ENH: Let Resampler objects have a pipe method

doc/source/api.rst

@@ -2274,6 +2274,7 @@ Function application
    Resampler.apply
    Resampler.aggregate
    Resampler.transform
+   Resampler.pipe
 
 Upsampling
 ~~~~~~~~~~

doc/source/whatsnew/v0.23.0.txt

@@ -142,6 +142,8 @@ Other Enhancements
 - ``Categorical.rename_categories``, ``CategoricalIndex.rename_categories`` and :attr:`Series.cat.rename_categories`
   can now take a callable as their argument (:issue:`18862`)
 - :class:`Interval` and :class:`IntervalIndex` have gained a ``length`` attribute (:issue:`18789`)
+- ``Resampler`` objects now have a functioning :attr:`~pandas.core.resample.Resampler.pipe` method.
+  Previously, calls to ``pipe`` were diverted to  the ``mean`` method (:issue:`17905`).
 
 .. _whatsnew_0230.api_breaking:
 

pandas/core/groupby.py

@@ -191,6 +191,60 @@
     dtype: int64
     """)
 
+_pipe_template = """\
+Apply a function ``func`` with arguments to this %(klass)s object and return
+the function's result.
+
+%(versionadded)s
+
+Use ``.pipe`` when you want to improve readability by chaining together
+functions that expect Series, DataFrames, GroupBy or Resampler objects.
+Instead of writing
+
+>>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)
+
+You can write
+
+>>> (df.groupby('group')
+...    .pipe(f)
+...    .pipe(g, arg1=a)
+...    .pipe(h, arg2=b, arg3=c))
+
+which is much more readable.
+
+Parameters
+----------
+func : callable or tuple of (callable, string)
+    Function to apply to this %(klass)s object or, alternatively,
+    a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
+    string indicating the keyword of ``callable`` that expects the
+    %(klass)s object.
+args : iterable, optional
+       positional arguments passed into ``func``.
+kwargs : dict, optional
+         a dictionary of keyword arguments passed into ``func``.
+
+Returns
+-------
+object : the return type of ``func``.
+
+Notes
+-----
+See more `here
+<http://pandas.pydata.org/pandas-docs/stable/groupby.html#pipe>`_
+
+Examples
+--------
+%(examples)s
+
+See Also
+--------
+pandas.Series.pipe : Apply a function with arguments to a series
+pandas.DataFrame.pipe: Apply a function with arguments to a dataframe
+apply : Apply function to each group instead of to the
+    full %(klass)s object.
+"""
+
 _transform_template = """
 Call function producing a like-indexed %(klass)s on each group and
 return a %(klass)s having the same indexes as the original object
@@ -676,6 +730,29 @@ def __getattr__(self, attr):
         raise AttributeError("%r object has no attribute %r" %
                              (type(self).__name__, attr))
 
+    @Substitution(klass='GroupBy',
+                  versionadded='.. versionadded:: 0.21.0',
+                  examples="""\
+>>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]})
+>>> df
+   A  B
+0  a  1
+1  b  2
+2  a  3
+3  b  4
+
+To get the difference between each groups maximum and minimum value in one
+pass, you can do
+
+>>> df.groupby('A').pipe(lambda x: x.max() - x.min())
+   B
+A
+a  2
+b  2""")
+    @Appender(_pipe_template)
+    def pipe(self, func, *args, **kwargs):
+        return _pipe(self, func, *args, **kwargs)
+
     plot = property(GroupByPlot)
 
     def _make_wrapper(self, name):
@@ -1779,54 +1856,6 @@ def tail(self, n=5):
         mask = self._cumcount_array(ascending=False) < n
         return self._selected_obj[mask]
 
-    def pipe(self, func, *args, **kwargs):
-        """ Apply a function with arguments to this GroupBy object,
-
-        .. versionadded:: 0.21.0
-
-        Parameters
-        ----------
-        func : callable or tuple of (callable, string)
-            Function to apply to this GroupBy object or, alternatively, a
-            ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
