Use black 19.10b0 (#29508)

Author: alimcmaster1

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/python/black
-    rev: stable
+    rev: 19.10b0
     hooks:
     -   id: black
         language_version: python3.7

doc/source/development/contributing.rst

@@ -654,6 +654,9 @@ submitting code to run the check yourself::
 to auto-format your code. Additionally, many editors have plugins that will
 apply ``black`` as you edit files.
 
+You should use a ``black`` version >= 19.10b0 as previous versions are not compatible
+with the pandas codebase.
+
 Optionally, you may wish to setup `pre-commit hooks <https://pre-commit.com/>`_
 to automatically run ``black`` and ``flake8`` when you make a git commit. This
 can be done by installing ``pre-commit``::

environment.yml

@@ -15,7 +15,7 @@ dependencies:
   - cython>=0.29.13
 
   # code checks
-  - black<=19.3b0
+  - black>=19.10b0
   - cpplint
   - flake8
   - flake8-comprehensions>=3.1.0  # used by flake8, linting of unnecessary comprehensions

pandas/core/algorithms.py

@@ -1159,7 +1159,7 @@ 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

@@ -4774,7 +4774,7 @@ 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

@@ -3599,7 +3599,7 @@ 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

@@ -292,9 +292,7 @@ def __init__(
                 self.grouper,
                 self._codes,
                 self._group_index,
-            ) = index._get_grouper_for_level(  # noqa: E501
-                self.grouper, level
-            )
+            ) = index._get_grouper_for_level(self.grouper, level)
 
         # a passed Grouper like, directly get the grouper in the same way
         # as single grouper groupby, use the group_info to get codes

pandas/core/indexes/base.py

@@ -1871,8 +1871,7 @@ def _isnan(self):
     @cache_readonly
     def _nan_idxs(self):
         if self._can_hold_na:
-            w = self._isnan.nonzero()[0]
-            return w
+            return self._isnan.nonzero()[0]
         else:
             return np.array([], dtype=np.int64)
 

pandas/core/indexing.py

@@ -319,7 +319,7 @@ 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)
@@ -1111,7 +1111,7 @@ 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
@@ -1255,7 +1255,7 @@ 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

@@ -1860,7 +1860,7 @@ def _shape_compat(x):
 
 
 def _interleaved_dtype(
-    blocks: List[Block]
+    blocks: List[Block],
 ) -> Optional[Union[np.dtype, ExtensionDtype]]:
     """Find the common dtype for `blocks`.