New attempt at Grouper.__repr__

Author: tp

pandas/core/groupby.py

@@ -234,6 +234,8 @@ class Grouper(object):
     >>> df.groupby(Grouper(level='date', freq='60s', axis=1))
     """
 
+    _attributes = ['key', 'level', 'freq', 'axis', 'sort']
+
     def __new__(cls, *args, **kwargs):
         if kwargs.get('freq') is not None:
             from pandas.core.resample import TimeGrouper
@@ -252,11 +254,6 @@ def __init__(self, key=None, level=None, freq=None, axis=0, sort=False):
         self.indexer = None
         self.binner = None
 
-    # _attributes is used in __repr__below
-    _attributes = collections.OrderedDict(
-        (k, v) for k, v in zip(signature(__init__).args[1:],
-                               signature(__init__).defaults))
-
     @property
     def ax(self):
         return self.grouper
@@ -339,10 +336,12 @@ def groups(self):
         return self.grouper.groups
 
     def __repr__(self):
+        grouper_defaults = compat.signature(self.__init__).defaults
         sd = self.__dict__
-        attr = self._attributes
-        attrs = collections.OrderedDict((k, sd[k]) for k, v in attr.items()
-                                        if k in sd and sd[k] != v)
+        attrs = collections.OrderedDict()
+        for k, v in zip(self._attributes, grouper_defaults):
+            if k in sd and sd[k] != v:
+                attrs[k] = sd[k]
         attrs = ", ".join("{}={!r}".format(k, v) for k, v in attrs.items())
         cls_name = self.__class__.__name__
         return "{}({})".format(cls_name, attrs)

pandas/core/resample.py

@@ -1023,6 +1023,11 @@ class TimeGrouper(Grouper):
     directly from the associated object
     """
 
+    _attributes = ['key', 'level', 'freq', 'axis', 'sort', 'closed', 'label',
+                   'how', 'nperiods', 'fill_method', 'limit',
+                   'loffset', 'kind', 'convention', 'base']
+    _end_types = {'M', 'A', 'Q', 'BM', 'BA', 'BQ', 'W'}
+
     def __init__(self, freq='Min', closed=None, label=None, how='mean',
                  nperiods=None, axis=0,
                  fill_method=None, limit=None, loffset=None, kind=None,
@@ -1066,14 +1071,6 @@ def __init__(self, freq='Min', closed=None, label=None, how='mean',
 
         super(TimeGrouper, self).__init__(freq=freq, axis=axis, **kwargs)
 
-    # _attributes is used in __repr__below
-    _attributes = Grouper._attributes.copy()
-    _attributes.update(
-        (k, v) for k, v in zip(compat.signature(__init__).args[1:],
-                               compat.signature(__init__).defaults))
-    _attributes.update(sort=True, convention='e')
-    _end_types = {'M', 'A', 'Q', 'BM', 'BA', 'BQ', 'W'}
-
     def _get_resampler(self, obj, kind=None):
         """
         return my resampler or raise if we have an invalid axis
@@ -1296,25 +1293,6 @@ def _get_period_bins(self, ax):
 
         return binner, bins, labels
 
-    def __repr__(self):
-        defaults = self._attributes.copy()
-        end_types = self._end_types
-        rule = self.freq.rule_code
-        if (rule in end_types or
-                ('-' in rule and rule[:rule.find('-')] in end_types)):
-            defaults.update(closed='right', label='right')
-        else:
-            defaults.update(closed='left', label='left')
-
-        sd = self.__dict__
-        attrs = collections.OrderedDict((k, sd[k]) for k, v in defaults.items()
-                                        if k in sd and sd[k] != v)
-        if 'freq' in attrs:
-            attrs['freq'] = attrs['freq'].freqstr
-        attrs = ", ".join("{}={!r}".format(k, v) for k, v in attrs.items())
-        cls_name = self.__class__.__name__
-        return "{}({})".format(cls_name, attrs)
-
 
 def _take_new_index(obj, indexer, new_index, axis=0):
     from pandas.core.api import Series, DataFrame