab8-4a7c-8c1b-2463568e847f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pv')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8654f2cf-8ab8-4a7c-8c1b-2463568e847f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('pv');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_df.groupby('Sex').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "OuD4HpFIHg66",
        "outputId": "2b1188a1-0b39-457b-e1fd-254c63015d62"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PassengerId  Survived  Pclass  Name  Age  SibSp  Parch  Ticket  Fare  \\\n",
              "Sex                                                                            \n",
              "female          314       314     314   314  261    314    314     314   314   \n",
              "male            577       577     577   577  453    577    577     577   577   \n",
              "\n",
              "        Cabin  ...    2    3    4    5    6    7    8    9   10   11  \n",
              "Sex            ...                                                    \n",
              "female     97  ...  314  314  314  314  314  314  314  314  314  314  \n",
              "male      107  ...  577  577  577  577  577  577  577  577  577  577  \n",
              "\n",
              "[2 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d6ebccf4-1695-40bd-be04-0d3c7c9ba20f\" 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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>...</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>261</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>97</td>\n",
              "      <td>...</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "      <td>314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>453</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>107</td>\n",
              "      <td>...</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "      <td>577</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 23 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-d6ebccf4-1695-40bd-be04-0d3c7c9ba20f')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-d6ebccf4-1695-40bd-be04-0d3c7c9ba20f 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-d6ebccf4-1695-40bd-be04-0d3c7c9ba20f');\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-333760ad-6e57-4546-a731-bbffec03c071\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-333760ad-6e57-4546-a731-bbffec03c071')\"\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-333760ad-6e57-4546-a731-bbffec03c071 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(titanic_df,x='Survived',y=pv['male'])\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "tcrZa-zw6Z1i",
        "outputId": "1173cdfc-728f-4f03-d1fd-802583ef788b"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# use [[]] to access more than two columns\n",
        "# create a df for survived passengers\n",
        "survived_passengers = titanic_df[titanic_df['Survived'] == True]\n",
        "\n",
        "# titanic_df[['Sex','Survived']].groupby(by='Sex').count()\n",
        "male_to_female_survivors =survived_passengers[['Survived','Sex']].groupby(by='Sex').count()\n",
        "# from the data we can tell that many women survived to men\n",
        "fig = px.pie(male_to_female_survivors,values=male_to_female_survivors['Survived'],\n",
        "             names=male_to_female_survivors.index,title=\"Percentage of male vs female survivors\",\n",
        "             hole=0.4)\n",
        "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ivqDkcsXBVFY",
        "outputId": "b5564793-66a6-4e81-f7c5-0560754fe010"
      },
      "execution_count": 48,
      "outputs": [
        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "group the  data by cabin to see if there is relationship between the survival rate"
      ],
      "metadata": {
        "id": "Z28yqshnj9IP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "survivor_per_cabin = survived_passengers[['Survived','Cabin']].groupby(by='Cabin').count().sort_values(by='Survived',ascending=False)\n",
        "# data shows that atleast every cabin had atleast one survivor\n"
      ],
      "metadata": {
        "id": "9rputXXDBezs"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cabin_highest_survival = survived_passengers[['Survived','Cabin']].groupby(by='Cabin').count().sort_values(by='Survived',ascending=False)\n",
        "print(f'the cabins with the highest survival {cabin_highest_survival[:5]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-usxO7sUkT2i",
        "outputId": "05222101-2d4d-4766-ffee-62f0ebfb217f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the cabins with the highest survival          Survived\n",
            "Cabin            \n",
            "B96 B98         4\n",
            "F33             3\n",
            "E101            3\n",
            "G6              2\n",
            "C126            2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dPfEw08q6SCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Average age of the passenger in the titanic"
      ],
      "metadata": {
        "id": "4WvZ_svts-gu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_clean_age = titanic_df.dropna(subset=['Age'])\n",
        "titanic_clean_age['Age'].mean()\n",
        "print(f\"the average age of the passengers in titanic is {titanic_clean_age['Age'].mean():.0f} years\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqVR2UoTtDBu",
        "outputId": "6db4b075-bf43-443a-f3c8-8b16cab6ef0a"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the average age of the passengers in titanic is 30 years\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **the average age of those who survived,you can go step further to see the oldest and youngest**"
