DAV_DataCleaning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df=pd.DataFrame(np.random.randn(5,3),index=['a','c','e','f','h'],columns=['one','two','three'])\n",
        "print(df)\n",
        "df=df.reindex(['a','b','c','d','e','f','g','h'])\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nExbtZLPN4NE",
        "outputId": "4b660315-dfb9-4fe7-9c3f-4e35d6ca4a5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        one       two     three\n",
            "a -1.468281 -1.568443  1.548528\n",
            "c -0.317975 -1.478968 -1.462209\n",
            "e -1.061967 -1.200375 -1.115532\n",
            "f  0.918337 -2.513320 -1.360113\n",
            "h -2.479150  0.780637  2.452944\n",
            "        one       two     three\n",
            "a -1.468281 -1.568443  1.548528\n",
            "b       NaN       NaN       NaN\n",
            "c -0.317975 -1.478968 -1.462209\n",
            "d       NaN       NaN       NaN\n",
            "e -1.061967 -1.200375 -1.115532\n",
            "f  0.918337 -2.513320 -1.360113\n",
            "g       NaN       NaN       NaN\n",
            "h -2.479150  0.780637  2.452944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TrA1iLJGcab",
        "outputId": "5c151215-0fde-4936-82b5-a61f43b33bb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    False\n",
            "b     True\n",
            "c    False\n",
            "d     True\n",
            "e    False\n",
            "f    False\n",
            "g     True\n",
            "h    False\n",
            "Name: one, dtype: bool\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df=pd.DataFrame(np.random.randn(5,3),index=['a','c','e','f','h'],columns=['one','two','three'])\n",
        "df=df.reindex(['a','b','c','d','e','f','g','h'])\n",
        "print(df['one'].isnull())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.random.rand(3,3),index=['a','c','e'],columns=['one','two','three'])\n",
        "df=df.reindex(['a','b','c'])\n",
        "print('Filling NaN with 0: ')\n",
        "print(df.fillna(0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jrqxlpBUO2ok",
        "outputId": "a4d1112f-f316-4df0-b378-f85331e28b80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filling NaN with 0: \n",
            "        one       two     three\n",
            "a  0.806088  0.764680  0.919489\n",
            "b  0.000000  0.000000  0.000000\n",
            "c  0.905234  0.894928  0.670956\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.random.randn(5,3),index=['a','c','e','f','h'],columns=['one','two','three'])\n",
        "df=df.reindex(['a','b','c','d','e','f','g','h'])\n",
        "print(df)\n",
        "print('\\nFilling NaN with previous values:')\n",
        "print(df.fillna(method='pad'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDRjnOPmPoXq",
        "outputId": "23919b23-443c-4491-89d4-561e8651c6db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        one       two     three\n",
            "a -1.621811  0.662613 -0.220696\n",
            "b       NaN       NaN       NaN\n",
            "c -0.208192  0.171725  0.855868\n",
            "d       NaN       NaN       NaN\n",
            "e  0.537776 -1.040111 -1.312575\n",
            "f -0.042064 -0.577189 -0.177003\n",
            "g       NaN       NaN       NaN\n",
            "h  0.639621  1.187459  1.238288\n",
            "\n",
            "Filling NaN with previous values:\n",
            "        one       two     three\n",
            "a -1.621811  0.662613 -0.220696\n",
            "b -1.621811  0.662613 -0.220696\n",
            "c -0.208192  0.171725  0.855868\n",
            "d -0.208192  0.171725  0.855868\n",
            "e  0.537776 -1.040111 -1.312575\n",
            "f -0.042064 -0.577189 -0.177003\n",
            "g -0.042064 -0.577189 -0.177003\n",
            "h  0.639621  1.187459  1.238288\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.random.randn(5,3),index=['a','c','e','f','h'],columns=['one','two','three'])\n",
        "df=df.reindex(['a','b','c','d','e','f','g','h'])\n",
        "print(df)\n",
        "print('\\nFilling NaN with next values: ')\n",
        "print(df.fillna(method='bfill'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdzL2BDnQABy",
        "outputId": "4fbd068e-4457-478e-aa9e-19bcff380610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        one       two     three\n",
            "a  0.543543  0.759285  0.243182\n",
            "b       NaN       NaN       NaN\n",
            "c -1.300828 -1.053479 -1.135769\n",
            "d       NaN       NaN       NaN\n",
            "e  0.239726 -0.714046 -1.995595\n",
            "f  1.432982  0.574981 -0.328493\n",
            "g       NaN       NaN       NaN\n",
