Pandas_1_29JanClass.ipynb

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      "display_name": "Python 3",
      "language": "python",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
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    "colab": {
      "name": "Pandas_1 - 29JanClass.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/plaban1981/INEURON/blob/master/Pandas_1_29JanClass.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GNtSeZcnujg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# read and write using pandas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TkyOGt_nujn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Or6FAMPnuju",
        "colab_type": "code",
        "colab": {},
        "outputId": "a648590b-e86c-4613-c1d2-5fa033f60697"
      },
      "source": [
        "# get the current working directory\n",
        "os.getcwd()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'D:\\\\datascience\\\\acad\\\\acad material\\\\Session-IPYNB 11'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9x2vsnfSnuj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#change current working directory\n",
        "os.chdir('D:\\\\datascience\\\\acad\\\\acad material\\\\Session-IPYNB 11')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78UE7RJcnuj4",
        "colab_type": "code",
        "colab": {},
        "outputId": "2b8f1b76-a793-4de4-8bf3-eeab5da1c670"
      },
      "source": [
        "os.getcwd()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'D:\\\\datascience\\\\acad\\\\acad material\\\\Session-IPYNB 11'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxU5lkNznuj8",
        "colab_type": "code",
        "colab": {},
        "outputId": "a434d60b-919e-4a32-88bb-0efbbf838756"
      },
      "source": [
        "# list down all objects in the current working directory\n",
        "os.listdir('D:\\\\datascience\\\\acad\\\\acad material\\\\Session-IPYNB 11')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.ipynb_checkpoints', 'College_Data.npy', 'Numpy_1.ipynb', 'Python_5.ipynb']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHgcfl5JnukC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06oqB1pWnukG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic_train = pd.read_csv(\"https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/ff414a1bcfcba32481e4d4e8db578e55872a2ca1/titanic.csv\",\n",
        "                           sep='\\t') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wy4kMx1rnukK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8522dc69-d2a3-4895-e20b-59ca7f52fd64"
      },
      "source": [
        "type(titanic_train)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ih2tcizunukO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "d200ed49-7918-4263-b634-cd791c9fe668"
      },
      "source": [
        "titanic_train.head(1)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...  Fare Cabin  Embarked\n",
              "0            1         0       3  ...  7.25   NaN         S\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NidDS4XEnukV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "981e57ab-08d4-4591-8103-9c6aded9aeb5"
      },
      "source": [
        "titanic_train.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>152</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113776</td>\n",
              "      <td>66.6000</td>\n",
              "      <td>C2</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>153</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Meo, Mr. Alfonzo</td>\n",
              "      <td>male</td>\n",
              "      <td>55.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 11206</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>154</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>van Billiard, Mr. Austin Blyler</td>\n",
              "      <td>male</td>\n",
              "      <td>40.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>A/5. 851</td>\n",
              "      <td>14.5000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Olsen, Mr. Ole Martin</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Fa 265302</td>\n",
              "      <td>7.3125</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>156</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Williams, Mr. Charles Duane</td>\n",
              "      <td>male</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>PC 17597</td>\n",
              "      <td>61.3792</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "151          152         1       1  ...  66.6000    C2         S\n",
              "152          153         0       3  ...   8.0500   NaN         S\n",
              "153          154         0       3  ...  14.5000   NaN         S\n",
              "154          155         0       3  ...   7.3125   NaN         S\n",
              "155          156         0       1  ...  61.3792   NaN         C\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JaZlF_0CnukZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7273d4b7-3362-49a7-95eb-81fcf159634c"
      },
      "source": [
        "titanic_train.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDD01s3snukd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c23a71e8-55b0-4d89-a78b-8c4a6b1e6403"
      },
      "source": [
        "titanic_train.dtypes"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      int64\n",
              "Survived         int64\n",
              "Pclass           int64\n",
              "Name            object\n",
              "Sex             object\n",
              "Age            float64\n",
              "SibSp            int64\n",
              "Parch            int64\n",
              "Ticket          object\n",
              "Fare           float64\n",
              "Cabin           object\n",
              "Embarked        object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byXYGv1pnukk",
        "colab_type": "code",
        "colab": {},
        "outputId": "a9b53e80-6938-4df1-9d2e-82899177636a"
      },
      "source": [
        "print( titanic_train.describe() )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
            "count   156.000000  156.000000  156.000000  126.000000  156.000000   \n",
            "mean     78.500000    0.346154    2.423077   28.141508    0.615385   \n",
            "std      45.177428    0.477275    0.795459   14.613880    1.056235   \n",
            "min       1.000000    0.000000    1.000000    0.830000    0.000000   \n",
            "25%      39.750000    0.000000    2.000000   19.000000    0.000000   \n",
            "50%      78.500000    0.000000    3.000000   26.000000    0.000000   \n",
            "75%     117.250000    1.000000    3.000000   35.000000    1.000000   \n",
            "max     156.000000    1.000000    3.000000   71.000000    5.000000   \n",
            "\n",
            "            Parch        Fare  \n",
            "count  156.000000  156.000000  \n",
            "mean     0.397436   28.109587  \n",
            "std      0.870146   39.401047  \n",
            "min      0.000000    6.750000  \n",
            "25%      0.000000    8.003150  \n",
            "50%      0.000000   14.454200  \n",
            "75%      0.000000   30.371850  \n",
            "max      5.000000  263.000000  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUB8adb_o5hP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.pandas.set_option('display.max_columns',None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IdqMqbMHp-HI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "54fe0ed7-1b59-497d-865a-acd66001f8b3"
      },
      "source": [
        "titanic_train.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UttLSxBLqDuW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d0366978-cd65-4586-d32a-0552dad91007"
      },
      "source": [
        "pd.get_dummies(titanic_train['Embarked']).head()#one-hot encoding"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
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              "  </thead>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   C  Q  S\n",
              "0  0  0  1\n",
              "1  1  0  0\n",
