Pandas_Top50.ipynb

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    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "1. Different kind of pandas:\n",
        "    1. Series\n",
        "    2. Dataframe"
      ],
      "metadata": {
        "id": "0_sBUux4jJtV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. how to intialise a series in pandas?\n",
        "import pandas as pd\n",
        "data = pd.Series()\n",
        "data.dtype\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yar1_6NRjGDi",
        "outputId": "319576e8-fcc6-4dfc-b611-e67683a8d7e1"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Series([], dtype: object)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJYrQhQZLx6Q",
        "outputId": "3aa5e768-17e7-488a-c603-30d5db5a1110"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "4    5\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "# 3.Create a series from an array\n",
        "import numpy as np\n",
        "data = np.array(['1','2','3','4','5'])\n",
        "df = pd.Series(data)\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Create a seies with custom index\n",
        "data = np.array([1,2,3,4,5])\n",
        "df = pd.Series(data, index = ['a','b', 'c', 'd', 'e'])\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sxedLEDkWCt",
        "outputId": "fd745bde-4948-47d6-e0b8-4ab1e3c69d04"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "d    4\n",
            "e    5\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Create a series from list\n",
        "l = [1,2,3,4,5]\n",
        "df = pd.Series(l)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hj4cMwo_k1eq",
        "outputId": "831a29cf-4551-4956-c16f-80d43ec7e1b2"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "4    5\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Create a series from dictionary\n",
        "d = {'a':1, 'b':2, 'c':3}\n",
        "df = pd.Series(d)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhvL9LLglJ_f",
        "outputId": "1b03bd79-0c71-4a1e-cea8-a7f920d217e7"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. Create a Series of length 3 of random number\n",
        "df = pd.Series(np.random.randn(3))\n",
        "print(df)\n",
        "\n",
        "df1 = pd.Series(np.random.randint(low = 1, high = 10, size=3))\n",
        "print(df1)\n",
        "\n",
        "df2 = pd.Series(range(3))\n",
        "print(df2)\n",
        "\n",
        "df3 = pd.Series(range(1,10,3))\n",
        "print(df3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcOCrFh8lcY4",
        "outputId": "242e595d-106a-4d7e-ffe4-93f76f10583f"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0   -0.012131\n",
            "1   -0.094413\n",
            "2   -0.003962\n",
            "dtype: float64\n",
            "0    5\n",
            "1    3\n",
            "2    2\n",
            "dtype: int64\n",
            "0    0\n",
            "1    1\n",
            "2    2\n",
            "dtype: int64\n",
            "0    1\n",
            "1    4\n",
            "2    7\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Create a copy in series\n",
        "data = [1,2,3,4,5]\n",
        "df = pd.Series(data)\n",
        "df1 = df.copy() #This is deep copy\n",
        "df[2] = 7\n",
        "#print(df,\"\\n\",df1)\n",
        "df2 = df.copy(deep=False) #this is shallow copy, change in original is reflected in copy also\n",
        "df[2] = 9\n",
        "print(df,\"\\n\",df2)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2z_huZIFmx19",
        "outputId": "9e6f1643-fd94-445d-c9a3-f868943705db"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "2    9\n",
            "3    4\n",
            "4    5\n",
            "dtype: int64 \n",
            " 0    1\n",
            "1    2\n",
            "2    9\n",
            "3    4\n",
            "4    5\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#9. Intialise a Empty DataFrame in pandas\n",
        "df = pd.DataFrame()\n",
        "print(df)\n",
        "\n",
        "data = []\n",
        "df1 = pd.DataFrame(data)\n",
        "print(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIrp86TPqd9W",
        "outputId": "c77569a9-a3da-4f87-dbcb-9d86dfd12209"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n",
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10. Create a dataframe using list\n",
        "l = [1,2,3,4,5]\n",
        "df = pd.DataFrame(l)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzJn4SUuupWM",
        "outputId": "765a099d-c4d3-4674-d945-e470dd0d5b1d"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   0\n",
            "0  1\n",
            "1  2\n",
            "2  3\n",
            "3  4\n",
            "4  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 11. Create a dataframe using dictionary\n",
        "d = {'ind': ['a', 'b', 'c'], 'value' : [1,2,3]}\n",
        "df = pd.DataFrame(d)\n",
        "print(df)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omhuUVn2vGNH",
        "outputId": "81a8378b-ff39-4234-f356-1fdf21de875e"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  ind  value\n",
            "0   a      1\n",
            "1   b      2\n",
            "2   c      3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 12. Series into dataframe\n",
        "df = pd.Series(['Hello', 'There', '!!'])\n",
        "print(pd.DataFrame(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lI4bhiHAvVQi",
        "outputId": "b5326c0c-65b9-4c0c-f526-2ae15a638b0e"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       0\n",
            "0  Hello\n",
            "1  There\n",
            "2     !!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 13. Read Csv files\n",
        "df = pd.read_csv('/content/data.csv')\n",
        "df"
      ],
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          "height": 774
        },
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      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  \\\n",
              "0             0     3846           3140.16                8                 4   \n",
              "1             1     8601           2811.00                4                 0   \n",
              "2             2     9121           4345.83                6                 3   \n",
              "3             3     5715           2246.86               10                 3   \n",
              "4             4      694           6640.74                3                 0   \n",
              "..          ...      ...               ...              ...               ...   \n",
              "995         995     8243           7324.89                9                 9   \n",
              "996         996     3447           3165.19                0                 0   \n",
              "997         997     4633           2174.33                5                 2   \n",
              "998         998     4762           2661.51                5                 2   \n",
              "999         999     6313           5117.22                5                 4   \n",
              "\n",
              "    User Location   Coupon Type  Coupon Usage Frequency  Website Visits  \\\n",
              "0             USA    Percentage                       4              69   \n",
              "1             USA    Percentage                       9              66   \n",
              "2           India  Fixed Amount                      10              33   \n",
              "3           India    Percentage                       3               5   \n",
              "4           India  Fixed Amount                       7               7   \n",
              "..            ...           ...                     ...             ...   \n",
              "995           USA  Fixed Amount                       3              95   \n",
              "996           USA  Fixed Amount                       4              97   \n",
              "997           USA    Percentage                       8              73   \n",
              "998           USA    Percentage                       0              40   \n",
              "999           USA    Percentage                       1              60   \n",
              "\n",
              "     Time Spent on Site (seconds)  \\\n",
              "0                            1207   \n",
              "1                             335   \n",
              "2                            1335   \n",
              "3                             724   \n",
              "4                             705   \n",
              "..                            ...   \n",
              "995                           563   \n",
              "996                          1173   \n",
              "997                           895   \n",
              "998                          1669   \n",
              "999                           509   \n",
              "\n",
              "                                    Purchase History  Purchased Completed  \n",
              "0                                          ['above']                 True  \n",
              "1                                       ['physical']                False  \n",
              "2    ['sense', 'receive', 'huge', 'rate', 'without']                False  \n",
              "3                       ['shoulder', 'let', 'class']                 True  \n",
              "4    ['camera', 'family', 'even', 'sell', 'brother']                False  \n",
              "..                                               ...                  ...  \n",
              "995                 ['throughout', 'happen', 'data']                 True  \n",
              "996  ['end', 'professional', 'good', 'trip', 'back']                 True  \n",
              "997                                ['deep', 'final']                 True  \n",
              "998                ['instead', 'particular', 'east']                False  \n",
              "999                               ['which', 'wrong']                False  \n",
              "\n",
              "[1000 rows x 12 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Minimum Purchase</th>\n",
