Pyspark_project_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a1917bff-acda-4873-b3c4-141d5b71bcd3",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_id</th><th>Customer_id</th><th>Order_date</th><th>Location</th><th>Source_order</th></tr></thead><tbody><tr><td>1</td><td>A</td><td>2023-01-01</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2022-01-01</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2023-01-07</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-01-10</td><td>India</td><td>Restaurant</td></tr><tr><td>3</td><td>A</td><td>2022-01-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-01-11</td><td>India</td><td>Restaurant</td></tr><tr><td>2</td><td>B</td><td>2022-02-01</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>B</td><td>2023-01-02</td><td>India</td><td>Swiggy</td></tr><tr><td>1</td><td>B</td><td>2023-01-04</td><td>India</td><td>Restaurant</td></tr><tr><td>1</td><td>B</td><td>2023-02-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>B</td><td>2023-01-16</td><td>India</td><td>zomato</td></tr><tr><td>3</td><td>B</td><td>2022-02-01</td><td>India</td><td>zomato</td></tr><tr><td>3</td><td>C</td><td>2023-01-01</td><td>India</td><td>zomato</td></tr><tr><td>1</td><td>C</td><td>2023-01-01</td><td>UK</td><td>Swiggy</td></tr><tr><td>6</td><td>C</td><td>2022-01-07</td><td>UK</td><td>zomato</td></tr><tr><td>3</td><td>D</td><td>2023-02-16</td><td>UK</td><td>Restaurant</td></tr><tr><td>5</td><td>D</td><td>2022-02-01</td><td>UK</td><td>zomato</td></tr><tr><td>3</td><td>E</td><td>2023-02-01</td><td>UK</td><td>Restaurant</td></tr><tr><td>4</td><td>E</td><td>2023-02-01</td><td>UK</td><td>Swiggy</td></tr><tr><td>4</td><td>E</td><td>2023-02-07</td><td>UK</td><td>Restaurant</td></tr><tr><td>2</td><td>A</td><td>2022-01-01</td><td>UK</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2023-01-07</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-01-10</td><td>UK</td><td>Restaurant</td></tr><tr><td>3</td><td>A</td><td>2022-01-11</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-01-11</td><td>UK</td><td>zomato</td></tr><tr><td>2</td><td>B</td><td>2022-03-01</td><td>UK</td><td>zomato</td></tr><tr><td>2</td><td>B</td><td>2023-01-02</td><td>UK</td><td>zomato</td></tr><tr><td>1</td><td>B</td><td>2023-01-04</td><td>UK</td><td>Swiggy</td></tr><tr><td>1</td><td>B</td><td>2023-03-11</td><td>UK</td><td>Restaurant</td></tr><tr><td>3</td><td>B</td><td>2023-01-16</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>B</td><td>2023-03-01</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>C</td><td>2023-01-01</td><td>UK</td><td>Restaurant</td></tr><tr><td>1</td><td>C</td><td>2023-01-01</td><td>USA</td><td>Swiggy</td></tr><tr><td>6</td><td>C</td><td>2023-01-07</td><td>USA</td><td>zomato</td></tr><tr><td>3</td><td>D</td><td>2022-03-16</td><td>USA</td><td>zomato</td></tr><tr><td>5</td><td>D</td><td>2023-03-01</td><td>USA</td><td>zomato</td></tr><tr><td>3</td><td>E</td><td>2023-03-01</td><td>USA</td><td>Swiggy</td></tr><tr><td>4</td><td>E</td><td>2022-03-01</td><td>USA</td><td>zomato</td></tr><tr><td>4</td><td>E</td><td>2023-03-07</td><td>USA</td><td>zomato</td></tr><tr><td>1</td><td>A</td><td>2023-05-05</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2022-05-05</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2023-05-07</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-05-10</td><td>India</td><td>Restaurant</td></tr><tr><td>3</td><td>A</td><td>2022-05-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-05-11</td><td>India</td><td>Restaurant</td></tr><tr><td>2</td><td>B</td><td>2022-02-05</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>B</td><td>2023-05-02</td><td>India</td><td>Swiggy</td></tr><tr><td>1</td><td>B</td><td>2023-05-04</td><td>India</td><td>Restaurant</td></tr><tr><td>1</td><td>B</td><td>2023-02-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>B</td><td>2023-05-16</td><td>India</td><td>zomato</td></tr><tr><td>3</td><td>B</td><td>2022-02-05</td><td>India</td><td>zomato</td></tr><tr><td>3</td><td>C</td><td>2023-05-05</td><td>India</td><td>zomato</td></tr><tr><td>1</td><td>C</td><td>2023-05-05</td><td>UK</td><td>Swiggy</td></tr><tr><td>6</td><td>C</td><td>2022-05-07</td><td>UK</td><td>zomato</td></tr><tr><td>3</td><td>D</td><td>2023-02-16</td><td>UK</td><td>Restaurant</td></tr><tr><td>5</td><td>D</td><td>2022-02-05</td><td>UK</td><td>zomato</td></tr><tr><td>3</td><td>E</td><td>2023-02-05</td><td>UK</td><td>Restaurant</td></tr><tr><td>4</td><td>E</td><td>2023-02-05</td><td>UK</td><td>Swiggy</td></tr><tr><td>4</td><td>E</td><td>2023-02-07</td><td>UK</td><td>Restaurant</td></tr><tr><td>2</td><td>A</td><td>2022-05-05</td><td>UK</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2023-05-07</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-05-10</td><td>UK</td><td>Restaurant</td></tr><tr><td>3</td><td>A</td><td>2022-05-11</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-05-11</td><td>UK</td><td>zomato</td></tr><tr><td>2</td><td>B</td><td>2022-07-05</td><td>UK</td><td>zomato</td></tr><tr><td>2</td><td>B</td><td>2023-05-02</td><td>UK</td><td>zomato</td></tr><tr><td>1</td><td>B</td><td>2023-05-04</td><td>UK</td><td>Swiggy</td></tr><tr><td>1</td><td>B</td><td>2023-07-11</td><td>UK</td><td>Restaurant</td></tr><tr><td>3</td><td>B</td><td>2023-05-16</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>B</td><td>2023-07-05</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>C</td><td>2023-05-05</td><td>UK</td><td>Restaurant</td></tr><tr><td>1</td><td>C</td><td>2023-05-05</td><td>USA</td><td>Swiggy</td></tr><tr><td>6</td><td>C</td><td>2023-05-07</td><td>USA</td><td>zomato</td></tr><tr><td>3</td><td>D</td><td>2022-07-16</td><td>USA</td><td>zomato</td></tr><tr><td>5</td><td>D</td><td>2023-07-05</td><td>USA</td><td>zomato</td></tr><tr><td>3</td><td>E</td><td>2023-07-05</td><td>USA</td><td>Swiggy</td></tr><tr><td>4</td><td>E</td><td>2022-07-05</td><td>USA</td><td>zomato</td></tr><tr><td>4</td><td>E</td><td>2023-07-07</td><td>USA</td><td>zomato</td></tr><tr><td>1</td><td>A</td><td>2023-06-06</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2022-06-06</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2023-06-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-06-10</td><td>India</td><td>Restaurant</td></tr><tr><td>3</td><td>A</td><td>2022-06-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-06-11</td><td>India</td><td>Restaurant</td></tr><tr><td>2</td><td>B</td><td>2022-02-06</td><td>India</td><td>Swiggy</td></tr><tr><td>2</td><td>B</td><td>2023-06-02</td><td>India</td><td>Swiggy</td></tr><tr><td>1</td><td>B</td><td>2023-06-04</td><td>India</td><td>Restaurant</td></tr><tr><td>1</td><td>B</td><td>2023-02-11</td><td>India</td><td>Swiggy</td></tr><tr><td>3</td><td>B</td><td>2023-06-16</td><td>India</td><td>zomato</td></tr><tr><td>3</td><td>B</td><td>2022-02-06</td><td>India</td><td>zomato</td></tr><tr><td>3</td><td>C</td><td>2023-06-06</td><td>India</td><td>zomato</td></tr><tr><td>1</td><td>C</td><td>2023-06-06</td><td>UK</td><td>Swiggy</td></tr><tr><td>6</td><td>C</td><td>2022-06-11</td><td>UK</td><td>zomato</td></tr><tr><td>3</td><td>D</td><td>2023-02-16</td><td>UK</td><td>Restaurant</td></tr><tr><td>5</td><td>D</td><td>2022-02-06</td><td>UK</td><td>zomato</td></tr><tr><td>3</td><td>E</td><td>2023-02-06</td><td>UK</td><td>Restaurant</td></tr><tr><td>4</td><td>E</td><td>2023-02-06</td><td>UK</td><td>Swiggy</td></tr><tr><td>4</td><td>E</td><td>2023-02-11</td><td>UK</td><td>Restaurant</td></tr><tr><td>2</td><td>A</td><td>2022-06-06</td><td>UK</td><td>Swiggy</td></tr><tr><td>2</td><td>A</td><td>2023-06-11</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-06-10</td><td>UK</td><td>Restaurant</td></tr><tr><td>3</td><td>A</td><td>2022-06-11</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>A</td><td>2023-06-11</td><td>UK</td><td>zomato</td></tr><tr><td>2</td><td>B</td><td>2022-11-06</td><td>UK</td><td>zomato</td></tr><tr><td>2</td><td>B</td><td>2023-06-02</td><td>UK</td><td>zomato</td></tr><tr><td>1</td><td>B</td><td>2023-06-04</td><td>UK</td><td>Swiggy</td></tr><tr><td>1</td><td>B</td><td>2023-11-11</td><td>UK</td><td>Restaurant</td></tr><tr><td>3</td><td>B</td><td>2023-06-16</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>B</td><td>2023-11-06</td><td>UK</td><td>Swiggy</td></tr><tr><td>3</td><td>C</td><td>2023-06-06</td><td>UK</td><td>Restaurant</td></tr><tr><td>1</td><td>C</td><td>2023-06-06</td><td>USA</td><td>Swiggy</td></tr><tr><td>6</td><td>C</td><td>2023-06-11</td><td>USA</td><td>zomato</td></tr><tr><td>3</td><td>D</td><td>2022-11-16</td><td>USA</td><td>zomato</td></tr><tr><td>5</td><td>D</td><td>2023-11-06</td><td>USA</td><td>zomato</td></tr><tr><td>3</td><td>E</td><td>2023-11-06</td><td>USA</td><td>Swiggy</td></tr><tr><td>4</td><td>E</td><td>2022-11-06</td><td>USA</td><td>zomato</td></tr><tr><td>4</td><td>E</td><td>2023-11-11</td><td>USA</td><td>zomato</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         1,
         "A",
         "2023-01-01",
         "India",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2022-01-01",
         "India",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2023-01-07",
         "India",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-01-10",
         "India",
         "Restaurant"
        ],
        [
         3,
         "A",
         "2022-01-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-01-11",
         "India",
         "Restaurant"
        ],
        [
         2,
         "B",
         "2022-02-01",
         "India",
         "Swiggy"
        ],
        [
         2,
         "B",
         "2023-01-02",
         "India",
         "Swiggy"
        ],
        [
         1,
         "B",
         "2023-01-04",
         "India",
         "Restaurant"
        ],
        [
         1,
         "B",
         "2023-02-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "B",
         "2023-01-16",
         "India",
         "zomato"
        ],
        [
         3,
         "B",
         "2022-02-01",
         "India",
         "zomato"
        ],
        [
         3,
         "C",
         "2023-01-01",
         "India",
         "zomato"
        ],
        [
         1,
         "C",
         "2023-01-01",
         "UK",
         "Swiggy"
        ],
        [
         6,
         "C",
         "2022-01-07",
         "UK",
         "zomato"
        ],
        [
         3,
         "D",
         "2023-02-16",
         "UK",
         "Restaurant"
        ],
        [
         5,
         "D",
         "2022-02-01",
         "UK",
         "zomato"
        ],
        [
         3,
         "E",
         "2023-02-01",
         "UK",
         "Restaurant"
        ],
        [
         4,
         "E",
         "2023-02-01",
         "UK",
         "Swiggy"
        ],
        [
         4,
         "E",
         "2023-02-07",
         "UK",
         "Restaurant"
        ],
        [
         2,
         "A",
         "2022-01-01",
         "UK",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2023-01-07",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-01-10",
         "UK",
         "Restaurant"
        ],
        [
         3,
         "A",
         "2022-01-11",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-01-11",
         "UK",
         "zomato"
        ],
        [
         2,
         "B",
         "2022-03-01",
         "UK",
         "zomato"
        ],
        [
         2,
         "B",
         "2023-01-02",
         "UK",
         "zomato"
        ],
        [
         1,
         "B",
         "2023-01-04",
         "UK",
         "Swiggy"
        ],
        [
         1,
         "B",
         "2023-03-11",
         "UK",
         "Restaurant"
        ],
        [
         3,
         "B",
         "2023-01-16",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "B",
         "2023-03-01",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "C",
         "2023-01-01",
         "UK",
         "Restaurant"
        ],
        [
         1,
         "C",
         "2023-01-01",
         "USA",
         "Swiggy"
        ],
        [
         6,
         "C",
         "2023-01-07",
         "USA",
         "zomato"
        ],
        [
         3,
         "D",
         "2022-03-16",
         "USA",
         "zomato"
        ],
        [
         5,
         "D",
         "2023-03-01",
         "USA",
         "zomato"
        ],
        [
         3,
         "E",
         "2023-03-01",
         "USA",
         "Swiggy"
        ],
        [
         4,
         "E",
         "2022-03-01",
         "USA",
         "zomato"
        ],
        [
         4,
         "E",
         "2023-03-07",
         "USA",
         "zomato"
        ],
        [
         1,
         "A",
         "2023-05-05",
         "India",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2022-05-05",
         "India",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2023-05-07",
         "India",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-05-10",
         "India",
         "Restaurant"
        ],
        [
         3,
         "A",
         "2022-05-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-05-11",
         "India",
         "Restaurant"
        ],
        [
         2,
         "B",
         "2022-02-05",
         "India",
         "Swiggy"
        ],
        [
         2,
         "B",
         "2023-05-02",
         "India",
         "Swiggy"
        ],
        [
         1,
         "B",
         "2023-05-04",
         "India",
         "Restaurant"
        ],
        [
         1,
         "B",
         "2023-02-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "B",
         "2023-05-16",
         "India",
         "zomato"
        ],
        [
         3,
         "B",
         "2022-02-05",
         "India",
         "zomato"
        ],
        [
         3,
         "C",
         "2023-05-05",
         "India",
         "zomato"
        ],
        [
         1,
         "C",
         "2023-05-05",
         "UK",
         "Swiggy"
        ],
        [
         6,
         "C",
         "2022-05-07",
         "UK",
         "zomato"
        ],
        [
         3,
         "D",
         "2023-02-16",
         "UK",
         "Restaurant"
        ],
        [
         5,
         "D",
         "2022-02-05",
         "UK",
         "zomato"
        ],
        [
         3,
         "E",
         "2023-02-05",
         "UK",
         "Restaurant"
        ],
        [
         4,
         "E",
         "2023-02-05",
         "UK",
         "Swiggy"
        ],
        [
         4,
         "E",
         "2023-02-07",
         "UK",
         "Restaurant"
        ],
        [
         2,
         "A",
         "2022-05-05",
         "UK",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2023-05-07",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-05-10",
         "UK",
         "Restaurant"
        ],
        [
         3,
         "A",
         "2022-05-11",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-05-11",
         "UK",
         "zomato"
        ],
        [
         2,
         "B",
         "2022-07-05",
         "UK",
         "zomato"
        ],
        [
         2,
         "B",
         "2023-05-02",
         "UK",
         "zomato"
        ],
        [
         1,
         "B",
         "2023-05-04",
         "UK",
         "Swiggy"
        ],
        [
         1,
         "B",
         "2023-07-11",
         "UK",
         "Restaurant"
        ],
        [
         3,
         "B",
         "2023-05-16",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "B",
         "2023-07-05",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "C",
         "2023-05-05",
         "UK",
         "Restaurant"
        ],
        [
         1,
         "C",
         "2023-05-05",
         "USA",
         "Swiggy"
        ],
        [
         6,
         "C",
         "2023-05-07",
         "USA",
         "zomato"
        ],
        [
         3,
         "D",
         "2022-07-16",
         "USA",
         "zomato"
        ],
        [
         5,
         "D",
         "2023-07-05",
         "USA",
         "zomato"
        ],
        [
         3,
         "E",
         "2023-07-05",
         "USA",
         "Swiggy"
        ],
        [
         4,
         "E",
         "2022-07-05",
         "USA",
         "zomato"
        ],
        [
         4,
         "E",
         "2023-07-07",
         "USA",
         "zomato"
        ],
        [
         1,
         "A",
         "2023-06-06",
         "India",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2022-06-06",
         "India",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2023-06-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-06-10",
         "India",
         "Restaurant"
        ],
        [
         3,
         "A",
         "2022-06-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-06-11",
         "India",
         "Restaurant"
        ],
        [
         2,
         "B",
         "2022-02-06",
         "India",
         "Swiggy"
        ],
        [
         2,
         "B",
         "2023-06-02",
         "India",
         "Swiggy"
        ],
        [
         1,
         "B",
         "2023-06-04",
         "India",
         "Restaurant"
        ],
        [
         1,
         "B",
         "2023-02-11",
         "India",
         "Swiggy"
        ],
        [
         3,
         "B",
         "2023-06-16",
         "India",
         "zomato"
        ],
        [
         3,
         "B",
         "2022-02-06",
         "India",
         "zomato"
        ],
        [
         3,
         "C",
         "2023-06-06",
         "India",
         "zomato"
        ],
        [
         1,
         "C",
         "2023-06-06",
         "UK",
         "Swiggy"
        ],
        [
         6,
         "C",
         "2022-06-11",
         "UK",
         "zomato"
        ],
        [
         3,
         "D",
         "2023-02-16",
         "UK",
         "Restaurant"
        ],
        [
         5,
         "D",
         "2022-02-06",
         "UK",
         "zomato"
        ],
        [
         3,
         "E",
         "2023-02-06",
         "UK",
         "Restaurant"
        ],
        [
         4,
         "E",
         "2023-02-06",
         "UK",
         "Swiggy"
        ],
        [
         4,
         "E",
         "2023-02-11",
         "UK",
         "Restaurant"
        ],
        [
         2,
         "A",
         "2022-06-06",
         "UK",
         "Swiggy"
        ],
        [
         2,
         "A",
         "2023-06-11",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-06-10",
         "UK",
         "Restaurant"
        ],
        [
         3,
         "A",
         "2022-06-11",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "A",
         "2023-06-11",
         "UK",
         "zomato"
        ],
        [
         2,
         "B",
         "2022-11-06",
         "UK",
         "zomato"
        ],
        [
         2,
         "B",
         "2023-06-02",
         "UK",
         "zomato"
        ],
        [
         1,
         "B",
         "2023-06-04",
         "UK",
         "Swiggy"
        ],
        [
         1,
         "B",
         "2023-11-11",
         "UK",
         "Restaurant"
        ],
        [
         3,
         "B",
         "2023-06-16",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "B",
         "2023-11-06",
         "UK",
         "Swiggy"
        ],
        [
         3,
         "C",
         "2023-06-06",
         "UK",
         "Restaurant"
        ],
        [
         1,
         "C",
         "2023-06-06",
         "USA",
         "Swiggy"
        ],
        [
         6,
         "C",
         "2023-06-11",
         "USA",
         "zomato"
        ],
        [
         3,
         "D",
         "2022-11-16",
         "USA",
         "zomato"
        ],
        [
         5,
         "D",
         "2023-11-06",
         "USA",
         "zomato"
        ],
        [
         3,
         "E",
         "2023-11-06",
         "USA",
         "Swiggy"
        ],
        [
         4,
         "E",
         "2022-11-06",
         "USA",
         "zomato"
        ],
        [
         4,
         "E",
         "2023-11-11",
         "USA",
         "zomato"
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_id",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Customer_id",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "Order_date",
         "type": "\"date\""
        },
        {
         "metadata": "{}",
         "name": "Location",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "Source_order",
         "type": "\"string\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.types import StructType, StructField, IntegerType, StringType,DateType\n",
    "schema = StructType([\n",
    "    StructField(\"Product_id\",IntegerType(),True),\n",
    "    StructField(\"Customer_id\",StringType(),True),\n",
    "    StructField(\"Order_date\",DateType(),True),\n",
    "    StructField(\"Location\",StringType(),True),\n",
    "    StructField(\"Source_order\",StringType(),True)\n",
    "])\n",
    "sales_df = spark.read.csv(\"/FileStore/tables/sales_csv.txt\",schema=schema)\n",
    "display(sales_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "c1432acd-9959-46a1-aea8-64c9841a3fd3",
     "showTitle": true,
     "title": "adding quarter"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_id</th><th>Customer_id</th><th>Order_date</th><th>Location</th><th>Source_order</th><th>order_year</th><th>order_month</th><th>order_quarter</th></tr></thead><tbody><tr><td>1</td><td>A</td><td>2023-01-01</td><td>India</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>2</td><td>A</td><td>2022-01-01</td><td>India</td><td>Swiggy</td><td>2022</td><td>1</td><td>1</td></tr><tr><td>2</td><td>A</td><td>2023-01-07</td><td>India</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>A</td><td>2023-01-10</td><td>India</td><td>Restaurant</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>A</td><td>2022-01-11</td><td>India</td><td>Swiggy</td><td>2022</td><td>1</td><td>1</td></tr><tr><td>3</td><td>A</td><td>2023-01-11</td><td>India</td><td>Restaurant</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>2</td><td>B</td><td>2022-02-01</td><td>India</td><td>Swiggy</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>2</td><td>B</td><td>2023-01-02</td><td>India</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>1</td><td>B</td><td>2023-01-04</td><td>India</td><td>Restaurant</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>1</td><td>B</td><td>2023-02-11</td><td>India</td><td>Swiggy</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>3</td><td>B</td><td>2023-01-16</td><td>India</td><td>zomato</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>B</td><td>2022-02-01</td><td>India</td><td>zomato</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>3</td><td>C</td><td>2023-01-01</td><td>India</td><td>zomato</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>1</td><td>C</td><td>2023-01-01</td><td>UK</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>6</td><td>C</td><td>2022-01-07</td><td>UK</td><td>zomato</td><td>2022</td><td>1</td><td>1</td></tr><tr><td>3</td><td>D</td><td>2023-02-16</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>5</td><td>D</td><td>2022-02-01</td><td>UK</td><td>zomato</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>3</td><td>E</td><td>2023-02-01</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-02-01</td><td>UK</td><td>Swiggy</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-02-07</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>2</td><td>A</td><td>2022-01-01</td><td>UK</td><td>Swiggy</td><td>2022</td><td>1</td><td>1</td></tr><tr><td>2</