              "2  0  0  1\n",
              "3  0  0  1\n",
              "4  0  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQTY0kZGqQU6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7df2759f-22b0-4d58-8e6a-ee6b4883ddb8"
      },
      "source": [
        "pd.get_dummies(titanic_train['Embarked'],drop_first=True).head()# Dummy Coding, drop first referrence"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Q  S\n",
              "0  0  1\n",
              "1  0  0\n",
              "2  0  1\n",
              "3  0  1\n",
              "4  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KxtDjhIrFHz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "de46341d-586c-4c0d-b69c-1fad8307e584"
      },
      "source": [
        "cat_cols = [col for col in titanic_train.columns if titanic_train[col].dtype == 'object']\n",
        "cat_cols"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QI9qwDO_rdUz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "31751633-f7c8-4750-c156-ed3fd06eb042"
      },
      "source": [
        "titanic_train.describe(include='O')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>31</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>156</td>\n",
              "      <td>2</td>\n",
              "      <td>145</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
              "      <td>male</td>\n",
              "      <td>347082</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     Name   Sex  Ticket        Cabin Embarked\n",
              "count                                 156   156     156           31      155\n",
              "unique                                156     2     145           28        3\n",
              "top     Pears, Mrs. Thomas (Edith Wearne)  male  347082  C23 C25 C27        S\n",
              "freq                                    1   100       2            2      110"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYNeebwZnukq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "9e5cfa0c-4a56-41d2-b34c-0f5662fd5b42"
      },
      "source": [
        "categorical = titanic_train.dtypes[titanic_train.dtypes == \"object\"].index\n",
        "print(categorical)\n",
        "\n",
        "titanic_train[categorical].describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>156</td>\n",
              "      <td>31</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>156</td>\n",
              "      <td>2</td>\n",
              "      <td>145</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
              "      <td>male</td>\n",
              "      <td>347082</td>\n",
              "      <td>C23 C25 C27</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     Name   Sex  Ticket        Cabin Embarked\n",
              "count                                 156   156     156           31      155\n",
              "unique                                156     2     145           28        3\n",
              "top     Pears, Mrs. Thomas (Edith Wearne)  male  347082  C23 C25 C27        S\n",
              "freq                                    1   100       2            2      110"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ip3rjD5Ynukx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "531a6e04-ecb6-4e47-f9d4-f21ecbfaa687"
      },
      "source": [
        "len(titanic_train[\"PassengerId\"])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "156"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPw55h2nnuk3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del titanic_train[\"PassengerId\"]     # Remove PassengerId"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gGbMmObnuk8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "71b2eaab-bccd-4b20-be95-388a3bbadfbf"
      },
      "source": [
        "sorted(titanic_train[\"Name\"])[0:15]   # Check the first 15 sorted names"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Ahlin, Mrs. Johan (Johanna Persdotter Larsson)',\n",
              " 'Allen, Mr. William Henry',\n",
              " 'Andersson, Miss. Ellis Anna Maria',\n",
              " 'Andersson, Miss. Erna Alexandra',\n",
              " 'Andersson, Mr. Anders Johan',\n",
              " 'Andersson, Mr. August Edvard (\"Wennerstrom\")',\n",
              " 'Andreasson, Mr. Paul Edvin',\n",
              " 'Andrew, Mr. Edgardo Samuel',\n",
              " 'Arnold-Franchi, Mrs. Josef (Josefine Franchi)',\n",
              " 'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)',\n",
              " 'Attalah, Miss. Malake',\n",
              " 'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)',\n",
              " 'Barton, Mr. David John',\n",
              " 'Bateman, Rev. Robert James',\n",
              " 'Baxter, Mr. Quigg Edmond']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "kMHI8ySQnulB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f06a4a44-e3db-4564-dd0a-05cc32503518"
      },
      "source": [
        "sorted(titanic_train[\"Name\"])[0:15]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Ahlin, Mrs. Johan (Johanna Persdotter Larsson)',\n",
              " 'Allen, Mr. William Henry',\n",
              " 'Andersson, Miss. Ellis Anna Maria',\n",
              " 'Andersson, Miss. Erna Alexandra',\n",
              " 'Andersson, Mr. Anders Johan',\n",
              " 'Andersson, Mr. August Edvard (\"Wennerstrom\")',\n",
              " 'Andreasson, Mr. Paul Edvin',\n",
              " 'Andrew, Mr. Edgardo Samuel',\n",
              " 'Arnold-Franchi, Mrs. Josef (Josefine Franchi)',\n",
              " 'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)',\n",
              " 'Attalah, Miss. Malake',\n",
              " 'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)',\n",
              " 'Barton, Mr. David John',\n",
              " 'Bateman, Rev. Robert James',\n",
              " 'Baxter, Mr. Quigg Edmond']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QZA7Im5nulF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "4066cbfd-f6b0-4be8-9b0f-6770352ad535"
      },
      "source": [
        "titanic_train[\"Name\"].describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                                   156\n",
              "unique                                  156\n",
              "top       Pears, Mrs. Thomas (Edith Wearne)\n",
              "freq                                      1\n",
              "Name: Name, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCnJ7jpInulJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "034b78fd-9270-4e7a-e8e5-35aef81da9f9"
      },
      "source": [
        "titanic_train[\"Ticket\"][0:15]       # Check the first 15 tickets"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            A/5 21171\n",
              "1             PC 17599\n",
              "2     STON/O2. 3101282\n",
              "3               113803\n",
              "4               373450\n",
              "5               330877\n",
              "6                17463\n",
              "7               349909\n",
              "8               347742\n",
              "9               237736\n",
              "10             PP 9549\n",
              "11              113783\n",
              "12           A/5. 2151\n",
              "13              347082\n",
              "14              350406\n",
              "Name: Ticket, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWM3S1L6nulV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "bb9d92da-3b77-49ab-b1b7-da4c6dc9cb32"
      },
      "source": [
        "titanic_train[\"Ticket\"].describe()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        156\n",
              "unique       145\n",
              "top       347082\n",
              "freq           2\n",
              "Name: Ticket, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69jw1uoenulY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del titanic_train[\"Ticket\"]        # Remove Ticket"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRIGiRX1nulb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "029d2bb8-ccc2-4a97-d170-2b9ba1acf461"
      },
      "source": [