              "      <th>Coupons Offered</th>\n",
              "      <th>Redeemed Coupons</th>\n",
              "      <th>User Location</th>\n",
              "      <th>Coupon Type</th>\n",
              "      <th>Coupon Usage Frequency</th>\n",
              "      <th>Website Visits</th>\n",
              "      <th>Time Spent on Site (seconds)</th>\n",
              "      <th>Purchase History</th>\n",
              "      <th>Purchased Completed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3846</td>\n",
              "      <td>3140.16</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>4</td>\n",
              "      <td>69</td>\n",
              "      <td>1207</td>\n",
              "      <td>['above']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>8601</td>\n",
              "      <td>2811.00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>9</td>\n",
              "      <td>66</td>\n",
              "      <td>335</td>\n",
              "      <td>['physical']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>9121</td>\n",
              "      <td>4345.83</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>India</td>\n",
              "      <td>Fixed Amount</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>1335</td>\n",
              "      <td>['sense', 'receive', 'huge', 'rate', 'without']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>5715</td>\n",
              "      <td>2246.86</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>India</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>724</td>\n",
              "      <td>['shoulder', 'let', 'class']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>694</td>\n",
              "      <td>6640.74</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>India</td>\n",
              "      <td>Fixed Amount</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>705</td>\n",
              "      <td>['camera', 'family', 'even', 'sell', 'brother']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>995</td>\n",
              "      <td>8243</td>\n",
              "      <td>7324.89</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>USA</td>\n",
              "      <td>Fixed Amount</td>\n",
              "      <td>3</td>\n",
              "      <td>95</td>\n",
              "      <td>563</td>\n",
              "      <td>['throughout', 'happen', 'data']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>996</td>\n",
              "      <td>3447</td>\n",
              "      <td>3165.19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Fixed Amount</td>\n",
              "      <td>4</td>\n",
              "      <td>97</td>\n",
              "      <td>1173</td>\n",
              "      <td>['end', 'professional', 'good', 'trip', 'back']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>997</td>\n",
              "      <td>4633</td>\n",
              "      <td>2174.33</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>8</td>\n",
              "      <td>73</td>\n",
              "      <td>895</td>\n",
              "      <td>['deep', 'final']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>998</td>\n",
              "      <td>4762</td>\n",
              "      <td>2661.51</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1669</td>\n",
              "      <td>['instead', 'particular', 'east']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>999</td>\n",
              "      <td>6313</td>\n",
              "      <td>5117.22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>509</td>\n",
              "      <td>['which', 'wrong']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 12 columns</p>\n",
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              "    </button>\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-f03bd8e9-c4e3-40a6-8b22-bf9e6bd5864a 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-f03bd8e9-c4e3-40a6-8b22-bf9e6bd5864a');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55e64bb2-50b0-4b93-b64e-bc51ae4a9443 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2d351b12-d280-426e-bb98-d6bf0bd6b345\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        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",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_2d351b12-d280-426e-bb98-d6bf0bd6b345 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288,\n        \"min\": 0,\n        \"max\": 999,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          521,\n          737,\n          740\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2874,\n        \"min\": 1,\n        \"max\": 9992,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2926,\n          5389,\n          7796\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2547.1060519388066,\n        \"min\": 1004.07,\n        \"max\": 9981.26,\n        \"num_unique_values\": 998,\n        \"samples\": [\n          6095.13,\n          3865.42,\n          2002.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"India\",\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Fixed Amount\",\n          \"Percentage\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          85,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent on Site (seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 517,\n        \"min\": 61,\n        \"max\": 1798,\n        \"num_unique_values\": 744,\n        \"samples\": [\n          407,\n          330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchase History\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 973,\n        \"samples\": [\n          \"['ready', 'half', 'box', 'cup']\",\n          \"['serious', 'prevent', 'room', 'seven']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased Completed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 14. Access first few rows of dataset\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "0LdEas-FxejS",
        "outputId": "0ef062ce-92e7-4850-e15d-6fea71434d82"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  \\\n",
              "0           0     3846           3140.16                8                 4   \n",
              "1           1     8601           2811.00                4                 0   \n",
              "2           2     9121           4345.83                6                 3   \n",
              "3           3     5715           2246.86               10                 3   \n",
              "4           4      694           6640.74                3                 0   \n",
              "\n",
              "  User Location   Coupon Type  Coupon Usage Frequency  Website Visits  \\\n",
              "0           USA    Percentage                       4              69   \n",
              "1           USA    Percentage                       9              66   \n",
              "2         India  Fixed Amount                      10              33   \n",
              "3         India    Percentage                       3               5   \n",
              "4         India  Fixed Amount                       7               7   \n",
              "\n",
              "   Time Spent on Site (seconds)  \\\n",
              "0                          1207   \n",
              "1                           335   \n",
              "2                          1335   \n",
              "3                           724   \n",
              "4                           705   \n",
              "\n",
              "                                  Purchase History  Purchased Completed  \n",
              "0                                        ['above']                 True  \n",
              "1                                     ['physical']                False  \n",
              "2  ['sense', 'receive', 'huge', 'rate', 'without']                False  \n",
              "3                     ['shoulder', 'let', 'class']                 True  \n",
              "4  ['camera', 'family', 'even', 'sell', 'brother']                False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7a877945-e46d-4085-ab60-4783b2f17c6e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Minimum Purchase</th>\n",
              "      <th>Coupons Offered</th>\n",
              "      <th>Redeemed Coupons</th>\n",
              "      <th>User Location</th>\n",
              "      <th>Coupon Type</th>\n",
              "      <th>Coupon Usage Frequency</th>\n",
              "      <th>Website Visits</th>\n",
              "      <th>Time Spent on Site (seconds)</th>\n",
              "      <th>Purchase History</th>\n",
              "      <th>Purchased Completed</th>\n",
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              "      <td>4</td>\n",
              "      <td>69</td>\n",
              "      <td>1207</td>\n",
              "      <td>['above']</td>\n",
              "      <td>True</td>\n",
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              "      <td>1</td>\n",
              "      <td>8601</td>\n",
              "      <td>2811.00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>9</td>\n",
              "      <td>66</td>\n",
              "      <td>335</td>\n",
              "      <td>['physical']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>9121</td>\n",
              "      <td>4345.83</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>India</td>\n",
              "      <td>Fixed Amount</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>1335</td>\n",
              "      <td>['sense', 'receive', 'huge', 'rate', 'without']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "      <td>2246.86</td>\n",
              "      <td>10</td>\n",
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              "      <td>India</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>724</td>\n",
              "      <td>['shoulder', 'let', 'class']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>6640.74</td>\n",
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              "      <td>Fixed Amount</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>705</td>\n",
