Mastering Pandas — Part 2: GroupBy & Indexing

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The result preserves the original row order within each group. df. groupby("gender"). first() # first row of each gender group df. groupby("gender"). last() # last row of each gender group You can group by more than one column at a time by passing a list. Use it for quick column selection or row slicing by position. It behaves like standard Python list indexing. df. iloc[0] # first row (position 0) df. iloc[0:3] # first 3 rows (positions 0, 1, 2 — end is exclusive) df. iloc[0, 2] # row at position 0, column at position 2 df.

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