        "titanic_train[\"Cabin\"][0:15]       # Check the first 15 tickets"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      NaN\n",
              "1      C85\n",
              "2      NaN\n",
              "3     C123\n",
              "4      NaN\n",
              "5      NaN\n",
              "6      E46\n",
              "7      NaN\n",
              "8      NaN\n",
              "9      NaN\n",
              "10      G6\n",
              "11    C103\n",
              "12     NaN\n",
              "13     NaN\n",
              "14     NaN\n",
              "Name: Cabin, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ax6i19BBnule",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "5367925a-b07d-4ec5-8b58-93a12ae91317"
      },
      "source": [
        "titanic_train[\"Cabin\"].describe()  # Check number of unique cabins"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count              31\n",
              "unique             28\n",
              "top       C23 C25 C27\n",
              "freq                2\n",
              "Name: Cabin, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYRP8s8tnulj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "261f6b37-bac8-4d8f-f7fa-f76b88d201d9"
      },
      "source": [
        "new_survived = pd.Categorical(titanic_train[\"Survived\"])\n",
        "print(new_survived)\n",
        "new_survived = new_survived.rename_categories([\"Died\",\"Survived\"])              \n",
        "\n",
        "new_survived.describe()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 1, 1, 0, ..., 1, 0, 0, 0, 0]\n",
            "Length: 156\n",
            "Categories (2, int64): [0, 1]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>counts</th>\n",
              "      <th>freqs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>categories</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Died</th>\n",
              "      <td>102</td>\n",
              "      <td>0.653846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>54</td>\n",
              "      <td>0.346154</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            counts     freqs\n",
              "categories                  \n",
              "Died           102  0.653846\n",
              "Survived        54  0.346154"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PheP6j0QtKF4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8fd8c6cd-40e0-4dd5-def9-9e57288fc2cd"
      },
      "source": [
        "titanic_train['Survived'].value_counts()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    102\n",
              "1     54\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgeHZw2knult",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "fbf452b1-7dbc-4a75-892a-b1c4ffe9bda7"
      },
      "source": [
        "new_Pclass = pd.Categorical(titanic_train[\"Pclass\"],ordered=True)\n",
        "print(titanic_train[\"Pclass\"].value_counts())\n",
        "print(new_Pclass)\n",
        "new_Pclass = new_Pclass.rename_categories([\"Class1\",\"Class2\",\"Class3\"])     \n",
        "new_Pclass.describe()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3    96\n",
            "2    30\n",
            "1    30\n",
            "Name: Pclass, dtype: int64\n",
            "[3, 1, 3, 1, 3, ..., 1, 3, 3, 3, 1]\n",
            "Length: 156\n",
            "Categories (3, int64): [1 < 2 < 3]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>counts</th>\n",
              "      <th>freqs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>categories</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Class1</th>\n",
              "      <td>30</td>\n",
              "      <td>0.192308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class2</th>\n",
              "      <td>30</td>\n",
              "      <td>0.192308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class3</th>\n",
              "      <td>96</td>\n",
              "      <td>0.615385</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            counts     freqs\n",
              "categories                  \n",
              "Class1          30  0.192308\n",
              "Class2          30  0.192308\n",
              "Class3          96  0.615385"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtYX6Izgnuly",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab13fd4b-af3a-45b7-884c-7619428a6cc8"
      },
      "source": [
        "titanic_train[\"Pclass\"].nunique()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gx_844akuMy3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "465c7186-e69b-47cd-e4f1-086903114b0d"
      },
      "source": [
        "titanic_train[\"Pclass\"].unique()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJr_q2kGnul1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic_train[\"Pclass\"] = new_Pclass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARkVSCg6nul5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d16db36c-a55c-492c-867e-c9f58d59c5b9"
      },
      "source": [
        "titanic_train.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</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>Fare</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>Class3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Class1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Class1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0         0  Class3                            Braund, Mr. Owen Harris   \n",
              "1         1  Class1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2         1  Class3                             Heikkinen, Miss. Laina   \n",
              "3         1  Class1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4         0  Class3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch     Fare Cabin Embarked  \n",
              "0    male  22.0      1      0   7.2500   NaN        S  \n",
              "1  female  38.0      1      0  71.2833   C85        C  \n",
              "2  female  26.0      0      0   7.9250   NaN        S  \n",
              "3  female  35.0      1      0  53.1000  C123        S  \n",
              "4    male  35.0      0      0   8.0500   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "UBXHXAPAnul-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "2a29116a-3ffc-4a71-c679-4db294fd0779"
      },
      "source": [
        "titanic_train[\"Cabin\"].unique()   # Check unique cabins"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
              "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
              "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
              "       'F E69', 'D47', 'B86', 'F2', 'C2'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cUaKX-cnumD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z388g_y8numH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "a4a02430-7e50-4457-c23c-4fddfbac6483"
      },
      "source": [
        "char_cabin = titanic_train[\"Cabin\"].astype(str) # Convert data to str\n",
        "char_cabin\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       nan\n",
              "1       C85\n",
              "2       nan\n",
              "3      C123\n",
              "4       nan\n",
              "       ... \n",
              "151      C2\n",
              "152     nan\n",
              "153     nan\n",
              "154     nan\n",
              "155     nan\n",
              "Name: Cabin, Length: 156, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41Q4qSbgnumM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tA2c7vfJnumP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "6db97c7c-f067-476c-a1a9-506c4ec3323f"
      },
      "source": [
        "new_Cabin = np.array([cabin[0] for cabin in char_cabin]) # Take first letter\n",
        "\n",
        "new_Cabin = pd.Categorical(new_Cabin)\n",
        "\n",
        "new_Cabin .describe()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>counts</th>\n",
              "      <th>freqs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>categories</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2</td>\n",
              "      <td>0.012821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>5</td>\n",
