Pivot — `pivot`

Reshape long → wide.

Use cases

Turn month rows into Jan/Feb/… columns of totals.
Build a cross-tab of category × metric.

What it does

Pivot spreads the unique values of one column (columns) out into new columns, filling each cell by aggregating the values column for that row key.

Pivot (index=region, columns=month, values=amount, aggfunc=sum)

Before

region	month	amount
North	Jan	100
North	Feb	150
South	Jan	80
South	Feb	200

4 rows · 3 cols

After

region	Jannew	Febnew
North	100	150
South	80	200

2 rows · 3 cols

Configuration

Config key	Type	Required	Description
`index`	string \| string[]	Yes	Row key(s)
`columns`	string	Yes	Column whose values become new columns
`values`	string	Yes	Column to aggregate into the cells
`aggfunc`	string	No	Aggregation (default `sum`): one of `sum`, `mean`, `min`, `max`, `median`, `first`, `last`, `count` — restricted to functions both the pandas and polars exports support

Generated Python code

python

df_2 = df_1.pivot_table(index='region', columns='month', values='amount', aggfunc='sum').reset_index()

Tips & common mistakes

aggfunc resolves collisions. When multiple rows share the same index/column pair, they're combined with this function (sum, mean, count, …).
New column names come from the values found in columns at run time.
To go the other way (wide → long), use Unpivot.

Pivot — pivot