Pandas groupby() allows you to split your data into groups and perform operations on them. It’s useful for data analysis and manipulation in Python.
With groupby(), you can split, apply a function, and combine the results. This allows efficient aggregation and transformation.
You can group data by one or multiple columns. Example: df.groupby('column_name'). This groups the data based on the specified column.
Apply aggregate functions like sum, mean, or count. Example: df.groupby('column_name').sum() computes the sum for each group.
You can apply custom functions using .apply() or .agg() on grouped data. This is useful for more complex transformations.
Groupby can handle multiple columns. Example: df.groupby(['col1', 'col2']).sum(). This groups data on multiple keys for complex analysis.