              "      <td>0.032051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>10</td>\n",
              "      <td>0.064103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>6</td>\n",
              "      <td>0.038462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>3</td>\n",
              "      <td>0.019231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>4</td>\n",
              "      <td>0.025641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <td>1</td>\n",
              "      <td>0.006410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n</th>\n",
              "      <td>125</td>\n",
              "      <td>0.801282</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            counts     freqs\n",
              "categories                  \n",
              "A                2  0.012821\n",
              "B                5  0.032051\n",
              "C               10  0.064103\n",
              "D                6  0.038462\n",
              "E                3  0.019231\n",
              "F                4  0.025641\n",
              "G                1  0.006410\n",
              "n              125  0.801282"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0k32ekXnumU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UO7ks54Qnuma",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic_train[\"Cabin\"] = new_Cabin"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPhKJJ56numf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b982d295-d07d-474e-ff92-3f566672a130"
      },
      "source": [
        "dummy_vector = pd.Series([1,None,3,None,7,8])\n",
        "\n",
        "dummy_vector.isnull()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    False\n",
              "1     True\n",
              "2    False\n",
              "3     True\n",
              "4    False\n",
              "5    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tp0VOmEnumk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6fba7822-70e0-47f9-9a2c-cc8e3b9a485b"
      },
      "source": [
        "missing = np.where(titanic_train[\"Age\"].isnull() == True)\n",
        "missing"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  5,  17,  19,  26,  28,  29,  31,  32,  36,  42,  45,  46,  47,\n",
              "         48,  55,  64,  65,  76,  77,  82,  87,  95, 101, 107, 109, 121,\n",
              "        126, 128, 140, 154]),)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNCFhw0_xMe4",
        "colab_type": "text"
      },
      "source": [
        "## Missing values in Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9Q2sjmUwnv9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "becb8084-ee06-4b78-b0b9-5f8d50e06e16"
      },
      "source": [
        "titanic_train.loc[missing].head()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</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>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>n</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>Class2</td>\n",
              "      <td>Williams, Mr. Charles Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>n</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Masselmani, Mrs. Fatima</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>n</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Emir, Mr. Farred Chehab</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>n</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>Class3</td>\n",
              "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8792</td>\n",
              "      <td>n</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived  Pclass                           Name     Sex  Age  SibSp  \\\n",
              "5          0  Class3               Moran, Mr. James    male  NaN      0   \n",
              "17         1  Class2   Williams, Mr. Charles Eugene    male  NaN      0   \n",
              "19         1  Class3        Masselmani, Mrs. Fatima  female  NaN      0   \n",
              "26         0  Class3        Emir, Mr. Farred Chehab    male  NaN      0   \n",
              "28         1  Class3  O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
              "\n",
              "    Parch     Fare Cabin Embarked  \n",
              "5       0   8.4583     n        Q  \n",
              "17      0  13.0000     n        S  \n",
              "19      0   7.2250     n        C  \n",
              "26      0   7.2250     n        C  \n",
              "28      0   7.8792     n        Q  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnlHoPu9nump",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f18057ae-0dc0-4fd2-bfb4-5ec50ab306b1"
      },
      "source": [
        "titanic_train['Age'][4]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbZTRGa2numt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53a6a314-fe2e-44bd-cf40-017d0db9195a"
      },
      "source": [
        "len(missing[0])"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22yZeFN5numx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "158f0904-dedd-4a6b-9e10-2227130fc505"
      },
      "source": [
        "index = np.where(titanic_train[\"Fare\"] == max(titanic_train[\"Fare\"]) )\n",
        "\n",
        "titanic_train.loc[index]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</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>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>Class1</td>\n",
              "      <td>Fortune, Mr. Charles Alexander</td>\n",
              "      <td>male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>263.0</td>\n",
              "      <td>C</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>1</td>\n",
              "      <td>Class1</td>\n",
              "      <td>Fortune, Miss. Mabel Helen</td>\n",
              "      <td>female</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>263.0</td>\n",
              "      <td>C</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived  Pclass                            Name     Sex   Age  SibSp  \\\n",
              "27         0  Class1  Fortune, Mr. Charles Alexander    male  19.0      3   \n",
              "88         1  Class1      Fortune, Miss. Mabel Helen  female  23.0      3   \n",
              "\n",
              "    Parch   Fare Cabin Embarked  \n",
              "27      2  263.0     C        S  \n",
              "88      2  263.0     C        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYrwaVblnum4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "16b0fd6f-e000-45ee-ac29-334829e1eeb8"
      },
      "source": [
        "titanic_train[\"Family\"] = titanic_train[\"SibSp\"] + titanic_train[\"Parch\"]\n",
        "\n",
        "most_family = np.where(titanic_train[\"Family\"] == max(titanic_train[\"Family\"]))\n",
        "\n",
        "titanic_train.loc[most_family]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</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>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Goodwin, Master. William Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>46.9</td>\n",
              "      <td>n</td>\n",
              "      <td>S</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>0</td>\n",
              "      <td>Class3</td>\n",
              "      <td>Goodwin, Miss. Lillian Amy</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>46.9</td>\n",
              "      <td>n</td>\n",
              "      <td>S</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived  Pclass                                Name     Sex   Age  SibSp  \\\n",
              "59         0  Class3  Goodwin, Master. William Frederick    male  11.0      5   \n",
              "71         0  Class3          Goodwin, Miss. Lillian Amy  female  16.0      5   \n",
              "\n",
              "    Parch  Fare Cabin Embarked  Family  \n",
              "59      2  46.9     n        S       7  \n",
              "71      2  46.9     n        S       7  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pugJfUmnum8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "581e31ef-03eb-4817-c512-db5009122bd1"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "labels = ['a','b','c']\n",
