Pandas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMRaxaQDnen1m3vPavSGEJT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning/blob/master/Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N9CHI-8fZJk_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  **Pandas(Beginer Level)**"
      ],
      "metadata": {
        "id": "8FcBowAEZN5e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1-**Installing Pandas**"
      ],
      "metadata": {
        "id": "r_gREz_oZeB6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conda install pandas"
      ],
      "metadata": {
        "id": "F9_SDT9dZnsY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas"
      ],
      "metadata": {
        "id": "McLSSQsSZrFY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2-Importing**"
      ],
      "metadata": {
        "id": "81yvE49DZ1Zj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "UtS8O87jaIww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3- Pandas Data Structures**\n"
      ],
      "metadata": {
        "id": "CdpSUCjmcb93"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.1- Series**"
      ],
      "metadata": {
        "id": "DMZqc4-TcmDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries = pd.Series(data , index)"
      ],
      "metadata": {
        "id": "Vpp4lxljcwSt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let’s build a series from Python List:**\n",
        "\n"
      ],
      "metadata": {
        "id": "70aC5mvmdFLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mylist = ['Ahmad','Machine Learning', 20, 'Pakistan']\n",
        "labels = ['Name', 'Career', 'Age', 'Country']\n",
        "newSeries = pd.Series(mylist,labels)\n",
        "print(newSeries)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wCCa-oHdQqs",
        "outputId": "7345c469-3bab-49b5-d6b7-cffef89d00f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name                  Ahmad\n",
            "Career     Machine Learning\n",
            "Age                      20\n",
            "Country            Pakistan\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is not necessary to add an index in a pandas Series. In that case, it will automatically start index from 0."
      ],
      "metadata": {
        "id": "SclW2Vx8durV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mylist = ['Ahmad','Machine Learning', 20, 'Pakistan']\n",
        "newSeries = pd.Series(mylist)\n",
        "print(newSeries)"
      ],
      "metadata": {
        "id": "5ZccTmPAdmZk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create a Series using a Python Dictionary**"
      ],
      "metadata": {
        "id": "V4I8ICSMd2YW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "myDict = {'Name': 'Ahmad',\n",
        "         'Career': 'Machine Learning',\n",
        "         'Age': 20,\n",
        "          'Country': 'Pakistan'}\n",
        "mySeries = pd.Series(myDict)\n",
        "print(mySeries)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7O8w0ped-9V",
        "outputId": "916d22c2-a503-456b-b3e5-85eb6e535ba2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name                  Ahmad\n",
            "Career     Machine Learning\n",
            "Age                      20\n",
            "Country            Pakistan\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accessing data from Series**\n"
      ],
      "metadata": {
        "id": "G4WWSNfUeHy0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seriesName['IndexName']"
      ],
      "metadata": {
        "id": "OPD613P-eNCL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(mySeries['Name'])\n",
        "print(mySeries['Age'])\n",
        "print(mySeries['Career'])"
      ],
      "metadata": {
        "id": "puzHiEo9eN-7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Basic Operations on Pandas Series**\n"
      ],
      "metadata": {
        "id": "pklm2nU4eZxc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries = pd.Series([10,20,30,40],index=['LONDON','NEWYORK','Washington','Manchester'])\n",
        "newSeries1 = pd.Series([10,20,35,46],index=['LONDON','NEWYORK','Istanbul','Karachi'])\n",
        "print(newSeries,newSeries1,sep='\\n\\n')"
      ],
      "metadata": {
        "id": "dgp8QWUIegEk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Basic Arithmetic operations include +-*/ operations. These are done over-index, so let’s perform them."
