Flake8

pandas/core/frame.py

@@ -2083,7 +2083,7 @@ def to_stata(
             data_label=data_label,
             write_index=write_index,
             variable_labels=variable_labels,
-            **kwargs
+            **kwargs,
         )
         writer.write_file()
 
@@ -2107,7 +2107,7 @@ def to_parquet(
         compression="snappy",
         index=None,
         partition_cols=None,
-        **kwargs
+        **kwargs,
     ):
         """
         Write a DataFrame to the binary parquet format.
@@ -2187,7 +2187,7 @@ def to_parquet(
             compression=compression,
             index=index,
             partition_cols=partition_cols,
-            **kwargs
+            **kwargs,
         )
 
     @Substitution(
@@ -2846,7 +2846,9 @@ def _getitem_bool_array(self, key):
                 stacklevel=3,
             )
         elif len(key) != len(self.index):
-            raise ValueError(f"Item wrong length {len(key)} instead of {len(self.index)}.")
+            raise ValueError(
+                f"Item wrong length {len(key)} instead of {len(self.index)}."
+            )
 
         # check_bool_indexer will throw exception if Series key cannot
         # be reindexed to match DataFrame rows
@@ -2955,7 +2957,9 @@ def _setitem_array(self, key, value):
         # also raises Exception if object array with NA values
         if com.is_bool_indexer(key):
             if len(key) != len(self.index):
-                raise ValueError(f"Item wrong length {len(key)} instead of {len(self.index)}!")
+                raise ValueError(
+                    f"Item wrong length {len(key)} instead of {len(self.index)}!"
+                )
             key = check_bool_indexer(self.index, key)
             indexer = key.nonzero()[0]
             self._check_setitem_copy()
@@ -4125,7 +4129,7 @@ def fillna(
         inplace=False,
         limit=None,
         downcast=None,
-        **kwargs
+        **kwargs,
     ):
         return super().fillna(
             value=value,
@@ -4134,7 +4138,7 @@ def fillna(
             inplace=inplace,
             limit=limit,
             downcast=downcast,
-            **kwargs
+            **kwargs,
         )
 
     @Appender(_shared_docs["replace"] % _shared_doc_kwargs)
@@ -4551,7 +4555,10 @@ def _maybe_casted_values(index, labels=None):
 
         if not drop:
             if isinstance(self.index, ABCMultiIndex):
-                names = [(n if n is not None else f"level_{i}") for i, n in enumerate(self.index.names)]
+                names = [
+                    (n if n is not None else f"level_{i}")
+                    for i, n in enumerate(self.index.names)
+                ]
                 to_insert = zip(self.index.levels, self.index.codes)
             else:
                 default = "index" if "index" not in self else "level_0"
@@ -4869,7 +4876,9 @@ def sort_values(
         if not isinstance(by, list):
             by = [by]
         if is_sequence(ascending) and len(by) != len(ascending):
-            raise ValueError(f"Length of ascending ({len(ascending)}) != length of by ({len(by)})")
+            raise ValueError(
+                f"Length of ascending ({len(ascending)}) != length of by ({len(by)})"
+            )
         if len(by) > 1:
             from pandas.core.sorting import lexsort_indexer
 
@@ -6575,7 +6584,7 @@ def _gotitem(
         see_also=_agg_summary_and_see_also_doc,
         examples=_agg_examples_doc,
         versionadded="\n.. versionadded:: 0.20.0\n",
-        **_shared_doc_kwargs
+        **_shared_doc_kwargs,
     )
     @Appender(_shared_docs["aggregate"])
     def aggregate(self, func, axis=0, *args, **kwargs):

pandas/core/generic.py

@@ -426,7 +426,9 @@ def _construct_axes_from_arguments(
             if alias is not None:
                 if a in kwargs:
                     if alias in kwargs:
-                        raise TypeError(f"arguments are mutually exclusive for [{a},{alias}]")
+                        raise TypeError(
+                            f"arguments are mutually exclusive for [{a},{alias}]"
+                        )
                     continue
                 if alias in kwargs:
                     kwargs[a] = kwargs.pop(alias)
@@ -2057,7 +2059,7 @@ def __getstate__(self):
             _typ=self._typ,
             _metadata=self._metadata,
             attrs=self.attrs,
-            **meta
+            **meta,
         )
 