        "my_data = [10,20,30]\n",
        "arr = np.array(my_data)\n",
        "d = {'a':10,'b':20,'c':30}\n",
        "\n",
        "print (\"Labels:\", labels)\n",
        "print(\"My data:\", my_data)\n",
        "print(\"Dictionary:\", d)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labels: ['a', 'b', 'c']\n",
            "My data: [10, 20, 30]\n",
            "Dictionary: {'a': 10, 'b': 20, 'c': 30}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q-VmOnAunum_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "877cc170-a29c-42c6-9171-a9c3c627956d"
      },
      "source": [
        "k=pd.Series(data=my_data, index=labels) \n",
        "k"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    10\n",
              "b    20\n",
              "c    30\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YN_Kwuw1nunD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "91622c8f-6dfb-4fd4-9e16-8eb7a139b9c4"
      },
      "source": [
        "#k['a']\n",
        "k[0]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnKLiODLnunS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "6afdd696-c3c9-4b98-cb46-2cc61bdb9ccb"
      },
      "source": [
        "print (\"\\nHolding numerical data\\n\",'-'*25, sep='')\n",
        "print(pd.Series(arr))\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Holding numerical data\n",
            "-------------------------\n",
            "0    10\n",
            "1    20\n",
            "2    30\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VztUmRiinunY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f4c2b85c-0344-4b89-decd-dc341fdcbb2b"
      },
      "source": [
        "print (\"\\nHolding text labels\\n\",'-'*20, sep='')\n",
        "print(pd.Series(labels))\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Holding text labels\n",
            "--------------------\n",
            "0    a\n",
            "1    b\n",
            "2    c\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p0YjYpDanunc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "ad643f53-da79-4838-abb3-945c80ae9c49"
      },
      "source": [
        "from numpy.random import randn as rn\n",
        "np.random.seed(101)\n",
        "matrix_data = rn(5,4)\n",
        "row_labels = ['A','B','C','D','E']\n",
        "column_headings = ['W','X','Y','Z']\n",
        "\n",
        "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
        "print(\"\\nThe data frame looks like\\n\",'-'*45, sep='')\n",
        "print(df)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The data frame looks like\n",
            "---------------------------------------------\n",
            "          W         X         Y         Z\n",
            "A  2.706850  0.628133  0.907969  0.503826\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n",
            "D  0.188695 -0.758872 -0.933237  0.955057\n",
            "E  0.190794  1.978757  2.605967  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FV2RyD13nung",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "e143fc0f-8dd3-416b-dd91-ad0845089da4"
      },
      "source": [
        "print(\"\\nThe 'X' column\\n\",'-'*25, sep='')\n",
        "print(df['X'])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The 'X' column\n",
            "-------------------------\n",
            "A    0.628133\n",
            "B   -0.319318\n",
            "C    0.740122\n",
            "D   -0.758872\n",
            "E    1.978757\n",
            "Name: X, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VL8SAj1Wnunn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8a83bdd5-d99f-443f-ad24-51a937e4cfcc"
      },
      "source": [
        "print(\"\\nType of the column: \", type(df['X']), sep='')\n",
        "print(\"\\nThe 'X' and 'Z' columns indexed by passing a list\\n\",'-'*55, sep='')\n",
        "print(df[['X','Z']])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Type of the column: <class 'pandas.core.series.Series'>\n",
            "\n",
            "The 'X' and 'Z' columns indexed by passing a list\n",
            "-------------------------------------------------------\n",
            "          X         Z\n",
            "A  0.628133  0.503826\n",
            "B -0.319318  0.605965\n",
            "C  0.740122 -0.589001\n",
            "D -0.758872  0.955057\n",
            "E  1.978757  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maiQU6DEnuns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "80d6242e-2c24-4f93-e2cb-2cea0ee4c79c"
      },
      "source": [
        "print(\"\\nType of the pair of columns: \", type(df[['X','Z']]), sep='')\n",
        "print (\"\\nSo, for more than one column, the object turns into a DataFrame\")"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Type of the pair of columns: <class 'pandas.core.frame.DataFrame'>\n",
            "\n",
            "So, for more than one column, the object turns into a DataFrame\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx_fZtYonunv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "ba9545c9-aa6e-4e85-b01e-492daef52a37"
      },
      "source": [
        "print(\"\\nThe 'X' column accessed by DOT method (NOT recommended)\\n\",'-'*55, sep='')\n",
        "print(df.X)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The 'X' column accessed by DOT method (NOT recommended)\n",
            "-------------------------------------------------------\n",
            "A    0.628133\n",
            "B   -0.319318\n",
            "C    0.740122\n",
            "D   -0.758872\n",
            "E    1.978757\n",
            "Name: X, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gon0hBkAnuny",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "c499175b-db7c-44db-e6ac-dd9de43c37e9"
      },
      "source": [
        "print(\"\\nA column is created by assigning it in relation to an existing column\\n\",'-'*75, sep='')\n",
        "df['New'] = df['X']+df['Z']\n",
        "df['New (Sum of X and Z)'] = df['X']+df['Z']\n",
        "print(df)\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "A column is created by assigning it in relation to an existing column\n",
            "---------------------------------------------------------------------------\n",
            "          W         X         Y         Z       New  New (Sum of X and Z)\n",
            "A  2.706850  0.628133  0.907969  0.503826  1.131958              1.131958\n",
            "B  0.651118 -0.319318 -0.848077  0.605965  0.286647              0.286647\n",
            "C -2.018168  0.740122  0.528813 -0.589001  0.151122              0.151122\n",
            "D  0.188695 -0.758872 -0.933237  0.955057  0.196184              0.196184\n",
            "E  0.190794  1.978757  2.605967  0.683509  2.662266              2.662266\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0qXoK4Enun0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "e3911b4f-b0bc-4d90-c6f2-a8ee134c41bc"
      },
      "source": [
        "print(\"\\nA column is dropped by using df.drop() method\\n\",'-'*55, sep='')\n",
        "df = df.drop('New', axis=1) # Notice the axis=1 option, axis = 0 is default, so one has to change it to 1\n",
        "print(df)\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "A column is dropped by using df.drop() method\n",
            "-------------------------------------------------------\n",
            "          W         X         Y         Z  New (Sum of X and Z)\n",
            "A  2.706850  0.628133  0.907969  0.503826              1.131958\n",
            "B  0.651118 -0.319318 -0.848077  0.605965              0.286647\n",
            "C -2.018168  0.740122  0.528813 -0.589001              0.151122\n",
            "D  0.188695 -0.758872 -0.933237  0.955057              0.196184\n",
            "E  0.190794  1.978757  2.605967  0.683509              2.662266\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "My4x-eIpnun2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "fbcd46e6-5222-41a4-ad17-7c18da22e4be"
      },
      "source": [
        "df1=df.drop('A')\n",
        "print(\"\\nA row (index) is dropped by using df.drop() method and axis=0\\n\",'-'*65, sep='')\n",