td><td>A</td><td>2023-01-07</td><td>UK</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>A</td><td>2023-01-10</td><td>UK</td><td>Restaurant</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>A</td><td>2022-01-11</td><td>UK</td><td>Swiggy</td><td>2022</td><td>1</td><td>1</td></tr><tr><td>3</td><td>A</td><td>2023-01-11</td><td>UK</td><td>zomato</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>2</td><td>B</td><td>2022-03-01</td><td>UK</td><td>zomato</td><td>2022</td><td>3</td><td>1</td></tr><tr><td>2</td><td>B</td><td>2023-01-02</td><td>UK</td><td>zomato</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>1</td><td>B</td><td>2023-01-04</td><td>UK</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>1</td><td>B</td><td>2023-03-11</td><td>UK</td><td>Restaurant</td><td>2023</td><td>3</td><td>1</td></tr><tr><td>3</td><td>B</td><td>2023-01-16</td><td>UK</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>B</td><td>2023-03-01</td><td>UK</td><td>Swiggy</td><td>2023</td><td>3</td><td>1</td></tr><tr><td>3</td><td>C</td><td>2023-01-01</td><td>UK</td><td>Restaurant</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>1</td><td>C</td><td>2023-01-01</td><td>USA</td><td>Swiggy</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>6</td><td>C</td><td>2023-01-07</td><td>USA</td><td>zomato</td><td>2023</td><td>1</td><td>1</td></tr><tr><td>3</td><td>D</td><td>2022-03-16</td><td>USA</td><td>zomato</td><td>2022</td><td>3</td><td>1</td></tr><tr><td>5</td><td>D</td><td>2023-03-01</td><td>USA</td><td>zomato</td><td>2023</td><td>3</td><td>1</td></tr><tr><td>3</td><td>E</td><td>2023-03-01</td><td>USA</td><td>Swiggy</td><td>2023</td><td>3</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2022-03-01</td><td>USA</td><td>zomato</td><td>2022</td><td>3</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-03-07</td><td>USA</td><td>zomato</td><td>2023</td><td>3</td><td>1</td></tr><tr><td>1</td><td>A</td><td>2023-05-05</td><td>India</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>2</td><td>A</td><td>2022-05-05</td><td>India</td><td>Swiggy</td><td>2022</td><td>5</td><td>2</td></tr><tr><td>2</td><td>A</td><td>2023-05-07</td><td>India</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-05-10</td><td>India</td><td>Restaurant</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2022-05-11</td><td>India</td><td>Swiggy</td><td>2022</td><td>5</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-05-11</td><td>India</td><td>Restaurant</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>2</td><td>B</td><td>2022-02-05</td><td>India</td><td>Swiggy</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>2</td><td>B</td><td>2023-05-02</td><td>India</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-05-04</td><td>India</td><td>Restaurant</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-02-11</td><td>India</td><td>Swiggy</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>3</td><td>B</td><td>2023-05-16</td><td>India</td><td>zomato</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>B</td><td>2022-02-05</td><td>India</td><td>zomato</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>3</td><td>C</td><td>2023-05-05</td><td>India</td><td>zomato</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>1</td><td>C</td><td>2023-05-05</td><td>UK</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>6</td><td>C</td><td>2022-05-07</td><td>UK</td><td>zomato</td><td>2022</td><td>5</td><td>2</td></tr><tr><td>3</td><td>D</td><td>2023-02-16</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>5</td><td>D</td><td>2022-02-05</td><td>UK</td><td>zomato</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>3</td><td>E</td><td>2023-02-05</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-02-05</td><td>UK</td><td>Swiggy</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-02-07</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>2</td><td>A</td><td>2022-05-05</td><td>UK</td><td>Swiggy</td><td>2022</td><td>5</td><td>2</td></tr><tr><td>2</td><td>A</td><td>2023-05-07</td><td>UK</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-05-10</td><td>UK</td><td>Restaurant</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2022-05-11</td><td>UK</td><td>Swiggy</td><td>2022</td><td>5</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-05-11</td><td>UK</td><td>zomato</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>2</td><td>B</td><td>2022-07-05</td><td>UK</td><td>zomato</td><td>2022</td><td>7</td><td>3</td></tr><tr><td>2</td><td>B</td><td>2023-05-02</td><td>UK</td><td>zomato</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-05-04</td><td>UK</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-07-11</td><td>UK</td><td>Restaurant</td><td>2023</td><td>7</td><td>3</td></tr><tr><td>3</td><td>B</td><td>2023-05-16</td><td>UK</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>B</td><td>2023-07-05</td><td>UK</td><td>Swiggy</td><td>2023</td><td>7</td><td>3</td></tr><tr><td>3</td><td>C</td><td>2023-05-05</td><td>UK</td><td>Restaurant</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>1</td><td>C</td><td>2023-05-05</td><td>USA</td><td>Swiggy</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>6</td><td>C</td><td>2023-05-07</td><td>USA</td><td>zomato</td><td>2023</td><td>5</td><td>2</td></tr><tr><td>3</td><td>D</td><td>2022-07-16</td><td>USA</td><td>zomato</td><td>2022</td><td>7</td><td>3</td></tr><tr><td>5</td><td>D</td><td>2023-07-05</td><td>USA</td><td>zomato</td><td>2023</td><td>7</td><td>3</td></tr><tr><td>3</td><td>E</td><td>2023-07-05</td><td>USA</td><td>Swiggy</td><td>2023</td><td>7</td><td>3</td></tr><tr><td>4</td><td>E</td><td>2022-07-05</td><td>USA</td><td>zomato</td><td>2022</td><td>7</td><td>3</td></tr><tr><td>4</td><td>E</td><td>2023-07-07</td><td>USA</td><td>zomato</td><td>2023</td><td>7</td><td>3</td></tr><tr><td>1</td><td>A</td><td>2023-06-06</td><td>India</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>2</td><td>A</td><td>2022-06-06</td><td>India</td><td>Swiggy</td><td>2022</td><td>6</td><td>2</td></tr><tr><td>2</td><td>A</td><td>2023-06-11</td><td>India</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-06-10</td><td>India</td><td>Restaurant</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2022-06-11</td><td>India</td><td>Swiggy</td><td>2022</td><td>6</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-06-11</td><td>India</td><td>Restaurant</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>2</td><td>B</td><td>2022-02-06</td><td>India</td><td>Swiggy</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>2</td><td>B</td><td>2023-06-02</td><td>India</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-06-04</td><td>India</td><td>Restaurant</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-02-11</td><td>India</td><td>Swiggy</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>3</td><td>B</td><td>2023-06-16</td><td>India</td><td>zomato</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>B</td><td>2022-02-06</td><td>India</td><td>zomato</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>3</td><td>C</td><td>2023-06-06</td><td>India</td><td>zomato</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>1</td><td>C</td><td>2023-06-06</td><td>UK</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>6</td><td>C</td><td>2022-06-11</td><td>UK</td><td>zomato</td><td>2022</td><td>6</td><td>2</td></tr><tr><td>3</td><td>D</td><td>2023-02-16</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>5</td><td>D</td><td>2022-02-06</td><td>UK</td><td>zomato</td><td>2022</td><td>2</td><td>1</td></tr><tr><td>3</td><td>E</td><td>2023-02-06</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-02-06</td><td>UK</td><td>Swiggy</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>4</td><td>E</td><td>2023-02-11</td><td>UK</td><td>Restaurant</td><td>2023</td><td>2</td><td>1</td></tr><tr><td>2</td><td>A</td><td>2022-06-06</td><td>UK</td><td>Swiggy</td><td>2022</td><td>6</td><td>2</td></tr><tr><td>2</td><td>A</td><td>2023-06-11</td><td>UK</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-06-10</td><td>UK</td><td>Restaurant</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2022-06-11</td><td>UK</td><td>Swiggy</td><td>2022</td><td>6</td><td>2</td></tr><tr><td>3</td><td>A</td><td>2023-06-11</td><td>UK</td><td>zomato</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>2</td><td>B</td><td>2022-11-06</td><td>UK</td><td>zomato</td><td>2022</td><td>11</td><td>4</td></tr><tr><td>2</td><td>B</td><td>2023-06-02</td><td>UK</td><td>zomato</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-06-04</td><td>UK</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>1</td><td>B</td><td>2023-11-11</td><td>UK</td><td>Restaurant</td><td>2023</td><td>11</td><td>4</td></tr><tr><td>3</td><td>B</td><td>2023-06-16</td><td>UK</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>B</td><td>2023-11-06</td><td>UK</td><td>Swiggy</td><td>2023</td><td>11</td><td>4</td></tr><tr><td>3</td><td>C</td><td>2023-06-06</td><td>UK</td><td>Restaurant</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>1</td><td>C</td><td>2023-06-06</td><td>USA</td><td>Swiggy</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>6</td><td>C</td><td>2023-06-11</td><td>USA</td><td>zomato</td><td>2023</td><td>6</td><td>2</td></tr><tr><td>3</td><td>D</td><td>2022-11-16</td><td>USA</td><td>zomato</td><td>2022</td><td>11</td><td>4</td></tr><tr><td>5</td><td>D</td><td>2023-11-06</td><td>USA</td><td>zomato</td><td>2023</td><td>11</td><td>4</td></tr><tr><td>3</td><td>E</td><td>2023-11-06</td><td>USA</td><td>Swiggy</td><td>2023</td><td>11</td><td>4</td></tr><tr><td>4</td><td>E</td><td>2022-11-06</td><td>USA</td><td>zomato</td><td>2022</td><td>11</td><td>4</td></tr><tr><td>4</td><td>E</td><td>2023-11-11</td><td>USA</td><td>zomato</td><td>2023</td><td>11</td><td>4</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         1,
         "A",
         "2023-01-01",
         "India",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         2,
         "A",
         "2022-01-01",
         "India",
         "Swiggy",
         2022,
         1,
         1
        ],
        [
         2,
         "A",
         "2023-01-07",
         "India",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         3,
         "A",
         "2023-01-10",
         "India",
         "Restaurant",
         2023,
         1,
         1
        ],
        [
         3,
         "A",
         "2022-01-11",
         "India",
         "Swiggy",
         2022,
         1,
         1
        ],
        [
         3,
         "A",
         "2023-01-11",
         "India",
         "Restaurant",
         2023,
         1,
         1
        ],
        [
         2,
         "B",
         "2022-02-01",
         "India",
         "Swiggy",
         2022,
         2,
         1
        ],
        [
         2,
         "B",
         "2023-01-02",
         "India",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         1,
         "B",
         "2023-01-04",
         "India",
         "Restaurant",
         2023,
         1,
         1
        ],
        [
         1,
         "B",
         "2023-02-11",
         "India",
         "Swiggy",
         2023,
         2,
         1
        ],
        [
         3,
         "B",
         "2023-01-16",
         "India",
         "zomato",
         2023,
         1,
         1
        ],
        [
         3,
         "B",
         "2022-02-01",
         "India",
         "zomato",
         2022,
         2,
         1
        ],
        [
         3,
         "C",
         "2023-01-01",
         "India",
         "zomato",
         2023,
         1,
         1
        ],
        [
         1,
         "C",
         "2023-01-01",
         "UK",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         6,
         "C",
         "2022-01-07",
         "UK",
         "zomato",
         2022,
         1,
         1
        ],