      ],
      "metadata": {
        "id": "BEp-hGbjetHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries + newSeries1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4JAuVsheo3s",
        "outputId": "0a554be5-100a-465c-9590-3d7282e2c2d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Istanbul       NaN\n",
              "Karachi        NaN\n",
              "LONDON        20.0\n",
              "Manchester     NaN\n",
              "NEWYORK       40.0\n",
              "Washington     NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can see that since London and NEWYORK index are present in both Series, so it has added the value of both and output of rest is NaN (Not a number)"
      ],
      "metadata": {
        "id": "2IhTAvBve2q7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries * newSeries1"
      ],
      "metadata": {
        "id": "6k0TtEQKgNjQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries / newSeries1"
      ],
      "metadata": {
        "id": "CNCwA3zDgR7q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Element Wise Operations/Broadcasting**\n"
      ],
      "metadata": {
        "id": "dnsF3wMUg0-t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries + 5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOp8JrauhAyD",
        "outputId": "0db5cf4c-b087-4e0a-8fc6-ab26ed2249e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LONDON        15\n",
              "NEWYORK       25\n",
              "Washington    35\n",
              "Manchester    45\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newSeries ** 1/2"
      ],
      "metadata": {
        "id": "FovpUsgihYkU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2-**DataFrame**"
      ],
      "metadata": {
        "id": "TycEViJnjw4e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Main ways to create Data Frame are**\n",
        "\n"
      ],
      "metadata": {
        "id": "6YelgeNXkBQN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Python Dictionary\n"
      ],
      "metadata": {
        "id": "tD1TZFnlkHgd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = {\"Name\":[\"Ahmad\",\"Ali\",'Ismail',\"John\"],\"Age\":  [20,21,19,17],\"Height\":[5.1,5.6,6.1,5.7]}"
      ],
      "metadata": {
        "id": "SzsE9gBGkMUd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame(df1)\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "tZf8qB4XkRGv",
        "outputId": "4e75a2e7-48ea-40da-e45f-b5da2fcb3996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Name  Age  Height\n",
              "0   Ahmad   20     5.1\n",
              "1     Ali   21     5.6\n",
              "2  Ismail   19     6.1\n",
              "3    John   17     5.7"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3a14962-945b-4e8b-a108-8f10545bd5dc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ahmad</td>\n",
              "      <td>20</td>\n",
              "      <td>5.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ali</td>\n",
              "      <td>21</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ismail</td>\n",
              "      <td>19</td>\n",
              "      <td>6.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>John</td>\n",
              "      <td>17</td>\n",
              "      <td>5.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3a14962-945b-4e8b-a108-8f10545bd5dc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b3a14962-945b-4e8b-a108-8f10545bd5dc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b3a14962-945b-4e8b-a108-8f10545bd5dc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting Values from a Column\n"
      ],
      "metadata": {
        "id": "azcIV9BEkZ8u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1['Age']\n",
        "#df1.columnname"
      ],
      "metadata": {
        "id": "cMMn1jy_kf7G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both of these syntaxes are correct, but we have to be careful about choosing one. If our column name has space in it, then definitely we can not use the 2nd method. We have to use the first method. We can only use the 2nd method when there is no space in the column name."
      ],
      "metadata": {
        "id": "qeVU1pgEkzYH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.Name"
      ],
      "metadata": {
        "id": "PhiE1PeUk2ZH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Values of Multiple Columns\n"
      ],
      "metadata": {
        "id": "_VPNy_vyk75U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1[[\"Name\",\"Age\"]]"
      ],
      "metadata": {
        "id": "tqI7IZ1Sk-3M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4-Important functions of Dataframe in Pandas**\n"
      ],
      "metadata": {
        "id": "pjwf1Np_lY5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "df=sns.load_dataset('titanic')\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "8bkzYEYSkTJx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.1-Data Importing**"
      ],
      "metadata": {
        "id": "SXH7aaZ1UQUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.read_fileExtensionName(\"File Path\")"
      ],
      "metadata": {
        "id": "q5pVc4cdUktx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**pd.read_csv()**\n"
      ],
      "metadata": {
        "id": "lo43FhAdUo9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file into a DataFrame\n",
        "df = pd.read_csv('data.csv')\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "id": "vB_-Ifv0UrDw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**pd.read_table()**"
      ],
      "metadata": {
        "id": "OpA5qfli0_w-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the delimited file into a DataFrame\n",
        "df = pd.read_table('data.txt', delimiter='\\t')\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "id": "SuAGxod41nW2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**pd.read_excel()**\n"
      ],
      "metadata": {
        "id": "gAyD8MLJ3TuB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the Excel file into a DataFrame\n",
        "df = pd.read_excel('data.xlsx')\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "id": "IUDBynUK4IcC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**pd.readl_sql()**\n"
      ],
      "metadata": {
        "id": "QrsrbtXv9Nvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sqlite3\n",
        "\n",
        "# Create a connection to the database\n",
        "conn = sqlite3.connect('my_database.db')\n",
        "\n",
        "# Read the SQL query into a DataFrame\n",
        "df = pd.read_sql('SELECT * FROM users', conn)\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "id": "awdvq66-9m_T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**pd.read_json()**\n"
      ],
      "metadata": {
        "id": "UWmzZAs3_lbv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the JSON file into a DataFrame\n",
        "df = pd.read_json('data.json')\n",
        "\n",
        "# Print the DataFrame\n",
        "print(df)"
      ],
      "metadata": {
        "id": "6k__j-CP_zZj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.2- Data Statistics**"
      ],
      "metadata": {
        "id": "eLX-EUxMVo6P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**head()**\n"
      ],
      "metadata": {
        "id": "n7TS_Rr5WQy3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "JLU_xWHQWVfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**tail()**\n"
      ],
      "metadata": {
        "id": "HWIabHcgbP2I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ErNChFSsbT2a",
        "outputId": "4897aa3c-baad-4fdd-be68-2ee190ab4498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
              "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
              "887         1       1  female  19.0      0      0  30.00        S   First   \n",
              "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
              "889         1       1    male  26.0      0      0  30.00        C   First   \n",
              "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
              "\n",
              "       who  adult_male deck  embark_town alive  alone  \n",
              "886    man        True  NaN  Southampton    no   True  \n",
              "887  woman       False    B  Southampton   yes   True  \n",
              "888  woman       False  NaN  Southampton    no  False  \n",
              "889    man        True    C    Cherbourg   yes   True  \n",
              "890    man        True  NaN   Queenstown    no   True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ecc7575d-9e9f-42f6-826e-7c20920677f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.00</td>\n",
              "      <td>S</td>\n",
              "      <td>Second</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.00</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>B</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.45</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.75</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecc7575d-9e9f-42f6-826e-7c20920677f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecc7575d-9e9f-42f6-826e-7c20920677f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ecc7575d-9e9f-42f6-826e-7c20920677f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**shape()**\n"
      ],
      "metadata": {
        "id": "1vLm1i-pdMEG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape()"
      ],
      "metadata": {
        "id": "sNhGl5y2dQGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**isnull()**\n"
      ],
      "metadata": {
        "id": "r2KbAVZxlLSY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().head()"
      ],
      "metadata": {
        "id": "__1ktE5VlFzx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**sum()**\n"
      ],
      "metadata": {
        "id": "FqwDwOKtlwsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "stzmARX_lsu2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**info()**\n"
      ],
      "metadata": {
        "id": "cl-5gpFNrBF0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "tlj4AAarrF77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**describe()**\n"
      ],
      "metadata": {
        "id": "42YPI_QKrL2C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "Mm78GzBFrNoS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**mean()**"
      ],
      "metadata": {
        "id": "NG7kxaegvdI3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.mean()"
      ],
      "metadata": {
        "id": "qPXWhAh3vh9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**median()**"
      ],
      "metadata": {
        "id": "cu-7f6co1tic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.median()"
      ],
      "metadata": {
        "id": "W-n_sKqa1rCM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**std()**"
      ],
      "metadata": {
        "id": "iNy_ME3A5Hb0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.std()"
      ],
      "metadata": {
        "id": "oSak7V-V19KU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Count()**"
      ],
      "metadata": {
        "id": "6VbxfpSBC1wK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.count()"
      ],
      "metadata": {
        "id": "kuJ3L42b5BlL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Max()**"
      ],
      "metadata": {
        "id": "yKY1_wBSYMGb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.max()"
      ],
      "metadata": {
        "id": "4ZQ6a7VcYILT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Min()**"
      ],
      "metadata": {
        "id": "7kQtDE4Lkcfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.min()"
      ],
      "metadata": {
        "id": "vL-0CwhLkW4i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4.3- Data Cleaning**"
      ],
      "metadata": {
        "id": "-jGhob5blC_T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####  **4.3.1-Handling Duplicated Value**"
      ],
      "metadata": {
        "id": "PXG1-EzofnR-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check if the data has any duplicate values using duplicated(). The sum() gives you the number of duplicate rows in the data frame"
      ],
      "metadata": {
        "id": "dYLWFTwdgLkP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()\n"
      ],
      "metadata": {
        "id": "NWUIHzKOf-_f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to see the duplicate rows, we can store the duplicated values inside a data frame."
      ],
      "metadata": {
        "id": "RKv3NX2FgQW4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows = df[df.duplicated()]\n",
        "duplicate_rows"
      ],
      "metadata": {
        "id": "XlyXvu5ggRyf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Duplicate values can be removed using drop_duplicates()"
      ],
      "metadata": {
        "id": "Uh-dxfWuinLW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.drop_duplicates(ignore_index=True)\n",
        "# ignore_index=True so that the index is relabeled"
      ],
      "metadata": {
        "id": "xhYJ_NmAipc-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4.3.2- Handling Null Value**"
      ],
      "metadata": {
        "id": "oNkwiuv2fViP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**isnull().**"
      ],
      "metadata": {
        "id": "-yjiWeQClx-O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to find the missing values in a dataset we can use isnull()."
      ],
      "metadata": {
        "id": "ZSE1vHGPmCaV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sort_values(ascending=False)\n",
        "# isnull() - To find the null values\n",
        "# sum() - To find the sum of null values for each attribute\n",
        "# sort_values() - To sort the values"
      ],
      "metadata": {
        "id": "NKofn8SOl149"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can find the percentage of values missing using the below code"
      ],
      "metadata": {
        "id": "HMsGFQBrmHAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(df.isnull().sum().sum()/df.size)*100\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJbocmjhmIX1",
        "outputId": "d190f152-6db2-4c87-cef3-3a265d47deb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0074822297044519264"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**dp.dropna()**"
      ],
      "metadata": {
        "id": "fjHxKmGtljoZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code drops rows that contain any missing values:\n",
        "\n"
      ],
      "metadata": {
        "id": "vg-yn3Fklx7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n"
      ],
      "metadata": {
        "id": "sVmUKwKJllzQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code drops columns that contain all missing values:\n",
        "\n"
      ],
      "metadata": {
        "id": "wmz0fZURl5pX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna(axis=1, how='all')\n"
      ],
      "metadata": {
        "id": "74DEr6ikl14A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=1)"
      ],
      "metadata": {
        "id": "VkcEYW9edRBB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**fillna()**"
      ],
      "metadata": {
        "id": "EVEmyy1XtZ6B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code fills missing values with the value 0:\n",
        "\n"
      ],
      "metadata": {
        "id": "aeVe7xmUqoJM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.fillna(0)"
      ],
      "metadata": {
        "id": "6qAgRMCoqZFE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code fills missing values with the previous valid value:\n",
        "\n"
      ],
      "metadata": {
        "id": "xyfDpEa1qwGr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.fillna(method='ffill')\n"
      ],
      "metadata": {
        "id": "HM5ERc6Nqzw7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**bfill()**"
      ],
      "metadata": {
        "id": "Gt8Mmn4Wd0JY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.bfill()"
      ],
      "metadata": {
        "id": "_qL3adftd2-g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ffill method**"
      ],
      "metadata": {
        "id": "y-oWa5BBeUgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.ffill()\n"
      ],
      "metadata": {
        "id": "Id68WJCgeZjn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4.3.3- Data Sorting**"
      ],
      "metadata": {
        "id": "KNgEuz_ziGF_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**df.sort_values()**"
      ],
      "metadata": {
        "id": "LW-f-NiztgGK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code sorts the DataFrame by the A column in ascending order:\n",
        "\n"
      ],
      "metadata": {
        "id": "VLQizPbItotx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "lz07JANjtu6a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " df = df.sort_values('sex')\n"
      ],
      "metadata": {
        "id": "BlyyYG2rtXxZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "s3-Z6v-kt1pr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following code sorts the DataFrame by the B column in descending order:\n",
        "\n"
      ],
      "metadata": {
        "id": "wPrAwd2Kt9xb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sort_values('sex', ascending=False)\n"
      ],
      "metadata": {
        "id": "InxAZMpHt875"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4.3.4Data Grouping**"
      ],
      "metadata": {
        "id": "aQRPRSDBiNKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**df.groupby()**"
      ],
      "metadata": {
        "id": "2Ti3-wmF71Nl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "Fl4lZ-6Q81QT",
        "outputId": "4aca5efd-d92e-4898-e6d0-d76a0a2e490d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
              "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
              "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
              "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
              "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
              "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
              "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
              "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
              "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
              "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
              "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
              "\n",
              "       who  adult_male deck  embark_town alive  alone  \n",
              "0      man        True  NaN  Southampton    no  False  \n",
              "1    woman       False    C    Cherbourg   yes  False  \n",
              "2    woman       False  NaN  Southampton   yes   True  \n",
              "3    woman       False    C  Southampton   yes  False  \n",
              "4      man        True  NaN  Southampton    no   True  \n",
              "..     ...         ...  ...          ...   ...    ...  \n",
              "886    man        True  NaN  Southampton    no   True  \n",
              "887  woman       False    B  Southampton   yes   True  \n",
              "888  woman       False  NaN  Southampton    no  False  \n",
              "889    man        True    C    Cherbourg   yes   True  \n",
              "890    man        True  NaN   Queenstown    no   True  \n",
              "\n",
              "[891 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91ff3265-bf03-4e99-8864-95f236f0023f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Second</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>B</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91ff3265-bf03-4e99-8864-95f236f0023f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91ff3265-bf03-4e99-8864-95f236f0023f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91ff3265-bf03-4e99-8864-95f236f0023f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying groupby() function to\n",
        "# group the data on sex value.\n",
        "gk = df.groupby('sex')"
      ],
      "metadata": {
        "id": "ZkYVgURz8zbk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's print the first entries\n",
        "# in all the groups formed.\n",
        "gk.first()"
      ],
      "metadata": {
        "id": "AWxs03Gj9AgZ",
        "outputId": "ca1879cd-c0f0-4e16-8620-1cc9ff838d93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        survived  pclass   age  sibsp  parch     fare embarked  class    who  \\\n",
              "sex                                                                            \n",
              "female         1       1  38.0      1      0  71.2833        C  First  woman   \n",
              "male           0       3  22.0      1      0   7.2500        S  Third    man   \n",
              "\n",
              "        adult_male deck  embark_town alive  alone  \n",
              "sex                                                \n",
              "female       False    C    Cherbourg   yes  False  \n",
              "male          True    E  Southampton    no  False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1807742b-22db-4175-8a0f-662308d49173\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>E</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1807742b-22db-4175-8a0f-662308d49173')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1807742b-22db-4175-8a0f-662308d49173 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1807742b-22db-4175-8a0f-662308d49173');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**df.apply()**"
      ],
      "metadata": {
        "id": "CLIw_M5gAekg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'Name':['John Doe','Jane Doe','John Smith'], 'Age':[30,25,40]})\n",
        "\n",
        "# Remove any rows where the name is missing\n",
        "df = df.apply(lambda row: row if row['Name'] else None, axis=1)\n",
        "\n",
        "# Drop any rows that are now empty\n",
        "df = df.dropna()\n",
        "\n",
        "print(df)\n"
      ],
      "metadata": {
        "id": "E7VAdxTS_bfv",
        "outputId": "25d9d0bc-7fe6-4b6e-fd79-6196bdbd166a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Name  Age\n",
            "0    John Doe   30\n",
            "1    Jane Doe   25\n",
            "2  John Smith   40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **4.3.4- Inconsistent Data Entry**"
      ],
      "metadata": {
        "id": "teu9rMFKmhCm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "3C_nyEj5CA_2",
        "outputId": "bd03c27b-925f-42c8-ef61-721ed74e0eca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False    C  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True    C  Southampton    no   True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-06fa17df-8a49-4284-9a11-77c72cbc6d20\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06fa17df-8a49-4284-9a11-77c72cbc6d20')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06fa17df-8a49-4284-9a11-77c72cbc6d20 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-06fa17df-8a49-4284-9a11-77c72cbc6d20');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['who'].unique()\n"
      ],
      "metadata": {
        "id": "eAirVg3QB4EG",
        "outputId": "fa5ac9eb-fcc9-4dca-8ba4-8d73f3361de2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['man', 'woman', 'child'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After using the unique function, we understood that there is a Graduate and graduate present in the Education column; hence it is necessary to normalize the entire column in either lowercase or uppercase."
      ],
      "metadata": {
        "id": "39u7UUZbCbCe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['who'] = df['who'].str.lower()\n",
        "df['who'].unique()"
      ],
      "metadata": {
        "id": "vbEfnp0hCNVu",
        "outputId": "e07ee1e4-9f33-4d02-8a8f-8170fc38b64d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['man', 'woman', 'child'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}