16352437_ICP6_Joins_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This ICP will mainly introduce you to the different types of joins in pandas and how to use them. It will also tell you how to deal with redundancy or duplicate values in the resulting dataframes.\n",
        "\n",
        "Learning Objectives\n",
        "\n",
        "1.   Get acquainted with the different types of python joins in Pandas\n",
        "2.   Learn how to join strings in Python pandas\n"
      ],
      "metadata": {
        "id": "gkBqPVae1tdZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inner Join in Pandas\n",
        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)\n",
        "\n",
        "\n",
        "Inner join is the most common type of join you’ll be working with. It returns a dataframe with only those rows that have common characteristics.\n",
        "\n",
        "An inner join requires each row in the two joined dataframes to have matching column values. This is similar to the intersection of two sets.\n",
        "Let’s start by importing the Pandas library:"
      ],
      "metadata": {
        "id": "5m7BT5Az3VKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "D1DxBtuX1s3v"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this ICP, we will create two dataframes – product and customer. The product dataframe will contain product details like Product_ID, Product_name, Category, Price, and Seller_City. The customer dataframe will contain details like id, name, age, Product_ID, Purchased_Product, and City.\n",
        "\n",
        "Our goal is to use our joining skills and generate meaningful information from the data"
      ],
      "metadata": {
        "id": "hH5stK_L6Br5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "product=pd.DataFrame({\n",
        "    'Product_ID':[101,102,103,104,105,106,107],\n",
        "    'Product_name':['Watch','Bag','Shoes','Smartphone','Books','Oil','Laptop'],\n",
        "    'Category':['Fashion','Fashion','Fashion','Electronics','Study','Grocery','Electronics'],\n",
        "    'Price':[299.0,1350.50,2999.0,14999.0,145.0,110.0,79999.0],\n",
        "    'Seller_City':[\"New York\", \"Los Angeles\", \"Chicago\", \"New York\", \"Houston\", \"Chicago\", \"Phoenix\"]\n",
        "})\n",
        "\n",
        "product"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "jVFS7sE41s6P",
        "outputId": "f7c6b496-2010-4384-930e-fdb8cd0dff68"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Product_ID Product_name     Category    Price  Seller_City\n",
              "0         101        Watch      Fashion    299.0     New York\n",
              "1         102          Bag      Fashion   1350.5  Los Angeles\n",
              "2         103        Shoes      Fashion   2999.0      Chicago\n",
              "3         104   Smartphone  Electronics  14999.0     New York\n",
              "4         105        Books        Study    145.0      Houston\n",
              "5         106          Oil      Grocery    110.0      Chicago\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "product",
              "summary": "{\n  \"name\": \"product\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 101,\n        \"max\": 107,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          101,\n          102,\n          106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Watch\",\n          \"Bag\",\n          \"Oil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Electronics\",\n          \"Grocery\",\n          \"Fashion\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29467.4731705914,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          299.0,\n          1350.5,\n          110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Los Angeles\",\n          \"Phoenix\",\n          \"Chicago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the above code snippet, we have passed a dict in the pandas DataFrame constructor. The dictionary has key-value pairs, where keys are Python strings, and the values are iterable objects; these could be lists, tuples, json, etc."