        "print(df1)\n"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "A row (index) is dropped by using df.drop() method and axis=0\n",
            "-----------------------------------------------------------------\n",
            "          W         X         Y         Z\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n",
            "D  0.188695 -0.758872 -0.933237  0.955057\n",
            "E  0.190794  1.978757  2.605967  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pTK9dAHnun6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "d6e9ba90-c752-4d44-dda7-5b94c7ac669a"
      },
      "source": [
        "print(\"\\nAn in-place change can be done by making inplace=True in the drop method\\n\",'-'*75, sep='')\n",
        "df.drop('New (Sum of X and Z)', axis=1, inplace=True)\n",
        "print(df)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "An in-place change can be done by making inplace=True in the drop method\n",
            "---------------------------------------------------------------------------\n",
            "          W         X         Y         Z\n",
            "A  2.706850  0.628133  0.907969  0.503826\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n",
            "D  0.188695 -0.758872 -0.933237  0.955057\n",
            "E  0.190794  1.978757  2.605967  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gbNA6IKnun8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### Selecting/indexing Rows\n",
        "#* Label-based 'loc' method\n",
        "#* Index (numeric) 'iloc' method"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbKIxINDnun-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d5148709-eb0f-42e0-b838-b16655572ce0"
      },
      "source": [
        "print(\"\\nLabel-based 'loc' method can be used for selecting row(s)\\n\",'-'*60, sep='')\n",
        "print(\"\\nSingle row\\n\")\n",
        "print(df.loc['C'])\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Label-based 'loc' method can be used for selecting row(s)\n",
            "------------------------------------------------------------\n",
            "\n",
            "Single row\n",
            "\n",
            "W   -2.018168\n",
            "X    0.740122\n",
            "Y    0.528813\n",
            "Z   -0.589001\n",
            "Name: C, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVOgBt6RnuoA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "955b3da0-7296-4f00-a83b-2c2edd67a3b5"
      },
      "source": [
        "print(\"\\nMultiple rows\\n\")\n",
        "print(df.loc[['B','C']])\n",
        "print('*'*80)\n",
        "\n"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Multiple rows\n",
            "\n",
            "          W         X         Y         Z\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n",
            "********************************************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwxQSIxfnuoF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6095141d-e8ea-4d59-e79f-0bc6d7a5f71a"
      },
      "source": [
        "print(\"\\nIndex position based 'iloc' method can be used for selecting row(s)\\n\",'-'*70, sep='')\n",
        "print(\"\\nSingle row\\n\")\n",
        "print(df.iloc[2])\n"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Index position based 'iloc' method can be used for selecting row(s)\n",
            "----------------------------------------------------------------------\n",
            "\n",
            "Single row\n",
            "\n",
            "W   -2.018168\n",
            "X    0.740122\n",
            "Y    0.528813\n",
            "Z   -0.589001\n",
            "Name: C, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32Zd_ObenuoM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "fff53064-08fd-4f38-bc73-f20d2a384157"
      },
      "source": [
        "print(\"\\nMultiple rows\\n\")\n",
        "print(df.iloc[[1,2]])"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Multiple rows\n",
            "\n",
            "          W         X         Y         Z\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjiP1YZhnuoP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#### Subsetting DataFrame"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA1rCUNCnuoT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(101)\n",
        "matrix_data = rn(5,4)\n",
        "row_labels = ['A','B','C','D','E']\n",
        "column_headings = ['W','X','Y','Z']\n",
        "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lC_2BgdVnuoX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "891f4078-2555-4796-c6c5-63ccc87d4e78"
      },
      "source": [
        "print(\"\\nThe DatFrame\\n\",'-'*45, sep='')\n",
        "print(df)\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The DatFrame\n",
            "---------------------------------------------\n",
            "          W         X         Y         Z\n",
            "A  2.706850  0.628133  0.907969  0.503826\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n",
            "D  0.188695 -0.758872 -0.933237  0.955057\n",
            "E  0.190794  1.978757  2.605967  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOibUzXnnuob",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "347dd499-fc3d-43e3-effc-81def6631f01"
      },
      "source": [
        "print(\"\\nElement at row 'B' and column 'Y' is\\n\") \n",
        "print(df.loc['B','Y'])#loc[index,column]\n"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Element at row 'B' and column 'Y' is\n",
            "\n",
            "-0.8480769834036315\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--y8MYXQnuod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "00a738a1-a4a3-4669-9132-01bae5a4df02"
      },
      "source": [
        "K=df.loc['B']\n",
        "K['Y']"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.8480769834036315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNdfYakPnuog",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "b472d493-5ee5-4f3f-fa7a-49735843416f"
      },
      "source": [
        "print(\"\\nSubset comprising of rows B and D, and columns W and Y, is\\n\")\n",
        "df.loc[['B','D'],['W','Y']]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Subset comprising of rows B and D, and columns W and Y, is\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>-0.848077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>-0.933237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         Y\n",
              "B  0.651118 -0.848077\n",
              "D  0.188695 -0.933237"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8CFkGQqnuoj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "f85d2e5d-8d52-4a26-d26a-fc3810e74597"
      },
      "source": [
        "print(\"\\nThe DataFrame\\n\",'-'*45, sep='')\n",
        "print(df)\n"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The DataFrame\n",
            "---------------------------------------------\n",
            "          W         X         Y         Z\n",
            "A  2.706850  0.628133  0.907969  0.503826\n",
            "B  0.651118 -0.319318 -0.848077  0.605965\n",
            "C -2.018168  0.740122  0.528813 -0.589001\n",
            "D  0.188695 -0.758872 -0.933237  0.955057\n",
            "E  0.190794  1.978757  2.605967  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKgOHyfvnuos",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "3e16b029-48ae-4ede-8da4-547b92b78c24"
      },
      "source": [
        "print(\"\\nBoolean DataFrame(s) where we are checking if the values are greater than 0\\n\",'-'*75, sep='')\n",
        "print(df>0)\n"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Boolean DataFrame(s) where we are checking if the values are greater than 0\n",
            "---------------------------------------------------------------------------\n",
            "       W      X      Y      Z\n",
            "A   True   True   True   True\n",
            "B   True  False  False   True\n",
            "C  False   True   True  False\n",
            "D   True  False  False   True\n",
            "E   True   True   True   True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnBrXC6O1le_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c5de8bc2-b271-4ce5-bc5a-987c6cfd52ff"