              "      <td>['camera', 'family', 'even', 'sell', 'brother']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      buttonEl.style.display =\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288,\n        \"min\": 0,\n        \"max\": 999,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          521,\n          737,\n          740\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2874,\n        \"min\": 1,\n        \"max\": 9992,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          2926,\n          5389,\n          7796\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2547.1060519388066,\n        \"min\": 1004.07,\n        \"max\": 9981.26,\n        \"num_unique_values\": 998,\n        \"samples\": [\n          6095.13,\n          3865.42,\n          2002.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"India\",\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Fixed Amount\",\n          \"Percentage\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          85,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent on Site (seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 517,\n        \"min\": 61,\n        \"max\": 1798,\n        \"num_unique_values\": 744,\n        \"samples\": [\n          407,\n          330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchase History\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 973,\n        \"samples\": [\n          \"['ready', 'half', 'box', 'cup']\",\n          \"['serious', 'prevent', 'room', 'seven']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased Completed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#15. Acess last few rows of dataset\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "AtUAKoTpxxmm",
        "outputId": "2cae156c-35f2-4c96-a198-7b8748e7d9cf"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  \\\n",
              "995         995     8243           7324.89                9                 9   \n",
              "996         996     3447           3165.19                0                 0   \n",
              "997         997     4633           2174.33                5                 2   \n",
              "998         998     4762           2661.51                5                 2   \n",
              "999         999     6313           5117.22                5                 4   \n",
              "\n",
              "    User Location   Coupon Type  Coupon Usage Frequency  Website Visits  \\\n",
              "995           USA  Fixed Amount                       3              95   \n",
              "996           USA  Fixed Amount                       4              97   \n",
              "997           USA    Percentage                       8              73   \n",
              "998           USA    Percentage                       0              40   \n",
              "999           USA    Percentage                       1              60   \n",
              "\n",
              "     Time Spent on Site (seconds)  \\\n",
              "995                           563   \n",
              "996                          1173   \n",
              "997                           895   \n",
              "998                          1669   \n",
              "999                           509   \n",
              "\n",
              "                                    Purchase History  Purchased Completed  \n",
              "995                 ['throughout', 'happen', 'data']                 True  \n",
              "996  ['end', 'professional', 'good', 'trip', 'back']                 True  \n",
              "997                                ['deep', 'final']                 True  \n",
              "998                ['instead', 'particular', 'east']                False  \n",
              "999                               ['which', 'wrong']                False  "
            ],
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bb71ab6a-9554-4e0c-aa17-c05e5094e9f0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb71ab6a-9554-4e0c-aa17-c05e5094e9f0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb71ab6a-9554-4e0c-aa17-c05e5094e9f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 995,\n        \"max\": 999,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          996,\n          999,\n          997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1850,\n        \"min\": 3447,\n        \"max\": 8243,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3447,\n          6313,\n          4633\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2126.3427283530755,\n        \"min\": 2174.33,\n        \"max\": 7324.89,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3165.19,\n          5117.22,\n          2174.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          9,\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          4,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Percentage\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 40,\n        \"max\": 97,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent on Site (seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 477,\n        \"min\": 509,\n        \"max\": 1669,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1173\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchase History\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"['end', 'professional', 'good', 'trip', 'back']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased Completed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 16. Selecting partcilar number of rows\n",
        "df.iloc[100:105]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "-wrFwRau9T36",
        "outputId": "f3694921-6156-458e-db3a-ff7b99d29978"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  \\\n",
              "100         100      456           9981.26                5                 2   \n",
              "101         101     4190           4875.50                9                 5   \n",
              "102         102     2252           6747.54                9                 3   \n",
              "103         103     7818           1378.73                6                 6   \n",
              "104         104      794           5840.53               10                 3   \n",
              "\n",
              "    User Location   Coupon Type  Coupon Usage Frequency  Website Visits  \\\n",
              "100           USA    Percentage                       8              24   \n",
              "101         India  Fixed Amount                       9               4   \n",
              "102         India    Percentage                      10              55   \n",
              "103         India    Percentage                       4              25   \n",
              "104         India    Percentage                       2              25   \n",
              "\n",
              "     Time Spent on Site (seconds)                        Purchase History  \\\n",
              "100                          1468                               ['water']   \n",
              "101                           414  ['away', 'effort', 'economic', 'stay']   \n",
              "102                           628     ['reality', 'hear', 'list', 'note']   \n",
              "103                          1667                                  ['as']   \n",
              "104                           184              ['administration', 'main']   \n",
              "\n",
              "     Purchased Completed  \n",
              "100                 True  \n",
              "101                False  \n",
              "102                 True  \n",
              "103                 True  \n",
              "104                 True  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7453a784-d2dd-4e87-82cd-fa7a9e12be19\" 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>Unnamed: 0</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Minimum Purchase</th>\n",
              "      <th>Coupons Offered</th>\n",
              "      <th>Redeemed Coupons</th>\n",
              "      <th>User Location</th>\n",
              "      <th>Coupon Type</th>\n",
              "      <th>Coupon Usage Frequency</th>\n",
              "      <th>Website Visits</th>\n",
              "      <th>Time Spent on Site (seconds)</th>\n",
              "      <th>Purchase History</th>\n",
              "      <th>Purchased Completed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>100</td>\n",
              "      <td>456</td>\n",
              "      <td>9981.26</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>USA</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "      <td>1468</td>\n",
              "      <td>['water']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>101</td>\n",
              "      <td>4190</td>\n",
              "      <td>4875.50</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>India</td>\n",
              "      <td>Fixed Amount</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>414</td>\n",
              "      <td>['away', 'effort', 'economic', 'stay']</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>102</td>\n",
              "      <td>2252</td>\n",
              "      <td>6747.54</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>India</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>10</td>\n",
              "      <td>55</td>\n",
              "      <td>628</td>\n",
              "      <td>['reality', 'hear', 'list', 'note']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>103</td>\n",
              "      <td>7818</td>\n",
              "      <td>1378.73</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>India</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>1667</td>\n",
              "      <td>['as']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>104</td>\n",
              "      <td>794</td>\n",
              "      <td>5840.53</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>India</td>\n",
              "      <td>Percentage</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>184</td>\n",