        [
         3,
         "D",
         "2023-02-16",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         5,
         "D",
         "2022-02-01",
         "UK",
         "zomato",
         2022,
         2,
         1
        ],
        [
         3,
         "E",
         "2023-02-01",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         4,
         "E",
         "2023-02-01",
         "UK",
         "Swiggy",
         2023,
         2,
         1
        ],
        [
         4,
         "E",
         "2023-02-07",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         2,
         "A",
         "2022-01-01",
         "UK",
         "Swiggy",
         2022,
         1,
         1
        ],
        [
         2,
         "A",
         "2023-01-07",
         "UK",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         3,
         "A",
         "2023-01-10",
         "UK",
         "Restaurant",
         2023,
         1,
         1
        ],
        [
         3,
         "A",
         "2022-01-11",
         "UK",
         "Swiggy",
         2022,
         1,
         1
        ],
        [
         3,
         "A",
         "2023-01-11",
         "UK",
         "zomato",
         2023,
         1,
         1
        ],
        [
         2,
         "B",
         "2022-03-01",
         "UK",
         "zomato",
         2022,
         3,
         1
        ],
        [
         2,
         "B",
         "2023-01-02",
         "UK",
         "zomato",
         2023,
         1,
         1
        ],
        [
         1,
         "B",
         "2023-01-04",
         "UK",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         1,
         "B",
         "2023-03-11",
         "UK",
         "Restaurant",
         2023,
         3,
         1
        ],
        [
         3,
         "B",
         "2023-01-16",
         "UK",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         3,
         "B",
         "2023-03-01",
         "UK",
         "Swiggy",
         2023,
         3,
         1
        ],
        [
         3,
         "C",
         "2023-01-01",
         "UK",
         "Restaurant",
         2023,
         1,
         1
        ],
        [
         1,
         "C",
         "2023-01-01",
         "USA",
         "Swiggy",
         2023,
         1,
         1
        ],
        [
         6,
         "C",
         "2023-01-07",
         "USA",
         "zomato",
         2023,
         1,
         1
        ],
        [
         3,
         "D",
         "2022-03-16",
         "USA",
         "zomato",
         2022,
         3,
         1
        ],
        [
         5,
         "D",
         "2023-03-01",
         "USA",
         "zomato",
         2023,
         3,
         1
        ],
        [
         3,
         "E",
         "2023-03-01",
         "USA",
         "Swiggy",
         2023,
         3,
         1
        ],
        [
         4,
         "E",
         "2022-03-01",
         "USA",
         "zomato",
         2022,
         3,
         1
        ],
        [
         4,
         "E",
         "2023-03-07",
         "USA",
         "zomato",
         2023,
         3,
         1
        ],
        [
         1,
         "A",
         "2023-05-05",
         "India",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         2,
         "A",
         "2022-05-05",
         "India",
         "Swiggy",
         2022,
         5,
         2
        ],
        [
         2,
         "A",
         "2023-05-07",
         "India",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         3,
         "A",
         "2023-05-10",
         "India",
         "Restaurant",
         2023,
         5,
         2
        ],
        [
         3,
         "A",
         "2022-05-11",
         "India",
         "Swiggy",
         2022,
         5,
         2
        ],
        [
         3,
         "A",
         "2023-05-11",
         "India",
         "Restaurant",
         2023,
         5,
         2
        ],
        [
         2,
         "B",
         "2022-02-05",
         "India",
         "Swiggy",
         2022,
         2,
         1
        ],
        [
         2,
         "B",
         "2023-05-02",
         "India",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         1,
         "B",
         "2023-05-04",
         "India",
         "Restaurant",
         2023,
         5,
         2
        ],
        [
         1,
         "B",
         "2023-02-11",
         "India",
         "Swiggy",
         2023,
         2,
         1
        ],
        [
         3,
         "B",
         "2023-05-16",
         "India",
         "zomato",
         2023,
         5,
         2
        ],
        [
         3,
         "B",
         "2022-02-05",
         "India",
         "zomato",
         2022,
         2,
         1
        ],
        [
         3,
         "C",
         "2023-05-05",
         "India",
         "zomato",
         2023,
         5,
         2
        ],
        [
         1,
         "C",
         "2023-05-05",
         "UK",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         6,
         "C",
         "2022-05-07",
         "UK",
         "zomato",
         2022,
         5,
         2
        ],
        [
         3,
         "D",
         "2023-02-16",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         5,
         "D",
         "2022-02-05",
         "UK",
         "zomato",
         2022,
         2,
         1
        ],
        [
         3,
         "E",
         "2023-02-05",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         4,
         "E",
         "2023-02-05",
         "UK",
         "Swiggy",
         2023,
         2,
         1
        ],
        [
         4,
         "E",
         "2023-02-07",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         2,
         "A",
         "2022-05-05",
         "UK",
         "Swiggy",
         2022,
         5,
         2
        ],
        [
         2,
         "A",
         "2023-05-07",
         "UK",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         3,
         "A",
         "2023-05-10",
         "UK",
         "Restaurant",
         2023,
         5,
         2
        ],
        [
         3,
         "A",
         "2022-05-11",
         "UK",
         "Swiggy",
         2022,
         5,
         2
        ],
        [
         3,
         "A",
         "2023-05-11",
         "UK",
         "zomato",
         2023,
         5,
         2
        ],
        [
         2,
         "B",
         "2022-07-05",
         "UK",
         "zomato",
         2022,
         7,
         3
        ],
        [
         2,
         "B",
         "2023-05-02",
         "UK",
         "zomato",
         2023,
         5,
         2
        ],
        [
         1,
         "B",
         "2023-05-04",
         "UK",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         1,
         "B",
         "2023-07-11",
         "UK",
         "Restaurant",
         2023,
         7,
         3
        ],
        [
         3,
         "B",
         "2023-05-16",
         "UK",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         3,
         "B",
         "2023-07-05",
         "UK",
         "Swiggy",
         2023,
         7,
         3
        ],
        [
         3,
         "C",
         "2023-05-05",
         "UK",
         "Restaurant",
         2023,
         5,
         2
        ],
        [
         1,
         "C",
         "2023-05-05",
         "USA",
         "Swiggy",
         2023,
         5,
         2
        ],
        [
         6,
         "C",
         "2023-05-07",
         "USA",
         "zomato",
         2023,
         5,
         2
        ],
        [
         3,
         "D",
         "2022-07-16",
         "USA",
         "zomato",
         2022,
         7,
         3
        ],
        [
         5,
         "D",
         "2023-07-05",
         "USA",
         "zomato",
         2023,
         7,
         3
        ],
        [
         3,
         "E",
         "2023-07-05",
         "USA",
         "Swiggy",
         2023,
         7,
         3
        ],
        [
         4,
         "E",
         "2022-07-05",
         "USA",
         "zomato",
         2022,
         7,
         3
        ],
        [
         4,
         "E",
         "2023-07-07",
         "USA",
         "zomato",
         2023,
         7,
         3
        ],
        [
         1,
         "A",
         "2023-06-06",
         "India",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         2,
         "A",
         "2022-06-06",
         "India",
         "Swiggy",
         2022,
         6,
         2
        ],
        [
         2,
         "A",
         "2023-06-11",
         "India",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         3,
         "A",
         "2023-06-10",
         "India",
         "Restaurant",
         2023,
         6,
         2
        ],
        [
         3,
         "A",
         "2022-06-11",
         "India",
         "Swiggy",
         2022,
         6,
         2
        ],
        [
         3,
         "A",
         "2023-06-11",
         "India",
         "Restaurant",
         2023,
         6,
         2
        ],
        [
         2,
         "B",
         "2022-02-06",
         "India",
         "Swiggy",
         2022,
         2,
         1
        ],
        [
         2,
         "B",
         "2023-06-02",
         "India",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         1,
         "B",
         "2023-06-04",
         "India",
         "Restaurant",
         2023,
         6,
         2
        ],
        [
         1,
         "B",
         "2023-02-11",
         "India",
         "Swiggy",
         2023,
         2,
         1
        ],
        [
         3,
         "B",
         "2023-06-16",
         "India",
         "zomato",
         2023,
         6,
         2
        ],
        [
         3,
         "B",
         "2022-02-06",
         "India",
         "zomato",
         2022,
         2,
         1
        ],
        [
         3,
         "C",
         "2023-06-06",
         "India",
         "zomato",
         2023,
         6,
         2
        ],
        [
         1,
         "C",
         "2023-06-06",
         "UK",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         6,
         "C",
         "2022-06-11",
         "UK",
         "zomato",
         2022,
         6,
         2
        ],
        [
         3,
         "D",
         "2023-02-16",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         5,
         "D",
         "2022-02-06",
         "UK",
         "zomato",
         2022,
         2,
         1
        ],
        [
         3,
         "E",
         "2023-02-06",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         4,
         "E",
         "2023-02-06",
         "UK",
         "Swiggy",
         2023,
         2,
         1
        ],
        [
         4,
         "E",
         "2023-02-11",
         "UK",
         "Restaurant",
         2023,
         2,
         1
        ],
        [
         2,
         "A",
         "2022-06-06",
         "UK",
         "Swiggy",
         2022,
         6,
         2
        ],
        [
         2,
         "A",
         "2023-06-11",
         "UK",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         3,
         "A",
         "2023-06-10",
         "UK",
         "Restaurant",
         2023,
         6,
         2
        ],
        [
         3,
         "A",
         "2022-06-11",
         "UK",
         "Swiggy",
         2022,
         6,
         2
        ],
        [
         3,
         "A",
         "2023-06-11",
         "UK",
         "zomato",
         2023,
         6,
         2
        ],
        [
         2,
         "B",
