DataFrame.
stack
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.
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.
The new index levels are sorted.
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 books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).
Examples
Single level columns
>>> df_single_level_cols = ps.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 height cat 0 1 dog 2 3 >>> df_single_level_cols.stack().sort_index() cat height 1 weight 0 dog height 3 weight 2 dtype: int64
Multi level columns: simple case
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'), ... ('weight', 'pounds')]) >>> df_multi_level_cols1 = ps.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 pounds cat 1 2 dog 2 4 >>> df_multi_level_cols1.stack().sort_index() weight cat kg 1 pounds 2 dog kg 2 pounds 4
Missing values
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'), ... ('height', 'm')]) >>> df_multi_level_cols2 = ps.DataFrame([[1.0, 2.0], [3.0, 4.0]], ... index=['cat', 'dog'], ... columns=multicol2)
It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:
>>> df_multi_level_cols2 weight height kg m cat 1.0 2.0 dog 3.0 4.0 >>> df_multi_level_cols2.stack().sort_index() height weight cat kg NaN 1.0 m 2.0 NaN dog kg NaN 3.0 m 4.0 NaN