black

Author: samuelsinayoko

pandas/core/algorithms.py

@@ -1155,7 +1155,7 @@ def compute(self, method):
         n = min(n, narr)
 
         kth_val = algos.kth_smallest(arr.copy(), n - 1)
-        ns, = np.nonzero(arr <= kth_val)
+        (ns,) = np.nonzero(arr <= kth_val)
         inds = ns[arr[ns].argsort(kind="mergesort")]
 
         if self.keep != "all":

pandas/core/frame.py

@@ -4829,7 +4829,7 @@ def drop_duplicates(self, subset=None, keep="first", inplace=False):
         duplicated = self.duplicated(subset, keep=keep)
 
         if inplace:
-            inds, = (-duplicated)._ndarray_values.nonzero()
+            (inds,) = (-duplicated)._ndarray_values.nonzero()
             new_data = self._data.take(inds)
             self._update_inplace(new_data)
         else:

pandas/core/generic.py

@@ -3613,7 +3613,7 @@ class   animal   locomotion
 
             if isinstance(loc, np.ndarray):
                 if loc.dtype == np.bool_:
-                    inds, = loc.nonzero()
+                    (inds,) = loc.nonzero()
                     return self.take(inds, axis=axis)
                 else:
                     return self.take(loc, axis=axis)

pandas/core/groupby/grouper.py

@@ -284,7 +284,11 @@ def __init__(
             if self.name is None:
                 self.name = index.names[level]
 
-            self.grouper, self._labels, self._group_index = index._get_grouper_for_level(  # noqa: E501
+            (
+                self.grouper,
+                self._labels,
+                self._group_index,
+            ) = index._get_grouper_for_level(  # noqa: E501
                 self.grouper, level
             )
 

pandas/core/indexes/base.py

@@ -1873,7 +1873,7 @@ def _isnan(self):
     @cache_readonly
     def _nan_idxs(self):
         if self._can_hold_na:
-            w, = self._isnan.nonzero()
+            (w,) = self._isnan.nonzero()
             return w
         else:
             return np.array([], dtype=np.int64)

pandas/core/indexing.py

@@ -320,7 +320,7 @@ def _setitem_with_indexer(self, indexer, value):
         # if there is only one block/type, still have to take split path
         # unless the block is one-dimensional or it can hold the value
         if not take_split_path and self.obj._data.blocks:
-            blk, = self.obj._data.blocks
+            (blk,) = self.obj._data.blocks
             if 1 < blk.ndim:  # in case of dict, keys are indices
                 val = list(value.values()) if isinstance(value, dict) else value
                 take_split_path = not blk._can_hold_element(val)
@@ -1120,7 +1120,7 @@ def _getitem_iterable(self, key, axis: int):
         if com.is_bool_indexer(key):
             # A boolean indexer
             key = check_bool_indexer(labels, key)
-            inds, = key.nonzero()
+            (inds,) = key.nonzero()
             return self.obj.take(inds, axis=axis)
         else:
             # A collection of keys
@@ -1264,7 +1264,7 @@ def _convert_to_indexer(self, obj, axis: int, raise_missing: bool = False):
 
             if com.is_bool_indexer(obj):
                 obj = check_bool_indexer(labels, obj)
-                inds, = obj.nonzero()
+                (inds,) = obj.nonzero()
                 return inds
             else:
                 # When setting, missing keys are not allowed, even with .loc:

pandas/core/internals/managers.py

@@ -1868,7 +1868,7 @@ def _shape_compat(x):
 
 
 def _interleaved_dtype(
-    blocks: List[Block]
+    blocks: List[Block],
 ) -> Optional[Union[np.dtype, ExtensionDtype]]:
     """Find the common dtype for `blocks`.
 

pandas/io/common.py

@@ -109,7 +109,7 @@ def _is_url(url) -> bool:
 
 
 def _expand_user(
-    filepath_or_buffer: FilePathOrBuffer[AnyStr]
+    filepath_or_buffer: FilePathOrBuffer[AnyStr],
 ) -> FilePathOrBuffer[AnyStr]:
     """Return the argument with an initial component of ~ or ~user
        replaced by that user's home directory.
@@ -139,7 +139,7 @@ def _validate_header_arg(header) -> None:
 
 
 def _stringify_path(
-    filepath_or_buffer: FilePathOrBuffer[AnyStr]
+    filepath_or_buffer: FilePathOrBuffer[AnyStr],
 ) -> FilePathOrBuffer[AnyStr]:
     """Attempt to convert a path-like object to a string.
 

pandas/io/parsers.py

@@ -1918,7 +1918,12 @@ def __init__(self, src, **kwds):
         else:
             if len(self._reader.header) > 1:
                 # we have a multi index in the columns
-                self.names, self.index_names, self.col_names, passed_names = self._extract_multi_indexer_columns(  # noqa: E501
+                (
+                    self.names,
+                    self.index_names,
+                    self.col_names,
+                    passed_names,
+                ) = self._extract_multi_indexer_columns(  # noqa: E501
                     self._reader.header, self.index_names, self.col_names, passed_names
                 )
             else:
@@ -2307,7 +2312,12 @@ def __init__(self, f, **kwds):
         # The original set is stored in self.original_columns.
         if len(self.columns) > 1:
             # we are processing a multi index column
-            self.columns, self.index_names, self.col_names, _ = self._extract_multi_indexer_columns(  # noqa: E501
+            (
+                self.columns,
+                self.index_names,
+                self.col_names,
+                _,
+            ) = self._extract_multi_indexer_columns(  # noqa: E501
                 self.columns, self.index_names, self.col_names
             )
             # Update list of original names to include all indices.

