CLN: Removing Python 3.6 or higher references that are always true

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 (
@@ -216,13 +215,7 @@ def try_sort(iterable):
 
 
 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
+    return list(mapping.keys())
 
 
 def asarray_tuplesafe(values, dtype=None):

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,
@@ -1278,11 +1277,7 @@ def _is_unorderable_exception(e: TypeError) -> bool:
     boolean
         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 is_numeric_v_string_like(a, b):

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,
@@ -3548,21 +3547,8 @@ 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

@@ -192,10 +192,7 @@ class NDFrame(PandasObject, SelectionMixin):
     _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 = {}  # type: Dict[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:
@@ -1814,9 +1809,6 @@ 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

@@ -8,8 +8,6 @@
 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,
@@ -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))
@@ -550,7 +545,7 @@ 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
+        types = (dict, OrderedDict)
         sort = not any(isinstance(d, types) for d in data)
         columns = lib.fast_unique_multiple_list_gen(gen, sort=sort)
 

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
@@ -362,13 +360,6 @@ 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

pandas/io/pickle.py

@@ -2,7 +2,7 @@
 import pickle
 import warnings
 
-from pandas.compat import PY36, pickle_compat as pc
+from pandas.compat import pickle_compat as pc
 
 from pandas.io.common import _get_handle, _stringify_path
 
@@ -140,9 +140,7 @@ def read_pickle(path, compression="infer"):
     # 1) try standard library Pickle
     # 2) try pickle_compat (older pandas version) to handle subclass changes
 
-    excs_to_catch = (AttributeError, ImportError)
-    if PY36:
-        excs_to_catch += (ModuleNotFoundError,)
+    excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError)
 
     try:
         with warnings.catch_warnings(record=True):

pandas/tests/extension/json/test_json.py

@@ -3,8 +3,6 @@
 
 import pytest
 
-from pandas.compat import PY36
-
 import pandas as pd
 from pandas.tests.extension import base
 import pandas.util.testing as tm
@@ -180,9 +178,6 @@ def test_fillna_frame(self):
 
 
 unhashable = pytest.mark.skip(reason="Unhashable")
-unstable = pytest.mark.skipif(
-    not PY36, reason="Dictionary order unstable"  # 3.6 or higher
-)
 
 
 class TestReduce(base.BaseNoReduceTests):
@@ -199,20 +194,16 @@ def test_sort_values_frame(self):
         # TODO (EA.factorize): see if _values_for_factorize allows this.
         pass
 
-    @unstable
     def test_argsort(self, data_for_sorting):
         super().test_argsort(data_for_sorting)
 
-    @unstable
     def test_argsort_missing(self, data_missing_for_sorting):
         super().test_argsort_missing(data_missing_for_sorting)
 
-    @unstable
     @pytest.mark.parametrize("ascending", [True, False])
     def test_sort_values(self, data_for_sorting, ascending):
         super().test_sort_values(data_for_sorting, ascending)
 
-    @unstable
     @pytest.mark.parametrize("ascending", [True, False])
     def test_sort_values_missing(self, data_missing_for_sorting, ascending):
         super().test_sort_values_missing(data_missing_for_sorting, ascending)
@@ -280,7 +271,6 @@ def test_groupby_extension_apply(self):
         we'll be able to dispatch unique.
         """
 
-    @unstable
     @pytest.mark.parametrize("as_index", [True, False])
     def test_groupby_extension_agg(self, as_index, data_for_grouping):
         super().test_groupby_extension_agg(as_index, data_for_grouping)

pandas/tests/frame/test_constructors.py

@@ -8,7 +8,7 @@
 import numpy.ma.mrecords as mrecords
 import pytest
 
-from pandas.compat import PY36, is_platform_little_endian
+from pandas.compat import is_platform_little_endian
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
 from pandas.core.dtypes.common import is_integer_dtype
@@ -387,7 +387,6 @@ def test_constructor_dict_nan_tuple_key(self, value):
         result = DataFrame(data, index=idx, columns=cols)
         tm.assert_frame_equal(result, expected)
 
-    @pytest.mark.skipif(not PY36, reason="Insertion order for Python>=3.6")
     def test_constructor_dict_order_insertion(self):
         datetime_series = tm.makeTimeSeries(nper=30)
         datetime_series_short = tm.makeTimeSeries(nper=25)
@@ -399,18 +398,6 @@ def test_constructor_dict_order_insertion(self):
         expected = DataFrame(data=d, columns=list("ba"))
         tm.assert_frame_equal(frame, expected)
 
-    @pytest.mark.skipif(PY36, reason="order by value for Python<3.6")
-    def test_constructor_dict_order_by_values(self):
-        datetime_series = tm.makeTimeSeries(nper=30)
-        datetime_series_short = tm.makeTimeSeries(nper=25)
-
-        # GH19018
-        # initialization ordering: by value if python<3.6
-        d = {"b": datetime_series_short, "a": datetime_series}
-        frame = DataFrame(data=d)
-        expected = DataFrame(data=d, columns=list("ab"))
-        tm.assert_frame_equal(frame, expected)
-
     def test_constructor_multi_index(self):
         # GH 4078
         # construction error with mi and all-nan frame
@@ -1373,7 +1360,7 @@ def test_constructor_list_of_dict_order(self):
             }
         )
         result = DataFrame(data)
-        tm.assert_frame_equal(result, expected, check_like=not PY36)
+        tm.assert_frame_equal(result, expected, check_like=False)
 
     def test_constructor_orient(self, float_string_frame):
         data_dict = float_string_frame.T._series

pandas/tests/frame/test_mutate_columns.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import PY36
-
 from pandas import DataFrame, Index, MultiIndex, Series
 import pandas.util.testing as tm
 
@@ -60,10 +58,7 @@ def test_assign_order(self):
         df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
         result = df.assign(D=df.A + df.B, C=df.A - df.B)
 
-        if PY36:
-            expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC"))
-        else:
-            expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD"))
+        expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC"))
         tm.assert_frame_equal(result, expected)
         result = df.assign(C=df.A - df.B, D=df.A + df.B)
 
@@ -80,25 +75,6 @@ def test_assign_bad(self):
         with pytest.raises(AttributeError):
             df.assign(C=df.A, D=df.A + df.C)
 
-    @pytest.mark.skipif(
-        PY36,
-        reason="""Issue #14207: valid for python
-                        3.6 and above""",
-    )
-    def test_assign_dependent_old_python(self):
-        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
-
-        # Key C does not exist at definition time of df
-        with pytest.raises(KeyError, match="^'C'$"):
-            df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"])
-        with pytest.raises(KeyError, match="^'C'$"):
-            df.assign(C=df.A, D=lambda x: x["A"] + x["C"])
-
-    @pytest.mark.skipif(
-        not PY36,
-        reason="""Issue #14207: not valid for
-                        python 3.5 and below""",
-    )
     def test_assign_dependent(self):
         df = DataFrame({"A": [1, 2], "B": [3, 4]})