pivot(data: 'DataFrame', index: 'IndexLabel | None' = None, columns: 'IndexLabel | None' = None, values: 'IndexLabel | None' = None) -> 'DataFrame'
Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified :None:None:`index` / :None:None:`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. See the User Guide <reshaping>
for more on reshaping.
For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.
Reference the user guide <reshaping.pivot>
for more examples.
Column to use to make new frame's index. If None, uses existing index.
Also accept list of index names.
Column to use to make new frame's columns.
Also accept list of columns names.
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.
When there are any :None:None:`index`, :None:None:`columns` combinations with multiple values. DataFrame.pivot_table
when you need to aggregate.
Returns reshaped DataFrame.
Return reshaped DataFrame organized by given index / column values.
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.
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',This example is valid syntax, but we were not able to check execution
... '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
>>> df.pivot(index='foo', columns='bar', values='baz') bar A B C foo one 1 2 3 two 4 5 6This example is valid syntax, but we were not able to check execution
>>> df.pivot(index='foo', columns='bar')['baz'] bar A B C foo one 1 2 3 two 4 5 6This example is valid syntax, but we were not able to check execution
>>> df.pivot(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
You could also assign a list of column names or a list of index names.
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame({This example is valid syntax, but we were not able to check execution
... "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
>>> df.pivot(index="lev1", columns=["lev2", "lev3"],values="values") lev2 1 2 lev3 1 2 1 2 lev1 1 0.0 1.0 2.0 NaN 2 4.0 3.0 NaN 5.0This example is valid syntax, but we were not able to check execution
>>> df.pivot(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
A ValueError is raised if there are any duplicates.
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
... "bar": ['A', 'A', 'B', 'C'],
... "baz": [1, 2, 3, 4]})
... df foo bar baz 0 one A 1 1 one A 2 2 two B 3 3 two C 4
Notice that the first two rows are the same for our :None:None:`index` and :None:None:`columns` arguments.
>>> df.pivot(index='foo', columns='bar', values='baz') Traceback (most recent call last): ... ValueError: Index contains duplicate entries, cannot reshapeSee :
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