            "h  0.150964  0.162664  0.084659\n",
            "\n",
            "Filling NaN with next values: \n",
            "        one       two     three\n",
            "a  0.543543  0.759285  0.243182\n",
            "b -1.300828 -1.053479 -1.135769\n",
            "c -1.300828 -1.053479 -1.135769\n",
            "d  0.239726 -0.714046 -1.995595\n",
            "e  0.239726 -0.714046 -1.995595\n",
            "f  1.432982  0.574981 -0.328493\n",
            "g  0.150964  0.162664  0.084659\n",
            "h  0.150964  0.162664  0.084659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame(np.random.randn(5,3),index=['a','c','e','f','h'],columns=['one','two','three'])\n",
        "df=df.reindex(['a','b','c','d','e','f','g','h'])\n",
        "print(df)\n",
        "print('\\nDropping the null values: ')\n",
        "print(df.dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GEgXuZa6QMwa",
        "outputId": "8e3994cd-02c0-47cb-999f-ec0641a7cf71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        one       two     three\n",
            "a -0.604943 -0.491333  0.329487\n",
            "b       NaN       NaN       NaN\n",
            "c  1.289913  1.727787  0.414517\n",
            "d       NaN       NaN       NaN\n",
            "e -0.312494 -1.204328  0.230497\n",
            "f  0.480227 -0.531103  0.573220\n",
            "g       NaN       NaN       NaN\n",
            "h -0.165039  1.662116 -0.129711\n",
            "\n",
            "Dropping the null values: \n",
            "        one       two     three\n",
            "a -0.604943 -0.491333  0.329487\n",
            "c  1.289913  1.727787  0.414517\n",
            "e -0.312494 -1.204328  0.230497\n",
            "f  0.480227 -0.531103  0.573220\n",
            "h -0.165039  1.662116 -0.129711\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df=pd.read_csv('/content/2,1 dataset titanic.csv')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFd6Vj-IQaC5",
        "outputId": "3b561cde-646a-44ae-9874-cc6734288c49"
      },
      "execution_count": null,
      "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": [
        "df=df.dropna()\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNnwTIGxQZ-x",
        "outputId": "70c66d83-30be-4d76-d726-581ef20b20b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 183 entries, 1 to 889\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  183 non-null    int64  \n",
            " 1   Survived     183 non-null    int64  \n",
            " 2   Pclass       183 non-null    int64  \n",
            " 3   Name         183 non-null    object \n",
            " 4   Sex          183 non-null    object \n",
            " 5   Age          183 non-null    float64\n",
            " 6   SibSp        183 non-null    int64  \n",
            " 7   Parch        183 non-null    int64  \n",
            " 8   Ticket       183 non-null    object \n",
            " 9   Fare         183 non-null    float64\n",
            " 10  Cabin        183 non-null    object \n",
            " 11  Embarked     183 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 18.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols=['Name','Ticket','Cabin']\n",
        "df=df.drop(cols,axis=1)\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDyhXzDHUKIo",
        "outputId": "fb85a7cb-5631-48c8-b79a-a9e8313c6942"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 183 entries, 1 to 889\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  183 non-null    int64  \n",
            " 1   Survived     183 non-null    int64  \n",
            " 2   Pclass       183 non-null    int64  \n",
            " 3   Sex          183 non-null    object \n",
            " 4   Age          183 non-null    float64\n",
            " 5   SibSp        183 non-null    int64  \n",
            " 6   Parch        183 non-null    int64  \n",
            " 7   Fare         183 non-null    float64\n",
            " 8   Embarked     183 non-null    object \n",
            "dtypes: float64(2), int64(5), object(2)\n",
            "memory usage: 14.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.dropna()\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6JuuUzSUqzC",
        "outputId": "0dfa2424-5ad4-4683-cc4d-ecc1a9dd5bbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 183 entries, 1 to 889\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  183 non-null    int64  \n",
            " 1   Survived     183 non-null    int64  \n",
            " 2   Pclass       183 non-null    int64  \n",
            " 3   Sex          183 non-null    object \n",
            " 4   Age          183 non-null    float64\n",
            " 5   SibSp        183 non-null    int64  \n",