     def __setstate__(self, state):
@@ -6795,7 +6797,10 @@ def replace(
             elif is_list_like(to_replace):  # [NA, ''] -> [0, 'missing']
                 if is_list_like(value):
                     if len(to_replace) != len(value):
-                        raise ValueError(f"Replacement lists must match in length. Expecting {len(to_replace)} got {len(value)} ")
+                        raise ValueError(
+                            f"Replacement lists must match in length."
+                            f" Expecting {len(to_replace)} got {len(value)} "
+                        )
 
                     new_data = self._data.replace_list(
                         src_list=to_replace,
@@ -7048,7 +7053,7 @@ def interpolate(
         limit_direction="forward",
         limit_area=None,
         downcast=None,
-        **kwargs
+        **kwargs,
     ):
         """
         Interpolate values according to different methods.
@@ -7122,7 +7127,7 @@ def interpolate(
             limit_area=limit_area,
             inplace=inplace,
             downcast=downcast,
-            **kwargs
+            **kwargs,
         )
 
         if inplace:
@@ -7822,7 +7827,7 @@ def groupby(
         group_keys=True,
         squeeze=False,
         observed=False,
-        **kwargs
+        **kwargs,
     ):
         """
         Group DataFrame or Series using a mapper or by a Series of columns.
@@ -7948,7 +7953,7 @@ def groupby(
             group_keys=group_keys,
             squeeze=squeeze,
             observed=observed,
-            **kwargs
+            **kwargs,
         )
 
     def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
@@ -9518,7 +9523,8 @@ def tshift(self, periods=1, freq=None, axis=0):
                 new_data = self._data.copy()
                 new_data.axes[block_axis] = index.shift(periods)
             else:
-                msg = (f"Given freq {freq.rule_code} does not match PeriodIndex freq {orig_freq.rule_code}")
+                msg = f"Given freq {freq.rule_code} does not match" \
+                      f" PeriodIndex freq {orig_freq.rule_code}"
                 raise ValueError(msg)
         else:
             new_data = self._data.copy()
@@ -9710,7 +9716,9 @@ def _tz_convert(ax, tz):
             if not hasattr(ax, "tz_convert"):
                 if len(ax) > 0:
                     ax_name = self._get_axis_name(axis)
-                    raise TypeError(f"{ax_name} is not a valid DatetimeIndex or PeriodIndex")
+                    raise TypeError(
+                        f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
+                    )
                 else:
                     ax = DatetimeIndex([], tz=tz)
             else:
@@ -9872,7 +9880,9 @@ def _tz_localize(ax, tz, ambiguous, nonexistent):
             if not hasattr(ax, "tz_localize"):
                 if len(ax) > 0:
                     ax_name = self._get_axis_name(axis)
-                    raise TypeError(f"{ax_name} is not a valid DatetimeIndex or PeriodIndex")
+                    raise TypeError(
+                        f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
+                    )
                 else:
                     ax = DatetimeIndex([], tz=tz)
             else:
@@ -11568,7 +11578,7 @@ def stat_func(
         level=None,
         numeric_only=None,
         min_count=0,
-        **kwargs
+        **kwargs,
     ):
         if name == "sum":
             nv.validate_sum(tuple(), kwargs)

pandas/core/indexes/base.py

@@ -265,7 +265,7 @@ def __new__(
         name=None,
         fastpath=None,
         tupleize_cols=True,
-        **kwargs
+        **kwargs,
     ) -> "Index":
 
         from .range import RangeIndex
@@ -961,7 +961,7 @@ def __repr__(self):
         data = self._format_data()
         attrs = self._format_attrs()
         space = self._format_space()
-        prepr = f",{space}".join(f'{k}={v}' for k, v in attrs)
+        prepr = f",{space}".join(f"{k}={v}" for k, v in attrs)
 
         # no data provided, just attributes
         if data is None:
@@ -1471,7 +1471,10 @@ def _validate_index_level(self, level):
         """
         if isinstance(level, int):
             if level < 0 and level != -1:
-                raise IndexError(f"Too many levels: Index has only 1 level, {level} is not a valid level number")
+                raise IndexError(
+                    f"Too many levels: Index has only 1 level,"
+                    f" {level} is not a valid level number"
+                )
             elif level > 0:
                 raise IndexError(
                     "Too many levels: Index has only 1 level, not %d" % (level + 1)
@@ -5064,7 +5067,10 @@ def get_slice_bound(self, label, side, kind):
         assert kind in ["ix", "loc", "getitem", None]
 
