modin.pandas.pivot¶
- modin.pandas.pivot(data, index=None, columns=None, values=None)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/general_overrides.py#L126-L134)¶
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a “pivot” table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns.
- Parameters:
data (
DataFrame
) –columns (str or object or a list of str) – Column to use to make new frame’s columns.
index (str or object or a list of str, optional) – Column to use to make new frame’s index. If not given, uses existing index.
values (str, object or a list of the previous, optional) – Column(s) to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.
- Return type:
Notes
Calls pivot_table with columns, values, index and aggregation “min”.
See also
DataFrame.pivot_table
Generalization of pivot that can handle duplicate values for one index/column pair.
DataFrame.unstack
Pivot based on the index values instead of a column.
wide_to_long
Wide panel to long format. Less flexible but more user-friendly than melt.
Examples
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', ... 'two'], ... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'], ... 'baz': [1, 2, 3, 4, 5, 6], ... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']}) >>> df foo bar baz zoo 0 one A 1 x 1 one B 2 y 2 one C 3 z 3 two A 4 q 4 two B 5 w 5 two C 6 t >>> pd.pivot(data=df, index='foo', columns='bar', values='baz') bar A B C foo one 1 2 3 two 4 5 6 >>> pd.pivot(data=df, index='foo', columns='bar')['baz'] bar A B C foo one 1 2 3 two 4 5 6 >>> pd.pivot(data=df, index='foo', columns='bar', values=['baz', 'zoo']) baz zoo bar A B C A B C foo one 1 2 3 x y z two 4 5 6 q w t >>> df = pd.DataFrame({ ... "lev1": [1, 1, 1, 2, 2, 2], ... "lev2": [1, 1, 2, 1, 1, 2], ... "lev3": [1, 2, 1, 2, 1, 2], ... "lev4": [1, 2, 3, 4, 5, 6], ... "values": [0, 1, 2, 3, 4, 5]}) >>> df lev1 lev2 lev3 lev4 values 0 1 1 1 1 0 1 1 1 2 2 1 2 1 2 1 3 2 3 2 1 2 4 3 4 2 1 1 5 4 5 2 2 2 6 5 >>> pd.pivot(data=df, index="lev1", columns=["lev2", "lev3"], values="values") lev2 1 2 lev3 1 2 1 2 lev1 1 0 1 2.0 NaN 2 4 3 NaN 5.0 >>> pd.pivot(data=df, index=["lev1", "lev2"], columns=["lev3"], values="values") lev3 1 2 lev1 lev2 1 1 0.0 1.0 2 2.0 NaN 2 1 4.0 3.0 2 NaN 5.0