      ],
      "metadata": {
        "id": "m2shMXtg7RRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "customer=pd.DataFrame({\n",
        "    'id':[1,2,3,4,5,6,7,8,9],\n",
        "    'name':['Olivia','Aditya','Cory','Isabell','Dominic','Tyler','Samuel','Daniel','Jeremy'],\n",
        "    'age':[20,25,15,10,30,65,35,18,23],\n",
        "    'Product_ID':[101,0,106,0,103,104,0,0,107],\n",
        "    'Purchased_Product':['Watch','NA','Oil','NA','Shoes','Smartphone','NA','NA','Laptop'],\n",
        "    'City':[\"New York\", \"Los Angeles\", \"Chicago\", \"New York\", \"Houston\", \"Chicago\", \"Phoenix\", \"Los Angeles\", \"San Antonio\"]\n",
        "})\n",
        "customer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "9BZvqwAg1s85",
        "outputId": "a7384a2f-5178-4ffb-c461-1ecb0ba4b195"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id     name  age  Product_ID Purchased_Product         City\n",
              "0   1   Olivia   20         101             Watch     New York\n",
              "1   2   Aditya   25           0                NA  Los Angeles\n",
              "2   3     Cory   15         106               Oil      Chicago\n",
              "3   4  Isabell   10           0                NA     New York\n",
              "4   5  Dominic   30         103             Shoes      Houston\n",
              "5   6    Tyler   65         104        Smartphone      Chicago\n",
              "6   7   Samuel   35           0                NA      Phoenix\n",
              "7   8   Daniel   18           0                NA  Los Angeles\n",
              "8   9   Jeremy   23         107            Laptop  San Antonio"
            ],
            "text/html": [
              "\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Isabell</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Dominic</td>\n",
              "      <td>30</td>\n",
              "      <td>103</td>\n",
              "      <td>Shoes</td>\n",
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              "      <td>6</td>\n",
              "      <td>Tyler</td>\n",
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              "      <td>Smartphone</td>\n",
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              "      <td>Samuel</td>\n",
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              "      <td>Phoenix</td>\n",
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              "    <tr>\n",
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              "      <td>Daniel</td>\n",
              "      <td>18</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "customer",
              "summary": "{\n  \"name\": \"customer\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Daniel\",\n          \"Aditya\",\n          \"Tyler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 10,\n        \"max\": 65,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          25,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54,\n        \"min\": 0,\n        \"max\": 107,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          101,\n          0,\n          107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Watch\",\n          \"NA\",\n          \"Laptop\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"New York\",\n          \"Los Angeles\",\n          \"San Antonio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We want to know about all the products sold online and who purchased them. Using an inner join, we can easily do this.\n",
        "\n",
        "The merge() function\n",
        "\n",
        "By default, the merge pandas function performs an inner join. The merge function only takes series or dataframe objects as input for merging; if we pass a different data type, it will result in a typeerror. The two dataframe / series objects are passed in the merge function with a comma as the separator."
      ],
      "metadata": {
        "id": "RLHP9dLD7W_J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(product,customer,on='Product_ID')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "enrE51Q01s_l",
        "outputId": "1766a5d8-5c3f-4414-a1fc-cefd59494281"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Product_ID Product_name     Category    Price Seller_City  id     name  \\\n",
              "0         101        Watch      Fashion    299.0    New York   1   Olivia   \n",
              "1         103        Shoes      Fashion   2999.0     Chicago   5  Dominic   \n",
              "2         104   Smartphone  Electronics  14999.0    New York   6    Tyler   \n",
              "3         106          Oil      Grocery    110.0     Chicago   3     Cory   \n",
              "4         107       Laptop  Electronics  79999.0     Phoenix   9   Jeremy   \n",
              "\n",
              "   age Purchased_Product         City  \n",
              "0   20             Watch     New York  \n",
              "1   30             Shoes      Houston  \n",
              "2   65        Smartphone      Chicago  \n",
              "3   15               Oil      Chicago  \n",
              "4   23            Laptop  San Antonio  "
            ],
            "text/html": [
              "\n",
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              "      <td>110.0</td>\n",
              "      <td>Chicago</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 101,\n        \"max\": 107,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          103,\n          107,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Shoes\",\n          \"Laptop\",\n          \"Smartphone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Fashion\",\n          \"Electronics\",\n          \"Grocery\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34267.905308028385,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2999.0,\n          79999.0,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"New York\",\n          \"Chicago\",\n          \"Phoenix\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          9,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Dominic\",\n          \"Jeremy\",\n          \"Tyler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 15,\n        \"max\": 65,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30,\n          23,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Shoes\",\n          \"Laptop\",\n          \"Smartphone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Houston\",\n          \"San Antonio\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above, I have performed inner join on the product and customer dataframes on the ‘Product_ID’ column.\n",
        "\n",
        "But what if the column name strings are different in the two dataframes? Then, we have to mention both column names str explicitly.\n",
        "\n",
        "‘left_on’ and ‘right_on’ are two parameters through which we can achieve this. ‘left_on’ is the name of the key in the left dataframe and ‘right_on’ in the right dataframe."
