modin.pandas.DataFrame.stack¶

DataFrame.stack(level: int | str | list = - 1, dropna: bool | NoDefault = _NoDefault.no_default, sort: bool | NoDefault = _NoDefault.no_default, future_stack: bool = False)[source]¶

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

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe. If the columns have a single level, the output is a Series. If the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Parameters:

  • level (int, str, list, default -1) – Level(s) to stack from the column axis onto the index axis, defined as one index or label, or a list of indices or labels.

  • dropna (bool, default True) – Whether to drop rows in the resulting Frame/Series with missing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe.

  • sort (bool, default True) – Whether to sort the levels of the resulting MultiIndex.

  • future_stack (bool, default False) – This argument is ignored in Snowpark pandas.

Returns:

Stacked dataframe or series.

Return type:

DataFrame or Series

Notes

level != -1 and MultiIndex dataframes are not yet supported by Snowpark pandas.

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.

Examples

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]], index=['cat', 'dog'], columns=['weight', 'height'])
>>> df_single_level_cols
     weight  height
cat       0       1
dog       2       3
>>> df_single_level_cols.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64
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