              "      <td>['administration', 'main']</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7453a784-d2dd-4e87-82cd-fa7a9e12be19')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7453a784-d2dd-4e87-82cd-fa7a9e12be19 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-7453a784-d2dd-4e87-82cd-fa7a9e12be19');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4bf53868-af4d-408e-8a9a-8b51dadad59c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bf53868-af4d-408e-8a9a-8b51dadad59c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bf53868-af4d-408e-8a9a-8b51dadad59c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 100,\n        \"max\": 104,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          101,\n          104,\n          102\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3019,\n        \"min\": 456,\n        \"max\": 7818,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4190,\n          794,\n          2252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3113.6197732012815,\n        \"min\": 1378.73,\n        \"max\": 9981.26,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4875.5,\n          5840.53,\n          6747.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 5,\n        \"max\": 10,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9,\n          10,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 6,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"India\",\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Fixed Amount\",\n          \"Percentage\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2,\n        \"max\": 10,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 4,\n        \"max\": 55,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent on Site (seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 657,\n        \"min\": 184,\n        \"max\": 1667,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          414,\n          184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchase History\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"['away', 'effort', 'economic', 'stay']\",\n          \"['administration', 'main']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased Completed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 17. print all column name\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbYYySzc5xEe",
        "outputId": "7687dfb3-de95-4052-b9d3-b8931348174d"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'User ID', 'Minimum Purchase', 'Coupons Offered',\n",
              "       'Redeemed Coupons', 'User Location', 'Coupon Type',\n",
              "       'Coupon Usage Frequency', 'Website Visits',\n",
              "       'Time Spent on Site (seconds)', 'Purchase History',\n",
              "       'Purchased Completed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 18. Select a column\n",
        "df['user_location']\n",
        "df.user_location"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9M-OVRelx8V7",
        "outputId": "749d2d7d-91dc-41fc-b32f-c7e93fb4910c"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        USA\n",
              "1        USA\n",
              "2      India\n",
              "3      India\n",
              "4      India\n",
              "       ...  \n",
              "995      USA\n",
              "996      USA\n",
              "997      USA\n",
              "998      USA\n",
              "999      USA\n",
              "Name: user_location, Length: 1000, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 19. Rename a column\n",
        "df.rename(columns = {'User Location' : 'user_location'}, inplace=True)"
      ],
      "metadata": {
        "id": "UmOpCWFX5mHh"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 20. get all the info of data\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oj_Wz7TC59Ah",
        "outputId": "e3f58230-4e80-4050-898b-c7faf2e117a5"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 12 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   Unnamed: 0                    1000 non-null   int64  \n",
            " 1   User ID                       1000 non-null   int64  \n",
            " 2   Minimum Purchase              1000 non-null   float64\n",
            " 3   Coupons Offered               1000 non-null   int64  \n",
            " 4   Redeemed Coupons              1000 non-null   int64  \n",
            " 5   user_location                 1000 non-null   object \n",
            " 6   Coupon Type                   1000 non-null   object \n",
            " 7   Coupon Usage Frequency        1000 non-null   int64  \n",
            " 8   Website Visits                1000 non-null   int64  \n",
            " 9   Time Spent on Site (seconds)  1000 non-null   int64  \n",
            " 10  Purchase History              1000 non-null   object \n",
            " 11  Purchased Completed           1000 non-null   bool   \n",
            "dtypes: bool(1), float64(1), int64(7), object(3)\n",
            "memory usage: 87.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#21. How to remove a column\n",
        "df.drop('Purchase History', axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "Zq4jaGfm-J7m"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#22. How to remove a row\n",
        "df.drop(999, axis=0, inplace=True)"
      ],
      "metadata": {
        "id": "FvGwnJLB-gdf"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "d7HyRUyF-s-2",
        "outputId": "083e08aa-0e40-4e65-d835-8076f434352a"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  \\\n",
              "994         994      332           9120.73                7                 0   \n",
              "995         995     8243           7324.89                9                 9   \n",
              "996         996     3447           3165.19                0                 0   \n",
              "997         997     4633           2174.33                5                 2   \n",
              "998         998     4762           2661.51                5                 2   \n",
              "\n",
              "    user_location   Coupon Type  Coupon Usage Frequency  Website Visits  \\\n",
              "994           USA  Fixed Amount                       1              58   \n",
              "995           USA  Fixed Amount                       3              95   \n",
              "996           USA  Fixed Amount                       4              97   \n",
              "997           USA    Percentage                       8              73   \n",
              "998           USA    Percentage                       0              40   \n",
              "\n",
              "     Time Spent on Site (seconds)  Purchased Completed  \n",
              "994                          1546                 True  \n",
              "995                           563                 True  \n",
              "996                          1173                 True  \n",
              "997                           895                 True  \n",
              "998                          1669                False  "
            ],
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              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd67a9db-325a-4aae-a73a-1ab1dc200b1c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cd67a9db-325a-4aae-a73a-1ab1dc200b1c 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-cd67a9db-325a-4aae-a73a-1ab1dc200b1c');\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",
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              "\n",
              "\n",
              "<div id=\"df-c6ce6ae8-e332-4513-8d2e-8548090a7839\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c6ce6ae8-e332-4513-8d2e-8548090a7839 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 994,\n        \"max\": 998,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          995,\n          998,\n          996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2843,\n        \"min\": 332,\n        \"max\": 8243,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8243,\n          4762,\n          3447\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3128.248877790896,\n        \"min\": 2174.33,\n        \"max\": 9120.73,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7324.89,\n          2661.51,\n          3165.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9,\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          9,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"USA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Percentage\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 40,\n        \"max\": 97,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent on Site (seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 456,\n        \"min\": 563,\n        \"max\": 1669,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          563\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased Completed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 24. Print the unique value in a column\n",
        "df['user_location'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ic7b2-jl_LFh",
        "outputId": "78754803-e862-4155-9007-b1cefeaa70a2"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['USA', 'India'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 23. How to map values in pandas\n",
        "df['user_location'] = df['user_location'].map({'USA':0, 'India':1})\n",
        "df['user_location'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-tLIj82-v0d",
        "outputId": "84ac4911-c03f-4be5-e9c3-cf7c9b007d29"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Coupon Type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBFSwW1HC89k",
        "outputId": "08cae337-c7ff-4385-e6dc-0da4fe8b224a"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Percentage', 'Fixed Amount'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Coupon Type'] = df['Coupon Type'].map({'Percentage':1, 'Fixed Amount':0})"
      ],
      "metadata": {
        "id": "MYtqRQm_BGfL"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 24. How to Find the Correlation Using Pandas\n",