pandas/io/stata.py

@@ -614,7 +614,7 @@ def _cast_to_stata_types(data):
                 data[col] = data[col].astype(np.int32)
             else:
                 data[col] = data[col].astype(np.float64)
-                if data[col].max() >= 2 ** 53 or data[col].min() <= -2 ** 53:
+                if data[col].max() >= 2 ** 53 or data[col].min() <= -(2 ** 53):
                     ws = precision_loss_doc % ("int64", "float64")
         elif dtype in (np.float32, np.float64):
             value = data[col].max()

pandas/tests/arrays/sparse/test_array.py

@@ -658,12 +658,16 @@ def test_getslice_tuple(self):
         dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])
 
         sparse = SparseArray(dense)
-        res = sparse[4:,]  # noqa: E231
+        res = sparse[
+            4:,
+        ]  # noqa: E231
         exp = SparseArray(dense[4:,])  # noqa: E231
         tm.assert_sp_array_equal(res, exp)
 
         sparse = SparseArray(dense, fill_value=0)
-        res = sparse[4:,]  # noqa: E231
+        res = sparse[
+            4:,
+        ]  # noqa: E231
         exp = SparseArray(dense[4:,], fill_value=0)  # noqa: E231
         tm.assert_sp_array_equal(res, exp)
 
@@ -823,11 +827,11 @@ def test_nonzero(self):
         # Tests regression #21172.
         sa = pd.SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
         expected = np.array([2, 5, 9], dtype=np.int32)
-        result, = sa.nonzero()
+        (result,) = sa.nonzero()
         tm.assert_numpy_array_equal(expected, result)
 
         sa = pd.SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
-        result, = sa.nonzero()
+        (result,) = sa.nonzero()
         tm.assert_numpy_array_equal(expected, result)
 
 

pandas/tests/dtypes/test_inference.py

@@ -505,7 +505,7 @@ def test_convert_numeric_int64_uint64(self, case, coerce):
         result = lib.maybe_convert_numeric(case, set(), coerce_numeric=coerce)
         tm.assert_almost_equal(result, expected)
 
-    @pytest.mark.parametrize("value", [-2 ** 63 - 1, 2 ** 64])
+    @pytest.mark.parametrize("value", [-(2 ** 63) - 1, 2 ** 64])
     def test_convert_int_overflow(self, value):
         # see gh-18584
         arr = np.array([value], dtype=object)

pandas/tests/frame/test_constructors.py

@@ -245,9 +245,9 @@ def test_constructor_overflow_int64(self):
             np.array([2 ** 64], dtype=object),
             np.array([2 ** 65]),
             [2 ** 64 + 1],
-            np.array([-2 ** 63 - 4], dtype=object),
-            np.array([-2 ** 64 - 1]),
-            [-2 ** 65 - 2],
+            np.array([-(2 ** 63) - 4], dtype=object),
+            np.array([-(2 ** 64) - 1]),
+            [-(2 ** 65) - 2],
         ],
     )
     def test_constructor_int_overflow(self, values):

pandas/tests/indexes/period/test_construction.py

@@ -434,7 +434,7 @@ def test_constructor_range_based_deprecated_different_freq(self):
         with tm.assert_produces_warning(FutureWarning) as m:
             PeriodIndex(start="2000", periods=2)
 
-        warning, = m
+        (warning,) = m
         assert 'freq="A-DEC"' in str(warning.message)
 
     def test_constructor(self):

pandas/tests/indexing/multiindex/test_getitem.py

@@ -108,7 +108,7 @@ def test_series_getitem_indexing_errors(
 
 
 def test_series_getitem_corner_generator(
-    multiindex_year_month_day_dataframe_random_data
+    multiindex_year_month_day_dataframe_random_data,
 ):
     s = multiindex_year_month_day_dataframe_random_data["A"]
     result = s[(x > 0 for x in s)]

pandas/tests/indexing/multiindex/test_xs.py

@@ -211,7 +211,7 @@ def test_xs_level_series_ymd(multiindex_year_month_day_dataframe_random_data):
 
 
 def test_xs_level_series_slice_not_implemented(
-    multiindex_year_month_day_dataframe_random_data
+    multiindex_year_month_day_dataframe_random_data,
 ):
     # this test is not explicitly testing .xs functionality
     # TODO: move to another module or refactor

pandas/tests/indexing/test_callable.py

@@ -17,10 +17,14 @@ def test_frame_loc_callable(self):
         res = df.loc[lambda x: x.A > 2]
         tm.assert_frame_equal(res, df.loc[df.A > 2])
 
