ENH: Provide dict object for to_dict() pandas-dev#16122

Author: dwkenefick

doc/source/whatsnew/v0.20.0.txt

@@ -521,7 +521,7 @@ Other Enhancements
 - The ``display.show_dimensions`` option can now also be used to specify
   whether the length of a ``Series`` should be shown in its repr (:issue:`7117`).
 - ``parallel_coordinates()`` has gained a ``sort_labels`` keyword arg that sorts class labels and the colours assigned to them (:issue:`15908`)
-
+- ``Series.to_dict()`` and ``DataFrame.to_dict()`` now support an ``into`` keyword which allows you to specify the ``collections.Mapping`` subclass that you would like returned.  The default is ``dict``, which is backwards compatible. (:issue:`16122`)
 
 .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations
 

pandas/core/frame.py

@@ -858,7 +858,7 @@ def from_dict(cls, data, orient='columns', dtype=None):
 
         return cls(data, index=index, columns=columns, dtype=dtype)
 
-    def to_dict(self, orient='dict'):
+    def to_dict(self, orient='dict', into=dict):
         """Convert DataFrame to dictionary.
 
         Parameters
@@ -880,32 +880,45 @@ def to_dict(self, orient='dict'):
             Abbreviations are allowed. `s` indicates `series` and `sp`
             indicates `split`.
 
+        into : class, default dict
+            The collections.Mapping subclass used for all Mappings
+            in the return value.
+            .. versionadded:: 0.20.0
+
         Returns
         -------
-        result : dict like {column -> {index -> value}}
+        result : collections.Mapping like {column -> {index -> value}}
+            If ``into`` is collections.defaultdict, the return
+            value's default_factory will be None.
         """
+        # GH16122
+        if not issubclass(into, collections.Mapping):
+            raise TypeError('unsupported type: {}'.format(type(into)))
         if not self.columns.is_unique:
             warnings.warn("DataFrame columns are not unique, some "
                           "columns will be omitted.", UserWarning)
+        into_c = (functools.partial(into, None)
+                  if into == collections.defaultdict else into)
         if orient.lower().startswith('d'):
-            return dict((k, v.to_dict()) for k, v in compat.iteritems(self))
+            return into_c(
+                (k, v.to_dict(into)) for k, v in compat.iteritems(self))
         elif orient.lower().startswith('l'):
-            return dict((k, v.tolist()) for k, v in compat.iteritems(self))
+            return into_c((k, v.tolist()) for k, v in compat.iteritems(self))
         elif orient.lower().startswith('sp'):
-            return {'index': self.index.tolist(),
-                    'columns': self.columns.tolist(),
-                    'data': lib.map_infer(self.values.ravel(),
-                                          _maybe_box_datetimelike)
-                    .reshape(self.values.shape).tolist()}
+            return into_c((('index', self.index.tolist()),
+                           ('columns', self.columns.tolist()),
+                           ('data', lib.map_infer(self.values.ravel(),
+                                                  _maybe_box_datetimelike)
+                            .reshape(self.values.shape).tolist())))
         elif orient.lower().startswith('s'):
-            return dict((k, _maybe_box_datetimelike(v))
-                        for k, v in compat.iteritems(self))
+            return into_c((k, _maybe_box_datetimelike(v))
+                          for k, v in compat.iteritems(self))
         elif orient.lower().startswith('r'):
-            return [dict((k, _maybe_box_datetimelike(v))
-                         for k, v in zip(self.columns, row))
+            return [into_c((k, _maybe_box_datetimelike(v))
+                           for k, v in zip(self.columns, row))
                     for row in self.values]
         elif orient.lower().startswith('i'):
-            return dict((k, v.to_dict()) for k, v in self.iterrows())
+            return into_c((k, v.to_dict(into)) for k, v in self.iterrows())
         else:
             raise ValueError("orient '%s' not understood" % orient)
 

pandas/core/series.py

@@ -8,6 +8,7 @@
 
 import types
 import warnings
+import collections
 
 from numpy import nan, ndarray
 import numpy as np
@@ -1072,15 +1073,30 @@ def tolist(self):
         """ Convert Series to a nested list """
         return list(self.asobject)
 
-    def to_dict(self):
+    def to_dict(self, into=dict):
         """
-        Convert Series to {label -> value} dict
+        Convert Series to {label -> value} dict or dict-like object
+        Parameters
+        ----------
+        into : class, default dict
+            The collections.Mapping subclass to use as the return
+            object.
+            .. versionadded:: 0.20.0
 
