pandas.DataFrame.pivot#

DataFrame.pivot(*,columns,index=<no_default>,values=<no_default>)[source]#

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a “pivot” table) based on column values. Usesunique values from specifiedindex /columns to form axes of theresulting DataFrame. This function does not support dataaggregation, multiple values will result in a MultiIndex in thecolumns. See theUser Guide for more on reshaping.

Parameters:

columnsstr or object or a list of str

Column to use to make new frame’s columns.

indexstr or object or a list of str, optional

Column to use to make new frame’s index. If not given, uses existing index.

valuesstr, object or a list of the previous, optional

Column(s) to use for populating new frame’s values. If notspecified, all remaining columns will be used and the result willhave hierarchically indexed columns.

Returns:

DataFrame

Returns reshaped DataFrame.

Raises:

ValueError:

When there are anyindex,columns combinations with multiplevalues.DataFrame.pivot_table when you need to aggregate.

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.

Notes

For finer-tuned control, see hierarchical indexing documentation alongwith the related stack/unstack methods.

Referencethe user guide for more examples.

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  zoo0   one   A    1    x1   one   B    2    y2   one   C    3    z3   two   A    4    q4   two   B    5    w5   two   C    6    t

>>>df.pivot(index='foo',columns='bar',values='baz')bar  A   B   Cfooone  1   2   3two  4   5   6

>>>df.pivot(index='foo',columns='bar')['baz']bar  A   B   Cfooone  1   2   3two  4   5   6

>>>df.pivot(index='foo',columns='bar',values=['baz','zoo'])      baz       zoobar   A  B  C   A  B  Cfooone   1  2  3   x  y  ztwo   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>>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 values0   1    1    1    1    01   1    1    2    2    12   1    2    1    3    23   2    1    2    4    34   2    1    1    5    45   2    2    2    6    5

>>>df.pivot(index="lev1",columns=["lev2","lev3"],values="values")lev2    1         2lev3    1    2    1    2lev11     0.0  1.0  2.0  NaN2     4.0  3.0  NaN  5.0

>>>df.pivot(index=["lev1","lev2"],columns=["lev3"],values="values")      lev3    1    2lev1  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.

>>>df=pd.DataFrame({"foo":['one','one','two','two'],..."bar":['A','A','B','C'],..."baz":[1,2,3,4]})>>>df   foo bar  baz0  one   A    11  one   A    22  two   B    33  two   C    4

Notice that the first two rows are the same for ourindexandcolumns arguments.

>>>df.pivot(index='foo',columns='bar',values='baz')Traceback (most recent call last):...ValueError:Index contains duplicate entries, cannot reshape