-        res = df.loc[lambda x: x.A > 2,]  # noqa: E231
+        res = df.loc[
+            lambda x: x.A > 2,
+        ]  # noqa: E231
         tm.assert_frame_equal(res, df.loc[df.A > 2,])  # noqa: E231
 
-        res = df.loc[lambda x: x.A > 2,]  # noqa: E231
+        res = df.loc[
+            lambda x: x.A > 2,
+        ]  # noqa: E231
         tm.assert_frame_equal(res, df.loc[df.A > 2,])  # noqa: E231
 
         res = df.loc[lambda x: x.B == "b", :]
@@ -90,7 +94,9 @@ def test_frame_loc_callable_labels(self):
         res = df.loc[lambda x: ["A", "C"]]
         tm.assert_frame_equal(res, df.loc[["A", "C"]])
 
-        res = df.loc[lambda x: ["A", "C"],]  # noqa: E231
+        res = df.loc[
+            lambda x: ["A", "C"],
+        ]  # noqa: E231
         tm.assert_frame_equal(res, df.loc[["A", "C"],])  # noqa: E231
 
         res = df.loc[lambda x: ["A", "C"], :]

pandas/tests/io/parser/test_index_col.py

@@ -22,8 +22,8 @@ def test_index_col_named(all_parsers, with_header):
 KORD5,19990127, 22:00:00, 21:56:00, -0.5900, 1.7100, 5.1000, 0.0000, 290.0000
 KORD6,19990127, 23:00:00, 22:56:00, -0.5900, 1.7100, 4.6000, 0.0000, 280.0000"""  # noqa
     header = (
-        "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n"
-    )  # noqa
+        "ID,date,NominalTime,ActualTime,TDew,TAir,Windspeed,Precip,WindDir\n"  # noqa
+    )
 
     if with_header:
         data = header + no_header

pandas/tests/reductions/test_reductions.py

@@ -179,8 +179,8 @@ class TestIndexReductions:
         [
             (0, 400, 3),
             (500, 0, -6),
-            (-10 ** 6, 10 ** 6, 4),
-            (10 ** 6, -10 ** 6, -4),
+            (-(10 ** 6), 10 ** 6, 4),
+            (10 ** 6, -(10 ** 6), -4),
             (0, 10, 20),
         ],
     )

pandas/tests/test_algos.py

@@ -216,10 +216,10 @@ def test_uint64_factorize(self, writable):
         tm.assert_numpy_array_equal(uniques, exp_uniques)
 
     def test_int64_factorize(self, writable):
-        data = np.array([2 ** 63 - 1, -2 ** 63, 2 ** 63 - 1], dtype=np.int64)
+        data = np.array([2 ** 63 - 1, -(2 ** 63), 2 ** 63 - 1], dtype=np.int64)
         data.setflags(write=writable)
         exp_labels = np.array([0, 1, 0], dtype=np.intp)
-        exp_uniques = np.array([2 ** 63 - 1, -2 ** 63], dtype=np.int64)
+        exp_uniques = np.array([2 ** 63 - 1, -(2 ** 63)], dtype=np.int64)
 
         labels, uniques = algos.factorize(data)
         tm.assert_numpy_array_equal(labels, exp_labels)
@@ -258,7 +258,7 @@ def test_deprecate_order(self):
         "data",
         [
             np.array([0, 1, 0], dtype="u8"),
-            np.array([-2 ** 63, 1, -2 ** 63], dtype="i8"),
+            np.array([-(2 ** 63), 1, -(2 ** 63)], dtype="i8"),
             np.array(["__nan__", "foo", "__nan__"], dtype="object"),
         ],
     )
@@ -275,8 +275,8 @@ def test_parametrized_factorize_na_value_default(self, data):
         [
             (np.array([0, 1, 0, 2], dtype="u8"), 0),
             (np.array([1, 0, 1, 2], dtype="u8"), 1),
-            (np.array([-2 ** 63, 1, -2 ** 63, 0], dtype="i8"), -2 ** 63),
-            (np.array([1, -2 ** 63, 1, 0], dtype="i8"), 1),
+            (np.array([-(2 ** 63), 1, -(2 ** 63), 0], dtype="i8"), -(2 ** 63)),
+            (np.array([1, -(2 ** 63), 1, 0], dtype="i8"), 1),
             (np.array(["a", "", "a", "b"], dtype=object), "a"),
             (np.array([(), ("a", 1), (), ("a", 2)], dtype=object), ()),
             (np.array([("a", 1), (), ("a", 1), ("a", 2)], dtype=object), ("a", 1)),

pandas/tests/test_nanops.py

@@ -302,7 +302,7 @@ def test_nanmean_overflow(self):
         # In the previous implementation mean can overflow for int dtypes, it
         # is now consistent with numpy
 
-        for a in [2 ** 55, -2 ** 55, 20150515061816532]:
+        for a in [2 ** 55, -(2 ** 55), 20150515061816532]:
             s = Series(a, index=range(500), dtype=np.int64)
             result = s.mean()
             np_result = s.values.mean()