Univariate_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMz3EQudbM1Q0xiIMTPSHC9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sohil344/EDA/blob/main/Univariate_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Bo7WijEt-s3x"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/titanic_train.csv')"
      ],
      "metadata": {
        "id": "PgpeI-L0_k0N"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "Z431MVTm_nl4",
        "outputId": "6dd70e94-1147-4824-d0ff-1e46ac4c8f4b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     passenger_id  pclass                                          name  \\\n",
              "116           668       3                        Barton, Mr. David John   \n",
              "714           116       1           Fortune, Mrs. Mark (Mary McDougald)   \n",
              "350           925       3                              Kelly, Mr. James   \n",
              "179          1281       3                          Waelens, Mr. Achille   \n",
              "723           715       3                          Christmann, Mr. Emil   \n",
              "259          1147       3  Riihivouri, Miss. Susanna Juhantytar \"Sanni\"   \n",
              "162           814       3                         Gallagher, Mr. Martin   \n",
              "66            710       3                           Carr, Miss. Jeannie   \n",
              "324           334       2                 Banfield, Mr. Frederick James   \n",
              "305          1127       3                          Petroff, Mr. Nedelio   \n",
              "\n",
              "        sex   age  sibsp  parch            ticket      fare        cabin  \\\n",
              "116    male  22.0      0      0            324669    8.0500          NaN   \n",
              "714  female  60.0      1      4             19950  263.0000  C23 C25 C27   \n",
              "350    male  44.0      0      0            363592    8.0500          NaN   \n",
              "179    male  22.0      0      0            345767    9.0000          NaN   \n",
              "723    male  29.0      0      0            343276    8.0500          NaN   \n",
              "259  female  22.0      0      0           3101295   39.6875          NaN   \n",
              "162    male  25.0      0      0             36864    7.7417          NaN   \n",
              "66   female  37.0      0      0            368364    7.7500          NaN   \n",
              "324    male  28.0      0      0  C.A./SOTON 34068   10.5000          NaN   \n",
              "305    male  19.0      0      0            349212    7.8958          NaN   \n",
              "\n",
              "    embarked boat  body                        home.dest  survived  \n",
              "116        S  NaN   NaN             England New York, NY         0  \n",
              "714        S   10   NaN                     Winnipeg, MB         1  \n",
              "350        S  NaN   NaN                              NaN         0  \n",
              "179        S  NaN   NaN   Antwerp, Belgium / Stanton, OH         0  \n",
              "723        S  NaN   NaN                              NaN         0  \n",
              "259        S  NaN   NaN                              NaN         0  \n",
              "162        Q  NaN   NaN                     New York, NY         0  \n",
              "66         Q  NaN   NaN   Co Sligo, Ireland Hartford, CT         0  \n",
              "324        S  NaN   NaN  Plymouth, Dorset / Houghton, MI         0  \n",
              "305        S  NaN   NaN                              NaN         0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-126c6b6d-8f9f-4728-ba0a-fa20a7b84484\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>passenger_id</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",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>668</td>\n",
              "      <td>3</td>\n",
              "      <td>Barton, Mr. David John</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>324669</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>England New York, NY</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>116</td>\n",
              "      <td>1</td>\n",
              "      <td>Fortune, Mrs. Mark (Mary McDougald)</td>\n",
              "      <td>female</td>\n",
              "      <td>60.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>19950</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Winnipeg, MB</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>925</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>363592</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>1281</td>\n",
              "      <td>3</td>\n",
              "      <td>Waelens, Mr. Achille</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>345767</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>723</th>\n",
              "      <td>715</td>\n",
              "      <td>3</td>\n",
              "      <td>Christmann, Mr. Emil</td>\n",
              "      <td>male</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>343276</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>1147</td>\n",
              "      <td>3</td>\n",
              "      <td>Riihivouri, Miss. Susanna Juhantytar \"Sanni\"</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3101295</td>\n",
              "      <td>39.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>814</td>\n",
              "      <td>3</td>\n",
              "      <td>Gallagher, Mr. Martin</td>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36864</td>\n",
              "      <td>7.7417</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>New York, NY</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>710</td>\n",
