CLN: OrderedDict->dict

pandas/compat/numpy/function.py

@@ -17,7 +17,6 @@
 and methods that are spread throughout the codebase. This module will make it
 easier to adjust to future upstream changes in the analogous numpy signatures.
 """
-from collections import OrderedDict
 from distutils.version import LooseVersion
 from typing import Any, Dict, Optional, Union
 
@@ -117,7 +116,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs):
     return skipna
 
 
-ARGSORT_DEFAULTS: "OrderedDict[str, Optional[Union[int, str]]]" = OrderedDict()
+ARGSORT_DEFAULTS: Dict[str, Optional[Union[int, str]]] = {}
 ARGSORT_DEFAULTS["axis"] = -1
 ARGSORT_DEFAULTS["kind"] = "quicksort"
 ARGSORT_DEFAULTS["order"] = None
@@ -133,7 +132,7 @@ def validate_argmax_with_skipna(skipna, args, kwargs):
 
 # two different signatures of argsort, this second validation
 # for when the `kind` param is supported
-ARGSORT_DEFAULTS_KIND: "OrderedDict[str, Optional[int]]" = OrderedDict()
+ARGSORT_DEFAULTS_KIND: Dict[str, Optional[int]] = {}
 ARGSORT_DEFAULTS_KIND["axis"] = -1
 ARGSORT_DEFAULTS_KIND["order"] = None
 validate_argsort_kind = CompatValidator(
@@ -178,7 +177,7 @@ def validate_clip_with_axis(axis, args, kwargs):
     return axis
 
 
-CUM_FUNC_DEFAULTS: "OrderedDict[str, Any]" = OrderedDict()
+CUM_FUNC_DEFAULTS: Dict[str, Any] = {}
 CUM_FUNC_DEFAULTS["dtype"] = None
 CUM_FUNC_DEFAULTS["out"] = None
 validate_cum_func = CompatValidator(
@@ -204,7 +203,7 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     return skipna
 
 
-ALLANY_DEFAULTS: "OrderedDict[str, Optional[bool]]" = OrderedDict()
+ALLANY_DEFAULTS: Dict[str, Optional[bool]] = {}
 ALLANY_DEFAULTS["dtype"] = None
 ALLANY_DEFAULTS["out"] = None
 ALLANY_DEFAULTS["keepdims"] = False
@@ -241,13 +240,13 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
 )
 
-SORT_DEFAULTS: "OrderedDict[str, Optional[Union[int, str]]]" = OrderedDict()
+SORT_DEFAULTS: Dict[str, Optional[Union[int, str]]] = {}
 SORT_DEFAULTS["axis"] = -1
 SORT_DEFAULTS["kind"] = "quicksort"
 SORT_DEFAULTS["order"] = None
 validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
 
-STAT_FUNC_DEFAULTS: "OrderedDict[str, Optional[Any]]" = OrderedDict()
+STAT_FUNC_DEFAULTS: Dict[str, Optional[Any]] = {}
 STAT_FUNC_DEFAULTS["dtype"] = None
 STAT_FUNC_DEFAULTS["out"] = None
 
@@ -281,13 +280,13 @@ def validate_cum_func_with_skipna(skipna, args, kwargs, name):
     MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
 )
 
-STAT_DDOF_FUNC_DEFAULTS: "OrderedDict[str, Optional[bool]]" = OrderedDict()
+STAT_DDOF_FUNC_DEFAULTS: Dict[str, Optional[bool]] = {}
 STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
 STAT_DDOF_FUNC_DEFAULTS["out"] = None
 STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
 validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
 
-TAKE_DEFAULTS: "OrderedDict[str, Optional[str]]" = OrderedDict()
+TAKE_DEFAULTS: Dict[str, Optional[str]] = {}
 TAKE_DEFAULTS["out"] = None
 TAKE_DEFAULTS["mode"] = "raise"
 validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")

pandas/tests/frame/apply/test_frame_apply.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from datetime import datetime
 from itertools import chain
 import warnings
@@ -1225,7 +1224,7 @@ def test_agg_reduce(self, axis, float_frame):
         tm.assert_frame_equal(result, expected)
 
