拆分—应用—合并模式

The GroupBy operation is one of the most powerful patterns in data analysis. It lets you split a DataFrame into groups, apply a function to each group independently, and then combine the results into a new structure. This workflow is known as the split-apply-combine pattern, a term coined by statistician Hadley Wickham.

In the split step, Pandas divides the DataFrame into sub-DataFrames based on the unique values in one or more columns. For example, if your data has a region column with values like 'North', 'South', and 'West', the split step creates three separate groups. No data is moved or copied yet — Pandas just records which rows belong to which group.

In the apply step, a function is applied to each group independently. The function can be a built-in aggregation like sum() or mean(), or a custom function you define. Each group's rows are passed to the function, and it returns a scalar, a Series, or a DataFrame as the result.

In the combine step, Pandas automatically assembles the per-group results back into a single object — typically a Series or DataFrame. The group keys become the index of the result. This step happens invisibly when you call an aggregation method on a GroupBy object, giving you a clean summary table with one row per group.

You create a GroupBy object by calling df.groupby(column). Nothing is computed at this stage — it is a lazy object. You can iterate over it to see the groups, or chain an aggregation method to trigger computation. Passing as_index=False keeps the grouping column as a regular column instead of the index in the result.

You can iterate over a GroupBy object to inspect each group individually. Each iteration yields a tuple of (group_key, sub_dataframe). This is useful for debugging or when you want to process each group with arbitrary Python code. However, for performance in production, prefer vectorised aggregation methods over explicit iteration.

Pandas GroupBy supports many built-in aggregation functions: sum(), mean(), count(), min(), max(), std(), median(), and more. These are highly optimised and run on all groups in a single pass. Always prefer them over writing a custom Python function when the built-in exists.

You can apply aggregations to multiple columns simultaneously by selecting them before calling the aggregation method, or by omitting column selection to aggregate all numeric columns. The result is a DataFrame instead of a Series, with one column per aggregated variable.

Think of GroupBy like SQL's GROUP BY clause. The Pandas code df.groupby('region')['sales'].sum() is equivalent to SELECT region, SUM(sales) FROM df GROUP BY region in SQL. Understanding this parallel helps you translate between the two tools and choose the right abstraction for your pipeline.

You might wonder why not just loop over unique values and compute statistics manually. The answer is performance and readability. A Python loop over groups is much slower than a single vectorised groupby call, especially for large datasets. GroupBy operations run in compiled C code internally, making them 10-100x faster than equivalent Python loops.

When you need finer granularity, group by multiple columns by passing a list to groupby(). The result has a MultiIndex where each level corresponds to one grouping column. For example, grouping by both 'region' and 'quarter' gives totals for every region-quarter combination.

Test your understanding of the split-apply-combine pattern from this lesson.

In this lesson you learned: the split-apply-combine pattern that underlies all GroupBy operations, how to create a GroupBy object with df.groupby(), and how to apply built-in aggregations like sum() and mean() to get one result per group. Next up we explore grouping by single and multiple keys with more aggregation methods.