-            string indicating the keyword of ``callable`` that expects the
-            GroupBy object.
-        args : iterable, optional
-               positional arguments passed into ``func``.
-        kwargs : dict, optional
-                 a dictionary of keyword arguments passed into ``func``.
-
-        Returns
-        -------
-        object : the return type of ``func``.
-
-        Notes
-        -----
-        Use ``.pipe`` when chaining together functions that expect
-        Series, DataFrames or GroupBy objects. Instead of writing
-
-        >>> f(g(h(df.groupby('group')), arg1=a), arg2=b, arg3=c)
-
-        You can write
-
-        >>> (df
-        ...    .groupby('group')
-        ...    .pipe(f, arg1)
-        ...    .pipe(g, arg2)
-        ...    .pipe(h, arg3))
-
-        See more `here
-        <http://pandas.pydata.org/pandas-docs/stable/groupby.html#pipe>`_
-
-        See Also
-        --------
-        pandas.Series.pipe : Apply a function with arguments to a series
-        pandas.DataFrame.pipe: Apply a function with arguments to a dataframe
-        apply : Apply function to each group instead of to the
-            full GroupBy object.
-        """
-        return _pipe(self, func, *args, **kwargs)
-
 
 GroupBy._add_numeric_operations()
 

pandas/core/resample.py

@@ -8,7 +8,8 @@
 from pandas.core.base import AbstractMethodError, GroupByMixin
 
 from pandas.core.groupby import (BinGrouper, Grouper, _GroupBy, GroupBy,
-                                 SeriesGroupBy, groupby, PanelGroupBy)
+                                 SeriesGroupBy, groupby, PanelGroupBy,
+                                 _pipe_template)
 
 from pandas.tseries.frequencies import to_offset, is_subperiod, is_superperiod
 from pandas.core.indexes.datetimes import DatetimeIndex, date_range
@@ -26,7 +27,7 @@
 from pandas._libs.lib import Timestamp
 from pandas._libs.tslibs.period import IncompatibleFrequency
 
-from pandas.util._decorators import Appender
+from pandas.util._decorators import Appender, Substitution
 from pandas.core.generic import _shared_docs
 _shared_docs_kwargs = dict()
 
@@ -257,6 +258,29 @@ def _assure_grouper(self):
         """ make sure that we are creating our binner & grouper """
         self._set_binner()
 
+    @Substitution(klass='Resampler',
+                  versionadded='.. versionadded:: 0.23.0',
+                  examples="""
+>>> df = pd.DataFrame({'A': [1, 2, 3, 4]},
+...                   index=pd.date_range('2012-08-02', periods=4))
+>>> df
+            A
+2012-08-02  1
+2012-08-03  2
+2012-08-04  3
+2012-08-05  4
+
+To get the difference between each 2-day period's maximum and minimum value in
+one pass, you can do
+
+>>> df.resample('2D').pipe(lambda x: x.max() - x.min())
+            A
+2012-08-02  1
+2012-08-04  1""")
+    @Appender(_pipe_template)
+    def pipe(self, func, *args, **kwargs):
+        return super(Resampler, self).pipe(func, *args, **kwargs)
+
     def plot(self, *args, **kwargs):
         # for compat with prior versions, we want to
         # have the warnings shown here and just have this work

pandas/tests/test_resample.py

@@ -235,6 +235,21 @@ def test_groupby_resample_on_api(self):
         result = df.groupby('key').resample('D', on='dates').mean()
         assert_frame_equal(result, expected)
 
+    def test_pipe(self):
+        # GH17905
+
+        # series
+        r = self.series.resample('H')
+        expected = r.max() - r.mean()
+        result = r.pipe(lambda x: x.max() - x.mean())
+        tm.assert_series_equal(result, expected)
+
+        # dataframe
+        r = self.frame.resample('H')
+        expected = r.max() - r.mean()
+        result = r.pipe(lambda x: x.max() - x.mean())
+        tm.assert_frame_equal(result, expected)
+
     @td.skip_if_no_mpl
     def test_plot_api(self):
         # .resample(....).plot(...)