CLN: fix flake8 C406, C409, and some of C408

asv_bench/benchmarks/groupby.py

@@ -486,7 +486,7 @@ def setup(self):
         tmp2 = (np.random.random(10000) * 10.0).astype(np.float32)
         tmp = np.concatenate((tmp1, tmp2))
         arr = np.repeat(tmp, 10)
-        self.df = DataFrame(dict(a=arr, b=arr))
+        self.df = DataFrame({"a": arr, "b": arr})
 
     def time_sum(self):
         self.df.groupby(["a"])["b"].sum()

pandas/conftest.py

@@ -472,8 +472,8 @@ def index_with_missing(request):
     if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]:
         # For setting missing values in the top level of MultiIndex
         vals = ind.tolist()
-        vals[0] = tuple([None]) + vals[0][1:]
-        vals[-1] = tuple([None]) + vals[-1][1:]
+        vals[0] = (None,) + vals[0][1:]
+        vals[-1] = (None,) + vals[-1][1:]
         return MultiIndex.from_tuples(vals)
     else:
         vals[0] = None

pandas/core/indexers.py

@@ -105,7 +105,7 @@ def is_empty_indexer(indexer, arr_value: np.ndarray) -> bool:
         return True
     if arr_value.ndim == 1:
         if not isinstance(indexer, tuple):
-            indexer = tuple([indexer])
+            indexer = (indexer,)
         return any(isinstance(idx, np.ndarray) and len(idx) == 0 for idx in indexer)
     return False
 

pandas/core/indexes/base.py

@@ -3558,7 +3558,7 @@ def _reindex_non_unique(self, target):
             cur_labels = self.take(indexer[check]).values
             cur_indexer = ensure_int64(length[check])
 
-            new_labels = np.empty(tuple([len(indexer)]), dtype=object)
+            new_labels = np.empty((len(indexer),), dtype=object)
             new_labels[cur_indexer] = cur_labels
             new_labels[missing_indexer] = missing_labels
 

pandas/core/indexing.py

@@ -1934,7 +1934,7 @@ def _align_series(self, indexer, ser: "Series", multiindex_indexer: bool = False
         to the locations selected by `indexer`
         """
         if isinstance(indexer, (slice, np.ndarray, list, Index)):
-            indexer = tuple([indexer])
+            indexer = (indexer,)
 
         if isinstance(indexer, tuple):
 
@@ -2073,7 +2073,7 @@ def __getitem__(self, key):
 
             # we could have a convertible item here (e.g. Timestamp)
             if not is_list_like_indexer(key):
-                key = tuple([key])
+                key = (key,)
             else:
                 raise ValueError("Invalid call for scalar access (getting)!")
 

pandas/core/internals/managers.py

@@ -1544,7 +1544,7 @@ def __init__(
             )
 
         self.axes = [axis]
-        self.blocks = tuple([block])
+        self.blocks = (block,)
 
     @classmethod
     def from_blocks(

pandas/core/series.py

@@ -368,7 +368,7 @@ def _init_dict(self, data, index=None, dtype=None):
             values = na_value_for_dtype(dtype)
             keys = index
         else:
-            keys, values = tuple([]), []
+            keys, values = tuple(), []
 
         # Input is now list-like, so rely on "standard" construction:
 

pandas/io/stata.py

@@ -873,30 +873,26 @@ def __init__(self):
                 (255, np.dtype(np.float64)),
             ]
         )
-        self.DTYPE_MAP_XML = dict(
-            [
-                (32768, np.dtype(np.uint8)),  # Keys to GSO
-                (65526, np.dtype(np.float64)),
-                (65527, np.dtype(np.float32)),
-                (65528, np.dtype(np.int32)),
-                (65529, np.dtype(np.int16)),
-                (65530, np.dtype(np.int8)),
-            ]
-        )
+        self.DTYPE_MAP_XML = {
+            32768: np.dtype(np.uint8),  # Keys to GSO
+            65526: np.dtype(np.float64),
+            65527: np.dtype(np.float32),
+            65528: np.dtype(np.int32),
+            65529: np.dtype(np.int16),
+            65530: np.dtype(np.int8),
+        }
         # error: Argument 1 to "list" has incompatible type "str";
         #  expected "Iterable[int]"  [arg-type]
         self.TYPE_MAP = list(range(251)) + list("bhlfd")  # type: ignore[arg-type]
-        self.TYPE_MAP_XML = dict(
-            [
-                # Not really a Q, unclear how to handle byteswap
-                (32768, "Q"),
-                (65526, "d"),
-                (65527, "f"),
-                (65528, "l"),
-                (65529, "h"),
-                (65530, "b"),
-            ]
-        )
+        self.TYPE_MAP_XML = {
+            # Not really a Q, unclear how to handle byteswap
+            32768: "Q",
+            65526: "d",
+            65527: "f",
+            65528: "l",
+            65529: "h",
+            65530: "b",
+        }
         # NOTE: technically, some of these are wrong. there are more numbers
         # that can be represented. it's the 27 ABOVE and BELOW the max listed
         # numeric data type in [U] 12.2.2 of the 11.2 manual
@@ -3138,24 +3134,22 @@ def _write_map(self) -> None:
         all blocks have been written.
         """
         if not self._map:
-            self._map = dict(
-                (
-                    ("stata_data", 0),
-                    ("map", self.handles.handle.tell()),
-                    ("variable_types", 0),
-                    ("varnames", 0),
-                    ("sortlist", 0),
-                    ("formats", 0),
-                    ("value_label_names", 0),
-                    ("variable_labels", 0),
-                    ("characteristics", 0),
-                    ("data", 0),
-                    ("strls", 0),
-                    ("value_labels", 0),
-                    ("stata_data_close", 0),
-                    ("end-of-file", 0),
-                )
-            )
+            self._map = {
+                "stata_data": 0,
+                "map": self.handles.handle.tell(),
+                "variable_types": 0,
+                "varnames": 0,
+                "sortlist": 0,
+                "formats": 0,
+                "value_label_names": 0,
+                "variable_labels": 0,
+                "characteristics": 0,
+                "data": 0,
+                "strls": 0,
+                "value_labels": 0,
+                "stata_data_close": 0,
+                "end-of-file": 0,
+            }
         # Move to start of map
         self.handles.handle.seek(self._map["map"])
         bio = BytesIO()

