Welcome back to our Apache Superset course.
Today, we’re going to create a Word Cloud chart using a product sales dataset to visualize how different product categories contribute to overall revenue.
Word Cloud charts are a powerful way to represent categorical data, where the size of each word reflects its importance. In this case, we’ll use it to quickly identify which product categories are generating the highest sales.
Let’s take a look at the dataset we’ll be using:
sales_by_category.csv
order_id,product_category,region,sales_amount,order_date 1,Electronics,North,120000,2026-03-01 2,Clothing,South,45000,2026-03-01 3,Groceries,West,30000,2026-03-01 4,Electronics,East,80000,2026-03-02 5,Furniture,North,60000,2026-03-02 6,Clothing,West,50000,2026-03-03 7,Groceries,South,35000,2026-03-03 8,Electronics,North,90000,2026-03-04 9,Furniture,East,70000,2026-03-04 10,Clothing,North,40000,2026-03-05 11,Electronics,South,110000,2026-03-05 12,Groceries,East,25000,2026-03-06 13,Furniture,West,65000,2026-03-06 14,Clothing,South,48000,2026-03-07 15,Electronics,North,130000,2026-03-07 16,Groceries,West,32000,2026-03-08 17,Furniture,East,72000,2026-03-08 18,Clothing,North,42000,2026-03-09 19,Electronics,South,115000,2026-03-09 20,Groceries,East,28000,2026-03-10 21,Furniture,West,69000,2026-03-10 22,Clothing,South,46000,2026-03-11 23,Electronics,North,125000,2026-03-11 24,Groceries,West,34000,2026-03-12 25,Furniture,East,71000,2026-03-12 26,Clothing,North,39000,2026-03-13 27,Electronics,South,118000,2026-03-13 28,Groceries,East,27000,2026-03-14 29,Furniture,West,67000,2026-03-14 30,Clothing,South,44000,2026-03-15 31,Electronics,North,135000,2026-03-15 32,Groceries,West,36000,2026-03-16 33,Furniture,East,74000,2026-03-16 34,Clothing,North,41000,2026-03-17 35,Electronics,South,122000,2026-03-17 36,Groceries,East,29000,2026-03-18 37,Furniture,West,68000,2026-03-18 38,Clothing,South,47000,2026-03-19 39,Electronics,North,140000,2026-03-19 40,Groceries,West,31000,2026-03-20 41,Furniture,East,76000,2026-03-20 42,Clothing,North,43000,2026-03-21 43,Electronics,South,119000,2026-03-21 44,Groceries,East,26000,2026-03-22 45,Furniture,West,70000,2026-03-22 46,Clothing,South,45000,2026-03-23 47,Electronics,North,138000,2026-03-23 48,Groceries,West,33000,2026-03-24 49,Furniture,East,73000,2026-03-24 50,Clothing,North,40000,2026-03-25 51,Electronics,South,121000,2026-03-25 52,Groceries,East,28000,2026-03-26 53,Furniture,West,69000,2026-03-26 54,Clothing,South,46000,2026-03-27 55,Electronics,North,132000,2026-03-27 56,Groceries,West,35000,2026-03-28 57,Furniture,East,75000,2026-03-28 58,Clothing,North,42000,2026-03-29 59,Electronics,South,117000,2026-03-29 60,Groceries,East,27000,2026-03-30 61,Furniture,West,68000,2026-03-30 62,Clothing,South,44000,2026-03-31 63,Electronics,North,145000,2026-04-01 64,Groceries,West,36000,2026-04-01 65,Furniture,East,77000,2026-04-02 66,Clothing,North,43000,2026-04-02 67,Electronics,South,123000,2026-04-03 68,Groceries,East,29000,2026-04-03 69,Furniture,West,71000,2026-04-04 70,Clothing,South,47000,2026-04-04 71,Electronics,North,142000,2026-04-05 72,Groceries,West,34000,2026-04-05 73,Furniture,East,76000,2026-04-06 74,Clothing,North,41000,2026-04-06 75,Electronics,South,120000,2026-04-07 76,Groceries,East,28000,2026-04-07 77,Furniture,West,70000,2026-04-08 78,Clothing,South,45000,2026-04-08 79,Electronics,North,137000,2026-04-09 80,Groceries,West,33000,2026-04-09 81,Furniture,East,74000,2026-04-10 82,Clothing,North,42000,2026-04-10 83,Electronics,South,119000,2026-04-11 84,Groceries,East,27000,2026-04-11 85,Furniture,West,69000,2026-04-12 86,Clothing,South,46000,2026-04-12 87,Electronics,North,134000,2026-04-13 88,Groceries,West,35000,2026-04-13 89,Furniture,East,72000,2026-04-14 90,Clothing,North,40000,2026-04-14 91,Electronics,South,121000,2026-04-15 92,Groceries,East,29000,2026-04-15 93,Furniture,West,68000,2026-04-16 94,Clothing,South,44000,2026-04-16 95,Electronics,North,139000,2026-04-17 96,Groceries,West,36000,2026-04-17 97,Furniture,East,75000,2026-04-18 98,Clothing,North,43000,2026-04-18 99,Electronics,South,122000,2026-04-19 100,Groceries,East,28000,2026-04-19 101,Furniture,West,70000,2026-04-20 102,Clothing,South,45000,2026-04-20 103,Electronics,North,141000,2026-04-21 104,Groceries,West,34000,2026-04-21 105,Furniture,East,76000,2026-04-22 106,Clothing,North,42000,2026-04-22 107,Electronics,South,118000,2026-04-23 108,Groceries,East,27000,2026-04-23 109,Furniture,West,69000,2026-04-24 110,Clothing,South,46000,2026-04-24 111,Electronics,North,136000,2026-04-25 112,Groceries,West,35000,2026-04-25 113,Furniture,East,73000,2026-04-26 114,Clothing,North,41000,2026-04-26 115,Electronics,South,120000,2026-04-27 116,Groceries,East,29000,2026-04-27 117,Furniture,West,71000,2026-04-28 118,Clothing,South,44000,2026-04-28 119,Electronics,North,138000,2026-04-29 120,Groceries,West,36000,2026-04-29
This dataset contains information about orders, including product category, region, and sales amount. Follow below step-by-step procedure to build Word Cloud Chart.
Step 1: Upload 'sales_by_category.csv' to Apache Superset.
Data -> Upload CSV to database
· Upload the csv file
· Select the database and schema name where you want to upload the csv file.
· Give the table name as 'sales_by_category'
Expand Column section.
· Select all the columns in 'columns to read' section.
· Set Column data types to {"order_id": "int64", "product_category": "string", "region" : "string", "sales_amount" : "float32", "order_date" : "datetime64[ns]"}
Click on Upload button.
Navigate to Datasets listing page, you can able to see sales_by_category dataset.
Step 2: Create Word Cloud Chart.
Click on sales_by_category dataset. You will be taken to chart configuration page.
Click on ‘view all charts’ link.
Select Word Cloud map.
Drag and drop product_category to Dimension.
Drag and drop sales_amount to Metric section, and select SUM as aggregation.
Click on ‘Create chart’ button, you can see Word cloud map like below.
That’s it, you're good to go.
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