            " 6   Parch        183 non-null    int64  \n",
            " 7   Fare         183 non-null    float64\n",
            " 8   Embarked     183 non-null    object \n",
            "dtypes: float64(2), int64(5), object(2)\n",
            "memory usage: 14.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vVEXKIuvVnT3",
        "outputId": "583f3c62-985a-48e4-d7ef-200dfe09a36a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
            "1              2         1       1  female  38.0      1      0  71.2833   \n",
            "3              4         1       1  female  35.0      1      0  53.1000   \n",
            "6              7         0       1    male  54.0      0      0  51.8625   \n",
            "10            11         1       3  female   4.0      1      1  16.7000   \n",
            "11            12         1       1  female  58.0      0      0  26.5500   \n",
            "..           ...       ...     ...     ...   ...    ...    ...      ...   \n",
            "871          872         1       1  female  47.0      1      1  52.5542   \n",
            "872          873         0       1    male  33.0      0      0   5.0000   \n",
            "879          880         1       1  female  56.0      0      1  83.1583   \n",
            "887          888         1       1  female  19.0      0      0  30.0000   \n",
            "889          890         1       1    male  26.0      0      0  30.0000   \n",
            "\n",
            "    Embarked  \n",
            "1          C  \n",
            "3          S  \n",
            "6          S  \n",
            "10         S  \n",
            "11         S  \n",
            "..       ...  \n",
            "871        S  \n",
            "872        S  \n",
            "879        C  \n",
            "887        S  \n",
            "889        C  \n",
            "\n",
            "[183 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummies=[]\n",
        "cols=['Pclass','Sex','Embarked']\n",
        "for col in cols:\n",
        "  dummies.append(pd.get_dummies(df[col]))\n",
        "print(dummies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VaQtqV_ZQZ2Z",
        "outputId": "b3bf7ccc-b81e-4a00-cb2e-df25f3f50cc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[     1  2  3\n",
            "1    1  0  0\n",
            "3    1  0  0\n",
            "6    1  0  0\n",
            "10   0  0  1\n",
            "11   1  0  0\n",
            "..  .. .. ..\n",
            "871  1  0  0\n",
            "872  1  0  0\n",
            "879  1  0  0\n",
            "887  1  0  0\n",
            "889  1  0  0\n",
            "\n",
            "[183 rows x 3 columns],      female  male\n",
            "1         1     0\n",
            "3         1     0\n",
            "6         0     1\n",
            "10        1     0\n",
            "11        1     0\n",
            "..      ...   ...\n",
            "871       1     0\n",
            "872       0     1\n",
            "879       1     0\n",
            "887       1     0\n",
            "889       0     1\n",
            "\n",
            "[183 rows x 2 columns],      C  Q  S\n",
            "1    1  0  0\n",
            "3    0  0  1\n",
            "6    0  0  1\n",
            "10   0  0  1\n",
            "11   0  0  1\n",
            "..  .. .. ..\n",
            "871  0  0  1\n",
            "872  0  0  1\n",
            "879  1  0  0\n",
            "887  0  0  1\n",
            "889  1  0  0\n",
            "\n",
            "[183 rows x 3 columns]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic_dummies=pd.concat(dummies,axis=1)\n",
        "print(titanic_dummies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBj_tVgzQZzR",
        "outputId": "f4e233f2-71ad-4713-f3cb-3dceeb34660d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     1  2  3  female  male  C  Q  S\n",
            "1    1  0  0       1     0  1  0  0\n",
            "3    1  0  0       1     0  0  0  1\n",
            "6    1  0  0       0     1  0  0  1\n",
            "10   0  0  1       1     0  0  0  1\n",
            "11   1  0  0       1     0  0  0  1\n",
            "..  .. .. ..     ...   ... .. .. ..\n",
            "871  1  0  0       1     0  0  0  1\n",
            "872  1  0  0       0     1  0  0  1\n",
            "879  1  0  0       1     0  1  0  0\n",
            "887  1  0  0       1     0  0  0  1\n",
            "889  1  0  0       0     1  1  0  0\n",
            "\n",
            "[183 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.concat((df,titanic_dummies),axis=1)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9vTaUwESWbw",