      ],
      "metadata": {
        "id": "Cyiqd8KZAs5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(product,customer,left_on='Product_name',right_on='Purchased_Product')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FDJiAQlOA7QG",
        "outputId": "fa35cfda-5cfc-44fa-eb61-4727c48889b5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Product_ID_x Product_name     Category    Price Seller_City  id     name  \\\n",
              "0           101        Watch      Fashion    299.0    New York   1   Olivia   \n",
              "1           103        Shoes      Fashion   2999.0     Chicago   5  Dominic   \n",
              "2           104   Smartphone  Electronics  14999.0    New York   6    Tyler   \n",
              "3           106          Oil      Grocery    110.0     Chicago   3     Cory   \n",
              "4           107       Laptop  Electronics  79999.0     Phoenix   9   Jeremy   \n",
              "\n",
              "   age  Product_ID_y Purchased_Product         City  \n",
              "0   20           101             Watch     New York  \n",
              "1   30           103             Shoes      Houston  \n",
              "2   65           104        Smartphone      Chicago  \n",
              "3   15           106               Oil      Chicago  \n",
              "4   23           107            Laptop  San Antonio  "
            ],
            "text/html": [
              "\n",
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              "      <td>110.0</td>\n",
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              "      <td>San Antonio</td>\n",
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              "                                                    [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",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID_x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 101,\n        \"max\": 107,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          103,\n          107,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Shoes\",\n          \"Laptop\",\n          \"Smartphone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Fashion\",\n          \"Electronics\",\n          \"Grocery\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34267.905308028385,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2999.0,\n          79999.0,\n          14999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"New York\",\n          \"Chicago\",\n          \"Phoenix\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          9,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Dominic\",\n          \"Jeremy\",\n          \"Tyler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 15,\n        \"max\": 65,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30,\n          23,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_ID_y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 101,\n        \"max\": 107,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          103,\n          107,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Shoes\",\n          \"Laptop\",\n          \"Smartphone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Houston\",\n          \"San Antonio\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What if we want to know about all the products the seller sells to the same city, i.e., both the seller and the customer belong to the same city.\n",
        "\n",
        "In this case, we have to perform an inner join on both Product_ID and Seller_City of the product and Product_ID and City columns of the customer dataframe.\n",
        "\n",
        " We only passed single strings in the left_on or right_on arguments in the above example. We can pass an iterable object of column names to the left_on and right_on arguments, which can be a list or a tuple, as they are iterable in left_on or right_on.\n",
        "\n",
        "Below the list items are column names, which are the names of the columns on which we want to join two dataframes."