         Returns
         -------
-        value_dict : dict
-        """
-        return dict(compat.iteritems(self))
+        value_dict : collections.Mapping
+            If ``into`` is collections.defaultdict, the return
+            value's default_factory will be None.
+        """
+        # GH16122
+        if issubclass(into, collections.Mapping):
+            if into == collections.defaultdict:
+                return into(None, compat.iteritems(self))
+            else:
+                return into(compat.iteritems(self))
+        else:
+            raise TypeError('unsupported type: {}'.format(type(into)))
 
     def to_frame(self, name=None):
         """

pandas/tests/frame/test_convert_to.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 
 import pytest
+import collections
 import numpy as np
 
 from pandas import compat
@@ -18,44 +19,48 @@ def test_to_dict(self):
             'A': {'1': 1, '2': 2},
             'B': {'1': '1', '2': '2', '3': '3'},
         }
-        recons_data = DataFrame(test_data).to_dict()
-
-        for k, v in compat.iteritems(test_data):
-            for k2, v2 in compat.iteritems(v):
-                self.assertEqual(v2, recons_data[k][k2])
-
-        recons_data = DataFrame(test_data).to_dict("l")
-
-        for k, v in compat.iteritems(test_data):
-            for k2, v2 in compat.iteritems(v):
-                self.assertEqual(v2, recons_data[k][int(k2) - 1])
-
-        recons_data = DataFrame(test_data).to_dict("s")
-
-        for k, v in compat.iteritems(test_data):
-            for k2, v2 in compat.iteritems(v):
-                self.assertEqual(v2, recons_data[k][k2])
-
-        recons_data = DataFrame(test_data).to_dict("sp")
-        expected_split = {'columns': ['A', 'B'], 'index': ['1', '2', '3'],
-                          'data': [[1.0, '1'], [2.0, '2'], [np.nan, '3']]}
-        tm.assert_dict_equal(recons_data, expected_split)
-
-        recons_data = DataFrame(test_data).to_dict("r")
-        expected_records = [{'A': 1.0, 'B': '1'},
-                            {'A': 2.0, 'B': '2'},
-                            {'A': np.nan, 'B': '3'}]
-        assert isinstance(recons_data, list)
-        self.assertEqual(len(recons_data), 3)
-        for l, r in zip(recons_data, expected_records):
-            tm.assert_dict_equal(l, r)
-
-        # GH10844
-        recons_data = DataFrame(test_data).to_dict("i")
-
-        for k, v in compat.iteritems(test_data):
-            for k2, v2 in compat.iteritems(v):
-                self.assertEqual(v2, recons_data[k2][k])
+        # GH16122
+        test_maps = (
+            dict, collections.defaultdict, collections.OrderedDict)
+        for mapping in test_maps:
+            recons_data = DataFrame(test_data).to_dict(into=mapping)
+
+            for k, v in compat.iteritems(test_data):
+                for k2, v2 in compat.iteritems(v):
+                    self.assertEqual(v2, recons_data[k][k2])
+
+            recons_data = DataFrame(test_data).to_dict("l", mapping)
+
+            for k, v in compat.iteritems(test_data):
+                for k2, v2 in compat.iteritems(v):
+                    self.assertEqual(v2, recons_data[k][int(k2) - 1])
+
+            recons_data = DataFrame(test_data).to_dict("s", mapping)
+
+            for k, v in compat.iteritems(test_data):
+                for k2, v2 in compat.iteritems(v):
+                    self.assertEqual(v2, recons_data[k][k2])
+
+            recons_data = DataFrame(test_data).to_dict("sp", mapping)
+            expected_split = {'columns': ['A', 'B'], 'index': ['1', '2', '3'],
+                              'data': [[1.0, '1'], [2.0, '2'], [np.nan, '3']]}
+            tm.assert_dict_equal(recons_data, expected_split)
+
+            recons_data = DataFrame(test_data).to_dict("r", mapping)
+            expected_records = [{'A': 1.0, 'B': '1'},
+                                {'A': 2.0, 'B': '2'},
+                                {'A': np.nan, 'B': '3'}]
+            assert isinstance(recons_data, list)
+            self.assertEqual(len(recons_data), 3)
+            for l, r in zip(recons_data, expected_records):
+                tm.assert_dict_equal(l, r)
+
+            # GH10844
+            recons_data = DataFrame(test_data).to_dict("i")
+
+            for k, v in compat.iteritems(test_data):
+                for k2, v2 in compat.iteritems(v):
+                    self.assertEqual(v2, recons_data[k2][k])
 
     def test_to_dict_timestamp(self):
 

pandas/tests/series/test_io.py

@@ -2,6 +2,7 @@
 # pylint: disable-msg=E1101,W0612
 
 from datetime import datetime
+import collections
 
 import numpy as np
 import pandas as pd
@@ -127,7 +128,15 @@ def test_to_frame(self):
         assert_frame_equal(rs, xp)
 
     def test_to_dict(self):
-        tm.assert_series_equal(Series(self.ts.to_dict(), name='ts'), self.ts)
+        # GH16122
+        test_maps = (
+            dict, collections.defaultdict, collections.OrderedDict)
+        for mapping in test_maps:
+            tm.assert_series_equal(
+                Series(self.ts.to_dict(mapping), name='ts'), self.ts)
+        from_method = Series(self.ts.to_dict(collections.Counter))
+        from_constructor = Series(collections.Counter(self.ts.iteritems()))
+        tm.assert_series_equal(from_method, from_constructor)
 
     def test_timeseries_periodindex(self):
         # GH2891