        "outputId": "3765474d-d388-4059-9752-2b21532d4236"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
            "1              2         1       1  female  38.0      1      0  71.2833   \n",
            "3              4         1       1  female  35.0      1      0  53.1000   \n",
            "6              7         0       1    male  54.0      0      0  51.8625   \n",
            "10            11         1       3  female   4.0      1      1  16.7000   \n",
            "11            12         1       1  female  58.0      0      0  26.5500   \n",
            "..           ...       ...     ...     ...   ...    ...    ...      ...   \n",
            "871          872         1       1  female  47.0      1      1  52.5542   \n",
            "872          873         0       1    male  33.0      0      0   5.0000   \n",
            "879          880         1       1  female  56.0      0      1  83.1583   \n",
            "887          888         1       1  female  19.0      0      0  30.0000   \n",
            "889          890         1       1    male  26.0      0      0  30.0000   \n",
            "\n",
            "    Embarked  1  2  3  female  male  C  Q  S  \n",
            "1          C  1  0  0       1     0  1  0  0  \n",
            "3          S  1  0  0       1     0  0  0  1  \n",
            "6          S  1  0  0       0     1  0  0  1  \n",
            "10         S  0  0  1       1     0  0  0  1  \n",
            "11         S  1  0  0       1     0  0  0  1  \n",
            "..       ... .. .. ..     ...   ... .. .. ..  \n",
            "871        S  1  0  0       1     0  0  0  1  \n",
            "872        S  1  0  0       0     1  0  0  1  \n",
            "879        C  1  0  0       1     0  1  0  0  \n",
            "887        S  1  0  0       1     0  0  0  1  \n",
            "889        C  1  0  0       0     1  1  0  0  \n",
            "\n",
            "[183 rows x 17 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove unwanted columns\n",
        "df=df.drop(['Pclass','Sex','Embarked'],axis=1)\n",
        "print(df)"
      ],
      "metadata": {
        "id": "A1HRRKozSWYZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4525f5b4-3847-475f-9861-dccabd7c8079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived   Age  SibSp  Parch     Fare  1  2  3  female  \\\n",
            "1              2         1  38.0      1      0  71.2833  1  0  0       1   \n",
            "3              4         1  35.0      1      0  53.1000  1  0  0       1   \n",
            "6              7         0  54.0      0      0  51.8625  1  0  0       0   \n",
            "10            11         1   4.0      1      1  16.7000  0  0  1       1   \n",
            "11            12         1  58.0      0      0  26.5500  1  0  0       1   \n",
            "..           ...       ...   ...    ...    ...      ... .. .. ..     ...   \n",
            "871          872         1  47.0      1      1  52.5542  1  0  0       1   \n",
            "872          873         0  33.0      0      0   5.0000  1  0  0       0   \n",
            "879          880         1  56.0      0      1  83.1583  1  0  0       1   \n",
            "887          888         1  19.0      0      0  30.0000  1  0  0       1   \n",
            "889          890         1  26.0      0      0  30.0000  1  0  0       0   \n",
            "\n",
            "     male  C  Q  S  \n",
            "1       0  1  0  0  \n",
            "3       0  0  0  1  \n",
            "6       1  0  0  1  \n",
            "10      0  0  0  1  \n",
            "11      0  0  0  1  \n",
            "..    ... .. .. ..  \n",
            "871     0  0  0  1  \n",
            "872     1  0  0  1  \n",
            "879     0  1  0  0  \n",
            "887     0  0  0  1  \n",
            "889     1  1  0  0  \n",
            "\n",
            "[183 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age']=df['Age'].interpolate()\n",
        "print(df)"
      ],
      "metadata": {
        "id": "JgajPxvJSWU5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "380473f4-3f80-443e-b7b8-dc53514fd763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived   Age  SibSp  Parch     Fare  1  2  3  female  \\\n",
            "1              2         1  38.0      1      0  71.2833  1  0  0       1   \n",
            "3              4         1  35.0      1      0  53.1000  1  0  0       1   \n",
            "6              7         0  54.0      0      0  51.8625  1  0  0       0   \n",
            "10            11         1   4.0      1      1  16.7000  0  0  1       1   \n",
            "11            12         1  58.0      0      0  26.5500  1  0  0       1   \n",
            "..           ...       ...   ...    ...    ...      ... .. .. ..     ...   \n",
