+pd.DataFrame.pivot_table

Author: ResidentMario

pandas/core/frame.py

@@ -109,8 +109,10 @@
     versionadded_to_excel='',
     versionadded_melt='\n.. versionadded:: 0.20.0\n',
     other_melt='melt',
-    versionadded_crosstab = '\n.. versionadded:: 0.20.0\n',
-    other_crosstab = 'crosstab')
+    versionadded_crosstab='\n.. versionadded:: 0.20.0\n',
+    other_crosstab='crosstab',
+    versionadded_pivot_table='\n.. versionadded:: 0.20.0\n',
+    other_pivot_table='pivot_table')
 
 _numeric_only_doc = """numeric_only : boolean, default None
     Include only float, int, boolean data. If None, will attempt to use
@@ -3935,6 +3937,85 @@ def pivot(self, index=None, columns=None, values=None):
         from pandas.core.reshape import pivot
         return pivot(self, index=index, columns=columns, values=values)
 
+    _shared_docs['pivot_table'] = """
+    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
+
+    %(versionadded_pivot_table)s
+
+    Parameters
+    ----------
+    data : DataFrame
+    values : column to aggregate, optional
+    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 or list of functions, 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)
+    fill_value : scalar, default None
+        Value to replace missing values with
+    margins : boolean, default False
+        Add all row / columns (e.g. for subtotal / grand totals)
+    dropna : boolean, default True
+        Do not include columns whose entries are all NaN
+    margins_name : string, default 'All'
+        Name of the row / column that will contain the totals
+        when margins is True.
+
+    Examples
+    --------
+    >>> df
+       A   B   C      D
+    0  foo one small  1
+    1  foo one large  2
+    2  foo one large  2
+    3  foo two small  3
+    4  foo two small  3
+    5  bar one large  4
+    6  bar one small  5
+    7  bar two small  6
+    8  bar two large  7
+
+    >>> table = pivot_table(df, values='D', index=['A', 'B'],
+    ...                     columns=['C'], aggfunc=np.sum)
+    >>> table
+              small  large
+    foo  one  1      4
+         two  6      NaN
+    bar  one  5      4
+         two  6      7
+
+    Returns
+    -------
+    table : DataFrame
+
+    See also
+    --------
+    DataFrame.pivot : pivot without aggregation that can handle
+        non-numeric data
+    %(other_pivot_table)s
+    """
+
+    @Appender(_shared_docs['pivot_table'] % _shared_doc_kwargs)
+    def pivot_table(self, values=None, index=None, columns=None,
+                    aggfunc='mean', fill_value=None, margins=False,
+                    dropna=True, margins_name='All'):
+        from pandas.tools.pivot import pivot_table
+        return pivot_table(self, values=values, index=index, columns=columns,
+                           aggfunc=aggfunc, fill_value=fill_value,
+                           margins=margins, dropna=dropna,
+                           margins_name=margins_name)
+
     def stack(self, level=-1, dropna=True):
         """
         Pivot a level of the (possibly hierarchical) column labels, returning a

pandas/tools/pivot.py

@@ -12,78 +12,16 @@
 
 from pandas.core.frame import _shared_docs
 from pandas.util.decorators import Appender
-_shared_docs_kwargs = dict(versionadded_crosstab="",
-                           other_crosstab="DataFrame.crosstab")
+_shared_doc_kwargs = dict(versionadded_crosstab='',
+                          other_crosstab='DataFrame.crosstab',
+                          versionadded_pivot_table='',
+                          other_pivot_table='DataFrame.pivot_table')
 
 
+@Appender(_shared_docs['pivot_table'] % _shared_doc_kwargs)
 def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
                 fill_value=None, margins=False, dropna=True,
                 margins_name='All'):
-    """
-    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 : column to aggregate, optional
-    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 or list of functions, 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)
-    fill_value : scalar, default None
-        Value to replace missing values with
-    margins : boolean, default False
-        Add all row / columns (e.g. for subtotal / grand totals)
-    dropna : boolean, default True
-        Do not include columns whose entries are all NaN
-    margins_name : string, default 'All'
-        Name of the row / column that will contain the totals
-        when margins is True.
-
-    Examples
-    --------
-    >>> df
-       A   B   C      D
-    0  foo one small  1
-    1  foo one large  2
-    2  foo one large  2
-    3  foo two small  3
-    4  foo two small  3
-    5  bar one large  4
-    6  bar one small  5
-    7  bar two small  6
-    8  bar two large  7
-
-    >>> table = pivot_table(df, values='D', index=['A', 'B'],
-    ...                     columns=['C'], aggfunc=np.sum)
-    >>> table
-              small  large
-    foo  one  1      4
-         two  6      NaN
-    bar  one  5      4
-         two  6      7
-
-    Returns
-    -------
-    table : DataFrame
-
-    See also
-    --------
-    DataFrame.pivot : pivot without aggregation that can handle
-        non-numeric data
-    """
     index = _convert_by(index)
     columns = _convert_by(columns)
 
@@ -183,9 +121,6 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
     return table
 
 
-DataFrame.pivot_table = pivot_table
-
-
 def _add_margins(table, data, values, rows, cols, aggfunc,
                  margins_name='All'):
     if not isinstance(margins_name, compat.string_types):
@@ -386,7 +321,7 @@ def _convert_by(by):
     return by
 
 
-@Appender(_shared_docs['crosstab'] % _shared_docs_kwargs)
+@Appender(_shared_docs['crosstab'] % _shared_doc_kwargs)
 def crosstab(index, columns, values=None, rownames=None, colnames=None,
              aggfunc=None, margins=False, dropna=True, normalize=False):
     index = com._maybe_make_list(index)