changes made by black pandas

Author: hweecat

pandas/core/frame.py

@@ -1984,7 +1984,7 @@ def to_feather(self, path):
     @Substitution(klass="DataFrame")
     @Appender(_shared_docs["to_markdown"])
     def to_markdown(
-        self, buf: Optional[IO[str]] = None, mode: Optional[str] = None, **kwargs,
+        self, buf: Optional[IO[str]] = None, mode: Optional[str] = None, **kwargs
     ) -> Optional[str]:
         kwargs.setdefault("headers", "keys")
         kwargs.setdefault("tablefmt", "pipe")

pandas/core/indexes/base.py

@@ -265,7 +265,7 @@ def _outer_indexer(self, left, right):
     # Constructors
 
     def __new__(
-        cls, data=None, dtype=None, copy=False, name=None, tupleize_cols=True, **kwargs,
+        cls, data=None, dtype=None, copy=False, name=None, tupleize_cols=True, **kwargs
     ) -> "Index":
 
         from .range import RangeIndex

pandas/core/indexes/category.py

@@ -164,13 +164,7 @@ def _engine_type(self):
     # Constructors
 
     def __new__(
-        cls,
-        data=None,
-        categories=None,
-        ordered=None,
-        dtype=None,
-        copy=False,
-        name=None,
+        cls, data=None, categories=None, ordered=None, dtype=None, copy=False, name=None
     ):
 
         dtype = CategoricalDtype._from_values_or_dtype(data, categories, ordered, dtype)

pandas/core/indexes/range.py

@@ -81,7 +81,7 @@ class RangeIndex(Int64Index):
     # Constructors
 
     def __new__(
-        cls, start=None, stop=None, step=None, dtype=None, copy=False, name=None,
+        cls, start=None, stop=None, step=None, dtype=None, copy=False, name=None
     ):
 
         cls._validate_dtype(dtype)

pandas/core/ops/__init__.py

@@ -82,14 +82,7 @@
 }
 
 
-COMPARISON_BINOPS: Set[str] = {
-    "eq",
-    "ne",
-    "lt",
-    "gt",
-    "le",
-    "ge",
-}
+COMPARISON_BINOPS: Set[str] = {"eq", "ne", "lt", "gt", "le", "ge"}
 
 # -----------------------------------------------------------------------------
 # Ops Wrapping Utilities

pandas/core/ops/dispatch.py

@@ -96,7 +96,7 @@ def should_series_dispatch(left, right, op):
 
 
 def dispatch_to_extension_op(
-    op, left: Union[ABCExtensionArray, np.ndarray], right: Any,
+    op, left: Union[ABCExtensionArray, np.ndarray], right: Any
 ):
     """
     Assume that left or right is a Series backed by an ExtensionArray,

pandas/core/series.py

@@ -1433,7 +1433,7 @@ def to_string(
     @Substitution(klass="Series")
     @Appender(_shared_docs["to_markdown"])
     def to_markdown(
-        self, buf: Optional[IO[str]] = None, mode: Optional[str] = None, **kwargs,
+        self, buf: Optional[IO[str]] = None, mode: Optional[str] = None, **kwargs
     ) -> Optional[str]:
         return self.to_frame().to_markdown(buf, mode, **kwargs)
 
@@ -4395,9 +4395,7 @@ def to_period(self, freq=None, copy=True):
     hist = pandas.plotting.hist_series
 
 
-Series._setup_axes(
-    ["index"], docs={"index": "The index (axis labels) of the Series."},
-)
+Series._setup_axes(["index"], docs={"index": "The index (axis labels) of the Series."})
 Series._add_numeric_operations()
 Series._add_series_or_dataframe_operations()
 

pandas/core/window/indexers.py

@@ -35,7 +35,7 @@ class BaseIndexer:
     """Base class for window bounds calculations"""
 
     def __init__(
-        self, index_array: Optional[np.ndarray] = None, window_size: int = 0, **kwargs,
+        self, index_array: Optional[np.ndarray] = None, window_size: int = 0, **kwargs
     ):
         """
         Parameters
@@ -100,7 +100,7 @@ def get_window_bounds(
     ) -> Tuple[np.ndarray, np.ndarray]:
 
         return calculate_variable_window_bounds(
-            num_values, self.window_size, min_periods, center, closed, self.index_array,
+            num_values, self.window_size, min_periods, center, closed, self.index_array
         )
 
