← NumPy & Pandas learning pathNumPy & Pandas · Module 7 of 10
Grouping and aggregation
Summarise data by categories.
📖 Key ideas
- df.groupby("city")["sales"].sum().
- agg applies several functions: .agg(["mean","max"]).
- value_counts() counts categories.
- pivot_table makes Excel-like summaries.
- reset_index() turns group labels back into columns.
💡 Real-life example: groupby is sorting exam papers into class piles, then calculating each pile's average.
groupbySplits data into groups
aggApplies aggregate functions
value_countsCounts unique values
pivot_tableSpreadsheet-style summary