pandas.pivot_table#
pandas. pivot_table ( data , values = None , index = None , columns = None , aggfunc = ‘mean’ , fill_value = None , margins = False , dropna = True , margins_name = ‘All’ , observed = False , sort = True ) [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 data DataFrame values list-like or scalar, optional
Column or columns to aggregate.
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.
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.
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. If margin=True , aggfunc will be used to calculate the partial aggregates.
fill_value scalar, default None
Value to replace missing values with (in the resulting pivot table, after aggregation).
margins bool, default False
If margins=True , special All columns and rows will be added with partial group aggregates across the categories on the rows and columns.
dropna bool, default True
Do not include columns whose entries are all NaN. If True, rows with a NaN value in any column will be omitted before computing margins.
margins_name str, default ‘All’
Name of the row / column that will contain the totals when margins is True.
observed bool, 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.
sort bool, default True
Specifies if the result should be sorted.
An Excel style pivot table.
Pivot without aggregation that can handle non-numeric data.
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
Wide panel to long format. Less flexible but more user-friendly than melt.
Reference the user guide for more 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 7.500000 6 small 5.500000 9 8.500000 8 foo large 2.000000 5 4.500000 4 small 2.333333 6 4.333333 2
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 , sort = True ) [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 values list-like or scalar, optional
Column or columns to aggregate.
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.
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.
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. If margin=True , aggfunc will be used to calculate the partial aggregates.
fill_value scalar, default None
Value to replace missing values with (in the resulting pivot table, after aggregation).
margins bool, default False
If margins=True , special All columns and rows will be added with partial group aggregates across the categories on the rows and columns.
dropna bool, default True
Do not include columns whose entries are all NaN. If True, rows with a NaN value in any column will be omitted before computing margins.
margins_name str, default ‘All’
Name of the row / column that will contain the totals when margins is True.
observed bool, 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.
sort bool, default True
Specifies if the result should be sorted.
An Excel style pivot table.
Pivot without aggregation that can handle non-numeric data.
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
Wide panel to long format. Less flexible but more user-friendly than melt.
Reference the user guide for more 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 7.500000 6 small 5.500000 9 8.500000 8 foo large 2.000000 5 4.500000 4 small 2.333333 6 4.333333 2