         if side not in ("left", "right"):
-            raise ValueError(f"Invalid value for side kwarg, must be either 'left' or 'right': {side}")
+            raise ValueError(
+                f"Invalid value for side kwarg, must be either"
+                f" 'left' or 'right': {side}"
+            )
 
         original_label = label
 

pandas/core/indexes/category.py

@@ -357,7 +357,7 @@ def _format_attrs(self):
         ]
         if self.name is not None:
             attrs.append(("name", ibase.default_pprint(self.name)))
-        attrs.append(('dtype', f"'{self.dtype.name}'"))
+        attrs.append(("dtype", f"'{self.dtype.name}'"))
         max_seq_items = get_option("display.max_seq_items") or len(self)
         if len(self) > max_seq_items:
             attrs.append(("length", len(self)))

pandas/core/indexes/datetimes.py

@@ -1422,7 +1422,7 @@ def date_range(
     normalize=False,
     name=None,
     closed=None,
-    **kwargs
+    **kwargs,
 ):
     """
     Return a fixed frequency DatetimeIndex.
@@ -1572,7 +1572,7 @@ def date_range(
         tz=tz,
         normalize=normalize,
         closed=closed,
-        **kwargs
+        **kwargs,
     )
     return DatetimeIndex._simple_new(dtarr, tz=dtarr.tz, freq=dtarr.freq, name=name)
 
@@ -1588,7 +1588,7 @@ def bdate_range(
     weekmask=None,
     holidays=None,
     closed=None,
-    **kwargs
+    **kwargs,
 ):
     """
     Return a fixed frequency DatetimeIndex, with business day as the default
@@ -1681,7 +1681,7 @@ def bdate_range(
         normalize=normalize,
         name=name,
         closed=closed,
-        **kwargs
+        **kwargs,
     )
 
 

pandas/core/indexes/multi.py

@@ -956,7 +956,7 @@ def copy(
         codes=None,
         deep=False,
         _set_identity=False,
-        **kwargs
+        **kwargs,
     ):
         """
         Make a copy of this object. Names, dtype, levels and codes can be
@@ -1020,7 +1020,7 @@ def _shallow_copy_with_infer(self, values, **kwargs):
             return MultiIndex(
                 levels=[[] for _ in range(self.nlevels)],
                 codes=[[] for _ in range(self.nlevels)],
-                **kwargs
+                **kwargs,
             )
         return self._shallow_copy(values, **kwargs)
 
@@ -1328,7 +1328,9 @@ def inferred_type(self):
     def _get_level_number(self, level):
         count = self.names.count(level)
         if (count > 1) and not is_integer(level):
-            raise ValueError(f"The name {level} occurs multiple times, use a level number")
+            raise ValueError(
+                f"The name {level} occurs multiple times, use a level number"
+            )
         try:
             level = self.names.index(level)
         except ValueError:
@@ -1338,7 +1340,10 @@ def _get_level_number(self, level):
                 level += self.nlevels
                 if level < 0:
                     orig_level = level - self.nlevels
-                    raise IndexError(f"Too many levels: Index has only {self.nlevels} levels, {orig_level} is not a valid level number")
+                    raise IndexError(
+                        f"Too many levels: Index has only {self.nlevels} levels,"
+                        f" {orig_level} is not a valid level number"
+                    )
             # Note: levels are zero-based
             elif level >= self.nlevels:
                 raise IndexError(
@@ -2284,7 +2289,10 @@ def reorder_levels(self, order):
         """
         order = [self._get_level_number(i) for i in order]
         if len(order) != self.nlevels:
-            raise AssertionError(f"Length of order must be same as number of levels ({self.nlevels}), got {len(order)}")
+            raise AssertionError(
+                f"Length of order must be same as number of levels ({self.nlevels}),"
+                f" got {len(order)}"
+            )
         new_levels = [self.levels[i] for i in order]
         new_codes = [self.codes[i] for i in order]
         new_names = [self.names[i] for i in order]
@@ -2594,7 +2602,10 @@ def slice_locs(self, start=None, end=None, step=None, kind=None):
 
     def _partial_tup_index(self, tup, side="left"):
         if len(tup) > self.lexsort_depth:
-            raise UnsortedIndexError(f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth ({self.lexsort_depth})")
+            raise UnsortedIndexError(
+                f"Key length ({len(tup)}) was greater than MultiIndex lexsort depth"
+                f" ({self.lexsort_depth})"
+            )
 
         n = len(tup)
         start, end = 0, len(self)