      ],
      "metadata": {
        "id": "h3W1sugTBU-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(product,customer,how='inner',left_on=['Product_ID','Seller_City'],right_on=['Product_ID','City'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "SocMVFgDBDAb",
        "outputId": "9f71e35d-4cf8-44e1-98f1-773b9655844c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Product_ID Product_name Category  Price Seller_City  id    name  age  \\\n",
              "0         101        Watch  Fashion  299.0    New York   1  Olivia   20   \n",
              "1         106          Oil  Grocery  110.0     Chicago   3    Cory   15   \n",
              "\n",
              "  Purchased_Product      City  \n",
              "0             Watch  New York  \n",
              "1               Oil   Chicago  "
            ],
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        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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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 101,\n        \"max\": 106,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          106,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Oil\",\n          \"Watch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Grocery\",\n          \"Fashion\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 133.64318164425748,\n        \"min\": 110.0,\n        \"max\": 299.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          110.0,\n          299.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Chicago\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Cory\",\n          \"Olivia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 15,\n        \"max\": 20,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          15,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Oil\",\n          \"Watch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Chicago\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Full Join in Pandas\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "What if we have to combine both dataframes to find all the products that are not sold and all the customers who didn’t purchase anything from us.\n",
        "\n",
        "We can use Full Join for this purpose.\n",
        "\n",
        "Full Join, also known as Full Outer Join, returns all those records which either have a match in the left or right dataframe.\n",
        "\n",
        "When rows in both the dataframes do not match, the resulting dataframe will have NaN for every dataframe column that lacks a matching row.\n",
        "\n",
        "We can perform Full join by passing the how parameter as ‘outer’ to the merge() function, which takes input python strings as arguments:"
      ],
      "metadata": {
        "id": "F3ezs9fOEA4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(product,customer,on='Product_ID',how='outer')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "0MMu6LLuELgz",
        "outputId": "226e7bf0-42a3-484d-a632-d9b2cd7c0f11"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product_ID Product_name     Category    Price  Seller_City   id     name  \\\n",
              "0            0          NaN          NaN      NaN          NaN  2.0   Aditya   \n",
              "1            0          NaN          NaN      NaN          NaN  4.0  Isabell   \n",
              "2            0          NaN          NaN      NaN          NaN  7.0   Samuel   \n",
              "3            0          NaN          NaN      NaN          NaN  8.0   Daniel   \n",
              "4          101        Watch      Fashion    299.0     New York  1.0   Olivia   \n",
              "5          102          Bag      Fashion   1350.5  Los Angeles  NaN      NaN   \n",
              "6          103        Shoes      Fashion   2999.0      Chicago  5.0  Dominic   \n",
              "7          104   Smartphone  Electronics  14999.0     New York  6.0    Tyler   \n",
              "8          105        Books        Study    145.0      Houston  NaN      NaN   \n",
              "9          106          Oil      Grocery    110.0      Chicago  3.0     Cory   \n",
              "10         107       Laptop  Electronics  79999.0      Phoenix  9.0   Jeremy   \n",
              "\n",
              "     age Purchased_Product         City  \n",
              "0   25.0                NA  Los Angeles  \n",
              "1   10.0                NA     New York  \n",
              "2   35.0                NA      Phoenix  \n",
              "3   18.0                NA  Los Angeles  \n",
              "4   20.0             Watch     New York  \n",
              "5    NaN               NaN          NaN  \n",
              "6   30.0             Shoes      Houston  \n",
              "7   65.0        Smartphone      Chicago  \n",
              "8    NaN               NaN          NaN  \n",
              "9   15.0               Oil      Chicago  \n",
              "10  23.0            Laptop  San Antonio  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-263b41c5-d047-4904-a743-b9688a1da9b6\" 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>Product_ID</th>\n",
              "      <th>Product_name</th>\n",
              "      <th>Category</th>\n",
              "      <th>Price</th>\n",
              "      <th>Seller_City</th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>Purchased_Product</th>\n",
              "      <th>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Aditya</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NA</td>\n",
              "      <td>Los Angeles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Isabell</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NA</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Samuel</td>\n",
              "      <td>35.0</td>\n",
              "      <td>NA</td>\n",
              "      <td>Phoenix</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Daniel</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NA</td>\n",
              "      <td>Los Angeles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101</td>\n",
              "      <td>Watch</td>\n",
              "      <td>Fashion</td>\n",
              "      <td>299.0</td>\n",
              "      <td>New York</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Olivia</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Watch</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>102</td>\n",
              "      <td>Bag</td>\n",
              "      <td>Fashion</td>\n",
              "      <td>1350.5</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>103</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Fashion</td>\n",
              "      <td>2999.0</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Dominic</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Houston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>104</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>New York</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Tyler</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>Chicago</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>145.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>9</th>\n",
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              "      <td>Oil</td>\n",
              "      <td>Grocery</td>\n",
              "      <td>110.0</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>Oil</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>79999.0</td>\n",
              "      <td>Phoenix</td>\n",
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              "      <td>Jeremy</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 0,\n        \"max\": 107,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          101,\n          105,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Watch\",\n          \"Bag\",\n          \"Oil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Electronics\",\n          \"Grocery\",\n          \"Fashion\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29467.4731705914,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          299.0,\n          1350.5,\n          110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Los Angeles\",\n          \"Phoenix\",\n          \"Chicago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7386127875258306,\n        \"min\": 1.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.0,\n          4.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Cory\",\n          \"Isabell\",\n          \"Dominic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.200137173630488,\n        \"min\": 10.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          15.0,\n          10.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"NA\",\n          \"Watch\",\n          \"Laptop\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Los Angeles\",\n          \"New York\",\n          \"San Antonio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Did you notice what happened here? All the non-matching rows of both the dataframes have NaN values for the columns of other dataframes. But wait – we still don’t know which row belongs to which dataframe.\n",
        "\n",
        "For this, Pandas provides us with a fantastic solution. By mentioning the indicator argument as True in the function, a new column of name _merge will be created in the resulting dataframe, which will mention which row belongs to which dataframe."
