CLN: Removing Python 3.6 or higher references that are always true (pandas-dev#29492)

Author: datapythonista

pandas/_libs/tslibs/timestamps.pyx

@@ -36,7 +36,6 @@ from pandas._libs.tslibs.tzconversion import (
 # Constants
 _zero_time = datetime_time(0, 0)
 _no_input = object()
-PY36 = sys.version_info >= (3, 6)
 
 # ----------------------------------------------------------------------
 
@@ -982,9 +981,8 @@ default 'raise'
         else:
             kwargs = {'year': dts.year, 'month': dts.month, 'day': dts.day,
                       'hour': dts.hour, 'minute': dts.min, 'second': dts.sec,
-                      'microsecond': dts.us, 'tzinfo': _tzinfo}
-            if PY36:
-                kwargs['fold'] = fold
+                      'microsecond': dts.us, 'tzinfo': _tzinfo,
+                      'fold': fold}
             ts_input = datetime(**kwargs)
 
         ts = convert_datetime_to_tsobject(ts_input, _tzinfo)

pandas/compat/__init__.py

@@ -12,7 +12,6 @@
 import sys
 import warnings
 
-PY36 = sys.version_info >= (3, 6)
 PY37 = sys.version_info >= (3, 7)
 PY38 = sys.version_info >= (3, 8)
 PYPY = platform.python_implementation() == "PyPy"

pandas/core/common.py

@@ -5,7 +5,7 @@
 """
 
 import collections
-from collections import OrderedDict, abc
+from collections import abc
 from datetime import datetime, timedelta
 from functools import partial
 import inspect
@@ -14,7 +14,6 @@
 import numpy as np
 
 from pandas._libs import lib, tslibs
-from pandas.compat import PY36
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import (
@@ -215,16 +214,6 @@ def try_sort(iterable):
         return listed
 
 
-def dict_keys_to_ordered_list(mapping):
-    # when pandas drops support for Python < 3.6, this function
-    # can be replaced by a simple list(mapping.keys())
-    if PY36 or isinstance(mapping, OrderedDict):
-        keys = list(mapping.keys())
-    else:
-        keys = try_sort(mapping)
-    return keys
-
-
 def asarray_tuplesafe(values, dtype=None):
 
     if not (isinstance(values, (list, tuple)) or hasattr(values, "__array__")):

pandas/core/dtypes/common.py

@@ -6,7 +6,6 @@
 
 from pandas._libs import algos, lib
 from pandas._libs.tslibs import conversion
-from pandas.compat import PY36
 
 from pandas.core.dtypes.dtypes import (
     CategoricalDtype,
@@ -1266,24 +1265,17 @@ def _is_unorderable_exception(e: TypeError) -> bool:
     """
     Check if the exception raised is an unorderable exception.
 
-    The error message differs for 3 <= PY <= 3.5 and PY >= 3.6, so
-    we need to condition based on Python version.
-
     Parameters
     ----------
     e : Exception or sub-class
         The exception object to check.
 
     Returns
     -------
-    boolean
+    bool
         Whether or not the exception raised is an unorderable exception.
     """
-
-    if PY36:
-        return "'>' not supported between instances of" in str(e)
-
-    return "unorderable" in str(e)
+    return "'>' not supported between instances of" in str(e)
 
 
 def needs_i8_conversion(arr_or_dtype) -> bool:

pandas/core/frame.py

@@ -34,7 +34,6 @@
 from pandas._config import get_option
 
 from pandas._libs import algos as libalgos, lib
-from pandas.compat import PY36
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
     Appender,
@@ -3498,16 +3497,12 @@ 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
         --------
@@ -3533,9 +3528,8 @@ 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)
@@ -3545,21 +3539,8 @@ def assign(self, **kwargs):
         """
         data = self.copy()
 
-        # >= 3.6 preserve order of kwargs
-        if PY36:
-            for k, v in kwargs.items():
-                data[k] = com.apply_if_callable(v, data)
-        else:
-            # <= 3.5: do all calculations first...
-            results = OrderedDict()
-            for k, v in kwargs.items():
-                results[k] = com.apply_if_callable(v, data)
-
-            # <= 3.5 and earlier
-            results = sorted(results.items())
-            # ... and then assign
-            for k, v in results:
-                data[k] = v
+        for k, v in kwargs.items():
+            data[k] = com.apply_if_callable(v, data)
         return data
 
     def _sanitize_column(self, key, value, broadcast=True):

pandas/core/generic.py

@@ -8,7 +8,6 @@
 import re
 from textwrap import dedent
 from typing import (
-    TYPE_CHECKING,
     Any,
     Callable,
     Dict,
@@ -190,12 +189,7 @@ class NDFrame(PandasObject, SelectionMixin):
     _metadata = []  # type: List[str]
     _is_copy = None
     _data = None  # type: BlockManager
-
-    if TYPE_CHECKING:
-        # TODO(PY36): replace with _attrs : Dict[Hashable, Any]
-        # We need the TYPE_CHECKING, because _attrs is not a class attribute
-        # and Py35 doesn't support the new syntax.
-        _attrs = {}  # type: Dict[Optional[Hashable], Any]
+    _attrs: Dict[Optional[Hashable], Any]
 
