D202 No blank lines allowed after function docstring (#31895)

Author: simonjayhawkins

pandas/_config/config.py

@@ -550,7 +550,6 @@ def _select_options(pat: str) -> List[str]:
 
     if pat=="all", returns all registered options
     """
-
     # short-circuit for exact key
     if pat in _registered_options:
         return [pat]
@@ -573,7 +572,6 @@ def _get_root(key: str) -> Tuple[Dict[str, Any], str]:
 
 def _is_deprecated(key: str) -> bool:
     """ Returns True if the given option has been deprecated """
-
     key = key.lower()
     return key in _deprecated_options
 
@@ -586,7 +584,6 @@ def _get_deprecated_option(key: str):
     -------
     DeprecatedOption (namedtuple) if key is deprecated, None otherwise
     """
-
     try:
         d = _deprecated_options[key]
     except KeyError:
@@ -611,7 +608,6 @@ def _translate_key(key: str) -> str:
     if key id deprecated and a replacement key defined, will return the
     replacement key, otherwise returns `key` as - is
     """
-
     d = _get_deprecated_option(key)
     if d:
         return d.rkey or key
@@ -627,7 +623,6 @@ def _warn_if_deprecated(key: str) -> bool:
     -------
     bool - True if `key` is deprecated, False otherwise.
     """
-
     d = _get_deprecated_option(key)
     if d:
         if d.msg:
@@ -649,7 +644,6 @@ def _warn_if_deprecated(key: str) -> bool:
 
 def _build_option_description(k: str) -> str:
     """ Builds a formatted description of a registered option and prints it """
-
     o = _get_registered_option(k)
     d = _get_deprecated_option(k)
 
@@ -674,7 +668,6 @@ def _build_option_description(k: str) -> str:
 
 def pp_options_list(keys: Iterable[str], width=80, _print: bool = False):
     """ Builds a concise listing of available options, grouped by prefix """
-
     from textwrap import wrap
     from itertools import groupby
 
@@ -738,7 +731,6 @@ def config_prefix(prefix):
     will register options "display.font.color", "display.font.size", set the
     value of "display.font.size"... and so on.
     """
-
     # Note: reset_option relies on set_option, and on key directly
     # it does not fit in to this monkey-patching scheme
 
@@ -801,7 +793,6 @@ def is_instance_factory(_type) -> Callable[[Any], None]:
                 ValueError if x is not an instance of `_type`
 
     """
-
     if isinstance(_type, (tuple, list)):
         _type = tuple(_type)
         type_repr = "|".join(map(str, _type))
@@ -848,7 +839,6 @@ def is_nonnegative_int(value: Optional[int]) -> None:
     ValueError
         When the value is not None or is a negative integer
     """
-
     if value is None:
         return
 

pandas/_config/localization.py

@@ -61,7 +61,6 @@ def can_set_locale(lc: str, lc_var: int = locale.LC_ALL) -> bool:
     bool
         Whether the passed locale can be set
     """
-
     try:
         with set_locale(lc, lc_var=lc_var):
             pass

pandas/_testing.py

@@ -1508,7 +1508,6 @@ def assert_sp_array_equal(
         create a new BlockIndex for that array, with consolidated
         block indices.
     """
-
     _check_isinstance(left, right, pd.arrays.SparseArray)
 
     assert_numpy_array_equal(left.sp_values, right.sp_values, check_dtype=check_dtype)
@@ -1876,7 +1875,6 @@ def makeCustomIndex(
 
         if unspecified, string labels will be generated.
     """
-
     if ndupe_l is None:
         ndupe_l = [1] * nlevels
     assert is_sequence(ndupe_l) and len(ndupe_l) <= nlevels
@@ -2025,7 +2023,6 @@ def makeCustomDataframe(
 
     >> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
     """
-
     assert c_idx_nlevels > 0
     assert r_idx_nlevels > 0
     assert r_idx_type is None or (
@@ -2229,7 +2226,6 @@ def can_connect(url, error_classes=None):
         Return True if no IOError (unable to connect) or URLError (bad url) was
         raised
     """
-
     if error_classes is None:
         error_classes = _get_default_network_errors()
 
@@ -2603,7 +2599,6 @@ def test_parallel(num_threads=2, kwargs_list=None):
     https://github.com/scikit-image/scikit-image/pull/1519
 