      },
      "source": [
        "np.where(df > 0)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 4, 4]),\n",
              " array([0, 1, 2, 3, 0, 3, 1, 2, 0, 3, 0, 1, 2, 3]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k38aI5TT1RJt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "48ed16ac-962d-43ab-c80f-44ab40d5bede"
      },
      "source": [
        "df[df > 0]"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>W</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>2.706850</td>\n",
              "      <td>0.628133</td>\n",
              "      <td>0.907969</td>\n",
              "      <td>0.503826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.651118</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.605965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.740122</td>\n",
              "      <td>0.528813</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>0.188695</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.955057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.190794</td>\n",
              "      <td>1.978757</td>\n",
              "      <td>2.605967</td>\n",
              "      <td>0.683509</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          W         X         Y         Z\n",
              "A  2.706850  0.628133  0.907969  0.503826\n",
              "B  0.651118       NaN       NaN  0.605965\n",
              "C       NaN  0.740122  0.528813       NaN\n",
              "D  0.188695       NaN       NaN  0.955057\n",
              "E  0.190794  1.978757  2.605967  0.683509"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4eLhg8Fnuov",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "c6d059fe-e05a-47ed-d8cb-d11f97c388a3"
      },
      "source": [
        "print(\"\\n\")\n",
        "print(df.loc[['A','B','C']]>0)\n"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "       W      X      Y      Z\n",
            "A   True   True   True   True\n",
            "B   True  False  False   True\n",
            "C  False   True   True  False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZ2NfyE5nuoy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "       W      X      Y      Z\n",
        "A   True   True   True   True\n",
        "B   True  False  False   True\n",
        "C  False   True   True  False\n",
        "D   True  False  False   True\n",
        "E   True   True   True   True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeDg7weunuo1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "403c6591-594d-473c-9f7f-479e71ae66f4"
      },
      "source": [
        "booldf = df>0\n",
        "print(\"\\nDataFrame indexed by boolean dataframe\\n\",'-'*45, sep='')\n",
        "print(df[booldf])"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "DataFrame indexed by boolean dataframe\n",
            "---------------------------------------------\n",
            "          W         X         Y         Z\n",
            "A  2.706850  0.628133  0.907969  0.503826\n",
            "B  0.651118       NaN       NaN  0.605965\n",
            "C       NaN  0.740122  0.528813       NaN\n",
            "D  0.188695       NaN       NaN  0.955057\n",
            "E  0.190794  1.978757  2.605967  0.683509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GezPSPNonuo3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "matrix_data = np.matrix('22,66,140;42,70,148;30,62,125;35,68,160;25,62,152')\n",
        "row_labels = ['A','B','C','D','E']\n",
        "column_headings = ['Age', 'Height', 'Weight']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwW9p4RBnuo8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "fce309ca-8714-4c26-c3d5-39cdfcf4fc74"
      },
      "source": [
        "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
        "print(\"\\nA new DataFrame\\n\",'-'*25, sep='')\n",
        "print(df)\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "A new DataFrame\n",
            "-------------------------\n",
            "   Age  Height  Weight\n",
            "A   22      66     140\n",
            "B   42      70     148\n",
            "C   30      62     125\n",
            "D   35      68     160\n",
            "E   25      62     152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OU7Sz69nuo_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "9e1963b4-e618-4a59-d0ab-b69b7830cf6f"
      },
      "source": [
        "print(\"\\nRows with Height > 65 inch\\n\",'-'*35, sep='')\n",
        "print(df[df['Height']>65])\n"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Rows with Height > 65 inch\n",
            "-----------------------------------\n",
            "   Age  Height  Weight\n",
            "A   22      66     140\n",
            "B   42      70     148\n",
            "D   35      68     160\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dp3KhuzOnupB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "booldf1 = df['Height']>65\n",
        "booldf2 = df['Weight']>145\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jpn5XFsJnupE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "96f8cfdf-50f4-4245-beaa-e91e7e0fb9f3"
      },
      "source": [
        "print(\"\\nRows with Height > 65 inch and Weight >145 lbs\\n\",'-'*55, sep='')\n",
        "print(df[(booldf1) & (booldf2)])\n"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Rows with Height > 65 inch and Weight >145 lbs\n",
            "-------------------------------------------------------\n",
            "   Age  Height  Weight\n",
            "B   42      70     148\n",
            "D   35      68     160\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGES4TzynupH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "d5669dcd-421b-4bfe-c108-512391456cd2"
      },
      "source": [
        "print(\"\\nDataFrame with only Age and Weight columns whose Height > 65 inch\\n\",'-'*68, sep='')\n",
        "print(df[booldf1][['Age','Weight']])"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "DataFrame with only Age and Weight columns whose Height > 65 inch\n",
            "--------------------------------------------------------------------\n",
            "   Age  Weight\n",
            "A   22     140\n",
            "B   42     148\n",
            "D   35     160\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zU3IDpY53BY3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2ba22457-ba74-44d7-d5e5-7c2055c716b0"
      },
      "source": [
        "df[df['Height']>65][['Age','Weight']]"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>22</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>42</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>35</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  Weight\n",
              "A   22     140\n",
              "B   42     148\n",
              "D   35     160"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iX4FoMI3nupJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "matrix_data = np.matrix('22,66,140;42,70,148;30,62,125;35,68,160;25,62,152')\n",
        "row_labels = ['A','B','C','D','E']\n",
        "column_headings = ['Age', 'Height', 'Weight']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P58HNoiLnupP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "fc083235-5d40-4358-9520-8f2f60c49995"
      },
      "source": [
        "df = pd.DataFrame(data=matrix_data, index=row_labels, columns=column_headings)\n",
        "print(\"\\nThe DataFrame\\n\",'-'*25, sep='')\n",
        "print(df)\n"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "The DataFrame\n",
            "-------------------------\n",
            "   Age  Height  Weight\n",
            "A   22      66     140\n",
            "B   42      70     148\n",
            "C   30      62     125\n",
            "D   35      68     160\n",
            "E   25      62     152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0XMISoYnupV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "289d534b-e242-4124-eef9-c248482f88b7"
      },
      "source": [
        "print(\"\\nAfter resetting index\\n\",'-'*35, sep='')\n",
        "print(df.reset_index())\n"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "After resetting index\n",
