pandas.DataFrame.stack#

DataFrame.stack(level=-1,dropna=<no_default>,sort=<no_default>,future_stack=False)[source]#

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-levelindex with one or more new inner-most levels compared to the currentDataFrame. The new inner-most levels are created by pivoting thecolumns of the current dataframe:

• if the columns have a single level, the output is a Series;

• if the columns have multiple levels, the new indexlevel(s) is (are) taken from the prescribed level(s) andthe output is a DataFrame.

Parameters:

levelint, str, list, default -1

Level(s) to stack from the column axis onto the indexaxis, defined as one index or label, or a list of indicesor labels.

dropnabool, default True

Whether to drop rows in the resulting Frame/Series withmissing values. Stacking a column level onto the indexaxis can create combinations of index and column valuesthat are missing from the original dataframe. See Examplessection.

sortbool, default True

Whether to sort the levels of the resulting MultiIndex.

future_stackbool, default False

Whether to use the new implementation that will replace the currentimplementation in pandas 3.0. When True, dropna and sort have no impacton the result and must remain unspecified. Seepandas 2.1.0 Releasenotes for more details.

Returns:

DataFrame or Series

Stacked dataframe or series.

See also

DataFrame.unstack

Unstack prescribed level(s) from index axis onto column axis.

DataFrame.pivot

Reshape dataframe from long format to wide format.

DataFrame.pivot_table

Create a spreadsheet-style pivot table as a DataFrame.

Notes

The function is named by analogy with a collection of booksbeing reorganized from being side by side on a horizontalposition (the columns of the dataframe) to being stackedvertically on top of each other (in the index of thedataframe).

Referencethe user guide for more examples.

Examples

Single level columns

>>>df_single_level_cols=pd.DataFrame([[0,1],[2,3]],...index=['cat','dog'],...columns=['weight','height'])

Stacking a dataframe with a single level column axis returns a Series:

>>>df_single_level_cols     weight heightcat       0      1dog       2      3>>>df_single_level_cols.stack(future_stack=True)cat  weight    0     height    1dog  weight    2     height    3dtype: int64

Multi level columns: simple case

>>>multicol1=pd.MultiIndex.from_tuples([('weight','kg'),...('weight','pounds')])>>>df_multi_level_cols1=pd.DataFrame([[1,2],[2,4]],...index=['cat','dog'],...columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>>df_multi_level_cols1     weight         kg    poundscat       1        2dog       2        4>>>df_multi_level_cols1.stack(future_stack=True)            weightcat kg           1    pounds       2dog kg           2    pounds       4

Missing values

>>>multicol2=pd.MultiIndex.from_tuples([('weight','kg'),...('height','m')])>>>df_multi_level_cols2=pd.DataFrame([[1.0,2.0],[3.0,4.0]],...index=['cat','dog'],...columns=multicol2)

It is common to have missing values when stacking a dataframewith multi-level columns, as the stacked dataframe typicallyhas more values than the original dataframe. Missing valuesare filled with NaNs:

>>>df_multi_level_cols2    weight height        kg      mcat    1.0    2.0dog    3.0    4.0>>>df_multi_level_cols2.stack(future_stack=True)        weight  heightcat kg     1.0     NaN    m      NaN     2.0dog kg     3.0     NaN    m      NaN     4.0

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>>df_multi_level_cols2.stack(0,future_stack=True)             kg    mcat weight  1.0  NaN    height  NaN  2.0dog weight  3.0  NaN    height  NaN  4.0>>>df_multi_level_cols2.stack([0,1],future_stack=True)cat  weight  kg    1.0     height  m     2.0dog  weight  kg    3.0     height  m     4.0dtype: float64