      ],
      "metadata": {
        "id": "PDO5kAp8Em6o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(product,customer,on='Product_ID',how='outer',indicator=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "Bu1vSq99E0Ci",
        "outputId": "8506f3b7-1daa-44ce-d705-ab859ad067ca"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product_ID Product_name     Category    Price  Seller_City   id     name  \\\n",
              "0            0          NaN          NaN      NaN          NaN  2.0   Aditya   \n",
              "1            0          NaN          NaN      NaN          NaN  4.0  Isabell   \n",
              "2            0          NaN          NaN      NaN          NaN  7.0   Samuel   \n",
              "3            0          NaN          NaN      NaN          NaN  8.0   Daniel   \n",
              "4          101        Watch      Fashion    299.0     New York  1.0   Olivia   \n",
              "5          102          Bag      Fashion   1350.5  Los Angeles  NaN      NaN   \n",
              "6          103        Shoes      Fashion   2999.0      Chicago  5.0  Dominic   \n",
              "7          104   Smartphone  Electronics  14999.0     New York  6.0    Tyler   \n",
              "8          105        Books        Study    145.0      Houston  NaN      NaN   \n",
              "9          106          Oil      Grocery    110.0      Chicago  3.0     Cory   \n",
              "10         107       Laptop  Electronics  79999.0      Phoenix  9.0   Jeremy   \n",
              "\n",
              "     age Purchased_Product         City      _merge  \n",
              "0   25.0                NA  Los Angeles  right_only  \n",
              "1   10.0                NA     New York  right_only  \n",
              "2   35.0                NA      Phoenix  right_only  \n",
              "3   18.0                NA  Los Angeles  right_only  \n",
              "4   20.0             Watch     New York        both  \n",
              "5    NaN               NaN          NaN   left_only  \n",
              "6   30.0             Shoes      Houston        both  \n",
              "7   65.0        Smartphone      Chicago        both  \n",
              "8    NaN               NaN          NaN   left_only  \n",
              "9   15.0               Oil      Chicago        both  \n",
              "10  23.0            Laptop  San Antonio        both  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>6</th>\n",
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              "      <td>5.0</td>\n",
              "      <td>Dominic</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Houston</td>\n",
              "      <td>both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>104</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>14999.0</td>\n",
              "      <td>New York</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Tyler</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Smartphone</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>105</td>\n",
              "      <td>Books</td>\n",
              "      <td>Study</td>\n",
              "      <td>145.0</td>\n",
              "      <td>Houston</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>left_only</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>106</td>\n",
              "      <td>Oil</td>\n",
              "      <td>Grocery</td>\n",
              "      <td>110.0</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Cory</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Oil</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>107</td>\n",
              "      <td>Laptop</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>79999.0</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Jeremy</td>\n",
              "      <td>23.0</td>\n",
              "      <td>Laptop</td>\n",
              "      <td>San Antonio</td>\n",
              "      <td>both</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-829abffa-029e-48e5-a501-7200e57e84eb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </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-829abffa-029e-48e5-a501-7200e57e84eb 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-829abffa-029e-48e5-a501-7200e57e84eb');\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",
              "<div id=\"df-b176d542-5983-4662-9386-73569e2a8bf6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b176d542-5983-4662-9386-73569e2a8bf6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </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-b176d542-5983-4662-9386-73569e2a8bf6 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\": \"pd\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 0,\n        \"max\": 107,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          101,\n          105,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Watch\",\n          \"Bag\",\n          \"Oil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Electronics\",\n          \"Grocery\",\n          \"Fashion\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29467.4731705914,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          299.0,\n          1350.5,\n          110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Los Angeles\",\n          \"Phoenix\",\n          \"Chicago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7386127875258306,\n        \"min\": 1.