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "BDOSVoEaCghA",
        "outputId": "37075da9-d1a5-431c-9241-67dc27ce3ab2"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Unnamed: 0   User ID  Minimum Purchase  \\\n",
              "Unnamed: 0                      1.000000 -0.002171          0.026005   \n",
              "User ID                        -0.002171  1.000000          0.007367   \n",
              "Minimum Purchase                0.026005  0.007367          1.000000   \n",
              "Coupons Offered                -0.039798  0.002602          0.022922   \n",
              "Redeemed Coupons               -0.027264 -0.017738          0.030320   \n",
              "user_location                   0.025349  0.009212         -0.023691   \n",
              "Coupon Type                     0.014543 -0.040165         -0.002389   \n",
              "Coupon Usage Frequency          0.007315 -0.021047         -0.013106   \n",
              "Website Visits                  0.006402  0.067028          0.026333   \n",
              "Time Spent on Site (seconds)   -0.002593 -0.045428          0.010706   \n",
              "Purchased Completed            -0.036374 -0.009235         -0.011193   \n",
              "\n",
              "                              Coupons Offered  Redeemed Coupons  \\\n",
              "Unnamed: 0                          -0.039798         -0.027264   \n",
              "User ID                              0.002602         -0.017738   \n",
              "Minimum Purchase                     0.022922          0.030320   \n",
              "Coupons Offered                      1.000000          0.614392   \n",
              "Redeemed Coupons                     0.614392          1.000000   \n",
              "user_location                       -0.009511          0.012804   \n",
              "Coupon Type                          0.019514          0.021676   \n",
              "Coupon Usage Frequency              -0.039666         -0.033027   \n",
              "Website Visits                      -0.008932          0.039285   \n",
              "Time Spent on Site (seconds)        -0.032751         -0.079040   \n",
              "Purchased Completed                 -0.009079         -0.056979   \n",
              "\n",
              "                              user_location  Coupon Type  \\\n",
              "Unnamed: 0                         0.025349     0.014543   \n",
              "User ID                            0.009212    -0.040165   \n",
              "Minimum Purchase                  -0.023691    -0.002389   \n",
              "Coupons Offered                   -0.009511     0.019514   \n",
              "Redeemed Coupons                   0.012804     0.021676   \n",
              "user_location                      1.000000    -0.010095   \n",
              "Coupon Type                       -0.010095     1.000000   \n",
              "Coupon Usage Frequency            -0.038936    -0.050167   \n",
              "Website Visits                    -0.002114     0.012650   \n",
              "Time Spent on Site (seconds)      -0.022119    -0.004501   \n",
              "Purchased Completed                0.042158    -0.005736   \n",
              "\n",
              "                              Coupon Usage Frequency  Website Visits  \\\n",
              "Unnamed: 0                                  0.007315        0.006402   \n",
              "User ID                                    -0.021047        0.067028   \n",
              "Minimum Purchase                           -0.013106        0.026333   \n",
              "Coupons Offered                            -0.039666       -0.008932   \n",
              "Redeemed Coupons                           -0.033027        0.039285   \n",
              "user_location                              -0.038936       -0.002114   \n",
              "Coupon Type                                -0.050167        0.012650   \n",
              "Coupon Usage Frequency                      1.000000       -0.024879   \n",
              "Website Visits                             -0.024879        1.000000   \n",
              "Time Spent on Site (seconds)               -0.055250       -0.015479   \n",
              "Purchased Completed                         0.069869        0.021245   \n",
              "\n",
              "                              Time Spent on Site (seconds)  \\\n",
              "Unnamed: 0                                       -0.002593   \n",
              "User ID                                          -0.045428   \n",
              "Minimum Purchase                                  0.010706   \n",
              "Coupons Offered                                  -0.032751   \n",
              "Redeemed Coupons                                 -0.079040   \n",
              "user_location                                    -0.022119   \n",
              "Coupon Type                                      -0.004501   \n",
              "Coupon Usage Frequency                           -0.055250   \n",
              "Website Visits                                   -0.015479   \n",
              "Time Spent on Site (seconds)                      1.000000   \n",
              "Purchased Completed                               0.019185   \n",
              "\n",
              "                              Purchased Completed  \n",
              "Unnamed: 0                              -0.036374  \n",
              "User ID                                 -0.009235  \n",
              "Minimum Purchase                        -0.011193  \n",
              "Coupons Offered                         -0.009079  \n",
              "Redeemed Coupons                        -0.056979  \n",
              "user_location                            0.042158  \n",
              "Coupon Type                             -0.005736  \n",
              "Coupon Usage Frequency                   0.069869  \n",
              "Website Visits                           0.021245  \n",
              "Time Spent on Site (seconds)             0.019185  \n",
              "Purchased Completed                      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61057fdb-0f90-4c7e-b83c-3895282d0129\" 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>Unnamed: 0</th>\n",
              "      <th>User ID</th>\n",
              "      <th>Minimum Purchase</th>\n",
              "      <th>Coupons Offered</th>\n",
              "      <th>Redeemed Coupons</th>\n",
              "      <th>user_location</th>\n",
              "      <th>Coupon Type</th>\n",
              "      <th>Coupon Usage Frequency</th>\n",
              "      <th>Website Visits</th>\n",
              "      <th>Time Spent on Site (seconds)</th>\n",
              "      <th>Purchased Completed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.002171</td>\n",
              "      <td>0.026005</td>\n",
              "      <td>-0.039798</td>\n",
              "      <td>-0.027264</td>\n",
              "      <td>0.025349</td>\n",
              "      <td>0.014543</td>\n",
              "      <td>0.007315</td>\n",
              "      <td>0.006402</td>\n",
              "      <td>-0.002593</td>\n",
              "      <td>-0.036374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>-0.002171</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.007367</td>\n",
              "      <td>0.002602</td>\n",
              "      <td>-0.017738</td>\n",
              "      <td>0.009212</td>\n",
              "      <td>-0.040165</td>\n",
              "      <td>-0.021047</td>\n",
              "      <td>0.067028</td>\n",
              "      <td>-0.045428</td>\n",
              "      <td>-0.009235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Minimum Purchase</th>\n",
              "      <td>0.026005</td>\n",
              "      <td>0.007367</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022922</td>\n",
              "      <td>0.030320</td>\n",
              "      <td>-0.023691</td>\n",
              "      <td>-0.002389</td>\n",
              "      <td>-0.013106</td>\n",
              "      <td>0.026333</td>\n",
              "      <td>0.010706</td>\n",
              "      <td>-0.011193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coupons Offered</th>\n",
              "      <td>-0.039798</td>\n",
              "      <td>0.002602</td>\n",
              "      <td>0.022922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.614392</td>\n",
              "      <td>-0.009511</td>\n",
              "      <td>0.019514</td>\n",
              "      <td>-0.039666</td>\n",
              "      <td>-0.008932</td>\n",
              "      <td>-0.032751</td>\n",
              "      <td>-0.009079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Redeemed Coupons</th>\n",
              "      <td>-0.027264</td>\n",
              "      <td>-0.017738</td>\n",
              "      <td>0.030320</td>\n",
              "      <td>0.614392</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.012804</td>\n",
              "      <td>0.021676</td>\n",
              "      <td>-0.033027</td>\n",
              "      <td>0.039285</td>\n",
              "      <td>-0.079040</td>\n",
              "      <td>-0.056979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_location</th>\n",
              "      <td>0.025349</td>\n",
              "      <td>0.009212</td>\n",
              "      <td>-0.023691</td>\n",
              "      <td>-0.009511</td>\n",
              "      <td>0.012804</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010095</td>\n",
              "      <td>-0.038936</td>\n",
              "      <td>-0.002114</td>\n",
              "      <td>-0.022119</td>\n",
              "      <td>0.042158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coupon Type</th>\n",
              "      <td>0.014543</td>\n",
              "      <td>-0.040165</td>\n",
              "      <td>-0.002389</td>\n",
              "      <td>0.019514</td>\n",
              "      <td>0.021676</td>\n",