pandas/tests/arithmetic/test_datetime64.py

@@ -1288,8 +1288,8 @@ def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array):
             ("seconds", 2),
             ("microseconds", 5),
         ]
-        for i, kwd in enumerate(relative_kwargs):
-            off = DateOffset(**dict([kwd]))
+        for i, (unit, value) in enumerate(relative_kwargs):
+            off = DateOffset(**{unit: value})
 
             expected = DatetimeIndex([x + off for x in vec_items])
             expected = tm.box_expected(expected, box_with_array)

pandas/tests/base/test_conversion.py

@@ -109,7 +109,7 @@ def test_iterable_map(self, index_or_series, dtype, rdtype):
         s = typ([1], dtype=dtype)
         result = s.map(type)[0]
         if not isinstance(rdtype, tuple):
-            rdtype = tuple([rdtype])
+            rdtype = (rdtype,)
         assert result in rdtype
 
     @pytest.mark.parametrize(

pandas/tests/frame/apply/test_frame_apply.py

@@ -562,11 +562,9 @@ def test_apply_dict(self):
 
         # GH 8735
         A = DataFrame([["foo", "bar"], ["spam", "eggs"]])
-        A_dicts = Series(
-            [dict([(0, "foo"), (1, "spam")]), dict([(0, "bar"), (1, "eggs")])]
-        )
+        A_dicts = Series([{0: "foo", 1: "spam"}, {0: "bar", 1: "eggs"}])
         B = DataFrame([[0, 1], [2, 3]])
-        B_dicts = Series([dict([(0, 0), (1, 2)]), dict([(0, 1), (1, 3)])])
+        B_dicts = Series([{0: 0, 1: 2}, {0: 1, 1: 3}])
         fn = lambda x: x.to_dict()
 
         for df, dicts in [(A, A_dicts), (B, B_dicts)]:
@@ -1221,7 +1219,7 @@ def test_agg_reduce(self, axis, float_frame):
         tm.assert_frame_equal(result, expected)
 
         # dict input with scalars
-        func = dict([(name1, "mean"), (name2, "sum")])
+        func = {name1: "mean", name2: "sum"}
         result = float_frame.agg(func, axis=axis)
         expected = Series(
             [
@@ -1233,7 +1231,7 @@ def test_agg_reduce(self, axis, float_frame):
         tm.assert_series_equal(result, expected)
 
         # dict input with lists
-        func = dict([(name1, ["mean"]), (name2, ["sum"])])
+        func = {name1: ["mean"], name2: ["sum"]}
         result = float_frame.agg(func, axis=axis)
         expected = DataFrame(
             {
@@ -1249,33 +1247,25 @@ def test_agg_reduce(self, axis, float_frame):
         tm.assert_frame_equal(result, expected)
 
         # dict input with lists with multiple
-        func = dict([(name1, ["mean", "sum"]), (name2, ["sum", "max"])])
+        func = {name1: ["mean", "sum"], name2: ["sum", "max"]}
         result = float_frame.agg(func, axis=axis)
         expected = pd.concat(
-            dict(
-                [
-                    (
-                        name1,
-                        Series(
-                            [
-                                float_frame.loc(other_axis)[name1].mean(),
-                                float_frame.loc(other_axis)[name1].sum(),
-                            ],
-                            index=["mean", "sum"],
-                        ),
-                    ),
-                    (
-                        name2,
-                        Series(
-                            [
-                                float_frame.loc(other_axis)[name2].sum(),
-                                float_frame.loc(other_axis)[name2].max(),
-                            ],
-                            index=["sum", "max"],
-                        ),
-                    ),
-                ]
-            ),
+            {
+                name1: Series(
+                    [
+                        float_frame.loc(other_axis)[name1].mean(),
+                        float_frame.loc(other_axis)[name1].sum(),
+                    ],
+                    index=["mean", "sum"],
+                ),
+                name2: Series(
+                    [
+                        float_frame.loc(other_axis)[name2].sum(),
+                        float_frame.loc(other_axis)[name2].max(),
+                    ],
+                    index=["sum", "max"],
+                ),
+            },
             axis=1,
         )
         expected = expected.T if axis in {1, "columns"} else expected

pandas/tests/frame/indexing/test_indexing.py

@@ -1503,10 +1503,29 @@ def test_loc_getitem_index_namedtuple(self):
         result = df.loc[IndexType("foo", "bar")]["A"]
         assert result == 1
 