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.0,\n          4.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Cory\",\n          \"Isabell\",\n          \"Dominic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.200137173630488,\n        \"min\": 10.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          15.0,\n          10.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"NA\",\n          \"Watch\",\n          \"Laptop\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Los Angeles\",\n          \"New York\",\n          \"San Antonio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"_merge\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"right_only\",\n          \"both\",\n          \"left_only\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Left Join in Pandas\n",
        "\n",
        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        "\n",
        "what if we want information about only those customers who bought something from us. We can use the Left Join concept here.\n",
        "\n",
        "Left join, also known as Left Outer Join, returns a dataframe containing all the rows of the left dataframe.\n",
        "\n",
        "All the non-matching rows of the left dataframe contain NaN for the columns in the right dataframe. It is simply an inner join plus all the non-matching rows of the left dataframe filled with NaN for columns of the right dataframe.\n",
        "\n",
        "Performing a left join is actually quite similar to a full join. Just change the how argument to a different str that is ‘left’:"
      ],
      "metadata": {
        "id": "nXAIufonFbAL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.merge(product,customer,on='Product_ID',how='left')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "dC9fUGPQF2E-",
        "outputId": "5111a7c2-24f6-4094-9f28-79faaf84bcbb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Product_ID Product_name     Category    Price  Seller_City   id     name  \\\n",
              "0         101        Watch      Fashion    299.0     New York  1.0   Olivia   \n",
              "1         102          Bag      Fashion   1350.5  Los Angeles  NaN      NaN   \n",
              "2         103        Shoes      Fashion   2999.0      Chicago  5.0  Dominic   \n",
              "3         104   Smartphone  Electronics  14999.0     New York  6.0    Tyler   \n",
              "4         105        Books        Study    145.0      Houston  NaN      NaN   \n",
              "5         106          Oil      Grocery    110.0      Chicago  3.0     Cory   \n",
              "6         107       Laptop  Electronics  79999.0      Phoenix  9.0   Jeremy   \n",
              "\n",
              "    age Purchased_Product         City  \n",
              "0  20.0             Watch     New York  \n",
              "1   NaN               NaN          NaN  \n",
              "2  30.0             Shoes      Houston  \n",
              "3  65.0        Smartphone      Chicago  \n",
              "4   NaN               NaN          NaN  \n",
              "5  15.0               Oil      Chicago  \n",
              "6  23.0            Laptop  San Antonio  "
            ],
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              "      <td>6.0</td>\n",
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              "      <td>145.0</td>\n",
              "      <td>Houston</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Chicago</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Cory</td>\n",
              "      <td>15.0</td>\n",
              "      <td>Oil</td>\n",
              "      <td>Chicago</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>Electronics</td>\n",
              "      <td>79999.0</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>9.0</td>\n",
              "      <td>Jeremy</td>\n",
              "      <td>23.0</td>\n",
              "      <td>Laptop</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 101,\n        \"max\": 107,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          101,\n          102,\n          106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Watch\",\n          \"Bag\",\n          \"Oil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Electronics\",\n          \"Grocery\",\n          \"Fashion\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29467.4731705914,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          299.0,\n          1350.5,\n          110.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Los Angeles\",\n          \"Phoenix\",\n          \"Chicago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.03315017762062,\n        \"min\": 1.0,\n        \"max\": 9.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5.0,\n          9.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Dominic\",\n          \"Jeremy\",\n          \"Tyler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.982492337043446,\n        \"min\": 15.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30.0,\n          23.0,\n          65.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Shoes\",\n          \"Laptop\",\n          \"Smartphone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Houston\",\n          \"San Antonio\",\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Applying the right join on the two data frames  to create a table of customers, including information about the products they bought.**"