         "2022-11-06",
         "UK",
         "zomato",
         2022,
         11,
         4
        ],
        [
         2,
         "B",
         "2023-06-02",
         "UK",
         "zomato",
         2023,
         6,
         2
        ],
        [
         1,
         "B",
         "2023-06-04",
         "UK",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         1,
         "B",
         "2023-11-11",
         "UK",
         "Restaurant",
         2023,
         11,
         4
        ],
        [
         3,
         "B",
         "2023-06-16",
         "UK",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         3,
         "B",
         "2023-11-06",
         "UK",
         "Swiggy",
         2023,
         11,
         4
        ],
        [
         3,
         "C",
         "2023-06-06",
         "UK",
         "Restaurant",
         2023,
         6,
         2
        ],
        [
         1,
         "C",
         "2023-06-06",
         "USA",
         "Swiggy",
         2023,
         6,
         2
        ],
        [
         6,
         "C",
         "2023-06-11",
         "USA",
         "zomato",
         2023,
         6,
         2
        ],
        [
         3,
         "D",
         "2022-11-16",
         "USA",
         "zomato",
         2022,
         11,
         4
        ],
        [
         5,
         "D",
         "2023-11-06",
         "USA",
         "zomato",
         2023,
         11,
         4
        ],
        [
         3,
         "E",
         "2023-11-06",
         "USA",
         "Swiggy",
         2023,
         11,
         4
        ],
        [
         4,
         "E",
         "2022-11-06",
         "USA",
         "zomato",
         2022,
         11,
         4
        ],
        [
         4,
         "E",
         "2023-11-11",
         "USA",
         "zomato",
         2023,
         11,
         4
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_id",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Customer_id",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "Order_date",
         "type": "\"date\""
        },
        {
         "metadata": "{}",
         "name": "Location",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "Source_order",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "order_year",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "order_month",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "order_quarter",
         "type": "\"integer\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.functions import month, year, quarter\n",
    "sales_df = sales_df.withColumn(\"order_year\",year(sales_df.Order_date))\n",
    "sales_df = sales_df.withColumn(\"order_month\",month(sales_df.Order_date))\n",
    "sales_df = sales_df.withColumn(\"order_quarter\",quarter(sales_df.Order_date))\n",
    "\n",
    "display(sales_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "3b9a0e94-2d20-4ead-8815-2294d170b77a",
     "showTitle": true,
     "title": "Menu dataframe"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_id</th><th>Product_name</th><th>Price</th></tr></thead><tbody><tr><td>1</td><td> PIZZA</td><td> 100</td></tr><tr><td>2</td><td> Chowmin</td><td> 150</td></tr><tr><td>3</td><td> sandwich</td><td> 120</td></tr><tr><td>4</td><td> Dosa</td><td> 110</td></tr><tr><td>5</td><td> Biryani</td><td> 80</td></tr><tr><td>6</td><td> Pasta</td><td> 180</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         1,
         " PIZZA",
         " 100"
        ],
        [
         2,
         " Chowmin",
         " 150"
        ],
        [
         3,
         " sandwich",
         " 120"
        ],
        [
         4,
         " Dosa",
         " 110"
        ],
        [
         5,
         " Biryani",
         " 80"
        ],
        [
         6,
         " Pasta",
         " 180"
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_id",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Product_name",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "Price",
         "type": "\"string\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.types import StructType, StructField, IntegerType, StringType\n",
    "schema = StructType([\n",
    "    StructField(\"Product_id\",IntegerType(),True),\n",
    "    StructField(\"Product_name\",StringType(),True),\n",
    "    StructField(\"Price\",StringType(),True)\n",
    "])\n",
    "menu_df = spark.read.csv(\"/FileStore/tables/menu_csv.txt\",schema=schema)\n",
    "display(menu_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "ea4b0268-0da4-4831-8284-103a3e3a4ce9",
     "showTitle": true,
     "title": "Total amount spent by each customer"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Customer_id</th><th>sum(Price)</th></tr></thead><tbody><tr><td>A</td><td>4260.0</td></tr><tr><td>B</td><td>4440.0</td></tr><tr><td>C</td><td>2400.0</td></tr><tr><td>D</td><td>1200.0</td></tr><tr><td>E</td><td>2040.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         "A",
         4260.0
        ],
        [
         "B",
         4440.0
        ],
        [
         "C",
         2400.0
        ],
        [
         "D",
         1200.0
        ],
        [
         "E",
         2040.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Customer_id",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"dG90YWxfYW1vdW50X3NwZW50ID0gKHNhbGVzX2RmLmpvaW4obWVudV9kZiwiUHJvZHVjdF9pZCIpLmdyb3VwQnkoJ0N1c3RvbWVyX2lkJykuYWdnKHsnUHJpY2UnOidzdW0nfSkub3JkZXJCeSgnQ3VzdG9tZXJfaWQnKSkKZGlzcGxheSh0b3RhbF9hbW91bnRfc3BlbnQp\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksView4a6c2a3\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksView4a6c2a3\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksView4a6c2a3\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksView4a6c2a3) SELECT `Customer_id`,SUM(`sum(Price)`) `column_745d92402` FROM q GROUP BY `Customer_id`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksView4a6c2a3\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Customer_id",
             "id": "column_745d92401"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d92402",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "area",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d92402": {
             "name": "sum(Price)",
             "type": "area",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": false,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 1704825443773,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": [
        [
         "table",
         5
        ]
       ],
       "metadata": {
        "byteLimit": 2048000,
        "rowLimit": 10000
       },
       "nuid": "98b17145-5e9a-497a-b708-5d050b88bd6a",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 6.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": {
        "addedWidgets": {},
        "arguments": {},
        "data": [
         {
          "addedWidgets": {},
          "aggData": [],
          "aggError": "",
          "aggOverflow": false,
          "aggSchema": [],
          "aggSeriesLimitReached": false,
          "aggType": "",
          "arguments": {},
          "columnCustomDisplayInfos": {},
          "data": [
           [
            "A",
            4260.0
           ],
           [
            "B",
            4440.0
           ],
           [
            "C",
            2400.0
           ],
           [
            "D",
            1200.0
           ],
           [
            "E",
            2040.0
           ]
          ],
          "datasetInfos": [],
          "dbfsResultPath": null,
          "isJsonSchema": true,
          "metadata": {},
          "overflow": false,
          "plotOptions": null,
          "removedWidgets": [],
          "schema": [
           {
            "metadata": "{}",
            "name": "Customer_id",
            "type": "\"string\""
           },
           {
            "metadata": "{}",
            "name": "column_745d92402",
            "type": "\"double\""
           }
          ],
          "type": "table"
         }
        ],
        "datasetInfos": [],
        "metadata": {
         "isDbfsCommandResult": false
        },
        "removedWidgets": [],
        "type": "listResults"
       },
       "showCommandTitle": false,
       "startTime": 1704825442393,
       "state": "finished",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Customer_id",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Customer_id",
           "type": "column"
          },
          {
           "alias": "column_745d92402",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 1704825442368,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_amount_spent = (sales_df.join(menu_df,\"Product_id\").groupBy('Customer_id').agg({'Price':'sum'}).orderBy('Customer_id'))\n",
    "display(total_amount_spent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "ff3af6a7-2f07-48cb-9478-03779ff311cd",
     "showTitle": true,
     "title": "Total amount spend on each product"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_name</th><th>sum(Price)</th></tr></thead><tbody><tr><td> Biryani</td><td>480.0</td></tr><tr><td> Chowmin</td><td>3600.0</td></tr><tr><td> Dosa</td><td>1320.0</td></tr><tr><td> PIZZA</td><td>2100.0</td></tr><tr><td> Pasta</td><td>1080.0</td></tr><tr><td> sandwich</td><td>5760.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         " Biryani",
         480.0
        ],
        [
         " Chowmin",
         3600.0
        ],
        [
         " Dosa",
         1320.0
        ],
        [
         " PIZZA",
         2100.0
        ],
        [
         " Pasta",
         1080.0
        ],
        [
         " sandwich",
         5760.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_name",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"dG90YWxfYW1vdW50X3NwZW50ID0gKHNhbGVzX2RmLmpvaW4obWVudV9kZiwiUHJvZHVjdF9pZCIpLmdyb3VwQnkoJ1Byb2R1Y3RfbmFtZScpLmFnZyh7J1ByaWNlJzonc3VtJ30pLm9yZGVyQnkoJ1Byb2R1Y3RfbmFtZScpKQpkaXNwbGF5KHRvdGFsX2Ftb3VudF9zcGVudCk=\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksView6986b0b\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksView6986b0b\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksView6986b0b\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksView6986b0b) SELECT `Product_name`,SUM(`sum(Price)`) `column_745d92404` FROM q GROUP BY `Product_name`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksView6986b0b\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Product_name",
             "id": "column_745d92403"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d92404",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "column",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d92404": {
             "color": "#8CFFB4",
             "name": "sum(Price)",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": true,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 1704825409818,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": [
        [
         "table",
         6
        ]
       ],
       "metadata": {
        "byteLimit": 2048000,
        "rowLimit": 10000
       },
       "nuid": "fd86e67e-a2e2-4c87-ab56-6fb3c318ef28",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 7.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": {
        "addedWidgets": {},
        "arguments": {},
        "data": [
         {
          "addedWidgets": {},
          "aggData": [],
          "aggError": "",
          "aggOverflow": false,
          "aggSchema": [],
          "aggSeriesLimitReached": false,
          "aggType": "",
          "arguments": {},
          "columnCustomDisplayInfos": {},
          "data": [
           [
            " Biryani",
            480.0
           ],
           [
            " Chowmin",
            3600.0
           ],
           [
            " Dosa",
            1320.0
           ],
           [