            "871          872         1  47.0      1      1  52.5542  1  0  0       1   \n",
            "872          873         0  33.0      0      0   5.0000  1  0  0       0   \n",
            "879          880         1  56.0      0      1  83.1583  1  0  0       1   \n",
            "887          888         1  19.0      0      0  30.0000  1  0  0       1   \n",
            "889          890         1  26.0      0      0  30.0000  1  0  0       0   \n",
            "\n",
            "     male  C  Q  S  \n",
            "1       0  1  0  0  \n",
            "3       0  0  0  1  \n",
            "6       1  0  0  1  \n",
            "10      0  0  0  1  \n",
            "11      0  0  0  1  \n",
            "..    ... .. .. ..  \n",
            "871     0  0  0  1  \n",
            "872     1  0  0  1  \n",
            "879     0  1  0  0  \n",
            "887     0  0  0  1  \n",
            "889     1  1  0  0  \n",
            "\n",
            "[183 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#min max scalar and standardization\n",
        "#y=(x-min)/(max-min)\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "data=[[-1,2],[-0.5,6],[0,10],[1,18]]\n",
        "scaler=MinMaxScaler()\n",
        "print(scaler.fit(data))\n",
        "print('-----------------')\n",
        "MinMaxScaler()\n",
        "print(scaler.data_max_)\n",
        "print('-----------------')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJABDaR5Nd5_",
        "outputId": "fa082c37-bc97-47ff-c957-82b11ab450cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MinMaxScaler()\n",
            "-----------------\n",
            "[ 1. 18.]\n",
            "-----------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import asarray\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "#define data\n",
        "data=asarray([[100,0.001],\n",
        "              [8,0.05],\n",
        "              [50,0.005],\n",
        "              [88,0.07],\n",
        "              [4,0.1]])\n",
        "print(data)\n",
        "#define standard scaler\n",
        "scaler=StandardScaler()\n",
        "#transform data\n",
        "scaled=scaler.fit_transform(data)\n",
        "print(scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NldpnXGyNd1n",
        "outputId": "3ec7e518-9dd3-4f23-8dc0-49453b7ad495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.0e+02 1.0e-03]\n",
            " [8.0e+00 5.0e-02]\n",
            " [5.0e+01 5.0e-03]\n",
            " [8.8e+01 7.0e-02]\n",
            " [4.0e+00 1.0e-01]]\n",
            "[[ 1.26398112 -1.16389967]\n",
            " [-1.06174414  0.12639634]\n",
            " [ 0.         -1.05856939]\n",
            " [ 0.96062565  0.65304778]\n",
            " [-1.16286263  1.44302493]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "data=[1,2,2,2,3,1,1,15,2,2,3,1,1,2]\n",
        "mean=np.mean(data)\n",
        "std=np.std(data)\n",
        "print('Mean is: ',mean)\n",
        "print(\"Standard deviation is: \",std)\n",
        "threshold=3\n",
        "outlier=[]\n",
        "for i in data:\n",
        "  z=(i-mean)/std\n",
        "  if z>threshold:\n",
        "    outlier.append(i)\n",
        "print('Outlier in dataset is: ',outlier)"
      ],
      "metadata": {
        "id": "jKb1AzI1Ndmn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c766414-deb4-414a-fba4-c819d221aaa9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean is:  2.7142857142857144\n",
            "Standard deviation is:  3.4729273660409197\n",
            "Outlier in dataset is:  [15]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Interquartile Range to detect outliers in data**\n",
        "\n",
        "\n",
        "> Q1- 25%\n",
        "\n",
        "> Q2- 50%\n",
        "\n",
        "> Q3- 75%\n",
        "\n",
        "\n",
        "\n",
        "*   Q1= median of the dataset\n",
        "*   Q2= median of n smallest data points\n",
        "\n",
        "*   Q3= median of n highest data points\n",
        "*   List item\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "64rKwW2PHQVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#step1\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "data=[6,2,3,4,5,1,50]\n",
        "sort_data=np.sort(data)\n",
        "sort_data\n",
        "\n",
        "Q1=np.percentile(data,25,interpolation='midpoint')\n",
        "Q2=np.percentile(data,50,interpolation='midpoint')\n",
        "Q3=np.percentile(data,75,interpolation='midpoint')\n",
        "\n",
        "print(\"Q1 : \",Q1)\n",
        "print(\"Q2 : \",Q2)\n",
        "print(\"Q3 : \",Q3)\n",
        "\n",
        "IQR=Q3-Q1\n",
        "\n",
        "print(\"Interquartile range is: \",IQR)"
