pandas.DataFrame.stack¶
-
DataFrame.
stack
(level=-1, dropna=True)[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.
The new index levels are sorted.
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. See Examples section.
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 books being re-organised from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of of each other (in the index of the dataframe).
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 height cat 0 1 dog 2 3 >>> df_single_level_cols.stack() cat weight 0 height 1 dog weight 2 height 3 dtype: 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 pounds cat 1 2 dog 2 4 >>> df_multi_level_cols1.stack() 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 = 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 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() height weight cat kg NaN 1.0 m 2.0 NaN dog kg NaN 3.0 m 4.0 NaN
Prescribing the level(s) to be stacked
The first parameter controls which level or levels are stacked:
>>> df_multi_level_cols2.stack(0) kg m cat height NaN 2.0 weight 1.0 NaN dog height NaN 4.0 weight 3.0 NaN >>> df_multi_level_cols2.stack([0, 1]) cat height m 2.0 weight kg 1.0 dog height m 4.0 weight kg 3.0 dtype: float64
Dropping missing values
>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]], ... index=['cat', 'dog'], ... columns=multicol2)
Note that rows where all values are missing are dropped by default but this behaviour can be controlled via the dropna keyword parameter:
>>> df_multi_level_cols3 weight height kg m cat NaN 1.0 dog 2.0 3.0 >>> df_multi_level_cols3.stack(dropna=False) height weight cat kg NaN NaN m 1.0 NaN dog kg NaN 2.0 m 3.0 NaN >>> df_multi_level_cols3.stack(dropna=True) height weight cat m 1.0 NaN dog kg NaN 2.0 m 3.0 NaN