            "-----------------------------------\n",
            "  index  Age  Height  Weight\n",
            "0     A   22      66     140\n",
            "1     B   42      70     148\n",
            "2     C   30      62     125\n",
            "3     D   35      68     160\n",
            "4     E   25      62     152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrofWD1QnupZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "84ef0a1a-595a-4454-cc37-a2e2b7f259ef"
      },
      "source": [
        "print(\"\\nAfter resetting index with 'drop' option TRUE\\n\",'-'*45, sep='')\n",
        "print(df.reset_index(drop=True))\n"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "After resetting index with 'drop' option TRUE\n",
            "---------------------------------------------\n",
            "   Age  Height  Weight\n",
            "0   22      66     140\n",
            "1   42      70     148\n",
            "2   30      62     125\n",
            "3   35      68     160\n",
            "4   25      62     152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Upmc-Eqnupd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "6b6300fe-19b9-464c-8551-39589dc85985"
      },
      "source": [
        "print(\"\\nAdding a new column 'Profession'\\n\",'-'*45, sep='')\n",
        "df['Profession'] = \"Student Teacher Engineer Doctor Nurse\".split()\n",
        "print(df)\n"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Adding a new column 'Profession'\n",
            "---------------------------------------------\n",
            "   Age  Height  Weight Profession\n",
            "A   22      66     140    Student\n",
            "B   42      70     148    Teacher\n",
            "C   30      62     125   Engineer\n",
            "D   35      68     160     Doctor\n",
            "E   25      62     152      Nurse\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNo3ob8Anuph",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "653dad95-62de-4f84-8561-bc4f692038c9"
      },
      "source": [
        "print(\"\\nSetting 'Profession' column as index\\n\",'-'*45, sep='')\n",
        "print (df.set_index('Profession'))"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Setting 'Profession' column as index\n",
            "---------------------------------------------\n",
            "            Age  Height  Weight\n",
            "Profession                     \n",
            "Student      22      66     140\n",
            "Teacher      42      70     148\n",
            "Engineer     30      62     125\n",
            "Doctor       35      68     160\n",
            "Nurse        25      62     152\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqeJJwFXnupm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "cdb6921c-fcc4-436d-edad-31a4ea5a39f5"
      },
      "source": [
        "# importing pandas as pd\n",
        "import pandas as pd\n",
        " \n",
        "# importing numpy as np\n",
        "import numpy as np\n",
        " \n",
        "# dictionary of lists\n",
        "dict = {'First Score':[100, 90, np.nan, 95],\n",
        "        'Second Score': [30, 45, 56, np.nan],\n",
        "        'Third Score':[np.nan, 40, 80, 98]}\n",
        " \n",
        "# creating a dataframe from list\n",
        "df = pd.DataFrame(dict)\n",
        " \n",
        "# using isnull() function  \n",
        "df.isnull()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>First Score</th>\n",
              "      <th>Second Score</th>\n",
              "      <th>Third Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   First Score  Second Score  Third Score\n",
              "0        False         False         True\n",
              "1        False         False        False\n",
              "2         True         False        False\n",
              "3        False          True        False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TueHYlH3nupo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0fc77b6a-90c3-4909-f510-a8bf973e5772"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "First Score     1\n",
              "Second Score    1\n",
              "Third Score     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "pJ_tMArfnupu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "1368471e-b575-4fc7-9e3c-2114800a716c"
      },
      "source": [
        "df.fillna(0)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>80.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>95.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>98.0</td>\n",
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              "   First Score  Second Score  Third Score\n",
              "0        100.0          30.0          0.0\n",
              "1         90.0          45.0         40.0\n",
              "2          0.0          56.0         80.0\n",
              "3         95.0           0.0         98.0"
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>3</th>\n",
              "      <td>95.0</td>\n",
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              "   First Score  Second Score  Third Score\n",
              "0        100.0          30.0          NaN\n",
              "1         90.0          45.0         40.0\n",
              "2          NaN          56.0         80.0\n",
              "3         95.0           NaN         98.0"
            ]
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          "execution_count": 125
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        "df.dropna()"
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      "execution_count": 126,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "   First Score  Second Score  Third Score\n",
              "1         90.0          45.0         40.0"
            ]
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          },
          "execution_count": 126
        }
      ]
    },
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        "df.columns"
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      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['First Score', 'Second Score', 'Third Score'], dtype='object')"
            ]
          },
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            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
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      "source": [
        "df.values"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[100.,  30.,  nan],\n",
              "       [ 90.,  45.,  40.],\n",
              "       [ nan,  56.,  80.],\n",
              "       [ 95.,  nan,  98.]])"
            ]
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            "tags": []
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          "execution_count": 128
        }
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        "outputId": "d27759ce-125e-480c-c11f-a4bdd27f7e0b"
      },
      "source": [
        "df['First Score'].unique()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([100.,  90.,  nan,  95.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
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      "metadata": {
        "id": "_TLo7l9PnuqT",
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          "height": 34
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        "outputId": "10ebffce-69ce-4698-e974-e2f0a1576490"
      },
      "source": [
        "df['First Score'].nunique()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
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          "height": 85
        },
        "outputId": "a68845a1-e28b-4064-b2a3-55977f9dbdc5"
      },
      "source": [
        "df['First Score'].value_counts()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.0     1\n",
              "90.0     1\n",
              "100.0    1\n",
              "Name: First Score, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
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        "colab": {}
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        ""
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}