     """
-
     assert num_threads > 0
     has_kwargs_list = kwargs_list is not None
     if has_kwargs_list:
@@ -2685,7 +2680,6 @@ def set_timezone(tz: str):
     ...
     'EDT'
     """
-
     import os
     import time
 

pandas/compat/numpy/function.py

@@ -99,7 +99,6 @@ def validate_argmin_with_skipna(skipna, args, kwargs):
     'skipna' parameter is either an instance of ndarray or
     is None, since 'skipna' itself should be a boolean
     """
-
     skipna, args = process_skipna(skipna, args)
     validate_argmin(args, kwargs)
     return skipna
@@ -113,7 +112,6 @@ def validate_argmax_with_skipna(skipna, args, kwargs):
     'skipna' parameter is either an instance of ndarray or
     is None, since 'skipna' itself should be a boolean
     """
-
     skipna, args = process_skipna(skipna, args)
     validate_argmax(args, kwargs)
     return skipna
@@ -151,7 +149,6 @@ def validate_argsort_with_ascending(ascending, args, kwargs):
     either integer type or is None, since 'ascending' itself should
     be a boolean
     """
-
     if is_integer(ascending) or ascending is None:
         args = (ascending,) + args
         ascending = True
@@ -173,7 +170,6 @@ def validate_clip_with_axis(axis, args, kwargs):
     so check if the 'axis' parameter is an instance of ndarray, since
     'axis' itself should either be an integer or None
     """
-
     if isinstance(axis, ndarray):
         args = (axis,) + args
         axis = None
@@ -298,7 +294,6 @@ def validate_take_with_convert(convert, args, kwargs):
     ndarray or 'None', so check if the 'convert' parameter is either
     an instance of ndarray or is None
     """
-
     if isinstance(convert, ndarray) or convert is None:
         args = (convert,) + args
         convert = True

pandas/compat/pickle_compat.py

@@ -229,7 +229,6 @@ def load(fh, encoding: Optional[str] = None, is_verbose: bool = False):
     encoding : an optional encoding
     is_verbose : show exception output
     """
-
     try:
         fh.seek(0)
         if encoding is not None:

pandas/conftest.py

@@ -118,7 +118,6 @@ def ip():
 
     Will raise a skip if IPython is not installed.
     """
-
     pytest.importorskip("IPython", minversion="6.0.0")
     from IPython.core.interactiveshell import InteractiveShell
 
@@ -679,7 +678,6 @@ def any_nullable_int_dtype(request):
     * 'UInt64'
     * 'Int64'
     """
-
     return request.param
 
 

pandas/core/algorithms.py

@@ -85,7 +85,6 @@ def _ensure_data(values, dtype=None):
     values : ndarray
     pandas_dtype : str or dtype
     """
-
     # we check some simple dtypes first
     if is_object_dtype(dtype):
         return ensure_object(np.asarray(values)), "object"
@@ -182,7 +181,6 @@ def _reconstruct_data(values, dtype, original):
     -------
     Index for extension types, otherwise ndarray casted to dtype
     """
-
     if is_extension_array_dtype(dtype):
         values = dtype.construct_array_type()._from_sequence(values)
     elif is_bool_dtype(dtype):
@@ -368,7 +366,6 @@ def unique(values):
     >>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
     array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
     """
-
     values = _ensure_arraylike(values)
 
     if is_extension_array_dtype(values):
@@ -791,7 +788,6 @@ def duplicated(values, keep="first") -> np.ndarray:
     -------
     duplicated : ndarray
     """
-
     values, _ = _ensure_data(values)
     ndtype = values.dtype.name
     f = getattr(htable, f"duplicated_{ndtype}")

pandas/core/apply.py

@@ -35,7 +35,6 @@ def frame_apply(
     kwds=None,
 ):
     """ construct and return a row or column based frame apply object """
-
     axis = obj._get_axis_number(axis)
     klass: Type[FrameApply]
     if axis == 0:
@@ -144,7 +143,6 @@ def agg_axis(self) -> "Index":
 
     def get_result(self):
         """ compute the results """
-
         # dispatch to agg
         if is_list_like(self.f) or is_dict_like(self.f):
             return self.obj.aggregate(self.f, axis=self.axis, *self.args, **self.kwds)
@@ -193,7 +191,6 @@ def apply_empty_result(self):
         we will try to apply the function to an empty
         series in order to see if this is a reduction function
         """
-
         # we are not asked to reduce or infer reduction
         # so just return a copy of the existing object
         if self.result_type not in ["reduce", None]:
@@ -396,7 +393,6 @@ def wrap_results_for_axis(
         self, results: ResType, res_index: "Index"
     ) -> "DataFrame":
         """ return the results for the rows """
-
         result = self.obj._constructor(data=results)
 
         if not isinstance(results[0], ABCSeries):
@@ -457,7 +453,6 @@ def wrap_results_for_axis(
 