              "      <td>3</td>\n",
              "      <td>Carr, Miss. Jeannie</td>\n",
              "      <td>female</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>368364</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Co Sligo, Ireland Hartford, CT</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>334</td>\n",
              "      <td>2</td>\n",
              "      <td>Banfield, Mr. Frederick James</td>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>C.A./SOTON 34068</td>\n",
              "      <td>10.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Plymouth, Dorset / Houghton, MI</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>1127</td>\n",
              "      <td>3</td>\n",
              "      <td>Petroff, Mr. Nedelio</td>\n",
              "      <td>male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>349212</td>\n",
              "      <td>7.8958</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-126c6b6d-8f9f-4728-ba0a-fa20a7b84484')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-126c6b6d-8f9f-4728-ba0a-fa20a7b84484 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-126c6b6d-8f9f-4728-ba0a-fa20a7b84484');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcR32lY8_oek",
        "outputId": "97b4751e-cb32-4d43-b65c-8b0ae54b26ac"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 850 entries, 0 to 849\n",
            "Data columns (total 15 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   passenger_id  850 non-null    int64  \n",
            " 1   pclass        850 non-null    int64  \n",
            " 2   name          850 non-null    object \n",
            " 3   sex           850 non-null    object \n",
            " 4   age           676 non-null    float64\n",
            " 5   sibsp         850 non-null    int64  \n",
            " 6   parch         850 non-null    int64  \n",
            " 7   ticket        850 non-null    object \n",
            " 8   fare          849 non-null    float64\n",
            " 9   cabin         191 non-null    object \n",
            " 10  embarked      849 non-null    object \n",
            " 11  boat          308 non-null    object \n",
            " 12  body          73 non-null     float64\n",
            " 13  home.dest     464 non-null    object \n",
            " 14  survived      850 non-null    int64  \n",
            "dtypes: float64(3), int64(5), object(7)\n",
            "memory usage: 99.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Categorical Data**\n",
        "\n",
        "Count plot- Mainly Used for categorical Column"
      ],
      "metadata": {
        "id": "QJ3-sO5f_3pi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['survived'])\n",
        "df['survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "jvvEfAa-_3Py",
        "outputId": "493660a9-f45a-4cf5-fa17-93efc642e234"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    537\n",
              "1    313\n",
              "Name: survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['pclass'])\n",
        "df['pclass'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "MJcTLSkV_uDM",
        "outputId": "272efa4f-f4e6-42d9-db14-e2262f98ac02"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    478\n",
              "1    206\n",
              "2    166\n",
              "Name: pclass, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['sex'])\n",
        "df['sex'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "GfhiRHeKAkjt",
        "outputId": "82bbb8fb-b994-44c2-8c0f-33ae5899c7f8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      551\n",
              "female    299\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['embarked'])\n",
        "df['embarked'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "AVO6vdLMA03J",
        "outputId": "9934c11c-fd27-4a9b-e2db-000deeec95d8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    589\n",
              "C    176\n",
              "Q     84\n",
              "Name: embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pie Chart**\n",
        "\n",
        "Can be used to know percentage distribution"
      ],
      "metadata": {
        "id": "0i1rLwUlBMZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['survived'].value_counts().plot(kind='pie',autopct='%.1f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "f_-6oVgeA7PY",
        "outputId": "782d3944-6f8b-46f6-ac1d-9af110943727"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2103512430>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['age'],bins=10,density=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "CNBW6qctBg6p",
        "outputId": "9781ef8f-e7da-47c5-cf80-f60e4cc54301"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.00907957, 0.00796779, 0.03279763, 0.03150055, 0.01852974,\n",
              "        0.01000606, 0.00889427, 0.00500303, 0.00092649, 0.00055589]),\n",
              " array([ 0.1667 ,  8.15003, 16.13336, 24.11669, 32.10002, 40.08335,\n",
              "        48.06668, 56.05001, 64.03334, 72.01667, 80.     ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['age'],bins=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "aUDrtUkEDEAK",
        "outputId": "2982da07-e263-4c6e-fb8e-d64325b83dfa"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f20ff48f9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df['age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "jkwYAvgKDy56",
        "outputId": "731e7845-9aac-4b75-ab97-323234d2370a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f20ff366760>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bcz_iJOYELbA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}