Grouping rows together

.groupby() splits a DataFrame into groups sharing the same value in one column -- it computes nothing by itself, it just sets up the split:

grouped = df.groupby("dept")

Aggregating each group

Follow it with a column and a summary function to actually compute something, one result per group:

totals = df.groupby("dept")["sales"].sum()
print(totals)

dept Eng 250 Sales 150 Name: sales, dtype: int64

The result is a Series indexed by the group names (Eng, Sales, ...) -- other common aggregations are .mean(), .count(), and .max().

Pulling out one group's result

Use .loc[] with the group's label to get a single value back out of that Series:

print(totals.loc["Eng"])   # 250

Sorting

print(df.sort_values("score", ascending=False))   # highest first

Why This Matters for Your Exams

"Group by X, then total/average Y" is the shape of most real data-analysis questions -- sales by region, average score by class, count by category. .groupby(X)[Y].sum() (or .mean(), .count()) is the whole pattern; everything else is just which column goes where.

Try it yourself