Addressing review comments

Author: datapythonista

pandas/core/common.py

@@ -214,10 +214,6 @@ def try_sort(iterable):
         return listed
 
 
-def dict_keys_to_ordered_list(mapping):
-    return list(mapping.keys())
-
-
 def asarray_tuplesafe(values, dtype=None):
 
     if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):

pandas/core/frame.py

@@ -3500,16 +3500,12 @@ def assign(self, **kwargs):
         Notes
         -----
         Assigning multiple columns within the same ``assign`` is possible.
-        For Python 3.6 and above, later items in '\*\*kwargs' may refer to
-        newly created or modified columns in 'df'; items are computed and
-        assigned into 'df' in order.  For Python 3.5 and below, the order of
-        keyword arguments is not specified, you cannot refer to newly created
-        or modified columns. All items are computed first, and then assigned
-        in alphabetical order.
+        Later items in '\*\*kwargs' may refer to newly created or modified
+        columns in 'df'; items are computed and assigned into 'df' in order.
 
         .. versionchanged:: 0.23.0
 
-           Keyword argument order is maintained for Python 3.6 and later.
+           Keyword argument order is maintained.
 
         Examples
         --------
@@ -3535,9 +3531,8 @@ def assign(self, **kwargs):
         Portland    17.0    62.6
         Berkeley    25.0    77.0
 
-        In Python 3.6+, you can create multiple columns within the same assign
-        where one of the columns depends on another one defined within the same
-        assign:
+        You can create multiple columns within the same assign where one
+        of the columns depends on another one defined within the same assign:
 
         >>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
         ...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)

pandas/core/generic.py

@@ -8,7 +8,6 @@
 import re
 from textwrap import dedent
 from typing import (
-    TYPE_CHECKING,
     Any,
     Callable,
     Dict,
@@ -190,9 +189,7 @@ class NDFrame(PandasObject, SelectionMixin):
     _metadata = []  # type: List[str]
     _is_copy = None
     _data = None  # type: BlockManager
-
-    if TYPE_CHECKING:
-        _attrs = {}  # type: Dict[Hashable, Any]
+    _attrs = {}  # type: Dict[Optional[Hashable], Any]
 
     # ----------------------------------------------------------------------
     # Constructors

pandas/core/internals/construction.py

@@ -233,7 +233,7 @@ def init_dict(data, index, columns, dtype=None):
             arrays.loc[missing] = [val] * missing.sum()
 
     else:
-        keys = com.dict_keys_to_ordered_list(data)
+        keys = list(data.keys())
         columns = data_names = Index(keys)
         arrays = (com.maybe_iterable_to_list(data[k]) for k in keys)
         # GH#24096 need copy to be deep for datetime64tz case
@@ -526,7 +526,7 @@ def _list_of_dict_to_arrays(data, columns, coerce_float=False, dtype=None):
     """Convert list of dicts to numpy arrays
 
     if `columns` is not passed, column names are inferred from the records
-    - for OrderedDict and (on Python>=3.6) dicts, the column names match
+    - for OrderedDict and dicts, the column names match
       the key insertion-order from the first record to the last.
     - For other kinds of dict-likes, the keys are lexically sorted.
 

pandas/core/reshape/concat.py

@@ -293,7 +293,7 @@ def __init__(
 
         if isinstance(objs, dict):
             if keys is None:
-                keys = com.dict_keys_to_ordered_list(objs)
+                keys = list(objs.keys())
             objs = [objs[k] for k in keys]
         else:
             objs = list(objs)

pandas/tests/frame/test_constructors.py

@@ -1360,7 +1360,7 @@ def test_constructor_list_of_dict_order(self):
             }
         )
         result = DataFrame(data)
-        tm.assert_frame_equal(result, expected, check_like=False)
+        tm.assert_frame_equal(result, expected)
 
     def test_constructor_orient(self, float_string_frame):
         data_dict = float_string_frame.T._series

pandas/tests/indexing/test_indexing.py

@@ -221,7 +221,7 @@ def test_setitem_dtype_upcast(self):
         expected = DataFrame(
             [{"a": 1, "b": np.nan, "c": "foo"}, {"a": 3, "b": 2, "c": np.nan}]
         )
-        tm.assert_frame_equal(df, expected, check_like=False)
+        tm.assert_frame_equal(df, expected)
 
         # GH10280
         df = DataFrame(

pandas/tests/io/json/test_normalize.py

@@ -380,7 +380,7 @@ def test_missing_field(self, author_missing_data):
             },
         ]
         expected = DataFrame(ex_data)
-        tm.assert_frame_equal(result, expected, check_like=False)
+        tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize(
         "max_level,expected",
@@ -522,7 +522,7 @@ def test_missing_meta(self, missing_metadata):
         columns = ["city", "number", "state", "street", "zip", "name"]
         columns = ["number", "street", "city", "state", "zip", "name"]
         expected = DataFrame(ex_data, columns=columns)
-        tm.assert_frame_equal(result, expected, check_like=False)
+        tm.assert_frame_equal(result, expected)
 
     def test_donot_drop_nonevalues(self):
         # GH21356

pandas/tests/reshape/test_concat.py

@@ -1264,7 +1264,7 @@ def test_concat_dict(self):
             "qux": DataFrame(np.random.randn(4, 3)),
         }
 
-        sorted_keys = com.dict_keys_to_ordered_list(frames)
+        sorted_keys = list(frames.keys())
 
         result = concat(frames)
         expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys)