Pandas_Operations.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "N9-d5sih3cma"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings (\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"students.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w7dKO2Z030Bj",
        "outputId": "8a90d4a4-fa1f-49a3-d416-3c2ff7ea25ca"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank    Name  Age Gender Background  Python  Machine_learning\n",
              "0     1    sara   25      F         IT      45                48\n",
              "1     2    lara   26      F         IT      45                47\n",
              "2     3   kiara   28      F     Non-IT      35                36\n",
              "3     4   rahul   32      M     Non-IT      42                41\n",
              "4     5  sachin   30      M     Non-IT      20                38"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bca0ad6-ac29-4c0e-bf09-39c30840ae51\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bca0ad6-ac29-4c0e-bf09-39c30840ae51')\"\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-5bca0ad6-ac29-4c0e-bf09-39c30840ae51 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-5bca0ad6-ac29-4c0e-bf09-39c30840ae51');\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": "markdown",
      "source": [
        "**Column Selection in Pandas**\n",
        "\n",
        "1.Dot Notation (Not Recommended) \n",
        "\n",
        "2.Bracket Notation"
      ],
      "metadata": {
        "id": "ecK3IsNKlr94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Python #Column name is case sensative"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVPH2BE_lmuP",
        "outputId": "fb2aa5ed-f1d8-4b23-ed64-9a14e99ce819"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    45\n",
              "1    45\n",
              "2    35\n",
              "3    42\n",
              "4    20\n",
              "5    40\n",
              "6    25\n",
              "7    28\n",
              "8    36\n",
              "9    22\n",
              "Name: Python, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Python\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jzezpID_mHr0",
        "outputId": "bb27f140-c65d-4ed2-c8d0-8322bf81ce1a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    45\n",
              "1    45\n",
              "2    35\n",
              "3    42\n",
              "4    20\n",
              "5    40\n",
              "6    25\n",
              "7    28\n",
              "8    36\n",
              "9    22\n",
              "Name: Python, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"Name\",\"Age\",\"Gender\"]]\n",
        "# in dot notation you can not select multiple columns hence not recommended"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "vvcOn4bqmXs6",
        "outputId": "66bee0a4-308b-46b6-adda-29cdf99d5a48"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Name  Age Gender\n",
              "0       sara   25      F\n",
              "1       lara   26      F\n",
              "2      kiara   28      F\n",
              "3      rahul   32      M\n",
              "4     sachin   30      M\n",
              "5     sarika   33      F\n",
              "6  dhanashri   35      F\n",
              "7      naush   34      M\n",
              "8     aditya   30      M\n",
              "9    nishant   28      M"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d25d3c4e-a5a1-4e33-911b-f23b59d53b48\">\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>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d25d3c4e-a5a1-4e33-911b-f23b59d53b48')\"\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-d25d3c4e-a5a1-4e33-911b-f23b59d53b48 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-d25d3c4e-a5a1-4e33-911b-f23b59d53b48');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating or Adding new column in pandas**\n",
        "\n",
        "dataframes are nothing but dictionaries. So adding values to it is similar to that of adding values in dictionaries.\n",
        "\n",
        "i.e. using key & value "
      ],
      "metadata": {
        "id": "GuCxHN8vmxiq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cities=[\"mumbai\",\"pune\",\"sangli\",\"nagpur\",\"nashik\",\"banglore\",\"chennai\",\"surat\",\"delhi\",\"thane\"]\n",
        "df[\"city\"]=cities\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DAWzdSWumkts",
        "outputId": "03c02ac2-f8b3-456c-c494-1604c20aedb0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank    Name  Age Gender Background  Python  Machine_learning    city\n",
              "0     1    sara   25      F         IT      45                48  mumbai\n",
              "1     2    lara   26      F         IT      45                47    pune\n",
              "2     3   kiara   28      F     Non-IT      35                36  sangli\n",
              "3     4   rahul   32      M     Non-IT      42                41  nagpur\n",
              "4     5  sachin   30      M     Non-IT      20                38  nashik"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0daf71ea-3e3a-49b3-8f6d-949c6b0f79eb\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0daf71ea-3e3a-49b3-8f6d-949c6b0f79eb')\"\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-0daf71ea-3e3a-49b3-8f6d-949c6b0f79eb 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-0daf71ea-3e3a-49b3-8f6d-949c6b0f79eb');\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": [
        "**Vectorization in Pandas**\n",
        "\n",
        "Here we will calculate the total marks by adding marks in python and machine learning also we will add this total column to our dataframe."
      ],
      "metadata": {
        "id": "ttfU4MDantOT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total=df[\"Python\"]+df[\"Machine_learning\"]\n",
        "df[\"Total\"]=total \n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "tptFa3TonkW5",
        "outputId": "bcaa8fe6-f858-4481-dd4b-ec5eaa7e61ab"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning      city  \\\n",
              "0     1       sara   25      F         IT      45                48    mumbai   \n",
              "1     2       lara   26      F         IT      45                47      pune   \n",
              "2     3      kiara   28      F     Non-IT      35                36    sangli   \n",
              "3     4      rahul   32      M     Non-IT      42                41    nagpur   \n",
              "4     5     sachin   30      M     Non-IT      20                38    nashik   \n",
              "5     6     sarika   33      F     Non-IT      40                25  banglore   \n",
              "6     7  dhanashri   35      F         IT      25                24   chennai   \n",
              "7     8      naush   34      M         IT      28                34     surat   \n",
              "8     9     aditya   30      M     Non-IT      36                34     delhi   \n",
              "9    10    nishant   28      M         IT      22                27     thane   \n",
              "\n",
              "   Total  \n",
              "0     93  \n",
              "1     92  \n",
              "2     71  \n",
              "3     83  \n",
              "4     58  \n",
              "5     65  \n",
              "6     49  \n",
              "7     62  \n",
              "8     70  \n",
              "9     49  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d3db7c9b-022c-43c7-86c4-a70f3d26cb2f\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>banglore</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>chennai</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>surat</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>delhi</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>thane</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d3db7c9b-022c-43c7-86c4-a70f3d26cb2f')\"\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-d3db7c9b-022c-43c7-86c4-a70f3d26cb2f 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-d3db7c9b-022c-43c7-86c4-a70f3d26cb2f');\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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Use of Apply method**\n",
        "\n",
        "Here we allot grades based on the total marks\n",
        "\n",
        "75% ==> A\n",
        "\n",
        "60% -75% ==> B\n",
        "\n",
        "35% -60% ==> C\n",
        "\n",
        "below that fail"
      ],
      "metadata": {
        "id": "zI7uUTQiohaV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def creategrade(marks):\n",
        "  p=(marks/100)*100\n",
        "  if (p>=75):\n",
        "   return \"A\"\n",
        "  elif (p>=60 and p<75):\n",
        "   return \"B\"\n",
        "  elif (p>=35 and p<60):\n",
        "   return \"C\"\n",
        "  else:\n",
        "   return \"Fail\""
      ],
      "metadata": {
        "id": "O-7Gn0cEoHe9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we have created the function and then we will apply this function to total column to generate the grades."
      ],
      "metadata": {
        "id": "e_Y1FmN1tSuo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "creategrade(80)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "O5tOGeziqyUs",
        "outputId": "6f7cb7dc-8e2e-44ca-a146-f0fe63761a00"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'A'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "creategrade(55)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "G3CP3JDoq-f4",
        "outputId": "40ab330f-2fa1-4eae-a2a7-e09b393809b2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'C'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Total\"].apply(creategrade)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bbvsf9MlrEK8",
        "outputId": "3c55b86a-daed-4f2d-8ef9-10e3778c576e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    A\n",
              "1    A\n",
              "2    B\n",
              "3    A\n",
              "4    C\n",
              "5    B\n",
              "6    C\n",
              "7    B\n",
              "8    B\n",
              "9    C\n",
              "Name: Total, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Grade\"]=df[\"Total\"].apply(creategrade)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "TB9bin5otO4q",
        "outputId": "32706e6c-7ead-48f5-e683-69439bd71922"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning      city  \\\n",
              "0     1       sara   25      F         IT      45                48    mumbai   \n",
              "1     2       lara   26      F         IT      45                47      pune   \n",
              "2     3      kiara   28      F     Non-IT      35                36    sangli   \n",
              "3     4      rahul   32      M     Non-IT      42                41    nagpur   \n",
              "4     5     sachin   30      M     Non-IT      20                38    nashik   \n",
              "5     6     sarika   33      F     Non-IT      40                25  banglore   \n",
              "6     7  dhanashri   35      F         IT      25                24   chennai   \n",
              "7     8      naush   34      M         IT      28                34     surat   \n",
              "8     9     aditya   30      M     Non-IT      36                34     delhi   \n",
              "9    10    nishant   28      M         IT      22                27     thane   \n",
              "\n",
              "   Total Grade  \n",
              "0     93     A  \n",
              "1     92     A  \n",
              "2     71     B  \n",
              "3     83     A  \n",
              "4     58     C  \n",
              "5     65     B  \n",
              "6     49     C  \n",
              "7     62     B  \n",
              "8     70     B  \n",
              "9     49     C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32ce93b5-37fb-486a-943c-6268d70ec99a\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "      <th>Total</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>93</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "      <td>71</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "      <td>83</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "      <td>58</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>banglore</td>\n",
              "      <td>65</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>chennai</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>surat</td>\n",
              "      <td>62</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>delhi</td>\n",
              "      <td>70</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>thane</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32ce93b5-37fb-486a-943c-6268d70ec99a')\"\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-32ce93b5-37fb-486a-943c-6268d70ec99a 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-32ce93b5-37fb-486a-943c-6268d70ec99a');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Value_counts method in pandas**\n",
        "\n",
        "Frequency of occurance is checked by using value_counts method.\n",
        "\n",
        "This method is used for categorical columns only."
      ],
      "metadata": {
        "id": "l3TemJocuhw-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Gender\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f6ByLBKt7xV",
        "outputId": "ba568ab0-f71a-4e63-cd34-1b8a567ca018"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F    5\n",
              "M    5\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Background\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaS0i3otu6HJ",
        "outputId": "29c4abf0-21ab-4f53-c009-3edd1604ee0d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IT        5\n",
              "Non-IT    5\n",
              "Name: Background, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Grade\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oKq6dD2vA-Q",
        "outputId": "f3fb0506-8c26-428d-dc0e-a8712c84a8a9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    4\n",
              "A    3\n",
              "C    3\n",
              "Name: Grade, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can have graphical representation using value_counts."
      ],
      "metadata": {
        "id": "GfLcL1CovTh-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Gender\"].value_counts().plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ttwsVDArvGsn",
        "outputId": "bd33c0f0-6e99-4a36-ccfd-1c609c54ae98"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By default it will have line graph"
      ],
      "metadata": {
        "id": "7mkK8NHcxWRy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Gender\"].value_counts().plot(kind=\"bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "6JaI9EIovmXn",
        "outputId": "bc5b6b34-fff1-48bc-96ae-625e3019fcec"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df[\"Gender\"].value_counts().plot(kind=\"bar\")\n",
        "plt.grid(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "0qYJOzwcxkNN",
        "outputId": "0fe32829-9fd7-4b50-c1e7-9bdda25bc2b3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGbCAYAAACYgdlIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAATt0lEQVR4nO3dX4xcdfn48WemXQYXdoplKdB0ixijBEgBqZomxlREsCqKNxoqoSnGC6wmpjGaXiC7QVPyTb4EjU0latN4sZRoqAZjKUjSNo02tiUaINGIolZpqQthp3/iMO7M74K4v+53u+3O9tmZzs7rlTSbOT2z55ns+bTvnJmdKTQajUYAACQotnsAAGDuEBYAQBphAQCkERYAQBphAQCkERYAQBphAQCkERYAQJr5rT5gvV6PV155Jfr6+qJQKLT68ADADDQajTh27FgsXrw4isWpr0u0PCxeeeWVGBgYaPVhAYAEhw4diiVLlkz59y0Pi76+voh4a7Byudzqw9NitVotnn766bjtttuip6en3eMAiazv7lKpVGJgYGD8//GptDws/vv0R7lcFhZdoFarRW9vb5TLZf/wwBxjfXens72MwYs3AYA0wgIASCMsAIA0wgIASCMsAIA0wgIASCMsAIA0wgIASCMsAIA0wgIASNNUWAwODkahUJjw55prrpmt2QCADtP0Z4Vcd9118atf/er/f4P5Lf+4EQDgPNV0FcyfPz+uuOKK2ZgFAOhwTYfFn/70p1i8eHFceOGFsWLFiti4cWMsXbp0yv2r1WpUq9Xx25VKJSLe+lS8Wq02g5HpJP/9GftZw9xjfXeX6f6cC41GozHdb7pjx444fvx4vOc974nDhw/H0NBQ/POf/4wXXnhhys9nHxwcjKGhoUnbh4eHo7e3d7qHBgDa6OTJk7F69eoYHR2Ncrk85X5NhcX/9cYbb8RVV10VDz/8cHzhC1847T6nu2IxMDAQIyMjZxxsLrp+cGe7R2i5UrERDy6vx/0HilGtF9o9Tku9MHh7u0eghaxv63uuq1Qq0d/ff9awOKdXXl5yySXx7ne/O1566aUp9ymVSlEqlSZt7+npiZ6ennM5fMepjnXXwjtVtV7ousffbed3t+u28/tU1nd3mO5jPqf3sTh+/Hj8+c9/jiuvvPJcvg0AMEc0FRZf+9rXYvfu3fHXv/41fv3rX8dnPvOZmDdvXtx1112zNR8A0EGaeirkH//4R9x1113x2muvxWWXXRYf/OAHY9++fXHZZZfN1nwAQAdpKiy2bds2W3MAAHOAzwoBANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgzTmFxUMPPRSFQiG++tWvJo0DAHSyGYfF/v3749FHH41ly5ZlzgMAdLAZhcXx48fj85//fPzgBz+It7/97dkzAQAdav5M7rRu3br4xCc+Ebfeemt861vfOuO+1Wo1qtXq+O1KpRIREbVaLWq12kwO37FK8xrtHqHlSsXGhK/dpNvO725nfXeXblzf033MTYfFtm3b4rnnnov9+/dPa/+NGzfG0NDQpO1PP/109Pb2Nnv4jvY/72/3BO3z4PJ6u0douV/+8pftHoEWsr67Szeu75MnT05rv0Kj0Zh2ah46dCiWL18ezzzzzPhrK1auXBk33nhjPPLII6e9z+muWAwMDMTIyEiUy+XpHnpOuH5wZ7tHaLlSsREPLq/H/QeKUa0X2j1OS70weHu7R6CFrG/re66rVCrR398fo6OjZ/z/u6krFgcPHoyjR4/Ge9/73vFtY2NjsWfPnvje974X1Wo15s2bN+E+pVIpSqXSpO/V09MTPT09zRy+41XHumvhnapaL3Td4++287vbddv5fSrruztM9zE3FRYf+chH4vnnn5+wbe3atXHNNdfEN77xjUlRAQB0l6bCoq+vL66//voJ2y666KK49NJLJ20HALqPd94EANLM6NdNT7Vr166EMQCAucAVCwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgjbAAANIICwAgTVNhsXnz5li2bFmUy+Uol8uxYsWK2LFjx2zNBgB0mKbCYsmSJfHQQw/FwYMH48CBA3HLLbfEpz/96XjxxRdnaz4AoIPMb2bnO+64Y8Ltb3/727F58+bYt29fXHfddamDAQCdp6mwONXY2Fj85Cc/iRMnTsSKFSum3K9arUa1Wh2/XalUIiKiVqtFrVab6eE7Umleo90jtFyp2JjwtZt02/nd7azv7tKN63u6j7nQaDSaOiOef/75WLFiRfz73/+Oiy++OIaHh+PjH//4lPsPDg7G0NDQpO3Dw8PR29vbzKEBgDY5efJkrF69OkZHR6NcLk+5X9Nh8eabb8bf//73GB0djZ/+9Kfxwx/+MHbv3h3XXnvtafc/3RWLgYGBGBkZOeNgc9H1gzvbPULLlYqNeHB5Pe4/UIxqvdDucVrqhcHb2z0CLWR9W99zXaVSif7+/rOGRdNPhVxwwQXxrne9KyIibr755ti/f3985zvfiUcfffS0+5dKpSiVSpO29/T0RE9PT7OH72jVse5aeKeq1gtd9/i77fzudt12fp/K+u4O033M5/w+FvV6fcIVCQCgezV1xWLDhg2xatWqWLp0aRw7diyGh4dj165dsXNn910CBAAmayosjh49Gvfcc08cPnw4FixYEMuWLYudO3fGRz/60dmaDwDoIE2FxY9+9KPZmgMAmAN8VggAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkEZYAABphAUAkKapsNi4cWO8733vi76+vli0aFHceeed8cc//nG2ZgMAOkxTYbF79+5Yt25d7Nu3L5555pmo1Wpx2223xYkTJ2ZrPgCgg8xvZuennnpqwu2tW7fGokWL4uDBg/GhD30odTAAoPM0FRb/1+joaERELFy4cMp9qtVqVKvV8duVSiUiImq1WtRqtXM5fMcpzWu0e4SWKxUbE752k247v7ud9d1dunF9T/cxFxqNxozOiHq9Hp/61KfijTfeiL1790653+DgYAwNDU3aPjw8HL29vTM5NADQYidPnozVq1fH6OholMvlKfebcVjcd999sWPHjti7d28sWbJkyv1Od8ViYGAgRkZGzjjYXHT94M52j9BypWIjHlxej/sPFKNaL7R7nJZ6YfD2do9AC1nf1vdcV6lUor+//6xhMaOnQr785S/HL37xi9izZ88ZoyIiolQqRalUmrS9p6cnenp6ZnL4jlUd666Fd6pqvdB1j7/bzu9u123n96ms7+4w3cfcVFg0Go34yle+Etu3b49du3bF1VdfPaPhAIC5qamwWLduXQwPD8fPf/7z6OvriyNHjkRExIIFC+Jtb3vbrAwIAHSOpt7HYvPmzTE6OhorV66MK6+8cvzP448/PlvzAQAdpOmnQgAApuKzQgCANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEjTdFjs2bMn7rjjjli8eHEUCoX42c9+NgtjAQCdqOmwOHHiRNxwww2xadOm2ZgHAOhg85u9w6pVq2LVqlWzMQsA0OGaDotmVavVqFar47crlUpERNRqtajVarN9+PNKaV6j3SO0XKnYmPC1m3Tb+d3trO/u0o3re7qPudBoNGZ8RhQKhdi+fXvceeedU+4zODgYQ0NDk7YPDw9Hb2/vTA8NALTQyZMnY/Xq1TE6OhrlcnnK/WY9LE53xWJgYCBGRkbOONhcdP3gznaP0HKlYiMeXF6P+w8Uo1ovtHuclnph8PZ2j0ALWd/W91xXqVSiv7//rGEx60+FlEqlKJVKk7b39PRET0/PbB/+vFId666Fd6pqvdB1j7/bzu9u123n96ms7+4w3cfsfSwAgDRNX7E4fvx4vPTSS+O3X3755fjd734XCxcujKVLl6YOBwB0lqbD4sCBA/HhD394/Pb69esjImLNmjWxdevWtMEAgM7TdFisXLkyzuH1ngDAHOY1FgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAGmEBAKQRFgBAmhmFxaZNm+Id73hHXHjhhfGBD3wgfvvb32bPBQB0oKbD4vHHH4/169fHAw88EM8991zccMMNcfvtt8fRo0dnYz4AoIM0HRYPP/xwfPGLX4y1a9fGtddeG9///vejt7c3tmzZMhvzAQAdZH4zO7/55ptx8ODB2LBhw/i2YrEYt956a/zmN7857X2q1WpUq9Xx26OjoxER8frrr0etVpvJzB1r/n9OtHuElptfb8TJk/WYXyvGWL3Q7nFa6rXXXmv3CLSQ9W19z3XHjh2LiIhGo3HG/ZoKi5GRkRgbG4vLL798wvbLL788/vCHP5z2Phs3boyhoaFJ26+++upmDk0HW93uAdqk/3/bPQHMPuu7+xw7diwWLFgw5d83FRYzsWHDhli/fv347Xq9Hq+//npceumlUSh0V+F2o0qlEgMDA3Ho0KEol8vtHgdIZH13l0ajEceOHYvFixefcb+mwqK/vz/mzZsXr7766oTtr776alxxxRWnvU+pVIpSqTRh2yWXXNLMYZkDyuWyf3hgjrK+u8eZrlT8V1Mv3rzgggvi5ptvjmeffXZ8W71ej2effTZWrFjR/IQAwJzS9FMh69evjzVr1sTy5cvj/e9/fzzyyCNx4sSJWLt27WzMBwB0kKbD4nOf+1z861//im9+85tx5MiRuPHGG+Opp56a9IJOiHjrqbAHHnhg0tNhQOezvjmdQuNsvzcCADBNPisEAEgjLACANMICAEgjLACANMICAEgjLEjzl7/85awfTgPA3ObXTUkzb968OHz4cCxatCgi3nrPk+9+97ve4wTmgHvvvXda+23ZsmWWJ+F8JyxIUywW48iRI+Nh0dfXF7///e/jne98Z5snA85VsViMq666Km666aYzXpncvn17C6fifDTrn24KQOe777774rHHHouXX3451q5dG3fffXcsXLiw3WNxHvIaC9IUCoUoFAqTtgGdb9OmTXH48OH4+te/Hk8++WQMDAzEZz/72di5c6fXVjGBp0JIUywWY9WqVeOfG/Dkk0/GLbfcEhdddNGE/Z544ol2jAck+tvf/hZbt26NH//4x/Gf//wnXnzxxbj44ovbPRbnAU+FkGbNmjUTbt99991tmgSYbcViMQqFQjQajRgbG2v3OJxHXLEAYFqq1Wo88cQTsWXLlti7d2988pOfjLVr18bHPvaxKBY9s85bXLEA4Ky+9KUvxbZt22JgYCDuvffeeOyxx6K/v7/dY3EecsUCgLMqFouxdOnSuOmmm874omyvocIVCwDO6p577vFbXkyLKxYAQBqvtgEA0ggLACCNsAAA0ggLACCNsAAA0ggLACCNsAAA0ggLACDN/wNEcpBc09SFbQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Gender\"].value_counts().plot(kind=\"pie\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "V7rOKEd_xxu0",
        "outputId": "a6901256-c152-45c6-af22-cf6cab13c2f2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Gender'>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Gender\"].value_counts().plot(kind=\"pie\",autopct=\"%.2f%%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "bYXa60lLyQEE",
        "outputId": "50e33b70-caa6-4cda-c469-daf35e60ef79"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Gender'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**For continuous data visualisation we will use Histogram**"
      ],
      "metadata": {
        "id": "je_VH1A-ztun"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Age\"].plot()\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "XlflEVEAykYM",
        "outputId": "78d6de72-5fe2-493e-e064-aed37e6d7ba2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Age\"].plot(kind=\"hist\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "B8BNaQ1Z0YOE",
        "outputId": "ebdd0830-3fc8-478e-92a2-c7855bcbcd64"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGdCAYAAAD60sxaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAusklEQVR4nO3de1RVdf7/8dcB5eYIXrklCuVtNIHSkTCdNEkkl0v0+zW0C0pmTcmMRtY35lta2RrK0rCVX+miotMY6mTUlKGGgTmifr2Q2SpTw9Dk4CXlCCY6cH5/9PPM9wQoHg8cDvv5WGuv6Xz2Z3/Oe3/mzPBqn88+22S1Wq0CAAAwEA9XFwAAANDcCEAAAMBwCEAAAMBwCEAAAMBwCEAAAMBwCEAAAMBwCEAAAMBwCEAAAMBw2ri6gJaotrZWx48fV/v27WUymVxdDgAAaASr1apz584pNDRUHh5XvsZDAKrH8ePHFRYW5uoyAACAA44ePapu3bpdsQ8BqB7t27eX9MsE+vv7u7gaAADQGBaLRWFhYba/41dCAKrH5a+9/P39CUAAALiZxixfYRE0AAAwHAIQAAAwHAIQAAAwHAIQAAAwHAIQAAAwHAIQAAAwHAIQAAAwHAIQAAAwHAIQAAAwHAIQAAAwHJcGoIyMDP3ud79T+/btFRgYqMTERB04cOCqx61du1Z9+/aVj4+PBgwYoPXr19vtt1qtmjNnjkJCQuTr66u4uDgdPHiwqU4DAAC4GZcGoMLCQs2YMUPbt2/Xpk2bdOnSJY0aNUpVVVUNHrNt2zZNnjxZ06ZN0969e5WYmKjExETt37/f1mf+/Pl6/fXXlZWVpR07dqhdu3aKj4/XhQsXmuO0AABAC2eyWq1WVxdx2cmTJxUYGKjCwkL9/ve/r7dPUlKSqqqq9PHHH9vabrvtNkVHRysrK0tWq1WhoaF64oknNHv2bElSRUWFgoKClJ2drUmTJl21DovFooCAAFVUVPAwVAAA3MS1/P1uUWuAKioqJEmdOnVqsE9RUZHi4uLs2uLj41VUVCRJKikpkdlstusTEBCgmJgYW59fq66ulsVisdsAAEDr1cbVBVxWW1urWbNm6fbbb9fNN9/cYD+z2aygoCC7tqCgIJnNZtv+y20N9fm1jIwMPf/889dTPuAU4U9/4uoSrtmRl8a4uoRrxjyjNeHz7JgWcwVoxowZ2r9/v3Jycpr9vdPT01VRUWHbjh492uw1AACA5tMirgClpqbq448/1pYtW9StW7cr9g0ODlZ5ebldW3l5uYKDg237L7eFhITY9YmOjq53TG9vb3l7e1/HGQAAAHfi0itAVqtVqamp+uCDD7R582ZFRERc9ZjY2Fjl5+fbtW3atEmxsbGSpIiICAUHB9v1sVgs2rFjh60PAAAwNpdeAZoxY4ZWrVqlDz/8UO3bt7et0QkICJCvr68kKTk5WTfccIMyMjIkSTNnztQdd9yhBQsWaMyYMcrJydGuXbv01ltvSZJMJpNmzZqlF198Ub169VJERISeffZZhYaGKjEx0SXnCQAAWhaXBqAlS5ZIkoYPH27Xvnz5ck2dOlWSVFpaKg+Pf1+oGjJkiFatWqVnnnlGf/7zn9WrVy/l5ubaLZx+6qmnVFVVpYcfflhnz57V0KFDlZeXJx8fnyY/JwAA0PK5NAA15ieICgoK6rRNnDhREydObPAYk8mkF154QS+88ML1lAcAAFqpFnMXGAAAQHMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMMhAAEAAMNxaQDasmWLxo4dq9DQUJlMJuXm5l6x/9SpU2Uymeps/fv3t/V57rnn6uzv27dvE58JAABwJy4NQFVVVYqKitLixYsb1X/RokUqKyuzbUePHlWnTp00ceJEu379+/e367d169amKB8AALipNq5884SEBCUkJDS6f0BAgAICAmyvc3NzdebMGaWkpNj1a9OmjYKDg51WJwAAaF3ceg3Q0qVLFRcXpx49eti1Hzx4UKGhobrxxht13333qbS09IrjVFdXy2Kx2G0AAKD1ctsAdPz4cX366ad66KGH7NpjYmKUnZ2tvLw8LVmyRCUlJRo2bJjOnTvX4FgZGRm2q0sBAQEKCwtr6vIBAIALuW0AWrFihTp06KDExES79oSEBE2cOFGRkZGKj4/X+vXrdfbsWa1Zs6bBsdLT01VRUWHbjh492sTVAwAAV3LpGiBHWa1WLVu2TA888IC8vLyu2LdDhw7q3bu3Dh061GAfb29veXt7O7tMAADQQrnlFaDCwkIdOnRI06ZNu2rfyspKHT58WCEhIc1QGQAAcAcuDUCVlZUqLi5WcXGxJKmkpETFxcW2Rcvp6elKTk6uc9zSpUsVExOjm2++uc6+2bNnq7CwUEeOHNG2bds0fvx4eXp6avLkyU16LgAAwH249CuwXbt2acSIEbbXaWlpkqQpU6YoOztbZWVlde7gqqio0Pvvv69FixbVO+axY8c0efJknT59Wl27dtXQoUO1fft2de3atelOBAAAuBWXBqDhw4fLarU2uD87O7tOW0BAgM6fP9/gMTk5Oc4oDQAAtGJuuQYIAADgehCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4bg0AG3ZskVjx45VaGioTCaTcnNzr9i/oKBAJpOpzmY2m+36LV68WOHh4fLx8VFMTIx27tzZhGcBAADcjUsDUFVVlaKiorR48eJrOu7AgQMqKyuzbYGBgbZ9q1evVlpamubOnas9e/YoKipK8fHxOnHihLPLBwAAbqqNK988ISFBCQkJ13xcYGCgOnToUO++hQsXavr06UpJSZEkZWVl6ZNPPtGyZcv09NNPX0+5AACglXDLNUDR0dEKCQnRXXfdpX/+85+29osXL2r37t2Ki4uztXl4eCguLk5FRUUNjlddXS2LxWK3AQCA1sutAlBISIiysrL0/vvv6/3331dYWJiGDx+uPXv2SJJOnTqlmpoaBQUF2R0XFBRUZ53Q/5WRkaGAgADbFhYW1qTnAQAAXMulX4Fdqz59+qhPnz6210OGDNHhw4f12muv6a9//avD46anpystLc322mKxEIIAAGjF3CoA1Wfw4MHaunWrJKlLly7y9PRUeXm5XZ/y8nIFBwc3OIa3t7e8vb2btE4AANByuNVXYPUpLi5WSEiIJMnLy0sDBw5Ufn6+bX9tba3y8/MVGxvrqhIBAEAL49IrQJWVlTp06JDtdUlJiYqLi9WpUyd1795d6enp+vHHH7Vy5UpJUmZmpiIiItS/f39duHBB77zzjjZv3qyNGzfaxkhLS9OUKVM0aNAgDR48WJmZmaqqqrLdFQYAAODSALRr1y6NGDHC9vryOpwpU6YoOztbZWVlKi0tte2/ePGinnjiCf3444/y8/NTZGSkPvvsM7sxkpKSdPLkSc2ZM0dms1nR0dHKy8urszAaAAAYl8lqtVpdXURLY7FYFBAQoIqKCvn7+7u6HBhI+NOfuLqEa3bkpTGuLuGaMc9oTfg8/9u1/P12+zVAAAAA14oABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADIcABAAADMelAWjLli0aO3asQkNDZTKZlJube8X+69at01133aWuXbvK399fsbGx2rBhg12f5557TiaTyW7r27dvE54FAABwNy4NQFVVVYqKitLixYsb1X/Lli266667tH79eu3evVsjRozQ2LFjtXfvXrt+/fv3V1lZmW3bunVrU5QPAADcVBtXvnlCQoISEhIa3T8zM9Pu9V/+8hd9+OGH+sc//qFbbrnF1t6mTRsFBwc7q0wAANDKuPUaoNraWp07d06dOnWyaz948KBCQ0N144036r777lNpaekVx6murpbFYrHbAABA6+XWAejVV19VZWWl7rnnHltbTEyMsrOzlZeXpyVLlqikpETDhg3TuXPnGhwnIyNDAQEBti0sLKw5ygcAAC7itgFo1apVev7557VmzRoFBgba2hMSEjRx4kRFRkYqPj5e69ev19mzZ7VmzZoGx0pPT1dFRYVtO3r0aHOcAgAAcBGXrgFyVE5Ojh566CGtXbtWcXFxV+zboUMH9e7dW4cOHWqwj7e3t7y9vZ1dJgAAaKEcugL0/fffO7uORnvvvfeUkpKi9957T2PGjLlq/8rKSh0+fFghISHNUB0AAHAHDgWgnj17asSIEXr33Xd14cIFh9+8srJSxcXFKi4uliSVlJSouLjYtmg5PT1dycnJtv6rVq1ScnKyFixYoJiYGJnNZpnNZlVUVNj6zJ49W4WFhTpy5Ii2bdum8ePHy9PTU5MnT3a4TgAA0Lo4FID27NmjyMhIpaWlKTg4WI888oh27tx5zePs2rVLt9xyi+0W9rS0NN1yyy2aM2eOJKmsrMzuDq633npL//rXvzRjxgyFhITYtpkzZ9r6HDt2TJMnT1afPn10zz33qHPnztq+fbu6du3qyKkCAIBWyKE1QNHR0Vq0aJEWLFigjz76SNnZ2Ro6dKh69+6tBx98UA888ECjAsfw4cNltVob3J+dnW33uqCg4Kpj5uTkXLUPAAAwtuu6C6xNmzaaMGGC1q5dq5dfflmHDh3S7NmzFRYWpuTkZJWVlTmrTgAAAKe5rgC0a9cuPfbYYwoJCdHChQs1e/ZsHT58WJs2bdLx48c1btw4Z9UJAADgNA59BbZw4UItX75cBw4c0N13362VK1fq7rvvlofHL3kqIiJC2dnZCg8Pd2atAAAATuFQAFqyZIkefPBBTZ06tcHbywMDA7V06dLrKg4AAKApOBSADh48eNU+Xl5emjJliiPDAwAANCmH1gAtX75ca9eurdO+du1arVix4rqLAgAAaEoOBaCMjAx16dKlTntgYKD+8pe/XHdRAAAATcmhAFRaWqqIiIg67T169LD74UIAAICWyKEAFBgYqH379tVp//LLL9W5c+frLgoAAKApORSAJk+erD/96U/6/PPPVVNTo5qaGm3evFkzZ87UpEmTnF0jAACAUzl0F9i8efN05MgRjRw5Um3a/DJEbW2tkpOTWQMEAABaPIcCkJeXl1avXq158+bpyy+/lK+vrwYMGKAePXo4uz4AAACncygAXda7d2/17t3bWbUAAAA0C4cCUE1NjbKzs5Wfn68TJ06otrbWbv/mzZudUhwAAEBTcCgAzZw5U9nZ2RozZoxuvvlmmUwmZ9cFAADQZBwKQDk5OVqzZo3uvvtuZ9cDAADQ5By6Dd7Ly0s9e/Z0di0AAADNwqEA9MQTT2jRokWyWq3OrgcAAKDJOfQV2NatW/X555/r008/Vf/+/dW2bVu7/evWrXNKcQAAAE3BoQDUoUMHjR8/3tm1AAAANAuHAtDy5cudXQcAAECzcWgNkCT961//0meffaY333xT586dkyQdP35clZWVTisOAACgKTh0BeiHH37Q6NGjVVpaqurqat11111q3769Xn75ZVVXVysrK8vZdQIAADiNQ1eAZs6cqUGDBunMmTPy9fW1tY8fP175+flOKw4AAKApOHQF6IsvvtC2bdvk5eVl1x4eHq4ff/zRKYUBAAA0FYeuANXW1qqmpqZO+7Fjx9S+ffvrLgoAAKApORSARo0apczMTNtrk8mkyspKzZ07l8djAACAFs+hr8AWLFig+Ph49evXTxcuXNC9996rgwcPqkuXLnrvvfecXSMAAIBTORSAunXrpi+//FI5OTnat2+fKisrNW3aNN133312i6IBAABaIocCkCS1adNG999/vzNrAQAAaBYOBaCVK1decX9ycrJDxQAAADQHhwLQzJkz7V5funRJ58+fl5eXl/z8/AhAAACgRXPoLrAzZ87YbZWVlTpw4ICGDh3KImgAANDiOfwssF/r1auXXnrppTpXh65ky5YtGjt2rEJDQ2UymZSbm3vVYwoKCnTrrbfK29tbPXv2VHZ2dp0+ixcvVnh4uHx8fBQTE6OdO3dew5kAAIDWzmkBSPplYfTx48cb3b+qqkpRUVFavHhxo/qXlJRozJgxGjFihIqLizVr1iw99NBD2rBhg63P6tWrlZaWprlz52rPnj2KiopSfHy8Tpw4cc3nAwAAWieH1gB99NFHdq+tVqvKysr0xhtv6Pbbb2/0OAkJCUpISGh0/6ysLEVERGjBggWSpN/+9rfaunWrXnvtNcXHx0uSFi5cqOnTpyslJcV2zCeffKJly5bp6aefbvR7AQCA1suhAJSYmGj32mQyqWvXrrrzzjtt4aQpFBUVKS4uzq4tPj5es2bNkiRdvHhRu3fvVnp6um2/h4eH4uLiVFRU1OC41dXVqq6utr22WCzOLRwAALQoDgWg2tpaZ9fRKGazWUFBQXZtQUFBslgs+vnnn3XmzBnV1NTU2+fbb79tcNyMjAw9//zzTVJzfcKf/qTZ3svIjrw0xtUlAIbG/9ehJXPqGiB3lZ6eroqKCtt29OhRV5cEAACakENXgNLS0hrdd+HChY68Rb2Cg4NVXl5u11ZeXi5/f3/5+vrK09NTnp6e9fYJDg5ucFxvb295e3s7rU4AANCyORSA9u7dq7179+rSpUvq06ePJOm7776Tp6enbr31Vls/k8nknCr/v9jYWK1fv96ubdOmTYqNjZUkeXl5aeDAgcrPz7etU6qtrVV+fr5SU1OdWgsAAHBfDgWgsWPHqn379lqxYoU6duwo6ZcfR0xJSdGwYcP0xBNPNGqcyspKHTp0yPa6pKRExcXF6tSpk7p376709HT9+OOPtkdv/OEPf9Abb7yhp556Sg8++KA2b96sNWvW6JNP/v09c1pamqZMmaJBgwZp8ODByszMVFVVle2uMAAAAIcC0IIFC7Rx40Zb+JGkjh076sUXX9SoUaMaHYB27dqlESNG2F5f/mptypQpys7OVllZmUpLS237IyIi9Mknn+jxxx/XokWL1K1bN73zzju2W+AlKSkpSSdPntScOXNkNpsVHR2tvLy8OgujAQCAcTkUgCwWi06ePFmn/eTJkzp37lyjxxk+fLisVmuD++v7lefhw4dr7969Vxw3NTWVr7wAAECDHLoLbPz48UpJSdG6det07NgxHTt2TO+//76mTZumCRMmOLtGAAAAp3LoClBWVpZmz56te++9V5cuXfploDZtNG3aNL3yyitOLRAAAMDZHApAfn5++p//+R+98sorOnz4sCTppptuUrt27ZxaHAAAQFO4rh9CLCsrU1lZmXr16qV27dpdcT0PAABAS+FQADp9+rRGjhyp3r176+6771ZZWZkkadq0aY2+AwwAAMBVHApAjz/+uNq2bavS0lL5+fnZ2pOSkpSXl+e04gAAAJqCQ2uANm7cqA0bNqhbt2527b169dIPP/zglMIAAACaikNXgKqqquyu/Fz2008/8UwtAADQ4jkUgIYNG2Z7PIX0yzO/amtrNX/+fLtfdgYAAGiJHPoKbP78+Ro5cqR27dqlixcv6qmnntLXX3+tn376Sf/85z+dXSMAAIBTOXQF6Oabb9Z3332noUOHaty4caqqqtKECRO0d+9e3XTTTc6uEQAAwKmu+QrQpUuXNHr0aGVlZem///u/m6ImAACAJnXNV4Datm2rffv2NUUtAAAAzcKhr8Duv/9+LV261Nm1AAAANAuHFkH/61//0rJly/TZZ59p4MCBdZ4BtnDhQqcUBwAA0BSuKQB9//33Cg8P1/79+3XrrbdKkr777ju7PiaTyXnVAQAANIFrCkC9evVSWVmZPv/8c0m/PPri9ddfV1BQUJMUBwAA0BSuaQ3Qr5/2/umnn6qqqsqpBQEAADQ1hxZBX/brQAQAAOAOrikAmUymOmt8WPMDAADczTWtAbJarZo6dartgacXLlzQH/7whzp3ga1bt855FQIAADjZNQWgKVOm2L2+//77nVoMAABAc7imALR8+fKmqgMAAKDZXNciaAAAAHdEAAIAAIZDAAIAAIZDAAIAAIZDAAIAAIZDAAIAAIZDAAIAAIZDAAIAAIZDAAIAAIZDAAIAAIbTIgLQ4sWLFR4eLh8fH8XExGjnzp0N9h0+fLjtqfT/dxszZoytz9SpU+vsHz16dHOcCgAAcAPX9CywprB69WqlpaUpKytLMTExyszMVHx8vA4cOKDAwMA6/detW6eLFy/aXp8+fVpRUVGaOHGiXb/Ro0fbPbvs8hPsAQAAXH4FaOHChZo+fbpSUlLUr18/ZWVlyc/PT8uWLau3f6dOnRQcHGzbNm3aJD8/vzoByNvb265fx44dm+N0AACAG3BpALp48aJ2796tuLg4W5uHh4fi4uJUVFTUqDGWLl2qSZMmqV27dnbtBQUFCgwMVJ8+ffToo4/q9OnTDY5RXV0ti8VitwEAgNbLpQHo1KlTqqmpUVBQkF17UFCQzGbzVY/fuXOn9u/fr4ceesiuffTo0Vq5cqXy8/P18ssvq7CwUAkJCaqpqal3nIyMDAUEBNi2sLAwx08KAAC0eC5fA3Q9li5dqgEDBmjw4MF27ZMmTbL984ABAxQZGambbrpJBQUFGjlyZJ1x0tPTlZaWZnttsVgIQQAAtGIuvQLUpUsXeXp6qry83K69vLxcwcHBVzy2qqpKOTk5mjZt2lXf58Ybb1SXLl106NChevd7e3vL39/fbgMAAK2XSwOQl5eXBg4cqPz8fFtbbW2t8vPzFRsbe8Vj165dq+rqat1///1XfZ9jx47p9OnTCgkJue6aAQCA+3P5XWBpaWl6++23tWLFCn3zzTd69NFHVVVVpZSUFElScnKy0tPT6xy3dOlSJSYmqnPnznbtlZWVevLJJ7V9+3YdOXJE+fn5GjdunHr27Kn4+PhmOScAANCyuXwNUFJSkk6ePKk5c+bIbDYrOjpaeXl5toXRpaWl8vCwz2kHDhzQ1q1btXHjxjrjeXp6at++fVqxYoXOnj2r0NBQjRo1SvPmzeO3gAAAgKQWEIAkKTU1VampqfXuKygoqNPWp08fWa3Wevv7+vpqw4YNziwPAAC0Mi7/CgwAAKC5EYAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhEIAAAIDhtIgAtHjxYoWHh8vHx0cxMTHauXNng32zs7NlMpnsNh8fH7s+VqtVc+bMUUhIiHx9fRUXF6eDBw829WkAAAA34fIAtHr1aqWlpWnu3Lnas2ePoqKiFB8frxMnTjR4jL+/v8rKymzbDz/8YLd//vz5ev3115WVlaUdO3aoXbt2io+P14ULF5r6dAAAgBtweQBauHChpk+frpSUFPXr109ZWVny8/PTsmXLGjzGZDIpODjYtgUFBdn2Wa1WZWZm6plnntG4ceMUGRmplStX6vjx48rNzW2GMwIAAC2dSwPQxYsXtXv3bsXFxdnaPDw8FBcXp6KiogaPq6ysVI8ePRQWFqZx48bp66+/tu0rKSmR2Wy2GzMgIEAxMTENjlldXS2LxWK3AQCA1sulAejUqVOqqamxu4IjSUFBQTKbzfUe06dPHy1btkwffvih3n33XdXW1mrIkCE6duyYJNmOu5YxMzIyFBAQYNvCwsKu99QAAEAL5vKvwK5VbGyskpOTFR0drTvuuEPr1q1T165d9eabbzo8Znp6uioqKmzb0aNHnVgxAABoaVwagLp06SJPT0+Vl5fbtZeXlys4OLhRY7Rt21a33HKLDh06JEm2465lTG9vb/n7+9ttAACg9XJpAPLy8tLAgQOVn59va6utrVV+fr5iY2MbNUZNTY2++uorhYSESJIiIiIUHBxsN6bFYtGOHTsaPSYAAGjd2ri6gLS0NE2ZMkWDBg3S4MGDlZmZqaqqKqWkpEiSkpOTdcMNNygjI0OS9MILL+i2225Tz549dfbsWb3yyiv64Ycf9NBDD0n65Q6xWbNm6cUXX1SvXr0UERGhZ599VqGhoUpMTHTVaQIAgBbE5QEoKSlJJ0+e1Jw5c2Q2mxUdHa28vDzbIubS0lJ5ePz7QtWZM2c0ffp0mc1mdezYUQMHDtS2bdvUr18/W5+nnnpKVVVVevjhh3X27FkNHTpUeXl5dX4wEQAAGJPJarVaXV1ES2OxWBQQEKCKioomWQ8U/vQnTh8TdR15aYyrS7hm7vjZYJ6bB/OM1qSpPs/X8vfb7e4CAwAAuF4EIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgEIAAAYDgtIgAtXrxY4eHh8vHxUUxMjHbu3Nlg37ffflvDhg1Tx44d1bFjR8XFxdXpP3XqVJlMJrtt9OjRTX0aAADATbg8AK1evVppaWmaO3eu9uzZo6ioKMXHx+vEiRP19i8oKNDkyZP1+eefq6ioSGFhYRo1apR+/PFHu36jR49WWVmZbXvvvfea43QAAIAbcHkAWrhwoaZPn66UlBT169dPWVlZ8vPz07Jly+rt/7e//U2PPfaYoqOj1bdvX73zzjuqra1Vfn6+XT9vb28FBwfbto4dOzbH6QAAADfg0gB08eJF7d69W3FxcbY2Dw8PxcXFqaioqFFjnD9/XpcuXVKnTp3s2gsKChQYGKg+ffro0Ucf1enTpxsco7q6WhaLxW4DAACtl0sD0KlTp1RTU6OgoCC79qCgIJnN5kaN8V//9V8KDQ21C1GjR4/WypUrlZ+fr5dfflmFhYVKSEhQTU1NvWNkZGQoICDAtoWFhTl+UgAAoMVr4+oCrsdLL72knJwcFRQUyMfHx9Y+adIk2z8PGDBAkZGRuummm1RQUKCRI0fWGSc9PV1paWm21xaLhRAEAEAr5tIrQF26dJGnp6fKy8vt2svLyxUcHHzFY1999VW99NJL2rhxoyIjI6/Y98Ybb1SXLl106NChevd7e3vL39/fbgMAAK2XSwOQl5eXBg4caLeA+fKC5tjY2AaPmz9/vubNm6e8vDwNGjToqu9z7NgxnT59WiEhIU6pGwAAuDeX3wWWlpamt99+WytWrNA333yjRx99VFVVVUpJSZEkJScnKz093db/5Zdf1rPPPqtly5YpPDxcZrNZZrNZlZWVkqTKyko9+eST2r59u44cOaL8/HyNGzdOPXv2VHx8vEvOEQAAtCwuXwOUlJSkkydPas6cOTKbzYqOjlZeXp5tYXRpaak8PP6d05YsWaKLFy/qP//zP+3GmTt3rp577jl5enpq3759WrFihc6ePavQ0FCNGjVK8+bNk7e3d7OeGwAAaJlcHoAkKTU1VampqfXuKygosHt95MiRK47l6+urDRs2OKkyAADQGrn8KzAAAIDmRgACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACGQwACAACG0yIC0OLFixUeHi4fHx/FxMRo586dV+y/du1a9e3bVz4+PhowYIDWr19vt99qtWrOnDkKCQmRr6+v4uLidPDgwaY8BQAA4EZcHoBWr16ttLQ0zZ07V3v27FFUVJTi4+N14sSJevtv27ZNkydP1rRp07R3714lJiYqMTFR+/fvt/WZP3++Xn/9dWVlZWnHjh1q166d4uPjdeHCheY6LQAA0IK5PAAtXLhQ06dPV0pKivr166esrCz5+flp2bJl9fZftGiRRo8erSeffFK//e1vNW/ePN1666164403JP1y9SczM1PPPPOMxo0bp8jISK1cuVLHjx9Xbm5uM54ZAABoqdq48s0vXryo3bt3Kz093dbm4eGhuLg4FRUV1XtMUVGR0tLS7Nri4+Nt4aakpERms1lxcXG2/QEBAYqJiVFRUZEmTZpUZ8zq6mpVV1fbXldUVEiSLBaLw+d2JbXV55tkXNhrqv/+mpI7fjaY5+bBPKM1aarP8+VxrVbrVfu6NACdOnVKNTU1CgoKsmsPCgrSt99+W+8xZrO53v5ms9m2/3JbQ31+LSMjQ88//3yd9rCwsMadCFqkgExXV2AMzHPzYJ7RmjT15/ncuXMKCAi4Yh+XBqCWIj093e6qUm1trX766Sd17txZJpPJhZW1DBaLRWFhYTp69Kj8/f1dXU6rxTw3D+a5eTDPzYN5tme1WnXu3DmFhoZeta9LA1CXLl3k6emp8vJyu/by8nIFBwfXe0xwcPAV+1/+z/LycoWEhNj1iY6OrndMb29veXt727V16NDhWk7FEPz9/fkfWDNgnpsH89w8mOfmwTz/29Wu/Fzm0kXQXl5eGjhwoPLz821ttbW1ys/PV2xsbL3HxMbG2vWXpE2bNtn6R0REKDg42K6PxWLRjh07GhwTAAAYi8u/AktLS9OUKVM0aNAgDR48WJmZmaqqqlJKSookKTk5WTfccIMyMjIkSTNnztQdd9yhBQsWaMyYMcrJydGuXbv01ltvSZJMJpNmzZqlF198Ub169VJERISeffZZhYaGKjEx0VWnCQAAWhCXB6CkpCSdPHlSc+bMkdlsVnR0tPLy8myLmEtLS+Xh8e8LVUOGDNGqVav0zDPP6M9//rN69eql3Nxc3XzzzbY+Tz31lKqqqvTwww/r7NmzGjp0qPLy8uTj49Ps59caeHt7a+7cuXW+JoRzMc/Ng3luHsxz82CeHWeyNuZeMQAAgFbE5T+ECAAA0NwIQAAAwHAIQAAAwHAIQAAAwHAIQJD0y+NAfve736l9+/YKDAxUYmKiDhw4UKdfUVGR7rzzTrVr107+/v76/e9/r59//tkFFbunxsyz2WzWAw88oODgYLVr10633nqr3n//fRdV7J6WLFmiyMhI24/DxcbG6tNPP7Xtv3DhgmbMmKHOnTvrN7/5jf7jP/6jzg+sonGuNNc//fST/vjHP6pPnz7y9fVV9+7d9ac//cn2vEU03tU+05dZrVYlJCTIZDLxAPCrIABBklRYWKgZM2Zo+/bt2rRpky5duqRRo0apqqrK1qeoqEijR4/WqFGjtHPnTv3v//6vUlNT7X6mAFfWmHlOTk7WgQMH9NFHH+mrr77ShAkTdM8992jv3r0urNy9dOvWTS+99JJ2796tXbt26c4779S4ceP09ddfS5Ief/xx/eMf/9DatWtVWFio48ePa8KECS6u2j1daa6PHz+u48eP69VXX9X+/fuVnZ2tvLw8TZs2zdVlu52rfaYvy8zM5BFOjWUF6nHixAmrJGthYaGtLSYmxvrMM8+4sKrWp755bteunXXlypV2/Tp16mR9++23m7u8VqVjx47Wd955x3r27Flr27ZtrWvXrrXt++abb6ySrEVFRS6ssPW4PNf1WbNmjdXLy8t66dKlZq6q9fn1PO/du9d6ww03WMvKyqySrB988IHrinMD/Ks76nX5EnWnTp0kSSdOnNCOHTsUGBioIUOGKCgoSHfccYe2bt3qyjLd3q/nWfrlxz5Xr16tn376SbW1tcrJydGFCxc0fPhwF1Xp3mpqapSTk6OqqirFxsZq9+7dunTpkuLi4mx9+vbtq+7du6uoqMiFlbq/X891fSoqKuTv7682bVz+O7xuq755Pn/+vO69914tXry4wWdpwh6fQNRRW1urWbNm6fbbb7f9wvb3338vSXruuef06quvKjo6WitXrtTIkSO1f/9+9erVy5Ulu6X65lmS1qxZo6SkJHXu3Flt2rSRn5+fPvjgA/Xs2dOF1bqfr776SrGxsbpw4YJ+85vf6IMPPlC/fv1UXFwsLy+vOg88DgoKktlsdk2xbq6huf61U6dOad68eXr44YddUKX7u9I8P/744xoyZIjGjRvn4irdBwEIdcyYMUP79++3u7pTW1srSXrkkUdsz2m75ZZblJ+fr2XLltme1YbGq2+eJenZZ5/V2bNn9dlnn6lLly7Kzc3VPffcoy+++EIDBgxwUbXup0+fPiouLlZFRYX+/ve/a8qUKSosLHR1Wa1SQ3P9f0OQxWLRmDFj1K9fPz333HOuK9aNNTTPhw4d0ubNm1kneI14FAbspKam6sMPP9SWLVsUERFhay8pKdGNN96ov/71r7r//vtt7UlJSWrTpo3+9re/uaJct9XQPB8+fFg9e/bU/v371b9/f1t7XFycevbsqaysLFeU2yrExcXppptuUlJSkkaOHKkzZ87YXQXq0aOHZs2apccff9x1RbYSl+f6zTfflCSdO3dO8fHx8vPz08cff8xzGZ3k8jz7+vrq9ddft7shpaamRh4eHho2bJgKCgpcV2QLxhogSPrl1snU1FR98MEH2rx5s90fZUkKDw9XaGhonVu2v/vuO/Xo0aM5S3VrV5vn8+fPS1KdO+s8PT1tV+HgmNraWlVXV2vgwIFq27at8vPzbfsOHDig0tLSBtet4Npcnmvplys/o0aNkpeXlz766CPCjxNdnuenn35a+/btU3FxsW2TpNdee03Lly93bZEtGF+BQdIvX8esWrVKH374odq3b29bCxEQECBfX1+ZTCY9+eSTmjt3rqKiohQdHa0VK1bo22+/1d///ncXV+8+rjbPffv2Vc+ePfXII4/o1VdfVefOnZWbm6tNmzbp448/dnH17iM9PV0JCQnq3r27zp07p1WrVqmgoEAbNmxQQECApk2bprS0NHXq1En+/v764x//qNjYWN12222uLt3tXGmuL4ef8+fP691335XFYpHFYpEkde3aVZ6eni6u3n1caZ6Dg4PrXfjcvXv3Ov+Shf/DtTehoaWQVO+2fPlyu34ZGRnWbt26Wf38/KyxsbHWL774wjUFu6nGzPN3331nnTBhgjUwMNDq5+dnjYyMrHNbPK7swQcftPbo0cPq5eVl7dq1q3XkyJHWjRs32vb//PPP1scee8zasWNHq5+fn3X8+PHWsrIyF1bsvq40159//nmDn/mSkhLXFu5mrvaZ/jVxG/xVsQYIAAAYDmuAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4RCAAACA4fw/THEYdDVo+bAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Age\"].plot(kind=\"hist\",bins=[5,10,15,20,25,30,35])\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "teY-cQk50jVK",
        "outputId": "d8646ab2-489e-4ab9-81c5-427c45ad6c84"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Age\"].plot(kind=\"hist\",bins=range(1,35,3))\n",
        "plt.grid()"
      ],
      "metadata": {
        "id": "YP507lbn031c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "ce4964a2-3eb9-4d1e-c43c-85d9020fb018"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most of the values are ranging in between 30 to 35 yrs "
      ],
      "metadata": {
        "id": "DGctAxFf3bQa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()[\"Total\"].sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45X4Gu-i44Ht",
        "outputId": "0e0b8f66-f281-4e9e-8f74-ac89861d8324"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rank               -0.793456\n",
              "Age                -0.593495\n",
              "Machine_learning    0.877388\n",
              "Python              0.903002\n",
              "Total               1.000000\n",
              "Name: Total, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "df.corr() ==> method will give you the corelation between two attributes \n",
        "\n",
        "Here Rank & Age columns are strongly co-related with Total column; which is obvious.\n",
        "\n",
        "Python & Machine_learning are strongly co-related since Total is nothing but the addition of this two attributes."
      ],
      "metadata": {
        "id": "8sLwyHHR5abq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.scatter(\"Python\",\"Total\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "balryKqK5QAG",
        "outputId": "61bb9964-3620-4013-d183-fe7aa93408ac"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Python', ylabel='Total'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAmK0lEQVR4nO3de3BU9f3/8dfmQkhCspALuUgIAVK5eItYMUajxVSKYKWillSnWBCsIgiC1nQG6tcbyGi1pK3Y1gmKgqMdVLQWCtQGYSIgkmorxQAhiUJCAmQ3JJJgcn5/dNhfl2s27O7JZ3k+Zs5M8vmcPXmfzxzY15zL5zgsy7IEAABgoDC7CwAAAOgqggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEi7C4g0Do6OrRv3z7FxcXJ4XDYXQ4AAOgEy7LU1NSk9PR0hYWd/rxLyAeZffv2KSMjw+4yAABAF9TU1Khfv36n7Q/5IBMXFyfpvwMRHx9vczUAAKAz3G63MjIyPN/jpxPyQeb45aT4+HiCDAAAhjnbbSHc7AsAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxgr5VxQAAIDA2FN/RFWHWjQgMVZZSbG21ECQAQAAPmlsadPMFeXaUFHvacvPTlZxYY6cMZFBrYVLSwAAwCczV5Rr064Gr7ZNuxo0Y8X2oNdCkAEAAJ22p/6INlTUq92yvNrbLUsbKupV2dAc1HoIMgAAoNOqDrWcsX/vQYIMAADopjITYs7YPyAxuDf9EmQAAECnDUzupfzsZIU7HF7t4Q6H8rOTg/70EkEGAAD4pLgwR3mDk7za8gYnqbgwJ+i18Pg1AADwiTMmUq9OuVIbvqzX9prDurx/H12bnWxLLQQZAADgE+aRAQAAxmIeGQAAYCTmkQEAAMZiHhkAAGAs5pEBAADGYh4ZAABgNOaRAQAAxjo+j0xlQ7P2HmzWgMTYoJ+JOY4gAwAAuiQryb4AcxyXlgAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGMvWINPU1KRZs2YpMzNT0dHRuvrqq7V161ZPv2VZmj9/vtLS0hQdHa2CggJVVFTYWDEAAOhObA0y99xzj9auXatly5bp888/14033qiCggJ9/fXXkqRFixZp8eLFWrJkiTZv3qzY2FiNHj1aR48etbNsAADQTTgs64TXVwbJN998o7i4OL377rsaO3asp33EiBEaM2aMnnjiCaWnp2vOnDmaO3euJMnlciklJUVLly7VxIkTO/V33G63nE6nXC6X4uPjA7IvAADAvzr7/W3bGZlvv/1W7e3t6tmzp1d7dHS0Nm7cqMrKStXW1qqgoMDT53Q6NXLkSJWVlZ12u62trXK73V4LAAAITbYFmbi4OOXm5uqJJ57Qvn371N7ertdee01lZWXav3+/amtrJUkpKSlen0tJSfH0ncqCBQvkdDo9S0ZGRkD3AwAA2MfWe2SWLVsmy7J0wQUXKCoqSosXL1ZhYaHCwrpeVlFRkVwul2epqanxY8UAAKA7sTXIDBo0SKWlpTpy5Ihqamq0ZcsWHTt2TAMHDlRqaqokqa6uzuszdXV1nr5TiYqKUnx8vNcCAABCU7eYRyY2NlZpaWk6fPiw1qxZo1tuuUVZWVlKTU3V+vXrPeu53W5t3rxZubm5NlYLAAC6C1vffr1mzRpZlqULL7xQu3bt0sMPP6whQ4boZz/7mRwOh2bNmqUnn3xS2dnZysrK0rx585Senq7x48fbWTYAAOgmbA0yLpdLRUVF+uqrr5SQkKAJEyboqaeeUmRkpCTpkUceUXNzs6ZNm6bGxkZdc801Wr169UlPOgEAgPOTbfPIBAvzyAAAYJ5uP48MAADAuSLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGNF2F0AAAChbk/9EVUdatGAxFhlJcXaXU5IIcgAABAgjS1tmrmiXBsq6j1t+dnJKi7MkTMm0sbKQgeXlgAACJCZK8q1aVeDV9umXQ2asWK7TRWFHoIMAAABsKf+iDZU1Kvdsrza2y1LGyrqVdnQbFNloYUgAwBAAFQdajlj/96DBBl/IMgAABAAmQkxZ+wfkMhNv/5AkAEAIAAGJvdSfnaywh0Or/Zwh0P52ck8veQnBBkAAAKkuDBHeYOTvNryBiepuDDHpopCD49fAwAQIM6YSL065UpVNjRr78Fm5pEJAIIMAAABlpVEgAkULi0BAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGMvWINPe3q558+YpKytL0dHRGjRokJ544glZluVZx7IszZ8/X2lpaYqOjlZBQYEqKipsrBoAAHQXtgaZZ555Ri+++KJ++9vfaseOHXrmmWe0aNEiFRcXe9ZZtGiRFi9erCVLlmjz5s2KjY3V6NGjdfToURsrBwAA3YHD+t/TH0E2btw4paSk6OWXX/a0TZgwQdHR0XrttddkWZbS09M1Z84czZ07V5LkcrmUkpKipUuXauLEiSdts7W1Va2trZ7f3W63MjIy5HK5FB8fH/idAgAA58ztdsvpdJ71+9vWMzJXX3211q9fry+//FKS9M9//lMbN27UmDFjJEmVlZWqra1VQUGB5zNOp1MjR45UWVnZKbe5YMECOZ1Oz5KRkRH4HQEAALaIsPOPP/roo3K73RoyZIjCw8PV3t6up556Snfeeackqba2VpKUkpLi9bmUlBRP34mKior00EMPeX4/fkYGAACEHluDzJtvvqnXX39dy5cv1/Dhw1VeXq5Zs2YpPT1dkyZN6tI2o6KiFBUV5edKAQBAd2RrkHn44Yf16KOPeu51ufjii1VVVaUFCxZo0qRJSk1NlSTV1dUpLS3N87m6ujpddtlldpQMAAC6EVvvkWlpaVFYmHcJ4eHh6ujokCRlZWUpNTVV69ev9/S73W5t3rxZubm5Qa0VAAB0P7aekbn55pv11FNPqX///ho+fLi2b9+uX//615o8ebIkyeFwaNasWXryySeVnZ2trKwszZs3T+np6Ro/frydpQMAgG7A1iBTXFysefPm6f7779eBAweUnp6ue++9V/Pnz/es88gjj6i5uVnTpk1TY2OjrrnmGq1evVo9e/a0sXIAANAd2DqPTDB09jl0AADQfRgxjwwAAMC5IMgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYK6IzK61atarTG/zhD3/Y5WIAAAB80akgM378+E5tzOFwqL29/VzqAQAA6LROBZmOjo5A1wEAAOAz7pEBAADG6tQZmRM1NzertLRU1dXVamtr8+qbOXOmXwoDAAA4G5+DzPbt23XTTTeppaVFzc3NSkhIUENDg2JiYtS3b1+CDAAACBqfLy3Nnj1bN998sw4fPqzo6Gh9/PHHqqqq0ogRI/Tss88GokYAAIBT8jnIlJeXa86cOQoLC1N4eLhaW1uVkZGhRYsW6Ze//GUgagQAADgln4NMZGSkwsL++7G+ffuqurpakuR0OlVTU+Pf6gAAAM7A53tkcnJytHXrVmVnZ+u6667T/Pnz1dDQoGXLlumiiy4KRI0AAACn5PMZmaefflppaWmSpKeeekp9+vTRfffdp/r6er300kt+LxAAQsme+iP6cOcBVTY0210KEBIclmVZdhcRSG63W06nUy6XS/Hx8XaXA+A81djSppkryrWhot7Tlp+drOLCHDljIm2sDOieOvv97fMZmVGjRqmxsfGUf3DUqFG+bg4AzgszV5Rr064Gr7ZNuxo0Y8V2myoCQoPPQeYf//jHSZPgSdLRo0f10Ucf+aUoAAgle+qPaENFvdpPOAHeblnaUFHPZSbgHHT6Zt/PPvvM8/MXX3yh2tpaz+/t7e1avXq1LrjgAv9WBwAhoOpQyxn79x5sVlZSbJCqAUJLp4PMZZddJofDIYfDccpLSNHR0SouLvZrcQAQCjITYs7YPyCREAN0VaeDTGVlpSzL0sCBA7VlyxYlJyd7+nr06KG+ffsqPDw8IEUCgMkGJvdSfnayNu1q8Lq8FO5wKG9wEmdjgHPQ6SCTmZkpSero6AhYMQAQqooLczRjxXavp5byBiepuDDHxqoA83Xp7de7d+/WCy+8oB07dkiShg0bpgcffFCDBg3yaTsDBgxQVVXVSe3333+/fve73+no0aOaM2eO3njjDbW2tmr06NH6/e9/r5SUlK6UDQC2ccZE6tUpV6qyoVl7DzZrQGLsOZ2J2VN/RFWHWs55O4DpfA4ya9as0Q9/+ENddtllysvLkyRt2rRJw4cP13vvvafvf//7nd7W1q1b1d7e7vn9X//6l77//e/r9ttvl/TfF1T+5S9/0VtvvSWn06kHHnhAt956qzZt2uRr2QDQLWQlnVvwYD4awJvPE+Ll5ORo9OjRWrhwoVf7o48+qr/97W/69NNPu1zMrFmz9P7776uiokJut1vJyclavny5brvtNknSf/7zHw0dOlRlZWW66qqrOrVNJsQDEEp++vKW095r8+qUK22sDPCvgE2It2PHDk2ZMuWk9smTJ+uLL77wdXMebW1teu211zR58mQ5HA5t27ZNx44dU0FBgWedIUOGqH///iorKzvtdlpbW+V2u70WAAgFzEcDnMznIJOcnKzy8vKT2svLy9W3b98uF/LOO++osbFRd999tySptrZWPXr0UO/evb3WS0lJ8ZrD5kQLFiyQ0+n0LBkZGV2uCQC6k87MRwOcbzodZB5//HG1tLRo6tSpmjZtmp555hl99NFH+uijj7Rw4ULde++9mjp1apcLefnllzVmzBilp6d3eRuSVFRUJJfL5VlqamrOaXsA0F0wHw1wsk7f7Pt///d/+vnPf6558+YpLi5Ozz33nIqKiiRJ6enpeuyxxzRz5swuFVFVVaV169Zp5cqVnrbU1FS1tbWpsbHR66xMXV2dUlNTT7utqKgoRUVFdakOAOjOmI8GOFmnz8gcvyfY4XBo9uzZ+uqrrzxnPb766is9+OCDcjgcXSqipKREffv21dixYz1tI0aMUGRkpNavX+9p27lzp6qrq5Wbm9ulvwMApisuzFHe4CSvNuajwfnMp8evTwwqcXFx51xAR0eHSkpKNGnSJEVE/P9ynE6npkyZooceekgJCQmKj4/XjBkzlJub2+knlgAg1Ph7PhrAdD4Fme985ztnPety6NAhnwpYt26dqqurNXny5JP6nn/+eYWFhWnChAleE+IBwPnuXOejAUJFp+eRCQsL0wsvvCCn03nG9SZNmuSXwvyFeWQAADBPZ7+/fTojM3HixHN6xBoAAMCfOn2zb1dv5AUAAAgUn59aAgAA6C46fWmpo6MjkHUAAAD4zOdXFAAAAHQXBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjRdhdAAAA/ran/oiqDrVoQGKsspJi7S4HAUSQAQCEjMaWNs1cUa4NFfWetvzsZBUX5sgZE2ljZQgULi0BAELGzBXl2rSrwatt064GzVix3aaKEGgEGQBASNhTf0QbKurVblle7e2WpQ0V9apsaLapMgQSQQYAEBKqDrWcsX/vQYJMKCLIAABCQmZCzBn7ByRy028oIsgAAELCwOReys9OVrjD4dUe7nAoPzuZp5dCFEEGABAyigtzlDc4yastb3CSigtzbKoIgcbj1wCAkOGMidSrU65UZUOz9h5sZh6Z8wBBBgAQcrKSCDDnCy4tAQAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWb78GbLKn/oiqDrVoQCJv6QWAriLIAEHW2NKmmSvKtaGi3tOWn52s4sIcOWMibawMAMzDpSUgyGauKNemXQ1ebZt2NWjGiu02VQQA5iLIAEG0p/6INlTUq92yvNrbLUsbKupV2dBsU2UAYCaCDBBEVYdazti/9yBBBgB8QZABgigzIeaM/QMSuekXAHxBkAGCaGByL+VnJyvc4fBqD3c4lJ+dzNNLAOAjggwQZMWFOcobnOTVljc4ScWFOTZVBADm4vFrIMicMZF6dcqVqmxo1t6DzcwjAwDngCAD2CQriQADAOeKS0sAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxle5D5+uuvdddddykxMVHR0dG6+OKL9cknn3j6LcvS/PnzlZaWpujoaBUUFKiiosLGigEAQHdha5A5fPiw8vLyFBkZqb/+9a/64osv9Nxzz6lPnz6edRYtWqTFixdryZIl2rx5s2JjYzV69GgdPXrUxsoBAEB34LCsE17DG0SPPvqoNm3apI8++uiU/ZZlKT09XXPmzNHcuXMlSS6XSykpKVq6dKkmTpx41r/hdrvldDrlcrkUHx/v1/oBAEBgdPb729YzMqtWrdIVV1yh22+/XX379lVOTo7++Mc/evorKytVW1urgoICT5vT6dTIkSNVVlZ2ym22trbK7XZ7LQAAIDTZGmT27NmjF198UdnZ2VqzZo3uu+8+zZw5U6+88ookqba2VpKUkpLi9bmUlBRP34kWLFggp9PpWTIyMgK7EwAAwDa2BpmOjg5dfvnlevrpp5WTk6Np06Zp6tSpWrJkSZe3WVRUJJfL5Vlqamr8WDEAAOhObA0yaWlpGjZsmFfb0KFDVV1dLUlKTU2VJNXV1XmtU1dX5+k7UVRUlOLj470WAAAQmmwNMnl5edq5c6dX25dffqnMzExJUlZWllJTU7V+/XpPv9vt1ubNm5WbmxvUWgEAQPdj69uvZ8+erauvvlpPP/207rjjDm3ZskV/+MMf9Ic//EGS5HA4NGvWLD355JPKzs5WVlaW5s2bp/T0dI0fP97O0gEAQDdga5D57ne/q7fffltFRUV6/PHHlZWVpRdeeEF33nmnZ51HHnlEzc3NmjZtmhobG3XNNddo9erV6tmzp42VAwCA7sDWeWSCgXlkAAAwjxHzyAAAAJwLggwAADAWQQYAABiLIAMAAIxl61NLJttTf0RVh1o0IDFWWUmxdpcDAMB5iSDjo8aWNs1cUa4NFfWetvzsZBUX5sgZE2ljZQAAnH+4tOSjmSvKtWlXg1fbpl0NmrFiu00VAQBw/iLI+GBP/RFtqKhX+wlT77RbljZU1KuyodmmygAAOD8RZHxQdajljP17DxJkAAAIJoKMDzITYs7YPyCRm34BAAgmgowPBib3Un52ssIdDq/2cIdD+dnJPL0EAECQEWR8VFyYo7zBSV5teYOTVFyYY1NFAACcv3j82kfOmEi9OuVKVTY0a+/BZuaRAQDARgSZLspKIsAAAGA3Li0BAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsW4PMY489JofD4bUMGTLE03/06FFNnz5diYmJ6tWrlyZMmKC6ujobKwYAAN2J7Wdkhg8frv3793uWjRs3evpmz56t9957T2+99ZZKS0u1b98+3XrrrTZWCwAAupMI2wuIiFBqaupJ7S6XSy+//LKWL1+uUaNGSZJKSko0dOhQffzxx7rqqqtOub3W1la1trZ6fne73YEpHAAA2M72MzIVFRVKT0/XwIEDdeedd6q6ulqStG3bNh07dkwFBQWedYcMGaL+/furrKzstNtbsGCBnE6nZ8nIyAj4PgAAAHvYGmRGjhyppUuXavXq1XrxxRdVWVmpa6+9Vk1NTaqtrVWPHj3Uu3dvr8+kpKSotrb2tNssKiqSy+XyLDU1NQHeCwAAYBdbLy2NGTPG8/Mll1yikSNHKjMzU2+++aaio6O7tM2oqChFRUX5q0QAANCN2X5p6X/17t1b3/nOd7Rr1y6lpqaqra1NjY2NXuvU1dWd8p4aAABw/ulWQebIkSPavXu30tLSNGLECEVGRmr9+vWe/p07d6q6ulq5ubk2Vtl97ak/og93HlBlQ7PdpfhVqO4XAODc2Xppae7cubr55puVmZmpffv26Ve/+pXCw8NVWFgop9OpKVOm6KGHHlJCQoLi4+M1Y8YM5ebmnvaJpfNVY0ubZq4o14aKek9bfnayigtz5IyJtLGycxOq+wUA8B9bz8h89dVXKiws1IUXXqg77rhDiYmJ+vjjj5WcnCxJev755zVu3DhNmDBB+fn5Sk1N1cqVK+0suVuauaJcm3Y1eLVt2tWgGSu221SRf4TqfgEA/MdhWZZldxGB5Ha75XQ65XK5FB8fb3c5fren/ohGPVd62v4P516vrKTYIFbkH6G6XwCAzuns93e3ukcGvqs61HLG/r0HzbyvJFT3CwDgXwQZw2UmxJyxf0CimWctQnW/AAD+RZAx3MDkXsrPTla4w+HVHu5wKD872djLL6G6XwAA/yLIhIDiwhzlDU7yassbnKTiwhybKvKPUN0vAID/cLNvCKlsaNbeg80akBgbUmcsQnW/AACn19nvb9vffg3/yUoKzS/6UN0vAMC549ISAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABgr5F9RcPxVUm632+ZKAABAZx3/3j7bKyFDPsg0NTVJkjIyMmyuBAAA+KqpqUlOp/O0/SH/9uuOjg7t27dPcXFxcjgcftuu2+1WRkaGampqQv6t2nZjrIODcQ4Oxjk4GOfgCOQ4W5alpqYmpaenKyzs9HfChPwZmbCwMPXr1y9g24+Pj+cfSZAw1sHBOAcH4xwcjHNwBGqcz3Qm5jhu9gUAAMYiyAAAAGMRZLooKipKv/rVrxQVFWV3KSGPsQ4Oxjk4GOfgYJyDozuMc8jf7AsAAEIXZ2QAAICxCDIAAMBYBBkAAGAsggwAADAWQeYsFixYoO9+97uKi4tT3759NX78eO3cudNrnaNHj2r69OlKTExUr169NGHCBNXV1dlUsZk6M87XX3+9HA6H1/Lzn//cporN9OKLL+qSSy7xTF6Vm5urv/71r55+jmX/ONs4cywHxsKFC+VwODRr1ixPG8e0/51qnO08pgkyZ1FaWqrp06fr448/1tq1a3Xs2DHdeOONam5u9qwze/Zsvffee3rrrbdUWlqqffv26dZbb7WxavN0ZpwlaerUqdq/f79nWbRokU0Vm6lfv35auHChtm3bpk8++USjRo3SLbfcon//+9+SOJb95WzjLHEs+9vWrVv10ksv6ZJLLvFq55j2r9ONs2TjMW3BJwcOHLAkWaWlpZZlWVZjY6MVGRlpvfXWW551duzYYUmyysrK7CrTeCeOs2VZ1nXXXWc9+OCD9hUVovr06WP96U9/4lgOsOPjbFkcy/7W1NRkZWdnW2vXrvUaW45p/zrdOFuWvcc0Z2R85HK5JEkJCQmSpG3btunYsWMqKCjwrDNkyBD1799fZWVlttQYCk4c5+Nef/11JSUl6aKLLlJRUZFaWlrsKC8ktLe364033lBzc7Nyc3M5lgPkxHE+jmPZf6ZPn66xY8d6HbsS/z/72+nG+Ti7jumQf2mkP3V0dGjWrFnKy8vTRRddJEmqra1Vjx491Lt3b691U1JSVFtba0OV5jvVOEvST37yE2VmZio9PV2fffaZfvGLX2jnzp1auXKljdWa5/PPP1dubq6OHj2qXr166e2339awYcNUXl7OsexHpxtniWPZn9544w19+umn2rp160l9/P/sP2caZ8neY5og44Pp06frX//6lzZu3Gh3KSHtdOM8bdo0z88XX3yx0tLSdMMNN2j37t0aNGhQsMs01oUXXqjy8nK5XC79+c9/1qRJk1RaWmp3WSHndOM8bNgwjmU/qamp0YMPPqi1a9eqZ8+edpcTsjozznYe01xa6qQHHnhA77//vj788EP169fP056amqq2tjY1NjZ6rV9XV6fU1NQgV2m+043zqYwcOVKStGvXrmCUFjJ69OihwYMHa8SIEVqwYIEuvfRS/eY3v+FY9rPTjfOpcCx3zbZt23TgwAFdfvnlioiIUEREhEpLS7V48WJFREQoJSWFY9oPzjbO7e3tJ30mmMc0QeYsLMvSAw88oLffflt///vflZWV5dU/YsQIRUZGav369Z62nTt3qrq62ut6OM7sbON8KuXl5ZKktLS0AFcX2jo6OtTa2sqxHGDHx/lUOJa75oYbbtDnn3+u8vJyz3LFFVfozjvv9PzMMX3uzjbO4eHhJ30mmMc0l5bOYvr06Vq+fLneffddxcXFea6rOp1ORUdHy+l0asqUKXrooYeUkJCg+Ph4zZgxQ7m5ubrqqqtsrt4cZxvn3bt3a/ny5brpppuUmJiozz77TLNnz1Z+fv4pHwPEqRUVFWnMmDHq37+/mpqatHz5cv3jH//QmjVrOJb96EzjzLHsP3FxcV730UlSbGysEhMTPe0c0+fubONs+zFty7NSBpF0yqWkpMSzzjfffGPdf//9Vp8+fayYmBjrRz/6kbV//377ijbQ2ca5urrays/PtxISEqyoqChr8ODB1sMPP2y5XC57CzfM5MmTrczMTKtHjx5WcnKydcMNN1h/+9vfPP0cy/5xpnHmWA6sEx8D5pgOjP8dZ7uPaYdlWVbg4xIAAID/cY8MAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAI919990aP3683WUAsBlBBkDA3X333XI4HHI4HJ63Qj/++OP69ttvz/rZvXv3yuFweF5CBwD/i5dGAgiKH/zgByopKVFra6s++OADTZ8+XZGRkSoqKrK7NAAG44wMgKCIiopSamqqMjMzdd9996mgoEBvvvmm4uPj9ec//9lr3XfeeUexsbFqampSVlaWJCknJ0cOh0PXX3+917rPPvus0tLSlJiYqOnTp+vYsWOevsOHD+unP/2p+vTpo5iYGI0ZM0YVFRWe/qVLl6p3795as2aNhg4dql69eukHP/iB9u/fH7iBAOBXBBkAtoiOjlZYWJgmTpyokpISr76SkhLddtttiouL05YtWyRJ69at0/79+7Vy5UrPeh9++KF2796tDz/8UK+88oqWLl2qpUuXevrvvvtuffLJJ1q1apXKyspkWZZuuukmr7DT0tKiZ599VsuWLdOGDRtUXV2tuXPnBnbnAfgNQQZAUFmWpXXr1mnNmjUaNWqU7rnnHq1Zs8ZzFuTAgQP64IMPNHnyZElScnKyJCkxMVGpqalKSEjwbKtPnz767W9/qyFDhmjcuHEaO3as1q9fL0mqqKjQqlWr9Kc//UnXXnutLr30Ur3++uv6+uuv9c4773i2cezYMS1ZskRXXHGFLr/8cj3wwAOebQDo/ggyAILi/fffV69evdSzZ0+NGTNGP/7xj/XYY4/pyiuv1PDhw/XKK69Ikl577TVlZmYqPz//rNscPny4wsPDPb+npaXpwIEDkqQdO3YoIiJCI0eO9PQnJibqwgsv1I4dOzxtMTExGjRo0Cm3AaD7I8gACIrvfe97Ki8vV0VFhb755hu98sorio2NlSTdc889nktCJSUl+tnPfiaHw3HWbUZGRnr97nA41NHR4VNdp9qGZVk+bQOAfQgyAIIiNjZWgwcPVv/+/RUR4f3A5F133aWqqiotXrxYX3zxhSZNmuTp69GjhySpvb3dp783dOhQffvtt9q8ebOn7eDBg9q5c6eGDRt2DnsCoDshyACwXZ8+fXTrrbfq4Ycf1o033qh+/fp5+vr27avo6GitXr1adXV1crlcndpmdna2brnlFk2dOlUbN27UP//5T91111264IILdMsttwRqVwAEGUEGQLcwZcoUtbW1eW7yPS4iIkKLFy/WSy+9pPT0dJ9CSElJiUaMGKFx48YpNzdXlmXpgw8+OOlyEgBzOSwuBgPoBpYtW6bZs2dr3759nstJAHA2zOwLwFYtLS3av3+/Fi5cqHvvvZcQA8AnXFoCYKtFixZpyJAhSk1N5XUFAHzGpSUAAGAszsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMb6f7wefHzOVThqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.scatter(\"Machine_learning\",\"Total\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "DUhMulvO6l5c",
        "outputId": "89266a12-c3ae-4291-de8d-9ad968922da2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Machine_learning', ylabel='Total'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot.scatter(\"Total\",\"Total\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "clMEVFvC9ETZ",
        "outputId": "32b2713f-38a4-49ef-edf2-909235bca336"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Total', ylabel='Total'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above visulization are giving straight line approximately.\n",
        "\n",
        "More the straighter line stronger the corelation between them."
      ],
      "metadata": {
        "id": "JFdJ1EQU65xk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YuocLudh9wVL",
        "outputId": "0fdcf7ae-1975-46d7-f544-ff2173e059ae"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank    Name  Age Gender Background  Python  Machine_learning    city  \\\n",
              "0     1    sara   25      F         IT      45                48  mumbai   \n",
              "1     2    lara   26      F         IT      45                47    pune   \n",
              "2     3   kiara   28      F     Non-IT      35                36  sangli   \n",
              "3     4   rahul   32      M     Non-IT      42                41  nagpur   \n",
              "4     5  sachin   30      M     Non-IT      20                38  nashik   \n",
              "\n",
              "   Total Grade  \n",
              "0     93     A  \n",
              "1     92     A  \n",
              "2     71     B  \n",
              "3     83     A  \n",
              "4     58     C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9c188b98-4a39-4114-af4e-bed9704ecd34\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "      <th>Total</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>93</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "      <td>71</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "      <td>83</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "      <td>58</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c188b98-4a39-4114-af4e-bed9704ecd34')\"\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-9c188b98-4a39-4114-af4e-bed9704ecd34 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-9c188b98-4a39-4114-af4e-bed9704ecd34');\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": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Slicing columns**\n",
        "\n",
        "using loc & iloc (location & indexlocation)\n",
        "\n",
        "In loc we use column names for fetching the data (label based indexing)\n",
        "\n",
        "In iloc we use index numbers for fetching the data ( Index based indexing)\n",
        "\n",
        "\n",
        "Consider if we want to fetch the value sangli from city column"
      ],
      "metadata": {
        "id": "DIJZkQSf9vRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#using loc\n",
        "df.loc[2,\"city\"]\n",
        "# here 2 is the index of row and city is name of column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "O4BcV_BS93P-",
        "outputId": "da7a9f0e-be3f-4282-ff77-4b8462de0ef1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sangli'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:,\"Name\":\"Machine_learning\"]\n",
        "#all rows and customized columns \n",
        "# here in loc start and end both are inclusive \n",
        "#syntax\n",
        "# df.loc[start row:end row,start col:end col]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "QD3xylRR_A74",
        "outputId": "48c06d6e-34c9-41f9-a277-16efd01cc9b8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Name  Age Gender Background  Python  Machine_learning\n",
              "0       sara   25      F         IT      45                48\n",
              "1       lara   26      F         IT      45                47\n",
              "2      kiara   28      F     Non-IT      35                36\n",
              "3      rahul   32      M     Non-IT      42                41\n",
              "4     sachin   30      M     Non-IT      20                38\n",
              "5     sarika   33      F     Non-IT      40                25\n",
              "6  dhanashri   35      F         IT      25                24\n",
              "7      naush   34      M         IT      28                34\n",
              "8     aditya   30      M     Non-IT      36                34\n",
              "9    nishant   28      M         IT      22                27"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a027fb39-8227-45a9-a08d-d9b1b076b59f\">\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>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a027fb39-8227-45a9-a08d-d9b1b076b59f')\"\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-a027fb39-8227-45a9-a08d-d9b1b076b59f 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-a027fb39-8227-45a9-a08d-d9b1b076b59f');\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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "miDZF-kEBO2u",
        "outputId": "c2ea9e50-adf9-43bd-d5a7-ca8b802b42e0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank    Name  Age Gender Background  Python  Machine_learning    city  \\\n",
              "0     1    sara   25      F         IT      45                48  mumbai   \n",
              "1     2    lara   26      F         IT      45                47    pune   \n",
              "2     3   kiara   28      F     Non-IT      35                36  sangli   \n",
              "3     4   rahul   32      M     Non-IT      42                41  nagpur   \n",
              "4     5  sachin   30      M     Non-IT      20                38  nashik   \n",
              "\n",
              "   Total Grade  \n",
              "0     93     A  \n",
              "1     92     A  \n",
              "2     71     B  \n",
              "3     83     A  \n",
              "4     58     C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-423920ef-0980-4c3b-b576-d8569a656d32\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "      <th>Total</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>93</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "      <td>71</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "      <td>83</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "      <td>58</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-423920ef-0980-4c3b-b576-d8569a656d32')\"\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-423920ef-0980-4c3b-b576-d8569a656d32 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-423920ef-0980-4c3b-b576-d8569a656d32');\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[:,[\"Rank\",\"Name\",\"Total\"]]\n",
        "# Here we can also slice the columns leaving some columns in between"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "pnVR5Qqc_hvV",
        "outputId": "1419f813-306e-421c-e736-dd19610f4b22"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Total\n",
              "0     1       sara     93\n",
              "1     2       lara     92\n",
              "2     3      kiara     71\n",
              "3     4      rahul     83\n",
              "4     5     sachin     58\n",
              "5     6     sarika     65\n",
              "6     7  dhanashri     49\n",
              "7     8      naush     62\n",
              "8     9     aditya     70\n",
              "9    10    nishant     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99029a8a-4daf-4375-a9f2-f751c5531de0\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99029a8a-4daf-4375-a9f2-f751c5531de0')\"\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-99029a8a-4daf-4375-a9f2-f751c5531de0 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-99029a8a-4daf-4375-a9f2-f751c5531de0');\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": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In iloc we use index numbers for fetching the data ( Index based indexing)**"
      ],
      "metadata": {
        "id": "uSSQBfXyBY18"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:,:7]\n",
        "# all rows and columns starting from index 0 to 6\n",
        "# here end is exclusive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "3YJotx4zA8lB",
        "outputId": "934a26c8-e2a2-4ec7-e9ec-3e46ea7c2c2b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning\n",
              "0     1       sara   25      F         IT      45                48\n",
              "1     2       lara   26      F         IT      45                47\n",
              "2     3      kiara   28      F     Non-IT      35                36\n",
              "3     4      rahul   32      M     Non-IT      42                41\n",
              "4     5     sachin   30      M     Non-IT      20                38\n",
              "5     6     sarika   33      F     Non-IT      40                25\n",
              "6     7  dhanashri   35      F         IT      25                24\n",
              "7     8      naush   34      M         IT      28                34\n",
              "8     9     aditya   30      M     Non-IT      36                34\n",
              "9    10    nishant   28      M         IT      22                27"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7dffa9cf-6e9f-43a5-be8e-d793094bffb4\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dffa9cf-6e9f-43a5-be8e-d793094bffb4')\"\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-7dffa9cf-6e9f-43a5-be8e-d793094bffb4 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-7dffa9cf-6e9f-43a5-be8e-d793094bffb4');\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": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In iloc -ve indexing is also supported.**\n",
        "\n",
        "let suppose we want columns till total in X variable.\n",
        "\n",
        "and Grade column in Y variable"
      ],
      "metadata": {
        "id": "J8xSu3JtBuii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1]\n",
        "x\n",
        "# you will get data frame 2D"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qE4COWB2Betd",
        "outputId": "38e58a5d-26f0-422f-a2d7-50f8a028a2c4"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning      city  \\\n",
              "0     1       sara   25      F         IT      45                48    mumbai   \n",
              "1     2       lara   26      F         IT      45                47      pune   \n",
              "2     3      kiara   28      F     Non-IT      35                36    sangli   \n",
              "3     4      rahul   32      M     Non-IT      42                41    nagpur   \n",
              "4     5     sachin   30      M     Non-IT      20                38    nashik   \n",
              "5     6     sarika   33      F     Non-IT      40                25  banglore   \n",
              "6     7  dhanashri   35      F         IT      25                24   chennai   \n",
              "7     8      naush   34      M         IT      28                34     surat   \n",
              "8     9     aditya   30      M     Non-IT      36                34     delhi   \n",
              "9    10    nishant   28      M         IT      22                27     thane   \n",
              "\n",
              "   Total  \n",
              "0     93  \n",
              "1     92  \n",
              "2     71  \n",
              "3     83  \n",
              "4     58  \n",
              "5     65  \n",
              "6     49  \n",
              "7     62  \n",
              "8     70  \n",
              "9     49  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e30dd05-8b92-4272-a278-f8b81310794f\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>banglore</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>chennai</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>surat</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>delhi</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>thane</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e30dd05-8b92-4272-a278-f8b81310794f')\"\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-6e30dd05-8b92-4272-a278-f8b81310794f 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-6e30dd05-8b92-4272-a278-f8b81310794f');\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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1]\n",
        "y\n",
        "# you will get series 1D"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1br3WczCUyq",
        "outputId": "6a242a03-6865-406d-87a1-8ec9cb5d937b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    A\n",
              "1    A\n",
              "2    B\n",
              "3    A\n",
              "4    C\n",
              "5    B\n",
              "6    C\n",
              "7    B\n",
              "8    B\n",
              "9    C\n",
              "Name: Grade, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dropping a column**"
      ],
      "metadata": {
        "id": "E6Cmr-jIl0UP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"city\",axis=1)\n",
        "#axis=1 for column axis=0 for row"
      ],
      "metadata": {
        "id": "3b1AtzqMCnn6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "336c95f0-f050-4266-b240-55bcd705135e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning  Total  \\\n",
              "0     1       sara   25      F         IT      45                48     93   \n",
              "1     2       lara   26      F         IT      45                47     92   \n",
              "2     3      kiara   28      F     Non-IT      35                36     71   \n",
              "3     4      rahul   32      M     Non-IT      42                41     83   \n",
              "4     5     sachin   30      M     Non-IT      20                38     58   \n",
              "5     6     sarika   33      F     Non-IT      40                25     65   \n",
              "6     7  dhanashri   35      F         IT      25                24     49   \n",
              "7     8      naush   34      M         IT      28                34     62   \n",
              "8     9     aditya   30      M     Non-IT      36                34     70   \n",
              "9    10    nishant   28      M         IT      22                27     49   \n",
              "\n",
              "  Grade  \n",
              "0     A  \n",
              "1     A  \n",
              "2     B  \n",
              "3     A  \n",
              "4     C  \n",
              "5     B  \n",
              "6     C  \n",
              "7     B  \n",
              "8     B  \n",
              "9     C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9af0227e-7d4d-46f7-8820-b9e4130f58e7\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>71</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>58</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>70</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9af0227e-7d4d-46f7-8820-b9e4130f58e7')\"\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-9af0227e-7d4d-46f7-8820-b9e4130f58e7 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-9af0227e-7d4d-46f7-8820-b9e4130f58e7');\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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#This change is not permanent \n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "WDKexLtEmOub",
        "outputId": "884b98db-6496-464e-8fc1-3928145b997f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning      city  \\\n",
              "0     1       sara   25      F         IT      45                48    mumbai   \n",
              "1     2       lara   26      F         IT      45                47      pune   \n",
              "2     3      kiara   28      F     Non-IT      35                36    sangli   \n",
              "3     4      rahul   32      M     Non-IT      42                41    nagpur   \n",
              "4     5     sachin   30      M     Non-IT      20                38    nashik   \n",
              "5     6     sarika   33      F     Non-IT      40                25  banglore   \n",
              "6     7  dhanashri   35      F         IT      25                24   chennai   \n",
              "7     8      naush   34      M         IT      28                34     surat   \n",
              "8     9     aditya   30      M     Non-IT      36                34     delhi   \n",
              "9    10    nishant   28      M         IT      22                27     thane   \n",
              "\n",
              "   Total Grade  \n",
              "0     93     A  \n",
              "1     92     A  \n",
              "2     71     B  \n",
              "3     83     A  \n",
              "4     58     C  \n",
              "5     65     B  \n",
              "6     49     C  \n",
              "7     62     B  \n",
              "8     70     B  \n",
              "9     49     C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e6f3bb5-9e93-4fe4-918d-016d65211083\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>city</th>\n",
              "      <th>Total</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>mumbai</td>\n",
              "      <td>93</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>pune</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>sangli</td>\n",
              "      <td>71</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>nagpur</td>\n",
              "      <td>83</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>nashik</td>\n",
              "      <td>58</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>banglore</td>\n",
              "      <td>65</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>chennai</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>surat</td>\n",
              "      <td>62</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>delhi</td>\n",
              "      <td>70</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>thane</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e6f3bb5-9e93-4fe4-918d-016d65211083')\"\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-1e6f3bb5-9e93-4fe4-918d-016d65211083 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-1e6f3bb5-9e93-4fe4-918d-016d65211083');\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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"city\",axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "yFP4piNHmjHU",
        "outputId": "4db983cf-c8dc-4518-e11b-b246c68b8733"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Gender Background  Python  Machine_learning  Total  \\\n",
              "0     1       sara   25      F         IT      45                48     93   \n",
              "1     2       lara   26      F         IT      45                47     92   \n",
              "2     3      kiara   28      F     Non-IT      35                36     71   \n",
              "3     4      rahul   32      M     Non-IT      42                41     83   \n",
              "4     5     sachin   30      M     Non-IT      20                38     58   \n",
              "5     6     sarika   33      F     Non-IT      40                25     65   \n",
              "6     7  dhanashri   35      F         IT      25                24     49   \n",
              "7     8      naush   34      M         IT      28                34     62   \n",
              "8     9     aditya   30      M     Non-IT      36                34     70   \n",
              "9    10    nishant   28      M         IT      22                27     49   \n",
              "\n",
              "  Grade  \n",
              "0     A  \n",
              "1     A  \n",
              "2     B  \n",
              "3     A  \n",
              "4     C  \n",
              "5     B  \n",
              "6     C  \n",
              "7     B  \n",
              "8     B  \n",
              "9     C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ff9bc5a-c740-41e6-bdbe-f30f9afe9dc0\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>71</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>58</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>F</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>M</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>70</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>M</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ff9bc5a-c740-41e6-bdbe-f30f9afe9dc0')\"\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-7ff9bc5a-c740-41e6-bdbe-f30f9afe9dc0 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-7ff9bc5a-c740-41e6-bdbe-f30f9afe9dc0');\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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for multiple column drop\n",
        "df.drop([\"Gender\",\"Grade\"],axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Z_13m757mtcq",
        "outputId": "3e0e4e64-189f-4098-ef1d-9f38012682ab"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Background  Python  Machine_learning  Total\n",
              "0     1       sara   25         IT      45                48     93\n",
              "1     2       lara   26         IT      45                47     92\n",
              "2     3      kiara   28     Non-IT      35                36     71\n",
              "3     4      rahul   32     Non-IT      42                41     83\n",
              "4     5     sachin   30     Non-IT      20                38     58\n",
              "5     6     sarika   33     Non-IT      40                25     65\n",
              "6     7  dhanashri   35         IT      25                24     49\n",
              "7     8      naush   34         IT      28                34     62\n",
              "8     9     aditya   30     Non-IT      36                34     70\n",
              "9    10    nishant   28         IT      22                27     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1af107a4-9c4e-464c-9728-ec58be6ec9b8\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>kiara</td>\n",
              "      <td>28</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1af107a4-9c4e-464c-9728-ec58be6ec9b8')\"\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-1af107a4-9c4e-464c-9728-ec58be6ec9b8 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-1af107a4-9c4e-464c-9728-ec58be6ec9b8');\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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For dropping rows we need to give index numbers of rows\n",
        "df.drop(2,axis=0,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "ehapxSemnCsn",
        "outputId": "b3fc8c0f-60c1-4bcc-8477-f137bc516ec8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank       Name  Age Background  Python  Machine_learning  Total\n",
              "0     1       sara   25         IT      45                48     93\n",
              "1     2       lara   26         IT      45                47     92\n",
              "3     4      rahul   32     Non-IT      42                41     83\n",
              "4     5     sachin   30     Non-IT      20                38     58\n",
              "5     6     sarika   33     Non-IT      40                25     65\n",
              "6     7  dhanashri   35         IT      25                24     49\n",
              "7     8      naush   34         IT      28                34     62\n",
              "8     9     aditya   30     Non-IT      36                34     70\n",
              "9    10    nishant   28         IT      22                27     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a3b3370-f452-4d91-a1b6-cdb2a382330e\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>sachin</td>\n",
              "      <td>30</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>20</td>\n",
              "      <td>38</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>dhanashri</td>\n",
              "      <td>35</td>\n",
              "      <td>IT</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>aditya</td>\n",
              "      <td>30</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a3b3370-f452-4d91-a1b6-cdb2a382330e')\"\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-5a3b3370-f452-4d91-a1b6-cdb2a382330e 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-5a3b3370-f452-4d91-a1b6-cdb2a382330e');\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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#For dropping multiple rows we need to give index numbers of rows\n",
        "df.drop([4,6,8],axis=0,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "tJhh_G0inVpp",
        "outputId": "9e1fd01f-c285-4683-f761-bf5a04ba4e38"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank     Name  Age Background  Python  Machine_learning  Total\n",
              "0     1     sara   25         IT      45                48     93\n",
              "1     2     lara   26         IT      45                47     92\n",
              "3     4    rahul   32     Non-IT      42                41     83\n",
              "5     6   sarika   33     Non-IT      40                25     65\n",
              "7     8    naush   34         IT      28                34     62\n",
              "9    10  nishant   28         IT      22                27     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e46ae49d-fac6-42ff-b8be-a0a720d90d19\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e46ae49d-fac6-42ff-b8be-a0a720d90d19')\"\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-e46ae49d-fac6-42ff-b8be-a0a720d90d19 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-e46ae49d-fac6-42ff-b8be-a0a720d90d19');\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": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reseting the row index**"
      ],
      "metadata": {
        "id": "U7wmNo5Annkp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "lP-6hXRjnldV",
        "outputId": "c1aaa250-f3be-46e2-9254-859e0fd9015f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  Rank     Name  Age Background  Python  Machine_learning  Total\n",
              "0      0     1     sara   25         IT      45                48     93\n",
              "1      1     2     lara   26         IT      45                47     92\n",
              "2      3     4    rahul   32     Non-IT      42                41     83\n",
              "3      5     6   sarika   33     Non-IT      40                25     65\n",
              "4      7     8    naush   34         IT      28                34     62\n",
              "5      9    10  nishant   28         IT      22                27     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd0c2a63-91a7-467c-adf3-063b8ae1c821\">\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>index</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd0c2a63-91a7-467c-adf3-063b8ae1c821')\"\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-cd0c2a63-91a7-467c-adf3-063b8ae1c821 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-cd0c2a63-91a7-467c-adf3-063b8ae1c821');\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": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here new index is assigned to the data frame.\n",
        "\n",
        "But we dont need this."
      ],
      "metadata": {
        "id": "htbYSxKvn83l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "otL3CVOgn686",
        "outputId": "4c1278f7-872d-4897-f61c-e741ec78ad12"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Rank     Name  Age Background  Python  Machine_learning  Total\n",
              "0     1     sara   25         IT      45                48     93\n",
              "1     2     lara   26         IT      45                47     92\n",
              "2     4    rahul   32     Non-IT      42                41     83\n",
              "3     6   sarika   33     Non-IT      40                25     65\n",
              "4     8    naush   34         IT      28                34     62\n",
              "5    10  nishant   28         IT      22                27     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea1d61d9-c3f2-4631-b1d8-3ade7a4a31a3\">\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>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10</td>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea1d61d9-c3f2-4631-b1d8-3ade7a4a31a3')\"\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-ea1d61d9-c3f2-4631-b1d8-3ade7a4a31a3 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-ea1d61d9-c3f2-4631-b1d8-3ade7a4a31a3');\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": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making particular column as index**"
      ],
      "metadata": {
        "id": "Ws9gl3YVoiPT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Here if we need Rank column as index\n",
        "#df.set(\"Column name\")\n",
        "df.set_index(\"Rank\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "4lEvkLXJoMfM",
        "outputId": "7b625410-606d-457c-ed0c-35ddd5fdfc3c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Name  Age Background  Python  Machine_learning  Total\n",
              "Rank                                                          \n",
              "1        sara   25         IT      45                48     93\n",
              "2        lara   26         IT      45                47     92\n",
              "4       rahul   32     Non-IT      42                41     83\n",
              "6      sarika   33     Non-IT      40                25     65\n",
              "8       naush   34         IT      28                34     62\n",
              "10    nishant   28         IT      22                27     49"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49a42fe6-4003-4c59-9369-31c6b3b2c179\">\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>Background</th>\n",
              "      <th>Python</th>\n",
              "      <th>Machine_learning</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</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>1</th>\n",
              "      <td>sara</td>\n",
              "      <td>25</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>48</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lara</td>\n",
              "      <td>26</td>\n",
              "      <td>IT</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>rahul</td>\n",
              "      <td>32</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>sarika</td>\n",
              "      <td>33</td>\n",
              "      <td>Non-IT</td>\n",
              "      <td>40</td>\n",
              "      <td>25</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>naush</td>\n",
              "      <td>34</td>\n",
              "      <td>IT</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>nishant</td>\n",
              "      <td>28</td>\n",
              "      <td>IT</td>\n",
              "      <td>22</td>\n",
              "      <td>27</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49a42fe6-4003-4c59-9369-31c6b3b2c179')\"\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-49a42fe6-4003-4c59-9369-31c6b3b2c179 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-49a42fe6-4003-4c59-9369-31c6b3b2c179');\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": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S2sEDKeCo7WA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}