     # ----------------------------------------------------------------------
     # Constructors

pandas/core/groupby/generic.py

@@ -28,7 +28,6 @@
 import numpy as np
 
 from pandas._libs import Timestamp, lib
-from pandas.compat import PY36
 from pandas.util._decorators import Appender, Substitution
 
 from pandas.core.dtypes.cast import (
@@ -233,10 +232,6 @@ def aggregate(self, func=None, *args, **kwargs):
         no_arg_message = "Must provide 'func' or named aggregation **kwargs."
         if relabeling:
             columns = list(kwargs)
-            if not PY36:
-                # sort for 3.5 and earlier
-                columns = list(sorted(columns))
-
             func = [kwargs[col] for col in columns]
             kwargs = {}
             if not columns:
@@ -1804,9 +1799,6 @@ def _normalize_keyword_aggregation(kwargs):
     >>> _normalize_keyword_aggregation({'output': ('input', 'sum')})
     (OrderedDict([('input', ['sum'])]), ('output',), [('input', 'sum')])
     """
-    if not PY36:
-        kwargs = OrderedDict(sorted(kwargs.items()))
-
     # Normalize the aggregation functions as Dict[column, List[func]],
     # process normally, then fixup the names.
     # TODO(Py35): When we drop python 3.5, change this to

pandas/core/internals/construction.py

@@ -2,14 +2,12 @@
 Functions for preparing various inputs passed to the DataFrame or Series
 constructors before passing them to a BlockManager.
 """
-from collections import OrderedDict, abc
+from collections import abc
 
 import numpy as np
 import numpy.ma as ma
 
 from pandas._libs import lib
-import pandas.compat as compat
-from pandas.compat import PY36
 
 from pandas.core.dtypes.cast import (
     construct_1d_arraylike_from_scalar,
@@ -235,7 +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
@@ -331,16 +329,13 @@ def extract_index(data):
         have_raw_arrays = False
         have_series = False
         have_dicts = False
-        have_ordered = False
 
         for val in data:
             if isinstance(val, ABCSeries):
                 have_series = True
                 indexes.append(val.index)
             elif isinstance(val, dict):
                 have_dicts = True
-                if isinstance(val, OrderedDict):
-                    have_ordered = True
                 indexes.append(list(val.keys()))
             elif is_list_like(val) and getattr(val, "ndim", 1) == 1:
                 have_raw_arrays = True
@@ -352,7 +347,7 @@ def extract_index(data):
         if have_series:
             index = _union_indexes(indexes)
         elif have_dicts:
-            index = _union_indexes(indexes, sort=not (compat.PY36 or have_ordered))
+            index = _union_indexes(indexes, sort=False)
 
         if have_raw_arrays:
             lengths = list(set(raw_lengths))
@@ -531,7 +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.
 
@@ -551,8 +546,7 @@ def _list_of_dict_to_arrays(data, columns, coerce_float=False, dtype=None):
 
     if columns is None:
         gen = (list(x.keys()) for x in data)
-        types = (dict, OrderedDict) if PY36 else OrderedDict
-        sort = not any(isinstance(d, types) for d in data)
+        sort = not any(isinstance(d, dict) for d in data)
         columns = lib.fast_unique_multiple_list_gen(gen, sort=sort)
 
     # assure that they are of the base dict class and not of derived

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/core/series.py

@@ -1,7 +1,6 @@
 """
 Data structure for 1-dimensional cross-sectional and time series data
 """
-from collections import OrderedDict
 from io import StringIO
 from shutil import get_terminal_size
 from textwrap import dedent
@@ -13,7 +12,6 @@
 from pandas._config import get_option
 
 from pandas._libs import index as libindex, lib, reshape, tslibs
-from pandas.compat import PY36
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution, deprecate
 from pandas.util._validators import validate_bool_kwarg, validate_percentile
@@ -364,13 +362,6 @@ def _init_dict(self, data, index=None, dtype=None):
         # Now we just make sure the order is respected, if any
         if data and index is not None:
             s = s.reindex(index, copy=False)
-        elif not PY36 and not isinstance(data, OrderedDict) and data:
-            # Need the `and data` to avoid sorting Series(None, index=[...])
-            # since that isn't really dict-like
-            try:
-                s = s.sort_index()
-            except TypeError:
-                pass
         return s._data, s.index
 
     @classmethod