 

pandas/core/window/numba_.py

@@ -63,7 +63,7 @@ def impl(window, *_args):
 
     @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
     def roll_apply(
-        values: np.ndarray, begin: np.ndarray, end: np.ndarray, minimum_periods: int,
+        values: np.ndarray, begin: np.ndarray, end: np.ndarray, minimum_periods: int
     ) -> np.ndarray:
         result = np.empty(len(begin))
         for i in loop_range(len(result)):

pandas/io/formats/css.py

@@ -20,9 +20,7 @@ def expand(self, prop, value: str):
         try:
             mapping = self.SIDE_SHORTHANDS[len(tokens)]
         except KeyError:
-            warnings.warn(
-                f'Could not expand "{prop}: {value}"', CSSWarning,
-            )
+            warnings.warn(f'Could not expand "{prop}: {value}"', CSSWarning)
             return
         for key, idx in zip(self.SIDES, mapping):
             yield prop_fmt.format(key), tokens[idx]
@@ -117,10 +115,7 @@ def __call__(self, declarations_str, inherited=None):
                 props[prop] = self.size_to_pt(
                     props[prop], em_pt=font_size, conversions=self.BORDER_WIDTH_RATIOS
                 )
-            for prop in [
-                f"margin-{side}",
-                f"padding-{side}",
-            ]:
+            for prop in [f"margin-{side}", f"padding-{side}"]:
                 if prop in props:
                     # TODO: support %
                     props[prop] = self.size_to_pt(

pandas/io/formats/excel.py

@@ -134,8 +134,7 @@ def build_border(self, props):
         return {
             side: {
                 "style": self._border_style(
-                    props.get(f"border-{side}-style"),
-                    props.get(f"border-{side}-width"),
+                    props.get(f"border-{side}-style"), props.get(f"border-{side}-width")
                 ),
                 "color": self.color_to_excel(props.get(f"border-{side}-color")),
             }

pandas/io/formats/style.py

@@ -291,10 +291,7 @@ def format_attr(pair):
 
             # ... except maybe the last for columns.names
             name = self.data.columns.names[r]
-            cs = [
-                BLANK_CLASS if name is None else INDEX_NAME_CLASS,
-                f"level{r}",
-            ]
+            cs = [BLANK_CLASS if name is None else INDEX_NAME_CLASS, f"level{r}"]
             name = BLANK_VALUE if name is None else name
             row_es.append(
                 {
@@ -308,11 +305,7 @@ def format_attr(pair):
 
             if clabels:
                 for c, value in enumerate(clabels[r]):
-                    cs = [
-                        COL_HEADING_CLASS,
-                        f"level{r}",
-                        f"col{c}",
-                    ]
+                    cs = [COL_HEADING_CLASS, f"level{r}", f"col{c}"]
                     cs.extend(
                         cell_context.get("col_headings", {}).get(r, {}).get(c, [])
                     )
@@ -356,11 +349,7 @@ def format_attr(pair):
         for r, idx in enumerate(self.data.index):
             row_es = []
             for c, value in enumerate(rlabels[r]):
-                rid = [
-                    ROW_HEADING_CLASS,
-                    f"level{c}",
-                    f"row{r}",
-                ]
+                rid = [ROW_HEADING_CLASS, f"level{c}", f"row{r}"]
                 es = {
                     "type": "th",
                     "is_visible": (_is_visible(r, c, idx_lengths) and not hidden_index),

pandas/io/orc.py

@@ -12,7 +12,7 @@
 
 
 def read_orc(
-    path: FilePathOrBuffer, columns: Optional[List[str]] = None, **kwargs,
+    path: FilePathOrBuffer, columns: Optional[List[str]] = None, **kwargs
 ) -> "DataFrame":
     """
     Load an ORC object from the file path, returning a DataFrame.

pandas/io/pytables.py

@@ -2830,7 +2830,7 @@ def read_index_node(
         # If the index was an empty array write_array_empty() will
         # have written a sentinel. Here we relace it with the original.
         if "shape" in node._v_attrs and np.prod(node._v_attrs.shape) == 0:
-            data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type,)
+            data = np.empty(node._v_attrs.shape, dtype=node._v_attrs.value_type)
         kind = _ensure_decoded(node._v_attrs.kind)
         name = None
 
@@ -3574,10 +3574,7 @@ def _read_axes(
         for a in self.axes:
             a.set_info(self.info)
             res = a.convert(
-                values,
-                nan_rep=self.nan_rep,
-                encoding=self.encoding,
-                errors=self.errors,
+                values, nan_rep=self.nan_rep, encoding=self.encoding, errors=self.errors
             )
             results.append(res)
 