      ],
      "metadata": {
        "id": "QSRrSB5Vm9R-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Right join on Product_ID to create a table of customers including product information\n",
        "rightjoin_merged_df = pd.merge(product, customer, on='Product_ID', how='right')\n",
        "rightjoin_merged_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "eMKAeK0WnEtd",
        "outputId": "cec9ded2-f181-45c6-bd75-b1683466a824"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Product_ID Product_name     Category    Price Seller_City  id     name  \\\n",
              "0         101        Watch      Fashion    299.0    New York   1   Olivia   \n",
              "1           0          NaN          NaN      NaN         NaN   2   Aditya   \n",
              "2         106          Oil      Grocery    110.0     Chicago   3     Cory   \n",
              "3           0          NaN          NaN      NaN         NaN   4  Isabell   \n",
              "4         103        Shoes      Fashion   2999.0     Chicago   5  Dominic   \n",
              "5         104   Smartphone  Electronics  14999.0    New York   6    Tyler   \n",
              "6           0          NaN          NaN      NaN         NaN   7   Samuel   \n",
              "7           0          NaN          NaN      NaN         NaN   8   Daniel   \n",
              "8         107       Laptop  Electronics  79999.0     Phoenix   9   Jeremy   \n",
              "\n",
              "   age Purchased_Product         City  \n",
              "0   20             Watch     New York  \n",
              "1   25                NA  Los Angeles  \n",
              "2   15               Oil      Chicago  \n",
              "3   10                NA     New York  \n",
              "4   30             Shoes      Houston  \n",
              "5   65        Smartphone      Chicago  \n",
              "6   35                NA      Phoenix  \n",
              "7   18                NA  Los Angeles  \n",
              "8   23            Laptop  San Antonio  "
            ],
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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",
              "      --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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      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-8ceb5f53-4cdf-498d-af21-ef7ca5e712f5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e36c8118-25a8-4842-9adb-0cf9f71e508d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-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",
              "        fill: #174EA6;\n",
              "      }\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('rightjoin_merged_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_e36c8118-25a8-4842-9adb-0cf9f71e508d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('rightjoin_merged_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rightjoin_merged_df",
              "summary": "{\n  \"name\": \"rightjoin_merged_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 54,\n        \"min\": 0,\n        \"max\": 107,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          101,\n          0,\n          107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Oil\",\n          \"Laptop\",\n          \"Shoes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Fashion\",\n          \"Grocery\",\n          \"Electronics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34267.905308028385,\n        \"min\": 110.0,\n        \"max\": 79999.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          110.0,\n          79999.0,\n          2999.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Seller_City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"New York\",\n          \"Chicago\",\n          \"Phoenix\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Daniel\",\n          \"Aditya\",\n          \"Tyler\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 10,\n        \"max\": 65,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          18,\n          25,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased_Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Watch\",\n          \"NA\",\n          \"Laptop\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"New York\",\n          \"Los Angeles\",\n          \"San Antonio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### All records from the right data frame (customer) are included in the resulting table.\n",
        "#### Records from the left data frame (product) are included only if they match a record in the right data frame based on the key (Product_ID).\n",
        "#### This approach ensures that every customer appears in the final table, even if they didn’t buy a product.This resulting table (merged_df) will display all customers along with product details for those who made a purchase."
      ],
      "metadata": {
        "id": "Ama-yORNohSr"
      }
    }
  ]
}