         # dict input with scalars
-        func = OrderedDict([(name1, "mean"), (name2, "sum")])
+        func = dict([(name1, "mean"), (name2, "sum")])
         result = float_frame.agg(func, axis=axis)
         expected = Series(
             [
@@ -1237,7 +1236,7 @@ def test_agg_reduce(self, axis, float_frame):
         tm.assert_series_equal(result, expected)
 
         # dict input with lists
-        func = OrderedDict([(name1, ["mean"]), (name2, ["sum"])])
+        func = dict([(name1, ["mean"]), (name2, ["sum"])])
         result = float_frame.agg(func, axis=axis)
         expected = DataFrame(
             {
@@ -1253,10 +1252,10 @@ def test_agg_reduce(self, axis, float_frame):
         tm.assert_frame_equal(result, expected)
 
         # dict input with lists with multiple
-        func = OrderedDict([(name1, ["mean", "sum"]), (name2, ["sum", "max"])])
+        func = dict([(name1, ["mean", "sum"]), (name2, ["sum", "max"])])
         result = float_frame.agg(func, axis=axis)
         expected = DataFrame(
-            OrderedDict(
+            dict(
                 [
                     (
                         name1,

pandas/tests/frame/methods/test_select_dtypes.py

@@ -1,5 +1,3 @@
-from collections import OrderedDict
-
 import numpy as np
 import pytest
 
@@ -202,9 +200,8 @@ def test_select_dtypes_include_exclude_mixed_scalars_lists(self):
 
     def test_select_dtypes_duplicate_columns(self):
         # GH20839
-        odict = OrderedDict
         df = DataFrame(
-            odict(
+            dict(
                 [
                     ("a", list("abc")),
                     ("b", list(range(1, 4))),

pandas/tests/frame/test_dtypes.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from datetime import timedelta
 
 import numpy as np
@@ -50,10 +49,9 @@ def test_empty_frame_dtypes(self):
             norows_int_df.dtypes, pd.Series(np.dtype("int32"), index=list("abc"))
         )
 
-        odict = OrderedDict
-        df = pd.DataFrame(odict([("a", 1), ("b", True), ("c", 1.0)]), index=[1, 2, 3])
+        df = pd.DataFrame(dict([("a", 1), ("b", True), ("c", 1.0)]), index=[1, 2, 3])
         ex_dtypes = pd.Series(
-            odict([("a", np.int64), ("b", np.bool_), ("c", np.float64)])
+            dict([("a", np.int64), ("b", np.bool_), ("c", np.float64)])
         )
         tm.assert_series_equal(df.dtypes, ex_dtypes)
 
@@ -85,17 +83,16 @@ def test_datetime_with_tz_dtypes(self):
     def test_dtypes_are_correct_after_column_slice(self):
         # GH6525
         df = pd.DataFrame(index=range(5), columns=list("abc"), dtype=np.float_)
-        odict = OrderedDict
         tm.assert_series_equal(
             df.dtypes,
-            pd.Series(odict([("a", np.float_), ("b", np.float_), ("c", np.float_)])),
+            pd.Series(dict([("a", np.float_), ("b", np.float_), ("c", np.float_)])),
         )
         tm.assert_series_equal(
-            df.iloc[:, 2:].dtypes, pd.Series(odict([("c", np.float_)]))
+            df.iloc[:, 2:].dtypes, pd.Series(dict([("c", np.float_)]))
         )
         tm.assert_series_equal(
             df.dtypes,
-            pd.Series(odict([("a", np.float_), ("b", np.float_), ("c", np.float_)])),
+            pd.Series(dict([("a", np.float_), ("b", np.float_), ("c", np.float_)])),
         )
 
     def test_dtypes_gh8722(self, float_string_frame):

pandas/tests/internals/test_internals.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from datetime import date, datetime
 import itertools
 import operator
@@ -165,7 +164,7 @@ def create_mgr(descr, item_shape=None):
 
     offset = 0
     mgr_items = []
-    block_placements = OrderedDict()
+    block_placements = {}
     for d in descr.split(";"):
         d = d.strip()
         if not len(d):