      ],
      "metadata": {
        "id": "qsPkQPY5m761"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "survived_passengers['Age'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xHZ4p-zmyPa",
        "outputId": "b4cef35c-33af-4908-b9b0-5e06c2ff9936"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop the NaN value in the Age column only\n",
        "\n",
        "clean_survived_passengers = survived_passengers.dropna(subset=['Age']).reset_index(drop=True)\n",
        "clean_survived_passengers.isna().any()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFzBqWkApovy",
        "outputId": "ae6f99cb-6c2b-4acc-cf8a-00ba0832c01f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    False\n",
              "Survived       False\n",
              "Pclass         False\n",
              "Name           False\n",
              "Sex            False\n",
              "Age            False\n",
              "SibSp          False\n",
              "Parch          False\n",
              "Ticket         False\n",
              "Fare           False\n",
              "Cabin           True\n",
              "Embarked        True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(survived_passengers['Age']))\n",
        "print(survived_passengers['Age'].mean())\n",
        "print(clean_survived_passengers['Age'].isna().sum())\n",
        "print(len(clean_survived_passengers['Age']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnAfkC_06C3c",
        "outputId": "b10e26f1-f134-44d3-9e02-44ad2a5290bc"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "342\n",
            "28.343689655172415\n",
            "0\n",
            "290\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C9CtL4Mcpgp3"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# average age of those who survived\n",
        "print(f\"the average age of the survivors is {clean_survived_passengers['Age'].mean():.0f} years\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyYyniFFnKyd",
        "outputId": "9cf45ec6-43a7-4dc1-f5fa-369caf9a4f71"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the average age of the survivors is 28 years\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **create a graph for survivors vs age**"
      ],
      "metadata": {
        "id": "XK6j3ikuJCHL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use the agg function to provide info of the survived passengers\n",
        "# including the youngest and oldest\n",
        "clean_survived_passengers[['Age','Sex']].groupby('Sex').agg(['min','max','mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FFuv3u7wqqyC",
        "outputId": "3b819f04-0106-4400-fc0f-7779fbd56b04"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Age                 \n",
              "         min   max       mean\n",
              "Sex                          \n",
              "female  0.75  63.0  28.847716\n",
              "male    0.42  80.0  27.276022"
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              "        document.querySelector('#df-78b33ce1-13fb-43ad-9d86-8bee55b5a8f0 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-78b33ce1-13fb-43ad-9d86-8bee55b5a8f0');\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-bc9a2c38-985a-4d04-955b-ce5935d1226a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc9a2c38-985a-4d04-955b-ce5935d1226a')\"\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-bc9a2c38-985a-4d04-955b-ce5935d1226a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "youngest_surv = clean_survived_passengers.iloc[clean_survived_passengers['Age'].idxmin()]\n",
        "oldest_surv = clean_survived_passengers.iloc[clean_survived_passengers['Age'].idxmax()]\n",
        "\n",
        "print(f\"the youngest survivor was {youngest_surv['Age']*12:.0f} months old and oldest was {oldest_surv['Age']:.0f} years old.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4O81-ScKB23",
        "outputId": "f950d156-b18b-467d-ef73-07a0ee75693f"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the youngest survivor was 5 months old and oldest was 80 years old.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "try to check if theres s relationship between the passenger class and the probability of survival"
      ],
      "metadata": {
        "id": "P1mG86b5AyKR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clean_survived_passengers[['Survived','Pclass']].groupby(by='Pclass').agg(['sum'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iIp4u3Oa6bzI",
        "outputId": "3cd6ac46-7bcf-4aa1-dc3e-0c8a81ff405b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Survived\n",
              "            sum\n",
              "Pclass         \n",
              "1           122\n",
              "2            83\n",
              "3            85"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55a2ea05-28d3-443d-bab5-46e4f4d7780b\" 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 tr th {\n",
              "        text-align: left;\n",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>83</td>\n",
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              "      <td>85</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55a2ea05-28d3-443d-bab5-46e4f4d7780b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-55a2ea05-28d3-443d-bab5-46e4f4d7780b 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-55a2ea05-28d3-443d-bab5-46e4f4d7780b');\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-c978e4fc-f48f-45bf-a796-e84c46378a63\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c978e4fc-f48f-45bf-a796-e84c46378a63')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </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",
