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Pandas: Data Manipulation - pivot_table() function

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:


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