pivot_table(self, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True) -> 'DataFrame'
The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.
Reference the user guide <reshaping.pivot>
for more examples.
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.
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.
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.
Value to replace missing values with (in the resulting pivot table, after aggregation).
Add all row / columns (e.g. for subtotal / grand totals).
Do not include columns whose entries are all NaN.
Name of the row / column that will contain the totals when margins is True.
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.
Specifies if the result should be sorted.
An Excel style pivot table.
Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.melt
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
DataFrame.pivot
Pivot without aggregation that can handle non-numeric data.
wide_to_long
Wide panel to long format. Less flexible but more user-friendly than melt.
>>> 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.
This example is valid syntax, but we were not able to check execution>>> 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 :None:None:`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.
This example is valid syntax, but we were not able to check execution>>> 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.
This example is valid syntax, but we were not able to check execution>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],See :
... 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
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.frame.DataFrame.pivot
pandas.core.frame.DataFrame.melt
pandas.core.reshape.pivot.pivot
pandas.core.frame.DataFrame.stack
pandas.core.reshape.melt.wide_to_long
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