source: kdnuggets: pandas groupby explained with examples
level: technical
pandas groupby helps answer questions that require grouping rows by one or more categories. for example, with sales data, you can calculate total revenue by region, average order value by product category, or the number of orders per sales representative. instead of manually filtering each category, groupby performs these calculations efficiently. the basic syntax selects a grouping column, a value column, and an aggregation function like sum. you can also use as_index=false to keep the grouped column as a regular column in the output, making it easier to export or merge.
groupby supports multiple aggregations on a single column using agg(), providing sum, mean, min, max, and count in one step. named aggregations let you assign custom column names like total_sales or average_order_value, which is helpful for dashboards and reports. grouping by multiple columns, such as region and category, gives a more detailed view. sorting results with sort_values() highlights top or bottom performers. the difference between count() and size() matters when missing values exist: size() counts all rows, while count() ignores missing values in a specific column.
transform() calculates group-level values and adds them back to the original dataframe, useful for feature engineering like computing each order's share of regional sales. filter() keeps or removes entire groups based on a condition, such as regions with total sales above a threshold. apply() allows custom logic per group, like finding the top order in each region, but can be slower than built-in methods. for time-based analysis, you can group by extracted date parts or use pd.grouper with a frequency like month. combining groupby with unstack() creates pivot-style summary tables for easy comparison.
why it matters: mastering groupby enables efficient data summarization and feature engineering, reducing manual coding and improving analysis speed for real-world datasets.