DOC: docstring for groupby.DataFrameGroupBy.resample.

Author: pandres

pandas/core/groupby.py

@@ -1477,31 +1477,36 @@ def describe(self, **kwargs):
     def resample(self, rule, *args, **kwargs):
         """Provide resampling when using a TimeGrouper.
 
-        Return a new grouper with our resampler appended
+        Given a grouper the function resamples it according to a string and an 
+        optional list and dictionary of parameters. Returns a new grouper with
+        our resampler appended.
 
         Parameters
         ----------
         rule : str
-            The offset string or object representing target conversion.
+            The offset string or object representing target grouper conversion.
         *args
             These parameters will be passed to the get_resampler_for_grouping
-            function.
+            function which builds the approriate resampler and checks for
+            deprecated parameters.
         **kwargs
             These parameters will be passed to the get_resampler_for_grouping
-            function.
+            function which builds the approriate resampler and checks for
+            deprecated parameters.
 
         Returns
         -------
         Grouper
-            Return a new grouper with our resampler appended
+            Return a new grouper with our resampler appended.
 
         Examples
         --------
 
         Start by creating a DataFrame with 9 one minute timestamps.
-        >>> index = pd.date_range('1/1/2000', periods=9, freq='T')
+
+        >>> idx = pd.date_range('1/1/2000', periods=9, freq='T')
         >>> df = pd.DataFrame(data=9*[range(4)],
-        ...                   index=index,
+        ...                   index=idx,
         ...                   columns=['a', 'b', 'c', 'd'])
         >>> df.iloc[[6], [0]] = 5  # change a value for grouping
         >>> df
@@ -1516,10 +1521,9 @@ def resample(self, rule, *args, **kwargs):
         2000-01-01 00:07:00  0  1  2  3
         2000-01-01 00:08:00  0  1  2  3
 
-        >>> series = pd.Series(range(9), index=index) # delete this
-
         Downsample the DataFrame into 3 minute bins and sum the values of
         the timestamps falling into a bin.
+
         >>> df.groupby('a').resample('3T').sum()
                                  a  b  c  d
         a
@@ -1529,6 +1533,7 @@ def resample(self, rule, *args, **kwargs):
         5   2000-01-01 00:06:00  5  1  2  3
 
         Upsample the series into 30 second bins.
+
         >>> df.groupby('a').resample('30S').sum()
                                  a  b  c  d
         a
@@ -1552,6 +1557,7 @@ def resample(self, rule, *args, **kwargs):
         5   2000-01-01 00:06:00  5  1  2  3
 
         Resample by month. Values are assigned to the month of the period.
+
         >>> df.groupby('a').resample('M').sum()
                         a  b   c   d
         a