     def infer_to_same_shape(self, results: ResType, res_index: "Index") -> "DataFrame":
         """ infer the results to the same shape as the input object """
-
         result = self.obj._constructor(data=results)
         result = result.T
 

pandas/core/arrays/categorical.py

@@ -701,7 +701,6 @@ def _set_categories(self, categories, fastpath=False):
         [a, c]
         Categories (2, object): [a, c]
         """
-
         if fastpath:
             new_dtype = CategoricalDtype._from_fastpath(categories, self.ordered)
         else:
@@ -1227,7 +1226,6 @@ def shape(self):
         -------
         shape : tuple
         """
-
         return tuple([len(self._codes)])
 
     def shift(self, periods, fill_value=None):
@@ -1384,7 +1382,6 @@ def isna(self):
         Categorical.notna : Boolean inverse of Categorical.isna.
 
         """
-
         ret = self._codes == -1
         return ret
 
@@ -1934,7 +1931,6 @@ def _repr_categories_info(self) -> str:
         """
         Returns a string representation of the footer.
         """
-
         category_strs = self._repr_categories()
         dtype = str(self.categories.dtype)
         levheader = f"Categories ({len(self.categories)}, {dtype}): "
@@ -2260,7 +2256,6 @@ def unique(self):
         Series.unique
 
         """
-
         # unlike np.unique, unique1d does not sort
         unique_codes = unique1d(self.codes)
         cat = self.copy()
@@ -2320,7 +2315,6 @@ def is_dtype_equal(self, other):
         -------
         bool
         """
-
         try:
             return hash(self.dtype) == hash(other.dtype)
         except (AttributeError, TypeError):

pandas/core/arrays/datetimelike.py

@@ -500,7 +500,6 @@ def __getitem__(self, key):
         This getitem defers to the underlying array, which by-definition can
         only handle list-likes, slices, and integer scalars
         """
-
         is_int = lib.is_integer(key)
         if lib.is_scalar(key) and not is_int:
             raise IndexError(
@@ -892,7 +891,6 @@ def _maybe_mask_results(self, result, fill_value=iNaT, convert=None):
 
         This is an internal routine.
         """
-
         if self._hasnans:
             if convert:
                 result = result.astype(convert)

pandas/core/arrays/integer.py

@@ -152,7 +152,6 @@ def safe_cast(values, dtype, copy: bool):
     ints.
 
     """
-
     try:
         return values.astype(dtype, casting="safe", copy=copy)
     except TypeError:
@@ -600,7 +599,6 @@ def _maybe_mask_result(self, result, mask, other, op_name: str):
         other : scalar or array-like
         op_name : str
         """
-
         # if we have a float operand we are by-definition
         # a float result
         # or our op is a divide

pandas/core/arrays/period.py

@@ -624,7 +624,6 @@ def _addsub_int_array(
         -------
         result : PeriodArray
         """
-
         assert op in [operator.add, operator.sub]
         if op is operator.sub:
             other = -other

pandas/core/arrays/sparse/array.py

@@ -1503,7 +1503,6 @@ def make_sparse(arr, kind="block", fill_value=None, dtype=None, copy=False):
     -------
     (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)
     """
-
     arr = com.values_from_object(arr)
 
     if arr.ndim > 1:

pandas/core/arrays/sparse/scipy_sparse.py

@@ -31,7 +31,6 @@ def _to_ijv(ss, row_levels=(0,), column_levels=(1,), sort_labels=False):
 
     def get_indexers(levels):
         """ Return sparse coords and dense labels for subset levels """
-
         # TODO: how to do this better? cleanly slice nonnull_labels given the
         # coord
         values_ilabels = [tuple(x[i] for i in levels) for x in nonnull_labels.index]
@@ -90,7 +89,6 @@ def _sparse_series_to_coo(ss, row_levels=(0,), column_levels=(1,), sort_labels=F
     levels row_levels, column_levels as the row and column
     labels respectively. Returns the sparse_matrix, row and column labels.
     """
-
     import scipy.sparse
 
     if ss.index.nlevels < 2:

pandas/core/common.py

@@ -337,7 +337,6 @@ def apply_if_callable(maybe_callable, obj, **kwargs):
     obj : NDFrame
     **kwargs
     """
-
     if callable(maybe_callable):
         return maybe_callable(obj, **kwargs)
 
@@ -412,7 +411,6 @@ def random_state(state=None):
     -------
     np.random.RandomState
     """
-
     if is_integer(state):
         return np.random.RandomState(state)
     elif isinstance(state, np.random.RandomState):