pandas/tests/io/json/test_pandas.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 import datetime
 from datetime import timedelta
 from io import StringIO
@@ -470,7 +469,7 @@ def test_blocks_compat_GH9037(self):
         index = pd.DatetimeIndex(list(index), freq=None)
 
         df_mixed = DataFrame(
-            OrderedDict(
+            dict(
                 float_1=[
                     -0.92077639,
                     0.77434435,

pandas/tests/resample/test_resample_api.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from datetime import datetime
 
 import numpy as np
@@ -428,7 +427,7 @@ def test_agg_misc():
     msg = r"Column\(s\) \['result1', 'result2'\] do not exist"
     for t in cases:
         with pytest.raises(pd.core.base.SpecificationError, match=msg):
-            t[["A", "B"]].agg(OrderedDict([("result1", np.sum), ("result2", np.mean)]))
+            t[["A", "B"]].agg(dict([("result1", np.sum), ("result2", np.mean)]))
 
     # agg with different hows
     expected = pd.concat(
@@ -438,7 +437,7 @@ def test_agg_misc():
         [("A", "sum"), ("A", "std"), ("B", "mean"), ("B", "std")]
     )
     for t in cases:
-        result = t.agg(OrderedDict([("A", ["sum", "std"]), ("B", ["mean", "std"])]))
+        result = t.agg(dict([("A", ["sum", "std"]), ("B", ["mean", "std"])]))
         tm.assert_frame_equal(result, expected, check_like=True)
 
     # equivalent of using a selection list / or not

pandas/tests/reshape/merge/test_merge.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from datetime import date, datetime, timedelta
 import random
 import re
@@ -1931,7 +1930,7 @@ def test_merge_index_types(index):
     result = left.merge(right, on=["index_col"])
 
     expected = DataFrame(
-        OrderedDict([("left_data", [1, 2]), ("right_data", [1.0, 2.0])]), index=index
+        dict([("left_data", [1, 2]), ("right_data", [1.0, 2.0])]), index=index
     )
     tm.assert_frame_equal(result, expected)
 

pandas/tests/reshape/test_concat.py

@@ -1,4 +1,4 @@
-from collections import OrderedDict, abc, deque
+from collections import abc, deque
 import datetime as dt
 from datetime import datetime
 from decimal import Decimal
@@ -2609,9 +2609,7 @@ def test_concat_odered_dict(self):
             [pd.Series(range(3)), pd.Series(range(4))], keys=["First", "Another"]
         )
         result = pd.concat(
-            OrderedDict(
-                [("First", pd.Series(range(3))), ("Another", pd.Series(range(4)))]
-            )
+            dict([("First", pd.Series(range(3))), ("Another", pd.Series(range(4)))])
         )
         tm.assert_series_equal(result, expected)
 

pandas/tests/reshape/test_get_dummies.py

@@ -1,5 +1,3 @@
-from collections import OrderedDict
-
 import numpy as np
 import pytest
 
@@ -569,9 +567,7 @@ def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
     @pytest.mark.parametrize("sparse", [True, False])
     def test_get_dummies_dont_sparsify_all_columns(self, sparse):
         # GH18914
-        df = DataFrame.from_dict(
-            OrderedDict([("GDP", [1, 2]), ("Nation", ["AB", "CD"])])
-        )
+        df = DataFrame.from_dict(dict([("GDP", [1, 2]), ("Nation", ["AB", "CD"])]))
         df = get_dummies(df, columns=["Nation"], sparse=sparse)
         df2 = df.reindex(columns=["GDP"])
 

pandas/tests/window/test_api.py

@@ -1,5 +1,3 @@
-from collections import OrderedDict
-
 import numpy as np
 import pytest
 
@@ -335,8 +333,6 @@ def test_multiple_agg_funcs(func, window_size, expected_vals):
     )
     expected = pd.DataFrame(expected_vals, index=index, columns=columns)
 
-    result = window.agg(
-        OrderedDict((("low", ["mean", "max"]), ("high", ["mean", "min"])))
-    )
+    result = window.agg(dict((("low", ["mean", "max"]), ("high", ["mean", "min"]))))
 
     tm.assert_frame_equal(result, expected)