initialization from dicts for py>=3.6 maintains insertion order

Author: tp

doc/source/whatsnew/v0.23.0.txt

@@ -3,7 +3,7 @@
 v0.23.0
 -------
 
-This is a major release from 0.21.1 and includes a number of API changes,
+This is a major release from 0.22.0 and includes a number of API changes,
 deprecations, new features, enhancements, and performance improvements along
 with a large number of bug fixes. We recommend that all users upgrade to this
 version.
@@ -240,7 +240,7 @@ The :func:`DataFrame.assign` now accepts dependent keyword arguments for python
   using ``.assign()`` to update an existing column. Previously, callables
   referring to other variables being updated would get the "old" values
 
-  Previous Behaviour:
+  Previous behaviour:
 
   .. code-block:: ipython
 
@@ -253,7 +253,7 @@ The :func:`DataFrame.assign` now accepts dependent keyword arguments for python
       1  3 -2
       2  4 -3
 
-  New Behaviour:
+  New behaviour:
 
   .. ipython:: python
 
@@ -320,6 +320,57 @@ If installed, we now require:
 | openpyxl        | 2.4.0           |          |
 +-----------------+-----------------+----------+
 
+.. _whatsnew_0230.api_breaking.dict_insertion_order:
+
+Creating dataframes and series from dicts preserves dict insertion order for python 3.6+
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Until Python 3.6, dicts in Python had no formally defined ordering. Python
+version 3.6 and later have changed the ordering definition of dicts, so dicts
+in these newer versions are ordered by insertion order
+(see also `PEP 468 <https://www.python.org/dev/peps/pep-0468/>`_).
+Pandas will from version 0.23 use insertion order, when creating series or
+data frames from dicts (:issue:`19018`) .
+
+Previous behaviour (and current behaviour if on Python < 3.6):
+
+.. code-block:: ipython
+
+  In [1]: pd.Series({'Income': 2000,
+  ...                 'Expenses': -1500,
+  ...                 'Taxes': -200,
+  ...                 'Net result': 300})
+  Expenses     -1500
+  Income        2000
+  Net result     300
+  Taxes         -200
+  dtype: int64
+
+Note the series above is ordered alphabetically by the index values.
+
+New behaviour (for Python >= 3.6):
+
+.. ipython:: python
+
+    pd.Series({'Income': 2000,
+               'Expenses': -1500,
+               'Taxes': -200,
+               'Net result': 300})
+
+Notice that the series is now ordered by insertion order. This new behaviour is
+used for all relevant pandas types (``Series``, ``DataFrame``, ``SparseSeries``
+and ``SparseDataFrame``).
+
+If you wish to retain the old behaviour while using Python >= 3.6, you can use
+``sort_index``:
+
+.. ipython:: python
+
+    pd.Series({'Income': 2000,
+               'Expenses': -1500,
+               'Taxes': -200,
+               'Net result': 300}).sort_index()
+
 .. _whatsnew_0230.api_breaking.deprecate_panel:
 
 Deprecate Panel

pandas/core/frame.py

@@ -460,7 +460,7 @@ def _init_dict(self, data, index, columns, dtype=None):
 
         else:
             keys = list(data.keys())
-            if not isinstance(data, OrderedDict):
+            if not PY36 and not isinstance(data, OrderedDict):
                 keys = com._try_sort(keys)
             columns = data_names = Index(keys)
             arrays = [data[k] for k in keys]

pandas/core/series.py

@@ -54,7 +54,7 @@
 from pandas import compat
 from pandas.io.formats.terminal import get_terminal_size
 from pandas.compat import (
-    zip, u, OrderedDict, StringIO, range, get_range_parameters)
+    zip, u, OrderedDict, StringIO, range, get_range_parameters, PY36)
 from pandas.compat.numpy import function as nv
 
 import pandas.core.ops as ops
@@ -286,7 +286,7 @@ def _init_dict(self, data, index=None, dtype=None):
         # Now we just make sure the order is respected, if any
         if index is not None:
             s = s.reindex(index, copy=False)
-        elif not isinstance(data, OrderedDict):
+        elif not PY36 and not isinstance(data, OrderedDict):
             try:
                 s = s.sort_index()
             except TypeError:

pandas/tests/frame/test_constructors.py

@@ -15,7 +15,7 @@
 
 from pandas.core.dtypes.common import is_integer_dtype
 from pandas.compat import (lmap, long, zip, range, lrange, lzip,
-                           OrderedDict, is_platform_little_endian)
+                           OrderedDict, is_platform_little_endian, PY36)
 from pandas import compat
 from pandas import (DataFrame, Index, Series, isna,
                     MultiIndex, Timedelta, Timestamp,
@@ -241,6 +241,17 @@ def test_constructor_dict(self):
         # Corner cases
         assert len(DataFrame({})) == 0
 
+        # GH19018
+        # initialization ordering: by insertion order if python>= 3.6, else
+        # by value
+        d = {'b': self.ts2, 'a': self.ts1}
+        frame = DataFrame(data=d)
+        if PY36:
+            expected = DataFrame(data=d, columns=list('ba'))
+        else:
+            expected = DataFrame(data=d, columns=list('ab'))
+        tm.assert_frame_equal(frame, expected)
+
         # mix dict and array, wrong size - no spec for which error should raise
         # first
         with pytest.raises(ValueError):

pandas/tests/series/test_constructors.py

@@ -22,7 +22,7 @@
 from pandas._libs import lib
 from pandas._libs.tslib import iNaT
 
-from pandas.compat import lrange, range, zip, long
+from pandas.compat import lrange, range, zip, long, PY36
 from pandas.util.testing import assert_series_equal
 import pandas.util.testing as tm
 
@@ -775,6 +775,17 @@ def test_constructor_dict(self):
         expected = Series([1, 2, nan, 0], index=['b', 'c', 'd', 'a'])
         assert_series_equal(result, expected)
 
+        # GH19018
+        # initialization ordering: by insertion order if python>= 3.6, else
+        # by value
+        d = {'b': 1, 'a': 0, 'c': 2}
+        result = Series(d)
+        if PY36:
+            expected = Series([1, 0, 2], index=list('bac'))
+        else:
+            expected = Series([0, 1, 2], index=list('abc'))
+        tm.assert_series_equal(result, expected)
+
         pidx = tm.makePeriodIndex(100)
         d = {pidx[0]: 0, pidx[1]: 1}
         result = Series(d, index=pidx)