      ],
      "metadata": {
        "id": "ptXLZHupNdZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7d930e1-1492-40e6-bbaa-ef4f9a109832"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Q1 :  2.5\n",
            "Q2 :  4.0\n",
            "Q3 :  5.5\n",
            "Interquartile range is:  3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#step 4\n",
        "low_lim=Q1-1.5*IQR\n",
        "up_lim=Q3+1.5*IQR\n",
        "print('Low limit is ',low_lim)\n",
        "print('Up limit is ',up_lim)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9KStv-iOQmwu",
        "outputId": "d34de5cf-d3de-4787-f29b-fd8ee0b54e55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Low limit is  -2.0\n",
            "Up limit is  10.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#step 5\n",
        "outlier=[]\n",
        "for x in data:\n",
        "  if(x>up_lim) or (x<low_lim):\n",
        "    outlier.append(x)\n",
        "    print(\"Outlier in the dataset is\",outlier)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jBEnbJsLBk3",
        "outputId": "f6f71b08-5d1c-4456-a8a0-e9baa15b9549"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outlier in the dataset is [50]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#step 6\n",
        "sns.boxplot(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "SWvTGSlvPwb-",
        "outputId": "07bf3505-6dfd-49c6-953d-d411f357783c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def load_data():\n",
        "  df_all=pd.read_csv('/content/2,1 dataset titanic (1) (1).csv')\n",
        "\n",
        "  return df_all.loc[:300,['Survived','Pclass','Sex','Cabin','Embarked']]\n",
        "\n",
        "df=load_data()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9i7WNcv7RIhm",
        "outputId": "8cb0aa4b-43b0-4b0c-9ae0-87b675a3d5fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex    Cabin Embarked\n",
              "0           0       3    male      NaN        S\n",
              "1           1       1  female      C85        C\n",
              "2           1       3  female      NaN        S\n",
              "3           1       1  female     C123        S\n",
              "4           0       3    male      NaN        S\n",
              "..        ...     ...     ...      ...      ...\n",
              "296         0       3    male      NaN        C\n",
              "297         0       1  female  C22 C26        S\n",
              "298         1       1    male     C106        S\n",
              "299         1       1  female  B58 B60        C\n",
              "300         1       3  female      NaN        Q\n",
              "\n",
              "[301 rows x 5 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Cabin</th>\n",
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              "      <td>male</td>\n",
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              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
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              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>female</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>C106</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>B58 B60</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 5 columns</p>\n",
              "</div>\n",
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              "            style=\"display:none;\">\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",
              "      } 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-ade96d0c-b26e-4d10-a0b7-106150682154 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\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",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 301,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 55,\n        \"samples\": [\n          \"C49\",\n          \"D56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Cabin.duplicated()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5lN8BkzRJwe",
        "outputId": "8ed367aa-af0d-4d9b-914b-2a2980033b78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2       True\n",
              "3      False\n",
              "4       True\n",
              "       ...  \n",
              "296     True\n",
              "297    False\n",
              "298    False\n",
              "299     True\n",
              "300     True\n",
              "Name: Cabin, Length: 301, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated(subset=['Survived','Pclass','Sex'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8qqAs2vRJem",
        "outputId": "36acab09-d815-42e7-a82b-68603440cb82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3       True\n",