            " PIZZA",
            2100.0
           ],
           [
            " Pasta",
            1080.0
           ],
           [
            " sandwich",
            5760.0
           ]
          ],
          "datasetInfos": [],
          "dbfsResultPath": null,
          "isJsonSchema": true,
          "metadata": {},
          "overflow": false,
          "plotOptions": null,
          "removedWidgets": [],
          "schema": [
           {
            "metadata": "{}",
            "name": "Product_name",
            "type": "\"string\""
           },
           {
            "metadata": "{}",
            "name": "column_745d92404",
            "type": "\"double\""
           }
          ],
          "type": "table"
         }
        ],
        "datasetInfos": [],
        "metadata": {
         "isDbfsCommandResult": false
        },
        "removedWidgets": [],
        "type": "listResults"
       },
       "showCommandTitle": false,
       "startTime": 1704825407798,
       "state": "finished",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Product_name",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Product_name",
           "type": "column"
          },
          {
           "alias": "column_745d92404",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 1704825407752,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_amount_spent = (sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg({'Price':'sum'}).orderBy('Product_name'))\n",
    "display(total_amount_spent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a127e5ed-b01e-4d70-b1b8-361486a9d4ba",
     "showTitle": true,
     "title": "Sales on each month"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Order_month</th><th>sum(Price)</th></tr></thead><tbody><tr><td>1</td><td>2960.0</td></tr><tr><td>2</td><td>2730.0</td></tr><tr><td>3</td><td>910.0</td></tr><tr><td>5</td><td>2960.0</td></tr><tr><td>6</td><td>2960.0</td></tr><tr><td>7</td><td>910.0</td></tr><tr><td>11</td><td>910.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         1,
         2960.0
        ],
        [
         2,
         2730.0
        ],
        [
         3,
         910.0
        ],
        [
         5,
         2960.0
        ],
        [
         6,
         2960.0
        ],
        [
         7,
         910.0
        ],
        [
         11,
         910.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Order_month",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"bW9udGhseV9hbW91bnQgPSAoc2FsZXNfZGYuam9pbihtZW51X2RmLCJQcm9kdWN0X2lkIikuZ3JvdXBCeSgnT3JkZXJfbW9udGgnKS5hZ2coeydQcmljZSc6J3N1bSd9KS5vcmRlckJ5KCdPcmRlcl9tb250aCcpKQpkaXNwbGF5KG1vbnRobHlfYW1vdW50KQ==\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksView0c99317\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksView0c99317\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksView0c99317\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksView0c99317) SELECT `Order_month`,SUM(`sum(Price)`) `column_745d92406` FROM q GROUP BY `Order_month`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksView0c99317\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Order_month",
             "id": "column_745d92405"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d92406",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "column",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d92406": {
             "color": "#FB8D3D",
             "name": "sum(Price)",
             "type": "column",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": false,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "d7b3d11f-4fac-4af5-9ac6-031a824d08e9",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 8.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Order_month",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Order_month",
           "type": "column"
          },
          {
           "alias": "column_745d92406",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_amount = (sales_df.join(menu_df,\"Product_id\").groupBy('Order_month').agg({'Price':'sum'}).orderBy('Order_month'))\n",
    "display(monthly_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "52e74e6b-5c0f-4825-85ec-256f47119535",
     "showTitle": true,
     "title": "Yearly sales"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Order_year</th><th>sum(Price)</th></tr></thead><tbody><tr><td>2022</td><td>4350.0</td></tr><tr><td>2023</td><td>9990.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         2022,
         4350.0
        ],
        [
         2023,
         9990.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Order_year",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"eWVhcmx5X2Ftb3VudCA9IChzYWxlc19kZi5qb2luKG1lbnVfZGYsIlByb2R1Y3RfaWQiKS5ncm91cEJ5KCdPcmRlcl95ZWFyJykuYWdnKHsnUHJpY2UnOidzdW0nfSkub3JkZXJCeSgnT3JkZXJfeWVhcicpKQpkaXNwbGF5KHllYXJseV9hbW91bnQp\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksViewf21114e\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksViewf21114e\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksViewf21114e\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksViewf21114e) SELECT `Order_year`,SUM(`sum(Price)`) `column_745d92408` FROM q GROUP BY `Order_year`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksViewf21114e\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Order_year",
             "id": "column_745d92407"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d92408",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "line",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d92408": {
             "color": "#E92828",
             "name": "sum(Price)",
             "type": "line",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": false,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "c34b50ee-4fce-45b9-bffd-f1cf11778b17",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 9.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Order_year",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Order_year",
           "type": "column"
          },
          {
           "alias": "column_745d92408",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_amount = (sales_df.join(menu_df,\"Product_id\").groupBy('Order_year').agg({'Price':'sum'}).orderBy('Order_year'))\n",
    "display(yearly_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "82e63cb1-cd19-483e-830e-072fe8fe76b8",
     "showTitle": true,
     "title": "Quaterly sales"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Order_quarter</th><th>sum(Price)</th></tr></thead><tbody><tr><td>1</td><td>6600.0</td></tr><tr><td>2</td><td>5920.0</td></tr><tr><td>3</td><td>910.0</td></tr><tr><td>4</td><td>910.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         1,
         6600.0
        ],
        [
         2,
         5920.0
        ],
        [
         3,
         910.0
        ],
        [
         4,
         910.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Order_quarter",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"cXVhdGVybHlfYW1vdW50ID0gKHNhbGVzX2RmLmpvaW4obWVudV9kZiwiUHJvZHVjdF9pZCIpLmdyb3VwQnkoJ09yZGVyX3F1YXJ0ZXInKS5hZ2coeydQcmljZSc6J3N1bSd9KS5vcmRlckJ5KCdPcmRlcl9xdWFydGVyJykpCmRpc3BsYXkocXVhdGVybHlfYW1vdW50KQ==\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksViewb6e6ed4\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksViewb6e6ed4\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksViewb6e6ed4\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksViewb6e6ed4) SELECT `Order_quarter`,SUM(`sum(Price)`) `column_745d924010` FROM q GROUP BY `Order_quarter`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksViewb6e6ed4\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Order_quarter",
             "id": "column_745d92409"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d924010",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "pie",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d924010": {
             "color": "#E92828",
             "name": "sum(Price)",
             "type": "pie",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": false,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "f005ec9e-91b9-4552-9906-1ce70d607db6",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 10.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Order_quarter",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Order_quarter",
           "type": "column"
          },
          {
           "alias": "column_745d924010",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "quaterly_amount = (sales_df.join(menu_df,\"Product_id\").groupBy('Order_quarter').agg({'Price':'sum'}).orderBy('Order_quarter'))\n",
    "display(quaterly_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "10a97131-fac6-46dd-b6ee-318d38658485",
     "showTitle": true,
     "title": "How many times each product ordered"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_name</th><th>product_count</th></tr></thead><tbody><tr><td> sandwich</td><td>48</td></tr><tr><td> Chowmin</td><td>24</td></tr><tr><td> PIZZA</td><td>21</td></tr><tr><td> Dosa</td><td>12</td></tr><tr><td> Pasta</td><td>6</td></tr><tr><td> Biryani</td><td>6</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         " sandwich",
         48
        ],
        [
         " Chowmin",
         24
        ],
        [
         " PIZZA",
         21
        ],
        [
         " Dosa",
         12
        ],
        [
         " Pasta",
         6
        ],
        [
         " Biryani",
         6
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_name",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "product_count",
         "type": "\"long\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "from pyspark.sql.functions import count\nproduct_sale=(sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg(count('product_id').alias('product_count')).\n              orderBy('product_count',ascending=0))\ndisplay(product_sale)",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "FUNNEL"
         },
         {
          "key": "options",
          "value": {
           "autoSort": true,
           "itemsLimit": 100,
           "numberFormat": "0,0.[00]",
           "percentFormat": "0[.]00%",
           "percentValuesRange": {
            "max": 1000,
            "min": 0.01
           },
           "sortKeyCol": {
            "colName": "product_count",
            "reverse": true
           },
           "stepCol": {
            "colName": "Product_name",
            "displayAs": "Steps"
           },
           "valueCol": {
            "colName": "product_count",
            "displayAs": "Value"
           }
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "a4c9f837-1029-4201-aec7-b4bd717fe228",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 11.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {},
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.functions import count\n",
    "product_sale=(sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg(count('product_id').alias('product_count')).\n",
    "              orderBy('product_count',ascending=0))\n",
    "display(product_sale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "b33e06f0-620d-4abd-8058-ed207fee48fa",
     "showTitle": true,
     "title": "Top 5 items"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_name</th><th>product_count</th></tr></thead><tbody><tr><td> sandwich</td><td>48</td></tr><tr><td> Chowmin</td><td>24</td></tr><tr><td> PIZZA</td><td>21</td></tr><tr><td> Dosa</td><td>12</td></tr><tr><td> Pasta</td><td>6</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         " sandwich",
         48
        ],
        [
         " Chowmin",
         24
        ],
        [
         " PIZZA",
         21
        ],
        [
         " Dosa",
         12
        ],
        [
         " Pasta",
         6
