pandas.DataFrame.pivot_table¶
- DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)[source]¶
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.
- Parameters
- valuescolumn to aggregate, optional
- indexcolumn, 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.
- columnscolumn, 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.
- aggfuncfunction, 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.
- fill_valuescalar, default None
Value to replace missing values with (in the resulting pivot table, after aggregation).
- marginsbool, default False
Add all row / columns (e.g. for subtotal / grand totals).
- dropnabool, default True
Do not include columns whose entries are all NaN.
- margins_namestr, default ‘All’
Name of the row / column that will contain the totals when margins is True.
- observedbool, 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.
- Returns
- DataFrame
An Excel style pivot table.
See also
DataFrame.pivotPivot without aggregation that can handle non-numeric data.
DataFrame.meltUnpivot a DataFrame from wide to long format, optionally leaving identifiers set.
wide_to_longWide panel to long format. Less flexible but more user-friendly than melt.
Examples
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo", ... "bar", "bar", "bar", "bar"], ... "B": ["one", "one", "one", "two", "two", ... "one", "one", "two", "two"], ... "C": ["small", "large", "large", "small", ... "small", "large", "small", "small", ... "large"], ... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7], ... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]}) >>> df A B C D E 0 foo one small 1 2 1 foo one large 2 4 2 foo one large 2 5 3 foo two small 3 5 4 foo two small 3 6 5 bar one large 4 6 6 bar one small 5 8 7 bar two small 6 9 8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'], ... columns=['C'], aggfunc=np.sum) >>> table C large small A B bar one 4.0 5.0 two 7.0 6.0 foo one 4.0 1.0 two NaN 6.0
We can also fill missing values using the fill_value parameter.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'], ... columns=['C'], aggfunc=np.sum, fill_value=0) >>> table C large small A B bar one 4 5 two 7 6 foo one 4 1 two 0 6
The next example aggregates by taking the mean across multiple columns.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'], ... aggfunc={'D': np.mean, ... 'E': np.mean}) >>> table D E A C bar large 5.500000 7.500000 small 5.500000 8.500000 foo large 2.000000 4.500000 small 2.333333 4.333333
We can also calculate multiple types of aggregations for any given value column.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'], ... aggfunc={'D': np.mean, ... 'E': [min, max, np.mean]}) >>> table D E mean max mean min A C bar large 5.500000 9.0 7.500000 6.0 small 5.500000 9.0 8.500000 8.0 foo large 2.000000 5.0 4.500000 4.0 small 2.333333 6.0 4.333333 2.0