-    @pytest.mark.parametrize("tpl", [tuple([1]), tuple([1, 2])])
+    @pytest.mark.parametrize(
+        "tpl",
+        [
+            (1,),
+            (
+                1,
+                2,
+            ),
+        ],
+    )
     def test_loc_getitem_index_single_double_tuples(self, tpl):
         # GH 20991
-        idx = Index([tuple([1]), tuple([1, 2])], name="A", tupleize_cols=False)
+        idx = Index(
+            [
+                (1,),
+                (
+                    1,
+                    2,
+                ),
+            ],
+            name="A",
+            tupleize_cols=False,
+        )
         df = DataFrame(index=idx)
 
         result = df.loc[[tpl]]

pandas/tests/frame/methods/test_dtypes.py

@@ -32,8 +32,8 @@ def test_empty_frame_dtypes(self):
             norows_int_df.dtypes, Series(np.dtype("int32"), index=list("abc"))
         )
 
-        df = DataFrame(dict([("a", 1), ("b", True), ("c", 1.0)]), index=[1, 2, 3])
-        ex_dtypes = Series(dict([("a", np.int64), ("b", np.bool_), ("c", np.float64)]))
+        df = DataFrame({"a": 1, "b": True, "c": 1.0}, index=[1, 2, 3])
+        ex_dtypes = Series({"a": np.int64, "b": np.bool_, "c": np.float64})
         tm.assert_series_equal(df.dtypes, ex_dtypes)
 
         # same but for empty slice of df
@@ -66,12 +66,12 @@ def test_dtypes_are_correct_after_column_slice(self):
         df = DataFrame(index=range(5), columns=list("abc"), dtype=np.float_)
         tm.assert_series_equal(
             df.dtypes,
-            Series(dict([("a", np.float_), ("b", np.float_), ("c", np.float_)])),
+            Series({"a": np.float_, "b": np.float_, "c": np.float_}),
         )
-        tm.assert_series_equal(df.iloc[:, 2:].dtypes, Series(dict([("c", np.float_)])))
+        tm.assert_series_equal(df.iloc[:, 2:].dtypes, Series({"c": np.float_}))
         tm.assert_series_equal(
             df.dtypes,
-            Series(dict([("a", np.float_), ("b", np.float_), ("c", np.float_)])),
+            Series({"a": np.float_, "b": np.float_, "c": np.float_}),
         )
 
     def test_dtypes_gh8722(self, float_string_frame):
@@ -90,10 +90,10 @@ def test_dtypes_gh8722(self, float_string_frame):
 
     def test_dtypes_timedeltas(self):
         df = DataFrame(
-            dict(
-                A=Series(date_range("2012-1-1", periods=3, freq="D")),
-                B=Series([timedelta(days=i) for i in range(3)]),
-            )
+            {
+                "A": Series(date_range("2012-1-1", periods=3, freq="D")),
+                "B": Series([timedelta(days=i) for i in range(3)]),
+            }
         )
         result = df.dtypes
         expected = Series(

pandas/tests/frame/methods/test_select_dtypes.py

@@ -201,16 +201,14 @@ def test_select_dtypes_include_exclude_mixed_scalars_lists(self):
     def test_select_dtypes_duplicate_columns(self):
         # GH20839
         df = DataFrame(
-            dict(
-                [
-                    ("a", list("abc")),
-                    ("b", list(range(1, 4))),
-                    ("c", np.arange(3, 6).astype("u1")),
-                    ("d", np.arange(4.0, 7.0, dtype="float64")),
-                    ("e", [True, False, True]),
-                    ("f", pd.date_range("now", periods=3).values),
-                ]
-            )
+            {
+                "a": ["a", "b", "c"],
+                "b": [1, 2, 3],
+                "c": np.arange(3, 6).astype("u1"),
+                "d": np.arange(4.0, 7.0, dtype="float64"),
+                "e": [True, False, True],
+                "f": pd.date_range("now", periods=3).values,
+            }
         )
         df.columns = ["a", "a", "b", "b", "b", "c"]
 
@@ -268,10 +266,10 @@ def test_select_dtypes_bad_datetime64(self):
     def test_select_dtypes_datetime_with_tz(self):
 
         df2 = DataFrame(
-            dict(
-                A=Timestamp("20130102", tz="US/Eastern"),
-                B=Timestamp("20130603", tz="CET"),
-            ),
+            {
+                "A": Timestamp("20130102", tz="US/Eastern"),
+                "B": Timestamp("20130603", tz="CET"),
+            },
             index=range(5),
         )
         df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1)