              "      <td>-0.010095</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.050167</td>\n",
              "      <td>0.012650</td>\n",
              "      <td>-0.004501</td>\n",
              "      <td>-0.005736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coupon Usage Frequency</th>\n",
              "      <td>0.007315</td>\n",
              "      <td>-0.021047</td>\n",
              "      <td>-0.013106</td>\n",
              "      <td>-0.039666</td>\n",
              "      <td>-0.033027</td>\n",
              "      <td>-0.038936</td>\n",
              "      <td>-0.050167</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.024879</td>\n",
              "      <td>-0.055250</td>\n",
              "      <td>0.069869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Website Visits</th>\n",
              "      <td>0.006402</td>\n",
              "      <td>0.067028</td>\n",
              "      <td>0.026333</td>\n",
              "      <td>-0.008932</td>\n",
              "      <td>0.039285</td>\n",
              "      <td>-0.002114</td>\n",
              "      <td>0.012650</td>\n",
              "      <td>-0.024879</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.015479</td>\n",
              "      <td>0.021245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time Spent on Site (seconds)</th>\n",
              "      <td>-0.002593</td>\n",
              "      <td>-0.045428</td>\n",
              "      <td>0.010706</td>\n",
              "      <td>-0.032751</td>\n",
              "      <td>-0.079040</td>\n",
              "      <td>-0.022119</td>\n",
              "      <td>-0.004501</td>\n",
              "      <td>-0.055250</td>\n",
              "      <td>-0.015479</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.019185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Purchased Completed</th>\n",
              "      <td>-0.036374</td>\n",
              "      <td>-0.009235</td>\n",
              "      <td>-0.011193</td>\n",
              "      <td>-0.009079</td>\n",
              "      <td>-0.056979</td>\n",
              "      <td>0.042158</td>\n",
              "      <td>-0.005736</td>\n",
              "      <td>0.069869</td>\n",
              "      <td>0.021245</td>\n",
              "      <td>0.019185</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7c05574f-04ca-497a-a72e-69f3333f10f0 button');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3032326781002591,\n        \"min\": -0.03979791356179781,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.025349056400467706,\n          1.0,\n          -0.0025926349494396436\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30447404706886083,\n        \"min\": -0.045428216639061124,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.009211819425411431,\n          -0.002171483802951862,\n          -0.045428216639061124\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2998545337745703,\n        \"min\": -0.023690678322248766,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.023690678322248766,\n          0.02600499340150594,\n          0.010706108852698138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3424794201064491,\n        \"min\": -0.03979791356179781,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.00951090645513091,\n          -0.03979791356179781,\n          -0.03275148537134614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n     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\"num_unique_values\": 11,\n        \"samples\": [\n          -0.010095336276700515,\n          0.014542852553167613,\n          -0.004501235602420712\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30944721102229356,\n        \"min\": -0.05525005160817979,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.03893644602530721,\n          0.007315499247228475,\n          -0.05525005160817979\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29899408542101047,\n        \"min\": -0.024879326806254064,\n        \"max\": 1.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          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            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 25. How to Convert a DataFrame into a Numpy Array\n",
        "df.to_numpy()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "8wkn6SHiDJcE",
        "outputId": "e0df5e29-01a4-4791-be08-6f93451ad343"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  \\\n",
              "0             0     3846           3140.16                8                 4   \n",
              "1             1     8601           2811.00                4                 0   \n",
              "2             2     9121           4345.83                6                 3   \n",
              "3             3     5715           2246.86               10                 3   \n",
              "4             4      694           6640.74                3                 0   \n",
              "..          ...      ...               ...              ...               ...   \n",
              "994         994      332           9120.73                7                 0   \n",
              "995         995     8243           7324.89                9                 9   \n",
              "996         996     3447           3165.19                0                 0   \n",
              "997         997     4633           2174.33                5                 2   \n",
              "998         998     4762           2661.51                5                 2   \n",
              "\n",
              "     user_location  Coupon Type  Coupon Usage Frequency  Website Visits  \\\n",
              "0                0            1                       4              69   \n",
              "1                0            1                       9              66   \n",
              "2                1            0                      10              33   \n",
              "3                1            1                       3               5   \n",
              "4                1            0                       7               7   \n",
              "..             ...          ...                     ...             ...   \n",
              "994              0            0                       1              58   \n",
              "995              0            0                       3              95   \n",
              "996              0            0                       4              97   \n",
              "997              0            1                       8              73   \n",
              "998              0            1                       0              40   \n",
              "\n",
              "     Time Spent on Site (seconds)  Purchased Completed  \n",
              "0                            1207                 True  \n",
              "1                             335                False  \n",
              "2                            1335                False  \n",
              "3                             724                 True  \n",
              "4                             705                False  \n",
              "..                            ...                  ...  \n",
              "994                          1546                 True  \n",
              "995                           563                 True  \n",
              "996                          1173                 True  \n",
              "997                           895                 True  \n",
              "998                          1669                False  \n",
              "\n",
              "[999 rows x 11 columns]"
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              "      <th>994</th>\n",
              "      <td>994</td>\n",
              "      <td>332</td>\n",
              "      <td>9120.73</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>58</td>\n",
              "      <td>1546</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>995</td>\n",
              "      <td>8243</td>\n",
              "      <td>7324.89</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>95</td>\n",
              "      <td>563</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>996</td>\n",
              "      <td>3447</td>\n",
              "      <td>3165.19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>97</td>\n",
              "      <td>1173</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>997</td>\n",
              "      <td>4633</td>\n",
              "      <td>2174.33</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>73</td>\n",
              "      <td>895</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>998</td>\n",
              "      <td>4762</td>\n",
              "      <td>2661.51</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>1669</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288,\n        \"min\": 0,\n        \"max\": 998,\n        \"num_unique_values\": 999,\n        \"samples\": [\n          453,\n          793,\n          209\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2875,\n        \"min\": 1,\n        \"max\": 9992,\n        \"num_unique_values\": 999,\n        \"samples\": [\n          5921,\n          1081,\n          3674\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2548.3345225512335,\n        \"min\": 1004.07,\n        \"max\": 9981.26,\n        \"num_unique_values\": 997,\n        \"samples\": [\n          6095.13,\n          3865.42,\n          2002.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupons Offered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          8,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          4,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_location\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coupon Usage Frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Website Visits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          85,\n          84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Spent on Site (seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 517,\n        \"min\": 61,\n        \"max\": 1798,\n        \"num_unique_values\": 744,\n        \"samples\": [\n          407,\n          330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased Completed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 26. Split a data based on some condition\n",
