pandas.DataFrame.pivot_table

Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

param values

column to aggregate, optional

param index

column, Grouper, array, or list of the previous

If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.

param columns

column, Grouper, array, or list of the previous

If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.

param aggfunc

function, list of functions, dict, default numpy.mean

If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions

param fill_value

scalar, default None

Value to replace missing values with

param margins

boolean, default False

Add all row / columns (e.g. for subtotal / grand totals)

param dropna

boolean, default True

Do not include columns whose entries are all NaN

param margins_name

string, default ‘All’

Name of the row / column that will contain the totals when margins is True.

param observed

boolean, default False

This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

Changed in version 0.25.0.

return

DataFrame

Warning

This feature is currently unsupported by Intel Scalable Dataframe Compiler