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_name",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "product_count",
         "type": "\"long\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "from pyspark.sql.functions import count\nproduct_sale=(sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg(count('product_id').alias('product_count')).\n              orderBy('product_count',ascending=0)).limit(5)\ndisplay(product_sale)",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "WORD_CLOUD"
         },
         {
          "key": "options",
          "value": {
           "column": "Product_name",
           "frequenciesColumn": "product_count",
           "wordCountLimit": {
            "max": null,
            "min": null
           },
           "wordLengthLimit": {
            "max": null,
            "min": null
           }
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "096ad08d-80e7-4d2c-ade2-aa9170bf9acf",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 12.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {},
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.functions import count\n",
    "product_sale=(sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg(count('product_id').alias('product_count')).\n",
    "              orderBy('product_count',ascending=0)).limit(5)\n",
    "display(product_sale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "9158478e-1273-4a1f-95ea-9e517c8ad48e",
     "showTitle": true,
     "title": "Most sold item"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Product_name</th><th>product_count</th></tr></thead><tbody><tr><td> sandwich</td><td>48</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         " sandwich",
         48
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Product_name",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "product_count",
         "type": "\"long\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "from pyspark.sql.functions import count\nproduct_sale=(sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg(count('product_id').alias('product_count')).\n              orderBy('product_count',ascending=0)).limit(1)\ndisplay(product_sale)",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "COUNTER"
         },
         {
          "key": "options",
          "value": {
           "counterColName": "product_count",
           "counterLabel": "Sandwich",
           "rowNumber": 1,
           "stringDecChar": ".",
           "stringDecimal": 0,
           "stringThouSep": ",",
           "targetRowNumber": 1,
           "tooltipFormat": "0,0.000"
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "cf72c61b-8de0-482f-9e11-05d8130b33bb",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 13.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {},
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.functions import count\n",
    "product_sale=(sales_df.join(menu_df,\"Product_id\").groupBy('Product_name').agg(count('product_id').alias('product_count')).\n",
    "              orderBy('product_count',ascending=0)).limit(1)\n",
    "display(product_sale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "0cde23b4-78d9-4282-af26-471267f310d6",
     "showTitle": true,
     "title": "frequency of customer visit to restaurant"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Customer_id</th><th>No of Visits</th></tr></thead><tbody><tr><td>A</td><td>6</td></tr><tr><td>B</td><td>6</td></tr><tr><td>C</td><td>3</td></tr><tr><td>D</td><td>1</td></tr><tr><td>E</td><td>5</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         "A",
         6
        ],
        [
         "B",
         6
        ],
        [
         "C",
         3
        ],
        [
         "D",
         1
        ],
        [
         "E",
         5
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Customer_id",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "No of Visits",
         "type": "\"long\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"ZnJvbSBweXNwYXJrLnNxbC5mdW5jdGlvbnMgaW1wb3J0IGNvdW50RGlzdGluY3QKCmN1c3RvbWVyX3Zpc2l0PSAoc2FsZXNfZGYuZmlsdGVyKHNhbGVzX2RmLlNvdXJjZV9vcmRlcj09J1Jlc3RhdXJhbnQnKS5ncm91cEJ5KCdDdXN0b21lcl9pZCcpLmFnZyhjb3VudERpc3RpbmN0KCdPcmRlcl9kYXRlJykuYWxpYXMoJ05vIG9mIFZpc2l0cycpKS5vcmRlckJ5KCdDdXN0b21lcl9pZCcpKQoKZGlzcGxheShjdXN0b21lcl92aXNpdCk=\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksView8f486fc\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksView8f486fc\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksView8f486fc\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksView8f486fc) SELECT `Customer_id`,SUM(`No of Visits`) `column_745d924021` FROM q GROUP BY `Customer_id`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksView8f486fc\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Customer_id",
             "id": "column_745d924020"
            },
            "y": [
             {
              "column": "No of Visits",
              "id": "column_745d924021",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "column",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d924021": {
             "color": "#B554FF",
             "name": "No of Visits",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": true,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "7216d67c-084b-4621-a520-5258276417a9",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 14.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Customer_id",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Customer_id",
           "type": "column"
          },
          {
           "alias": "column_745d924021",
           "args": [
            {
             "column": "No of Visits",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql.functions import countDistinct\n",
    "\n",
    "customer_visit= (sales_df.filter(sales_df.Source_order=='Restaurant').groupBy('Customer_id').agg(countDistinct('Order_date').alias('No of Visits')).orderBy('Customer_id'))\n",
    "\n",
    "display(customer_visit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "71cd641a-bcc4-42ab-b967-039b84420c6c",
     "showTitle": true,
     "title": "Total sales by each country"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Location</th><th>sum(Price)</th></tr></thead><tbody><tr><td>India</td><td>4860.0</td></tr><tr><td>UK</td><td>7020.0</td></tr><tr><td>USA</td><td>2460.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         "India",
         4860.0
        ],
        [
         "UK",
         7020.0
        ],
        [
         "USA",
         2460.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Location",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"Y291bnRyeV9zYWxlcz0gKHNhbGVzX2RmLmpvaW4obWVudV9kZiwnUHJvZHVjdF9pZCcpLmdyb3VwQnkoJ0xvY2F0aW9uJykuYWdnKHsnUHJpY2UnOidzdW0nfSkub3JkZXJCeSgnTG9jYXRpb24nKSkKCmRpc3BsYXkoY291bnRyeV9zYWxlcyk=\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksView2f226a1\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksView2f226a1\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksView2f226a1\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksView2f226a1) SELECT `Location`,SUM(`sum(Price)`) `column_745d924023` FROM q GROUP BY `Location`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksView2f226a1\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Location",
             "id": "column_745d924022"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d924023",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "column",
           "isAggregationOn": true,
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d924023": {
             "color": "#981717",
             "name": "sum(Price)",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": false,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "category"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 1704823747680,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": [
        [
         "table",
         3
        ]
       ],
       "metadata": {
        "byteLimit": 2048000,
        "rowLimit": 10000
       },
       "nuid": "041f76ba-eae5-469d-b85d-1b8912b8c815",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 15.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": {
        "addedWidgets": {},
        "arguments": {},
        "data": [
         {
          "addedWidgets": {},
          "aggData": [],
          "aggError": "",
          "aggOverflow": false,
          "aggSchema": [],
          "aggSeriesLimitReached": false,
          "aggType": "",
          "arguments": {},
          "columnCustomDisplayInfos": {},
          "data": [
           [
            "India",
            4860.0
           ],
           [
            "UK",
            7020.0
           ],
           [
            "USA",
            2460.0
           ]
          ],
          "datasetInfos": [],
          "dbfsResultPath": null,
          "isJsonSchema": true,
          "metadata": {},
          "overflow": false,
          "plotOptions": null,
          "removedWidgets": [],
          "schema": [
           {
            "metadata": "{}",
            "name": "Location",
            "type": "\"string\""
           },
           {
            "metadata": "{}",
            "name": "column_745d924023",
            "type": "\"double\""
           }
          ],
          "type": "table"
         }
        ],
        "datasetInfos": [],
        "metadata": {
         "isDbfsCommandResult": false
        },
        "removedWidgets": [],
        "type": "listResults"
       },
       "showCommandTitle": false,
       "startTime": 1704823744777,
       "state": "finished",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Location",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Location",
           "type": "column"
          },
          {
           "alias": "column_745d924023",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 1704823744728,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_sales= (sales_df.join(menu_df,'Product_id').groupBy('Location').agg({'Price':'sum'}).orderBy('Location'))\n",
    "\n",
    "display(country_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "5cf880f2-1115-4736-badd-9d236bfee154",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .table-result-container {\n",
       "    max-height: 300px;\n",
       "    overflow: auto;\n",
       "  }\n",
       "  table, th, td {\n",
       "    border: 1px solid black;\n",
       "    border-collapse: collapse;\n",
       "  }\n",
       "  th, td {\n",
       "    padding: 5px;\n",
       "  }\n",
       "  th {\n",
       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>Source_order</th><th>sum(Price)</th></tr></thead><tbody><tr><td>Restaurant</td><td>3090.0</td></tr><tr><td>Swiggy</td><td>6330.0</td></tr><tr><td>zomato</td><td>4920.0</td></tr></tbody></table></div>"
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
       "aggData": [],
       "aggError": "",
       "aggOverflow": false,
       "aggSchema": [],
       "aggSeriesLimitReached": false,
       "aggType": "",
       "arguments": {},
       "columnCustomDisplayInfos": {},
       "data": [
        [
         "Restaurant",
         3090.0
        ],
        [
         "Swiggy",
         6330.0
        ],
        [
         "zomato",
         4920.0
        ]
       ],
       "datasetInfos": [],
       "dbfsResultPath": null,
       "isJsonSchema": true,
       "metadata": {},
       "overflow": false,
       "plotOptions": {
        "customPlotOptions": {},
        "displayType": "table",
        "pivotAggregation": null,
        "pivotColumns": null,
        "xColumns": null,
        "yColumns": null
       },
       "removedWidgets": [],
       "schema": [
        {
         "metadata": "{}",
         "name": "Source_order",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "sum(Price)",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Databricks visualization. Run in Databricks to view."