        "df1 = df['user_location']>0\n",
        "print(df1.value_counts())\n",
        "\n",
        "df2 = df[df['user_location']==0]\n",
        "print(df2.value_counts())"
      ],
      "metadata": {
        "id": "UftMP-fUD2z5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e472cf2b-b250-4975-c336-11c7e471393f"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user_location\n",
            "True     518\n",
            "False    481\n",
            "Name: count, dtype: int64\n",
            "Unnamed: 0  User ID  Minimum Purchase  Coupons Offered  Redeemed Coupons  user_location  Coupon Type  Coupon Usage Frequency  Website Visits  Time Spent on Site (seconds)  Purchased Completed\n",
            "0           3846     3140.16           8                4                 0              1            4                       69              1207                          True                   1\n",
            "485         796      8367.73           1                1                 0              0            10                      44              566                           True                   1\n",
            "657         592      1127.98           3                3                 0              1            5                       20              850                           True                   1\n",
            "656         3459     8786.25           8                6                 0              1            10                      22              414                           True                   1\n",
            "655         9856     2776.39           5                1                 0              0            6                       48              1615                          False                  1\n",
            "                                                                                                                                                                                                  ..\n",
            "308         367      3244.14           8                7                 0              1            6                       58              1370                          False                  1\n",
            "307         4703     5720.60           9                8                 0              0            1                       14              373                           False                  1\n",
            "305         5353     5866.55           2                1                 0              1            5                       24              1036                          False                  1\n",
            "304         9053     7856.51           5                0                 0              0            2                       5               797                           True                   1\n",
            "998         4762     2661.51           5                2                 0              1            0                       40              1669                          False                  1\n",
            "Name: count, Length: 481, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 27. Data aggregration in pandas - summarizing of data\n",
        "df.agg({'Minimum Purchase': ['sum', 'min', 'max'], 'Redeemed Coupons': 'count'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "eWoSWu69miY7",
        "outputId": "7cc1f67c-3c7f-4a93-838e-c9db7b79855f"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Minimum Purchase  Redeemed Coupons\n",
              "sum          5602438.14               NaN\n",
              "min             1004.07               NaN\n",
              "max             9981.26               NaN\n",
              "count               NaN             999.0"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Minimum Purchase</th>\n",
              "      <th>Redeemed Coupons</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>5602438.14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1004.07</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9981.26</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>NaN</td>\n",
              "      <td>999.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea1d0b5f-982f-425d-bca5-ff54092020dd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ea1d0b5f-982f-425d-bca5-ff54092020dd 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-ea1d0b5f-982f-425d-bca5-ff54092020dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d76b9fd6-ff9e-4299-91de-8cea7a80917d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d76b9fd6-ff9e-4299-91de-8cea7a80917d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d76b9fd6-ff9e-4299-91de-8cea7a80917d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Minimum Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3231401.094085877,\n        \"min\": 1004.07,\n        \"max\": 5602438.140000001,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5602438.140000001,\n          1004.07,\n          9981.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Redeemed Coupons\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 999.0,\n        \"max\": 999.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yG8iRQpyD9q",
        "outputId": "9cf6f14a-6b5b-4e92-af2e-54830f7fa5af"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 12 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   Unnamed: 0                    1000 non-null   int64  \n",
            " 1   User ID                       1000 non-null   int64  \n",
            " 2   Minimum Purchase              1000 non-null   float64\n",
            " 3   Coupons Offered               1000 non-null   int64  \n",
            " 4   Redeemed Coupons              1000 non-null   int64  \n",
            " 5   User Location                 1000 non-null   object \n",
            " 6   Coupon Type                   1000 non-null   object \n",
            " 7   Coupon Usage Frequency        1000 non-null   int64  \n",
            " 8   Website Visits                1000 non-null   int64  \n",
            " 9   Time Spent on Site (seconds)  1000 non-null   int64  \n",
            " 10  Purchase History              1000 non-null   object \n",
            " 11  Purchased Completed           1000 non-null   bool   \n",
            "dtypes: bool(1), float64(1), int64(7), object(3)\n",
            "memory usage: 87.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 28. Map in pandas - used for element-wise operations on a Series\n",
        "df = pd.Series([1,2,3,4])\n",
        "df1 = df.map(lambda x: x**2)\n",
        "print(df,\"\\n\",df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uinaqkv1s1sT",
        "outputId": "7e17e87c-8496-4be8-cd74-090fc717c028"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "dtype: int64 \n",
            " 0     1\n",
            "1     4\n",
            "2     9\n",
            "3    16\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 28. ApplyMap in pandas - used for element-wise operations on a Dataframe\n",
        "df = pd.DataFrame([[1, 2], [3, 4], [5, 6]])\n",
        "df1 = df.applymap(lambda x: x**2)\n",
        "print(df,\"\\nNext\\n\",df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zZX2PUPuXvf",
        "outputId": "7bd67227-68e9-4092-e504-9b36b530bff2"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   0  1\n",
            "0  1  2\n",
            "1  3  4\n",
            "2  5  6 \n",
            "Next\n",
            "     0   1\n",
            "0   1   4\n",
            "1   9  16\n",
            "2  25  36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 29. Apply: Applying a function along the axis of a DataFrame\n",
        "df = pd.DataFrame([[1, 2], [3, 4], [5, 6]])\n",
        "df1 = df.apply(sum) #by default along axis 0\n",
        "df2 = df.apply(sum, axis=1)\n",
        "print(df,\"\\nNext\\n\",df1,\"\\nNext\\n\",df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcuCPO7avAVR",
        "outputId": "ec1b4689-13df-4afb-fbbf-f8670965c72c"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   0  1\n",
            "0  1  2\n",
            "1  3  4\n",
            "2  5  6 \n",
            "Next\n",
            " 0     9\n",
            "1    12\n",
            "dtype: int64 \n",
            "Next\n",
            " 0     3\n",
            "1     7\n",
            "2    11\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 30. Create a pivot table - summarizes and aggregates data in a tabular format\n",
        "data = {\n",
        "    'Date': ['2024-05-01', '2024-05-01', '2024-05-02', '2024-05-02', '2024-05-03'],\n",
        "    'City': ['New York', 'Los Angeles', 'New York', 'Los Angeles', 'New York'],\n",
        "    'Temperature': [70, 75, 72, 78, 74],\n",
        "    'Humidity': [50, 60, 55, 65, 58]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "pivot = df.pivot_table(index='Date', columns='City', values=['Temperature', 'Humidity'])\n",
        "print(pivot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDKxVgbKvX0K",
        "outputId": "12bba425-693f-45a0-87ba-38e37c327d18"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              Humidity          Temperature         \n",
            "City       Los Angeles New York Los Angeles New York\n",
            "Date                                                \n",
            "2024-05-01        60.0     50.0        75.0     70.0\n",
            "2024-05-02        65.0     55.0        78.0     72.0\n",
            "2024-05-03         NaN     58.0         NaN     74.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 31. grouoby in pandas - performs aggregation on grouped data of one or more columns\n",
        "df.groupby(['Date']).size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7UXVg8ZxUZJ",
        "outputId": "af009a9c-b0d4-49b0-e0b1-14691b18151b"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2024-05-01    2\n",
              "2024-05-02    2\n",
