Pivot Table
Learn what a pivot table is and how to use it for data summarization. Discover functions, use cases, and best practices for analyzing data by category.
Pivot Table: Summarizing and Analyzing Data by Category
What is a Pivot Table?
A pivot table is a data summarization tool commonly found in spreadsheets and BI platforms. It reorganizes and aggregates data to answer questions like “how many?”, “how much?” or “what’s the average?” by category. Unlike the raw data table, a pivot table doesn’t change the source data; it “pivots” it to view it from a new angle. Users can drag fields into row and column zones and choose summary functions (Sum, Count, Average, etc.) to compute values. For example, a pivot table can take monthly sales data and quickly show the total sales by product or average sales per region. Pivot tables are highly interactive: you can filter or sort categories and the table updates instantly.
Functions
By default, numeric fields are summed and text fields counted. You can change the aggregation (sum→average, count→min/max, percentage of total, etc.) for each value field. A typical pivot table has Rows and Columns (categories) and Values (aggregated measures). For instance, placing “Product” as Rows, “Quarter” as Columns, and “Sales” as Values would produce a table of quarterly sales per product.
Use cases
Pivot tables let analysts answer business questions quickly without writing formulas. Common uses include counting items per category, totaling sales by region, or finding averages. They can also generate interactive reports or charts that update when you drill into the data. Pivot tables are especially powerful for ad-hoc exploration of large datasets; they handle grouping, subtotals and grand totals automatically, saving hours of manual calculation.