Pivot

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.

Before

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

4 rows · 3 cols

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

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

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.