Pandas DataFrame: pivot_table() function

Last update on August 19 2022 21:50:51 (UTC/GMT +8 hours)

DataFrame - pivot_table() function

The pivot_table() function is used to 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.

Syntax:

DataFrame.pivot_table(self, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)

Parameters:

Name	Description	Type/Default Value	Required / Optional
values		column to aggregate	Optional
index	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.	column, Grouper, array, or list of the previous	Required
columns	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.	column, Grouper, array, or list of the previous	Required
aggfunc	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	function, list of functions, dict, Default Value: numpy.mean	Required
fill_value	Value to replace missing values with	scalar Default Value: None	Required
margins	Add all row / columns (e.g. for subtotal / grand totals)	boolean Default Value: False	Required
dropna	Do not include columns whose entries are all NaN	boolean Default Value: True	Required
margins_name	Name of the row / column that will contain the totals when margins is True.	string Default Value: ‘All’	Required
observed	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.	boolean Default Value: False	Required

Returns: DataFrame

Example:

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