              "4       True\n",
              "       ...  \n",
              "296     True\n",
              "297     True\n",
              "298     True\n",
              "299     True\n",
              "300     True\n",
              "Length: 301, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Cabin.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_SFpGfqRJbW",
        "outputId": "8c5ea5bb-6745-4d1a-8814-3266f1bbacff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "245"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJwBhXZIRJU2",
        "outputId": "bac00c58-1cc8-4aaa-9f26-869950b77597"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "218"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df.duplicated(keep='first'),:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jXp7YG8MXefs",
        "outputId": "53c7dd01-7706-45f9-bc35-9e01a9691f13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex Cabin Embarked\n",
              "4           0       3    male   NaN        S\n",
              "7           0       3    male   NaN        S\n",
              "8           1       3  female   NaN        S\n",
              "12          0       3    male   NaN        S\n",
              "13          0       3    male   NaN        S\n",
              "..        ...     ...     ...   ...      ...\n",
              "293         0       3  female   NaN        S\n",
              "294         0       3    male   NaN        S\n",
              "295         0       1    male   NaN        C\n",
              "296         0       3    male   NaN        C\n",
              "300         1       3  female   NaN        Q\n",
              "\n",
              "[218 rows x 5 columns]"
            ],
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>male</td>\n",
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              "      <td>S</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>12</th>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>male</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>218 rows × 5 columns</p>\n",
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              "  <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-a7609176-0121-416b-a123-467bd71da170 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-a7609176-0121-416b-a123-467bd71da170');\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-34cf5669-f914-4657-bfa8-cc32ec93275a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34cf5669-f914-4657-bfa8-cc32ec93275a')\"\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-34cf5669-f914-4657-bfa8-cc32ec93275a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 218,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"G6\",\n          \"D26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df.duplicated(keep=False),:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "64XLb-NDZUAF",
        "outputId": "cd0c619b-bb02-4480-e072-97d6e6a45b5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Survived  Pclass     Sex Cabin Embarked\n",
              "0           0       3    male   NaN        S\n",
              "2           1       3  female   NaN        S\n",
              "4           0       3    male   NaN        S\n",
              "5           0       3    male   NaN        Q\n",
              "7           0       3    male   NaN        S\n",
              "..        ...     ...     ...   ...      ...\n",
              "293         0       3  female   NaN        S\n",
              "294         0       3    male   NaN        S\n",
              "295         0       1    male   NaN        C\n",
              "296         0       3    male   NaN        C\n",
              "300         1       3  female   NaN        Q\n",
              "\n",
              "[239 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b05b183f-58b7-40da-b7db-21a5504d3d4b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>239 rows × 5 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-b05b183f-58b7-40da-b7db-21a5504d3d4b')\"\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-b05b183f-58b7-40da-b7db-21a5504d3d4b 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-b05b183f-58b7-40da-b7db-21a5504d3d4b');\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-6b306d2e-e8e0-4da7-8108-9f63b0bb4ed8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b306d2e-e8e0-4da7-8108-9f63b0bb4ed8')\"\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-6b306d2e-e8e0-4da7-8108-9f63b0bb4ed8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 239,\n  \"fields\": [\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"G6\",\n          \"D26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
  ]
}