              "2024-05-03    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 32. Convert a string to Datetime\n",
        "data = '23-07-2019'\n",
        "df = pd.to_datetime(data)\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVcxXXyK2DjP",
        "outputId": "8e5a5f09-75ec-48c3-dabf-1cd79a75e0e8"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2019-07-23 00:00:00\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-130-9f34c00b5ccb>:3: UserWarning: Parsing dates in %d-%m-%Y format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
            "  df = pd.to_datetime(data)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 33. How to drop duplicates\n",
        "df = pd.Series( ['2024-05-01', '2024-05-01', '2024-05-02', '2024-05-02', '2024-05-03'])\n",
        "print(df)\n",
        "df.drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MQ5XSrT2uZU",
        "outputId": "a6f98b51-2e63-4ca7-fb62-5af89447e5f5"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    2024-05-01\n",
            "1    2024-05-01\n",
            "2    2024-05-02\n",
            "3    2024-05-02\n",
            "4    2024-05-03\n",
            "dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2024-05-01\n",
              "2    2024-05-02\n",
              "4    2024-05-03\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 34. Calculate standard deviation\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "df.std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VY888eKK3TVD",
        "outputId": "7d05c4b1-976e-45ad-c92c-28c4cef4f928"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.5811388300841898"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 35. Calculate Variance\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "df.var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5P4sLIg63Mp9",
        "outputId": "0a9240ad-8ed0-48d0-ef99-4b7866859a97"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 36. Calculate Mean\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "df.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7k4743xAlPc",
        "outputId": "d252164f-4a77-45d2-b0c9-8cd2d94002d1"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 37. Calculate Mode\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "print(df.mode())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qrIkcVLAn7q",
        "outputId": "4235e03a-f2da-4e6a-e356-f49eb362af78"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "4    5\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 38. Calculate Median\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "df.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cszoypsuAohQ",
        "outputId": "5b10fe59-7f80-4031-9385-cf54438ad619"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 39. Calculate Cumulative Sum\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "df.cumsum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGpq7LX_3l1l",
        "outputId": "455ba541-9b1d-458e-cc69-aae65aaf9330"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     1\n",
              "1     3\n",
              "2     6\n",
              "3    10\n",
              "4    15\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 40. Check all true\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "print(df.all())\n",
        "df[len(df)] = False\n",
        "df.all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0V_K1a753sTg",
        "outputId": "0cf4a6fd-4f76-477b-91bf-f9d934140527"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 41. Check if any true\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "print(df.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rYG3u8A33V1",
        "outputId": "d68e9904-823d-4afd-b152-5f914ee02c62"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 42. Check if value exist\n",
        "df = pd.Series([1,2,3,4,5])\n",
        "print(df.isin([10]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OspaWqT54rvJ",
        "outputId": "d7f4a189-0a37-492d-a0e3-f11047b2f285"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    False\n",
            "1    False\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 43. Sort a Series\n",
        "df.sort_values()\n",
        "print(df)\n",
        "df1 = df.sort_values(ascending=False)\n",
        "print(df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAauEG844-yE",
        "outputId": "3e16ab2c-b64d-4e28-d620-bc6954dd2d73"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4    5\n",
            "3    4\n",
            "2    3\n",
            "1    2\n",
            "0    1\n",
            "dtype: int64\n",
            "4    5\n",
            "3    4\n",
            "2    3\n",
            "1    2\n",
            "0    1\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 44. Sum of a series\n",
        "data = {\n",
        "    'Date': ['2024-05-01', '2024-05-01', '2024-05-02', '2024-05-02', '2024-05-03'],\n",
        "    'City': ['New York', 'Los Angeles', 'New York', 'Los Angeles', 'New York'],\n",
        "    'Temperature': [70, 75, 72, 78, 74],\n",
        "    'Humidity': [50, 60, 55, 65, 58]\n",
        "}\n",
        "df = pd.DataFrame(data)\n",
        "df['Temperature'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYOujiV0-jZI",
        "outputId": "85d93be5-9ba1-4d61-e891-3cd944a569fa"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "369"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 45. Changing datatype of a column\n",
        "print(df['Temperature'].dtype)\n",
        "df['Temperature'] = df['Temperature'].astype(object)\n",
        "print(df['Temperature'].dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhbKiVG1B_kR",
        "outputId": "d56c8275-c320-49f0-e430-1771c941a349"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n",
            "object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 46. n largest rows\n",
        "df.nlargest(3, columns = ['Temperature'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "8oRqG24ACIEi",
        "outputId": "a885a10c-deb0-4e46-f6c3-aed8dc57ae35"
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      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date         City  Temperature  Humidity\n",
              "3  2024-05-02  Los Angeles           78        65\n",
              "1  2024-05-01  Los Angeles           75        60\n",
              "4  2024-05-03     New York           74        58"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# 47. n smallest rows\n",
        "df.nsmallest(3, columns = ['Temperature'])"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
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        "outputId": "15a30fbe-311f-413b-c247-73a60705bffd"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      City  Temperature  Humidity\n",
              "0  2024-05-01  New York           70        50\n",
              "2  2024-05-02  New York           72        55\n",
              "4  2024-05-03  New York           74        58"
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-05-02</td>\n",
              "      <td>New York</td>\n",
              "      <td>72</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-05-03</td>\n",
              "      <td>New York</td>\n",
              "      <td>74</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48dcb204-747f-4374-87ad-f3be96a35c9f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-48dcb204-747f-4374-87ad-f3be96a35c9f 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-48dcb204-747f-4374-87ad-f3be96a35c9f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-931e217c-0cb0-4bba-ba75-b121ad0036e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-931e217c-0cb0-4bba-ba75-b121ad0036e8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-931e217c-0cb0-4bba-ba75-b121ad0036e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2024-05-01\",\n          \"2024-05-02\",\n          \"2024-05-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 70,\n        \"max\": 74,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 50,\n        \"max\": 58,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 48. No of unique element in a column\n",
        "df['Temperature'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHrLNOOKDdvD",
        "outputId": "3e47135b-702c-48a7-96ef-35f06d8c123a"
      },
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 49. unique element in a column\n",
        "df['Temperature'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXdOkk9DDyKn",
        "outputId": "67e9ec0f-ad47-4776-a243-1e605b35f37f"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([70, 75, 72, 78, 74])"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 50. Sort a value based on column\n",
        "df.sort_values('Temperature')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IPt0msgHD3z5",
        "outputId": "f60e447a-4179-408d-949d-4990fc61eefa"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date         City  Temperature  Humidity\n",
              "0  2024-05-01     New York           70        50\n",
              "2  2024-05-02     New York           72        55\n",
              "4  2024-05-03     New York           74        58\n",
              "1  2024-05-01  Los Angeles           75        60\n",
              "3  2024-05-02  Los Angeles           78        65"
            ],
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              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5ab9aeb7-8e3a-4708-b024-86563978e0f3');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b60d45db-9d0d-4cac-beb3-0afdbd6b213f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2024-05-01\",\n          \"2024-05-02\",\n          \"2024-05-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Los Angeles\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 70,\n        \"max\": 78,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          72,\n          78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 50,\n        \"max\": 65,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          55,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    }
  ]
}