Pandas: Data Manipulation - pivot_table() function

Last update on August 19 2022 21:50:33 (UTC/GMT +8 hours)

pivot_table() function

Create a spreadsheet-style pivot table as a DataFrame.

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:

pandas.pivot_table(data, 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
data	DataFrame			Required
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: numpy.mean	Required
fill_value	Value to replace missing values with	scalar	Default: None	Required
margins	Add all row / columns (e.g. for subtotal / grand totals)	boolean	Default: False	Required
dropna	Do not include columns whose entries are all NaN	boolean	Default: True	Required
margins_name	Name of the row / column that will contain the totals when margins is True.	string	Default: ‘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: False	Required

Returns: DataFrame.

Example:

Download the Pandas DataFrame Notebooks from here.

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