Added repr string for Grouper

Author: tp

pandas/core/groupby.py

@@ -336,6 +336,17 @@ def _get_binner_for_grouping(self, obj):
     def groups(self):
         return self.grouper.groups
 
+    def __repr__(self):
+        if not hasattr(self, '_repr'):
+            defaults = dict(key=None, level=None, freq=None, axis=0,
+                            sort=False)
+            attrs = {k: v for k, v in self.__dict__.items()
+                     if k in defaults and v != defaults[k]}
+            attrs = ", ".join("{}={!r}".format(k, v) for k, v in attrs.items())
+            cls_name = self.__class__.__name__
+            self._repr = "{}({})".format(cls_name, attrs)
+        return self._repr
+
 
 class GroupByPlot(PandasObject):
     """

pandas/core/resample.py

@@ -1057,6 +1057,8 @@ def __init__(self, freq='Min', closed=None, label=None, how='mean',
                  convention=None, base=0, **kwargs):
         freq = to_offset(freq)
 
+        # _default_values is used for generating the __repr__ string
+        self._default_values = {}
         end_types = set(['M', 'A', 'Q', 'BM', 'BA', 'BQ', 'W'])
         rule = freq.rule_code
         if (rule in end_types or
@@ -1065,11 +1067,13 @@ def __init__(self, freq='Min', closed=None, label=None, how='mean',
                 closed = 'right'
             if label is None:
                 label = 'right'
+            self._default_values.update(closed='right', label='right')
         else:
             if closed is None:
                 closed = 'left'
             if label is None:
                 label = 'left'
+            self._default_values.update(closed='left', label='left')
 
         self.closed = closed
         self.label = label
@@ -1293,6 +1297,29 @@ def _get_time_period_bins(self, ax):
 
         return binner, bins, labels
 
+    def __repr__(self):
+        if not hasattr(self, '_repr'):
+            defaults = dict(key=None, level=None, freq=None,
+                            axis=0, sort=False)
+            timegrouper_defaults = dict(freq='Min',
+                                        how='mean',
+                                        nperiods=None, axis=0,
+                                        fill_method=None, limit=None,
+                                        loffset=None,
+                                        kind=None,
+                                        convention='e', base=0,
+                                        sort=True)
+            timegrouper_defaults.update(self._default_values)
+            defaults.update(timegrouper_defaults)
+
+            attrs = {k: v for k, v in self.__dict__.items()
+                     if k in defaults and v != defaults[k]}
+            attrs['freq'] = attrs['freq'].freqstr
+            attrs = ", ".join("{}={!r}".format(k, v) for k, v in attrs.items())
+            cls_name = self.__class__.__name__
+            self._repr = "{}({})".format(cls_name, attrs)
+        return self._repr
+
 
 def _take_new_index(obj, indexer, new_index, axis=0):
     from pandas.core.api import Series, DataFrame