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              "      --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-c978e4fc-f48f-45bf-a796-e84c46378a63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "survivors_by_class = clean_survived_passengers[['Survived','Pclass']].groupby(by='Pclass').sum().sort_values(by='Survived')"
      ],
      "metadata": {
        "id": "ceb3a2N3BH4B"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(survivors_by_class ,x = survivors_by_class['Survived'],\n",
        "             y=survivors_by_class.index, orientation='h',\n",
        "             color=survivors_by_class.index)\n",
        "fig.update_yaxes(type='category')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wiX7IC6GCGxd",
        "outputId": "4e44c91b-203c-40d3-96d9-41e863732baf"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Average Fare of those survived"
      ],
      "metadata": {
        "id": "ROBDi_nBMvNK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clean_survived_passengers['Fare'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O15P41rlC4Ah",
        "outputId": "dea376c4-c950-4c5f-8564-a29c3305f7e1"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51.843205172413796"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "significant part of the data was the sibsp(number of siblings/spouse on board) and parch (number of parents/ children aboard)"
      ],
      "metadata": {
        "id": "W6dE1jgJNIGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(clean_survived_passengers['SibSp'].isna().any())\n",
        "print(clean_survived_passengers['Parch'].isna().any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpc7-DZaMsdw",
        "outputId": "e5660a26-03d3-4d92-c0c3-c0908844aac2"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_survived_passengers[['SibSp','Survived']].groupby(by='SibSp').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_JZinY4kNp7i",
        "outputId": "1edec5b7-a6f6-4031-8597-e002544a5c66"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Survived\n",
              "SibSp          \n",
              "0           175\n",
              "1            97\n",
              "2            11\n",
              "3             4\n",
              "4             3"
            ],
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              "\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Number of survivors vs the number of Siblings/ spouse on board**"
      ],
      "metadata": {
        "id": "fPXVolRMncIe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = clean_survived_passengers[['SibSp','Survived']].groupby(by='SibSp').count()\n",
        "fig = px.bar(data, x=data.index, y=data['Survived'],title='Number of survivors vs the number of Siblings/ spouse on board')\n",
        "fig.update_traces()\n",
        "fig.update_layout(xaxis_title=\"SibSp\", yaxis_title=\"number of survivors\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "955x5JLERZWy",
        "outputId": "27fad340-73f3-4b31-aef2-78584a34d363"
      },
      "execution_count": 64,
      "outputs": [
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    {
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      "source": [
        "clean_survived_passengers[['Parch','Survived']].groupby(by='Parch').count()"
      ],
      "metadata": {
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      "execution_count": 65,
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            "text/plain": [
              "       Survived\n",
              "Parch          \n",
              "0           186\n",
              "1            61\n",
              "2            39\n",
              "3             3\n",
              "5             1"
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              "          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",
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            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **number of survivors in relation to the number of Parents or children**"
      ],
      "metadata": {
        "id": "TK3MyO1enQIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = clean_survived_passengers[['Parch','Survived']].groupby(by='Parch').count()\n",
        "fig = px.bar(data, x=data.index, y=data['Survived'],\n",
        "             title=\"number of survivors in relation to the number of Parents or children\",\n",
        "             color=data.index)\n",
        "fig.update_layout(xaxis_title=\"Parch\",yaxis_title=\"number of survivors\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VhqkCQlAmGp-",
        "outputId": "7116b181-704d-4300-d42d-7b2a18c8fa05"
      },
      "execution_count": 66,
      "outputs": [
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    },
    {
      "cell_type": "code",
      "source": [
        "data = clean_survived_passengers[['Parch','Survived','Sex']].groupby(by='Parch').count()\n",
        "fig = px.bar(data, x=data.index, y=data['Survived'],\n",
        "             title=\"number of survivors in relation to the number of Parents or children\",\n",
        "             color=data.index)\n",
        "fig.update_layout(xaxis_title=\"Parch\",yaxis_title=\"number of survivors\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "bUGkoI4Lv3Uz",
        "outputId": "768f697f-d933-4cdb-eb64-859c73c96e85"
      },
      "execution_count": 67,
      "outputs": [
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      "execution_count": 67,
