Joining combines separate pandas data frames when you need information that belongs together but changes at different rates. For example, product details can remain in products_df while each sale is recorded in sales_df. section 6.0 section 6.8
A join matches rows using a common key or index. By default, pandas compares the indexes, so set product_id as the index in both frames before calling products_df.join(sales_df). The result contains columns from both inputs. section 6.8
From product records to sales analysis
After joining the product and sales frames, group the result by name and sum quantity to find units sold. Group by name and sum retail_price, then sort_values(), to compare product revenue. Group by date for daily totals, or by date and name for a two-level breakdown.
Extend the basic operation by asking:
- Can several data frames be joined into one analysis? section 7.13
- Which join type preserves unmatched rows, and where do the resulting NaN values appear? section 7.13
- What relationship appears after joining country-level wine scores with tourism spending and calculating correlation? section 7.13