@@ -4003,7 +4000,7 @@ def create_description(
         return d
 
     def read_coordinates(
-        self, where=None, start: Optional[int] = None, stop: Optional[int] = None,
+        self, where=None, start: Optional[int] = None, stop: Optional[int] = None
     ):
         """select coordinates (row numbers) from a table; return the
         coordinates object
@@ -4264,7 +4261,7 @@ def write_data_chunk(
             self.table.flush()
 
     def delete(
-        self, where=None, start: Optional[int] = None, stop: Optional[int] = None,
+        self, where=None, start: Optional[int] = None, stop: Optional[int] = None
     ):
 
         # delete all rows (and return the nrows)
@@ -4695,7 +4692,7 @@ def _convert_index(name: str, index: Index, encoding: str, errors: str) -> Index
     if inferred_type == "date":
         converted = np.asarray([v.toordinal() for v in values], dtype=np.int32)
         return IndexCol(
-            name, converted, "date", _tables().Time32Col(), index_name=index_name,
+            name, converted, "date", _tables().Time32Col(), index_name=index_name
         )
     elif inferred_type == "string":
 
@@ -4711,13 +4708,13 @@ def _convert_index(name: str, index: Index, encoding: str, errors: str) -> Index
 
     elif inferred_type in ["integer", "floating"]:
         return IndexCol(
-            name, values=converted, kind=kind, typ=atom, index_name=index_name,
+            name, values=converted, kind=kind, typ=atom, index_name=index_name
         )
     else:
         assert isinstance(converted, np.ndarray) and converted.dtype == object
         assert kind == "object", kind
         atom = _tables().ObjectAtom()
-        return IndexCol(name, converted, kind, atom, index_name=index_name,)
+        return IndexCol(name, converted, kind, atom, index_name=index_name)
 
 
 def _unconvert_index(

pandas/tests/arithmetic/test_numeric.py

@@ -95,7 +95,7 @@ class TestNumericArraylikeArithmeticWithDatetimeLike:
     # TODO: also check name retentention
     @pytest.mark.parametrize("box_cls", [np.array, pd.Index, pd.Series])
     @pytest.mark.parametrize(
-        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype),
+        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
     )
     def test_mul_td64arr(self, left, box_cls):
         # GH#22390
@@ -115,7 +115,7 @@ def test_mul_td64arr(self, left, box_cls):
     # TODO: also check name retentention
     @pytest.mark.parametrize("box_cls", [np.array, pd.Index, pd.Series])
     @pytest.mark.parametrize(
-        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype),
+        "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
     )
     def test_div_td64arr(self, left, box_cls):
         # GH#22390

pandas/tests/arrays/test_boolean.py

@@ -565,9 +565,7 @@ def test_kleene_xor_scalar(self, other, expected):
             a, pd.array([True, False, None], dtype="boolean")
         )
 
-    @pytest.mark.parametrize(
-        "other", [True, False, pd.NA, [True, False, None] * 3],
-    )
+    @pytest.mark.parametrize("other", [True, False, pd.NA, [True, False, None] * 3])
     def test_no_masked_assumptions(self, other, all_logical_operators):
         # The logical operations should not assume that masked values are False!
         a = pd.arrays.BooleanArray(

pandas/tests/arrays/test_timedeltas.py

@@ -46,7 +46,7 @@ def test_incorrect_dtype_raises(self):
             TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
 
         with pytest.raises(
-            ValueError, match=r"dtype int64 cannot be converted to timedelta64\[ns\]",
+            ValueError, match=r"dtype int64 cannot be converted to timedelta64\[ns\]"
         ):
             TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("int64"))
 

pandas/tests/base/test_conversion.py

@@ -313,10 +313,7 @@ def test_array_multiindex_raises():
             pd.core.arrays.period_array(["2000", "2001"], freq="D"),
             np.array([pd.Period("2000", freq="D"), pd.Period("2001", freq="D")]),
         ),
-        (
-            pd.core.arrays.integer_array([0, np.nan]),
-            np.array([0, pd.NA], dtype=object),
-        ),
+        (pd.core.arrays.integer_array([0, np.nan]), np.array([0, pd.NA], dtype=object)),
         (
             pd.core.arrays.IntervalArray.from_breaks([0, 1, 2]),
             np.array([pd.Interval(0, 1), pd.Interval(1, 2)], dtype=object),

