modin.pandas.pivot

snowflake.snowpark.modin.pandas.general.pivot(data, index=None, columns=None, values=None)[source]

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:

DataFrame

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
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