      ]
     },
     "metadata": {
      "application/vnd.databricks.v1.subcommand+json": {
       "baseErrorDetails": null,
       "bindings": {},
       "collapsed": false,
       "command": "%python\n__backend_agg_display_orig = display\n__backend_agg_dfs = []\ndef __backend_agg_display_new(df):\n    __backend_agg_df_modules = [\"pandas.core.frame\", \"databricks.koalas.frame\", \"pyspark.sql.dataframe\", \"pyspark.pandas.frame\", \"pyspark.sql.connect.dataframe\"]\n    if (type(df).__module__ in __backend_agg_df_modules and type(df).__name__ == 'DataFrame') or isinstance(df, list):\n        __backend_agg_dfs.append(df)\n\ndisplay = __backend_agg_display_new\n\ndef __backend_agg_user_code_fn():\n    import base64\n    exec(base64.standard_b64decode(\"bW9kZV9vZl9zYWxlcz0gKHNhbGVzX2RmLmpvaW4obWVudV9kZiwnUHJvZHVjdF9pZCcpLmdyb3VwQnkoJ1NvdXJjZV9vcmRlcicpLmFnZyh7J1ByaWNlJzonc3VtJ30pLm9yZGVyQnkoJ1NvdXJjZV9vcmRlcicpKQoKZGlzcGxheShtb2RlX29mX3NhbGVzKQ==\").decode())\n\ntry:\n    # run user code\n    __backend_agg_user_code_fn()\n\n    #reset display function\n    display = __backend_agg_display_orig\n\n    if len(__backend_agg_dfs) > 0:\n        # create a temp view\n        if type(__backend_agg_dfs[0]).__module__ == \"databricks.koalas.frame\":\n            # koalas dataframe\n            __backend_agg_dfs[0].to_spark().createOrReplaceTempView(\"DatabricksViewdb24333\")\n        elif type(__backend_agg_dfs[0]).__module__ == \"pandas.core.frame\" or isinstance(__backend_agg_dfs[0], list):\n            # pandas dataframe\n            spark.createDataFrame(__backend_agg_dfs[0]).createOrReplaceTempView(\"DatabricksViewdb24333\")\n        else:\n            __backend_agg_dfs[0].createOrReplaceTempView(\"DatabricksViewdb24333\")\n        #run backend agg\n        display(spark.sql(\"\"\"WITH q AS (select * from DatabricksViewdb24333) SELECT `Source_order`,SUM(`sum(Price)`) `column_745d924025` FROM q GROUP BY `Source_order`\"\"\"))\n    else:\n        displayHTML(\"dataframe no longer exists. If you're using dataframe.display(), use display(dataframe) instead.\")\n\n\nfinally:\n    spark.sql(\"drop view if exists DatabricksViewdb24333\")\n    display = __backend_agg_display_orig\n    del __backend_agg_display_new\n    del __backend_agg_display_orig\n    del __backend_agg_dfs\n    del __backend_agg_user_code_fn\n\n",
       "commandTitle": "Visualization 1",
       "commandType": "auto",
       "commandVersion": 0,
       "commentThread": [],
       "commentsVisible": false,
       "contentSha256Hex": null,
       "customPlotOptions": {
        "redashChart": [
         {
          "key": "type",
          "value": "CHART"
         },
         {
          "key": "options",
          "value": {
           "alignYAxesAtZero": true,
           "coefficient": 1,
           "columnConfigurationMap": {
            "x": {
             "column": "Source_order",
             "id": "column_745d924024"
            },
            "y": [
             {
              "column": "sum(Price)",
              "id": "column_745d924025",
              "transform": "SUM"
             }
            ]
           },
           "dateTimeFormat": "DD/MM/YYYY HH:mm",
           "direction": {
            "type": "counterclockwise"
           },
           "error_y": {
            "type": "data",
            "visible": true
           },
           "globalSeriesType": "pie",
           "legend": {
            "traceorder": "normal"
           },
           "missingValuesAsZero": true,
           "numberFormat": "0,0.[00000]",
           "percentFormat": "0[.]00%",
           "series": {
            "error_y": {
             "type": "data",
             "visible": true
            },
            "stacking": null
           },
           "seriesOptions": {
            "column_745d924025": {
             "name": "sum(Price)",
             "type": "pie",
             "yAxis": 0
            }
           },
           "showDataLabels": true,
           "sizemode": "diameter",
           "sortX": true,
           "sortY": true,
           "swappedAxes": false,
           "textFormat": "",
           "useAggregationsUi": true,
           "valuesOptions": {},
           "version": 2,
           "xAxis": {
            "labels": {
             "enabled": true
            },
            "type": "-"
           },
           "yAxis": [
            {
             "type": "-"
            },
            {
             "opposite": true,
             "type": "-"
            }
           ]
          }
         }
        ]
       },
       "datasetPreviewNameToCmdIdMap": {},
       "diffDeletes": [],
       "diffInserts": [],
       "displayType": "redashChart",
       "error": null,
       "errorDetails": null,
       "errorSummary": null,
       "errorTraceType": null,
       "finishTime": 0,
       "globalVars": {},
       "guid": "",
       "height": "auto",
       "hideCommandCode": false,
       "hideCommandResult": false,
       "iPythonMetadata": null,
       "inputWidgets": {},
       "isLockedInExamMode": false,
       "latestUser": "a user",
       "latestUserId": null,
       "listResultMetadata": null,
       "metadata": {},
       "nuid": "0ba720f7-e76d-4eb5-aae5-c51d9f1a4e08",
       "origId": 0,
       "parentHierarchy": [],
       "pivotAggregation": null,
       "pivotColumns": null,
       "position": 16.0,
       "resultDbfsErrorMessage": null,
       "resultDbfsStatus": "INLINED_IN_TREE",
       "results": null,
       "showCommandTitle": false,
       "startTime": 0,
       "state": "input",
       "streamStates": {},
       "subcommandOptions": {
        "queryPlan": {
         "groups": [
          {
           "column": "Source_order",
           "type": "column"
          }
         ],
         "selects": [
          {
           "column": "Source_order",
           "type": "column"
          },
          {
           "alias": "column_745d924025",
           "args": [
            {
             "column": "sum(Price)",
             "type": "column"
            }
           ],
           "function": "SUM",
           "type": "function"
          }
         ]
        }
       },
       "submitTime": 0,
       "subtype": "tableResultSubCmd.visualization",
       "tableResultIndex": 0,
       "useConsistentColors": false,
       "version": "CommandV1",
       "width": "auto",
       "workflows": [],
       "xColumns": null,
       "yColumns": null
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_of_sales= (sales_df.join(menu_df,'Product_id').groupBy('Source_order').agg({'Price':'sum'}).orderBy('Source_order'))\n",
    "\n",
    "display(mode_of_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "05bb9ab3-c393-4406-a9e7-c9a369013b5b",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "application/vnd.databricks.v1+notebook": {
   "dashboards": [
    {
     "elements": [
      {
       "dashboardResultIndex": null,
       "elementNUID": "98b17145-5e9a-497a-b708-5d050b88bd6a",
       "elementType": "command",
       "guid": "4b135ca8-d8e8-4f78-9c55-5fb17a8570c1",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showRunButton": true,
        "showTitle": true,
        "title": "Total amount spent by each customer",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 13,
        "x": 11,
        "y": 12,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "fd86e67e-a2e2-4c87-ab56-6fb3c318ef28",
       "elementType": "command",
       "guid": "6b511e7e-33ed-4bab-a058-34a8fa085afe",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showRunButton": true,
        "showTitle": true,
        "title": "Total amount spent on each food category",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 13,
        "x": 11,
        "y": 6,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "0ba720f7-e76d-4eb5-aae5-c51d9f1a4e08",
       "elementType": "command",
       "guid": "7de3b6ad-8929-4ca3-87c9-0af9170718f2",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showTitle": true,
        "title": "Mode of Purchase",
        "titleAlign": "center"
       },
       "position": {
        "height": 8,
        "width": 15,
        "x": 0,
        "y": 24,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "d7b3d11f-4fac-4af5-9ac6-031a824d08e9",
       "elementType": "command",
       "guid": "99772643-0eea-46b8-887d-29388f944d89",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showTitle": true,
        "title": "Monthly Sales",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 12,
        "x": 0,
        "y": 18,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "041f76ba-eae5-469d-b85d-1b8912b8c815",
       "elementType": "command",
       "guid": "ac1647c2-ed6a-4c35-8c32-a328b932442a",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showRunButton": true,
        "showTitle": true,
        "title": "Country Revenue",
        "titleAlign": "center"
       },
       "position": {
        "height": 8,
        "width": 9,
        "x": 15,
        "y": 24,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "096ad08d-80e7-4d2c-ade2-aa9170bf9acf",
       "elementType": "command",
       "guid": "b23d1ca7-cd6e-4689-b651-7a009eece20b",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showTitle": true,
        "title": "Top 5 products",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 11,
        "x": 0,
        "y": 6,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "7216d67c-084b-4621-a520-5258276417a9",
       "elementType": "command",
       "guid": "bbbfadc1-257a-4c85-a1f7-513fed815096",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showTitle": true,
        "title": "Customer visits to Restaurant",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 11,
        "x": 0,
        "y": 12,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "f005ec9e-91b9-4552-9906-1ce70d607db6",
       "elementType": "command",
       "guid": "c1dd3da4-e928-43b5-98f4-fd4faa46a84b",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showTitle": true,
        "title": "Quaterly Sales",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 12,
        "x": 12,
        "y": 18,
        "z": null
       },
       "resultIndex": null
      },
      {
       "dashboardResultIndex": null,
       "elementNUID": "cf72c61b-8de0-482f-9e11-05d8130b33bb",
       "elementType": "command",
       "guid": "d2ca6f97-83e7-445f-83bf-f3a7e523a1c1",
       "options": {
        "autoScaleImg": false,
        "scale": 0,
        "showTitle": true,
        "title": "Top Food item",
        "titleAlign": "center"
       },
       "position": {
        "height": 6,
        "width": 24,
        "x": 0,
        "y": 0,
        "z": null
       },
       "resultIndex": null
      }
     ],
     "globalVars": {},
     "guid": "",
     "layoutOption": {
      "grid": true,
      "stack": false
     },
     "nuid": "84a4cee3-506a-4e98-b238-3913d6a11dc6",
     "origId": 3362387503275637,
     "title": "Sales Analysis",
     "version": "DashboardViewV1",
     "width": 1024
    }
   ],
   "language": "python",
   "notebookMetadata": {
    "pythonIndentUnit": 4
   },
   "notebookName": "Pyspark_project_notebook",
   "widgets": {}
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}