pandas/tests/indexing/common.py

@@ -170,7 +170,7 @@ def check_values(self, f, func, values=False):
             tm.assert_almost_equal(result, expected)
 
     def check_result(
-        self, method1, key1, method2, key2, typs=None, axes=None, fails=None,
+        self, method1, key1, method2, key2, typs=None, axes=None, fails=None
     ):
         def _eq(axis, obj, key1, key2):
             """ compare equal for these 2 keys """

pandas/tests/indexing/test_loc.py

@@ -116,7 +116,7 @@ def test_loc_getitem_label_out_of_range(self):
         self.check_result("loc", "f", "ix", "f", typs=["floats"], fails=KeyError)
         self.check_result("loc", "f", "loc", "f", typs=["floats"], fails=KeyError)
         self.check_result(
-            "loc", 20, "loc", 20, typs=["ints", "uints", "mixed"], fails=KeyError,
+            "loc", 20, "loc", 20, typs=["ints", "uints", "mixed"], fails=KeyError
         )
         self.check_result("loc", 20, "loc", 20, typs=["labels"], fails=TypeError)
         self.check_result("loc", 20, "loc", 20, typs=["ts"], axes=0, fails=TypeError)
@@ -129,7 +129,7 @@ def test_loc_getitem_label_list(self):
 
     def test_loc_getitem_label_list_with_missing(self):
         self.check_result(
-            "loc", [0, 1, 2], "loc", [0, 1, 2], typs=["empty"], fails=KeyError,
+            "loc", [0, 1, 2], "loc", [0, 1, 2], typs=["empty"], fails=KeyError
         )
         self.check_result(
             "loc",

pandas/tests/io/formats/test_format.py

@@ -2385,8 +2385,7 @@ def test_east_asian_unicode_series(self):
 
             # object dtype, longer than unicode repr
             s = Series(
-                [1, 22, 3333, 44444],
-                index=[1, "AB", pd.Timestamp("2011-01-01"), "あああ"],
+                [1, 22, 3333, 44444], index=[1, "AB", pd.Timestamp("2011-01-01"), "あああ"]
             )
             expected = (
                 "1                          1\n"

pandas/tests/io/parser/test_usecols.py

@@ -199,7 +199,7 @@ def test_usecols_with_whitespace(all_parsers):
         # Column selection by index.
         ([0, 1], DataFrame(data=[[1000, 2000], [4000, 5000]], columns=["2", "0"])),
         # Column selection by name.
-        (["0", "1"], DataFrame(data=[[2000, 3000], [5000, 6000]], columns=["0", "1"]),),
+        (["0", "1"], DataFrame(data=[[2000, 3000], [5000, 6000]], columns=["0", "1"])),
     ],
 )
 def test_usecols_with_integer_like_header(all_parsers, usecols, expected):

pandas/tests/series/methods/test_argsort.py

@@ -9,7 +9,7 @@ class TestSeriesArgsort:
     def _check_accum_op(self, name, ser, check_dtype=True):
         func = getattr(np, name)
         tm.assert_numpy_array_equal(
-            func(ser).values, func(np.array(ser)), check_dtype=check_dtype,
+            func(ser).values, func(np.array(ser)), check_dtype=check_dtype
         )
 
         # with missing values

pandas/tests/series/test_cumulative.py

@@ -17,7 +17,7 @@
 def _check_accum_op(name, series, check_dtype=True):
     func = getattr(np, name)
     tm.assert_numpy_array_equal(
-        func(series).values, func(np.array(series)), check_dtype=check_dtype,
+        func(series).values, func(np.array(series)), check_dtype=check_dtype
     )
 
     # with missing values

pandas/util/_test_decorators.py

@@ -181,10 +181,10 @@ def skip_if_no(package: str, min_version: Optional[str] = None) -> Callable:
     is_platform_windows(), reason="not used on win32"
 )
 skip_if_has_locale = pytest.mark.skipif(
-    _skip_if_has_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}",
+    _skip_if_has_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}"
 )
 skip_if_not_us_locale = pytest.mark.skipif(
-    _skip_if_not_us_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}",
+    _skip_if_not_us_locale(), reason=f"Specific locale is set {locale.getlocale()[0]}"
 )
 skip_if_no_scipy = pytest.mark.skipif(
     _skip_if_no_scipy(), reason="Missing SciPy requirement"