      "outputs": []
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      "source": [
        "clean_survived_passengers[['Parch','Survived','Sex']].groupby(by='Sex').count()"
      ],
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          "height": 0
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        "outputId": "88769a59-b19b-4341-e6d9-295643665c52"
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      "execution_count": 68,
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              "        Parch  Survived\n",
              "Sex                    \n",
              "female    197       197\n",
              "male       93        93"
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              "    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-2d6977be-1236-4af8-8bce-e4184da47f6c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_parch = clean_survived_passengers[['Parch','Survived',]].groupby(by='Parch').count()\n",
        "data_parch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wnYRiHbMTGvs",
        "outputId": "a2104ef5-4491-4e94-f247-f377b04d6599"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Survived\n",
              "Parch          \n",
              "0           186\n",
              "1            61\n",
              "2            39\n",
              "3             3\n",
              "5             1"
            ],
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              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c5b3092e-640e-4b87-99d4-73c1715bb679 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
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              "\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "  </svg>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_parch');\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_sibsp = clean_survived_passengers[['SibSp','Survived']].groupby(by='SibSp').count()\n",
        "data_sibsp\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Zq_acM1ITvf1",
        "outputId": "81d9527e-37ef-49b1-95b0-1147778575c3"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Survived\n",
              "SibSp          \n",
              "0           175\n",
              "1            97\n",
              "2            11\n",
              "3             4\n",
              "4             3"
            ],
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              "      const buttonEl =\n",
              "        document.querySelector('#df-e85218f1-bc64-40fc-8b37-b2e0902808cb 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-e85218f1-bc64-40fc-8b37-b2e0902808cb');\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-bed5df6a-5e90-4db4-a00c-f45f1d4ea8ba\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bed5df6a-5e90-4db4-a00c-f45f1d4ea8ba')\"\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-bed5df6a-5e90-4db4-a00c-f45f1d4ea8ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9109e6f2-d4c6-4efb-bf32-9a341531928f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_sibsp')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9109e6f2-d4c6-4efb-bf32-9a341531928f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_sibsp');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_sibsp.merge( data_parch, how='inner', on='Survived')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zVdNMA67UaO3",
        "outputId": "03377107-dcd0-4c9f-b838-59266c1cb313"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived\n",
              "0         3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88b968e6-0859-440e-bb6c-4cc67821a680\" 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</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-88b968e6-0859-440e-bb6c-4cc67821a680')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-88b968e6-0859-440e-bb6c-4cc67821a680 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-88b968e6-0859-440e-bb6c-4cc67821a680');\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>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create a model using linear regression and seaborn to predict the survival of a passenger in the titanic"
      ],
      "metadata": {
        "id": "AaRGfk_yo8Q0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(8,4),dpi=200)\n",
        "sns.set_theme()\n",
        "sns.scatterplot(titanic_df, x='Age',y='SibSp', hue='Survived',style='Survived',size='SibSp')\n"
      ],
      "metadata": {
        "id": "ZufMtDYcm3J6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bbc767c5-933f-4ff3-f412-91205b966798"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='SibSp'>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4),dpi=200)\n",
        "sns.set_theme()\n",
        "sns.scatterplot(survived_passengers,x='Age',y='Parch',hue='Survived',size='Parch')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "cWQ-xYh-afBT",
        "outputId": "85a26098-c6d0-40a2-8b6f-b8f791302afa"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Parch'>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4),dpi=200)\n",
        "sns.regplot(titanic_df,x='Age',y='Survived',scatter_kws = {'alpha': 0.4},\n",
        "            line_kws = {'color': 'black'})\n",
        "# the relationship does not seem vey strong"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KXmnyvaMTcVE",
        "outputId": "9551bd15-d82f-4136-d257-8e4dd52d560e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Survived'>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FiMys3whbH_O"
      },
      "execution_count": 74,
      "outputs": []
    }
  ]
}