style and more typing

pandas-dev · jreback · Nov 20, 2019 · Oct 17, 2019 · Oct 17, 2019 · Oct 17, 2019
commit 9eb7c7397ead15272882fbfceee43583f2f01c9f
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
@@ -340,8 +340,10 @@ def _aggregate_multiple_funcs(self, arg, _level):
 
         return DataFrame(results, columns=columns)
 
-    def _wrap_series_output(self, output: Dict[int, np.ndarray], index, names: List[Hashable]):
-        """ 
+    def _wrap_series_output(
+        self, output: Dict[int, np.ndarray], index: Index, names: List[Hashable]
+    ) -> Series:
+        """
         Wraps the output of a SeriesGroupBy operation into the expected result.
 
         Parameters
@@ -359,8 +361,8 @@ def _wrap_series_output(self, output: Dict[int, np.ndarray], index, names: List[
 
         Notes
         -----
-        output and names should only contain one element. These are containers for generic
-        compatability with the DataFrameGroupBy class.
+        output and names should only contain one element. These are containers
+        for generic compatability with the DataFrameGroupBy class.
         """
         assert len(names) == 1
         result = Series(output[0])
@@ -369,8 +371,10 @@ def _wrap_series_output(self, output: Dict[int, np.ndarray], index, names: List[
 
         return result
 
-    def _wrap_aggregated_output(self, output: Dict[int, np.ndarray], names: List[Hashable]):
-        """ 
+    def _wrap_aggregated_output(
+        self, output: Dict[int, np.ndarray], names: List[Hashable]
+    ) -> Series:
+        """
         Wraps the output of a SeriesGroupBy aggregation into the expected result.
 
         Parameters
@@ -388,16 +392,18 @@ def _wrap_aggregated_output(self, output: Dict[int, np.ndarray], names: List[Has
 
         Notes
         -----
-        output and names should only contain one element. These are containers for generic
-        compatability with the DataFrameGroupBy class.
+        output and names should only contain one element. These are containers
+        for generic compatability with the DataFrameGroupBy class.
         """
         result = self._wrap_series_output(
             output=output, index=self.grouper.result_index, names=names
         )
         return self._reindex_output(result)._convert(datetime=True)
 
-    def _wrap_transformed_output(self, output: Dict[int, np.ndarray], names: List[Hashable]):
-        """ 
+    def _wrap_transformed_output(
+        self, output: Dict[int, np.ndarray], names: List[Hashable]
+    ) -> Series:
+        """
         Wraps the output of a SeriesGroupBy aggregation into the expected result.
 
         Parameters
@@ -415,9 +421,9 @@ def _wrap_transformed_output(self, output: Dict[int, np.ndarray], names: List[Ha
 
         Notes
         -----
-        output and names should only contain one element. These are containers for generic
-        compatability with the DataFrameGroupBy class.
-        """        
+        output and names should only contain one element. These are containers
+        for generic compatability with the DataFrameGroupBy class.
+        """
         return self._wrap_series_output(
             output=output, index=self.obj.index, names=names
         )
@@ -1651,7 +1657,9 @@ def _insert_inaxis_grouper_inplace(self, result):
             if in_axis:
                 result.insert(0, name, lev)
 
-    def _wrap_aggregated_output(self, output: Dict[int, np.ndarray], names: List[Hashable]) -> DataFrame:
+    def _wrap_aggregated_output(
+        self, output: Dict[int, np.ndarray], names: List[Hashable]
+    ) -> DataFrame:
         """
         Wraps the output of DataFrameGroupBy aggregations into the expected result.
 
@@ -1683,7 +1691,9 @@ def _wrap_aggregated_output(self, output: Dict[int, np.ndarray], names: List[Has
 
         return self._reindex_output(result)._convert(datetime=True)
 
-    def _wrap_transformed_output(self, output: Dict[int, np.ndarray], names: List[Hashable]) -> DataFrame:
+    def _wrap_transformed_output(
+        self, output: Dict[int, np.ndarray], names: List[Hashable]
+    ) -> DataFrame:
         """
         Wraps the output of DataFrameGroupBy transformations into the expected result.
 
@@ -1700,7 +1710,7 @@ def _wrap_transformed_output(self, output: Dict[int, np.ndarray], names: List[Ha
         -------
         DataFrame
         """
-        
+
         result = DataFrame(output)
         result.columns = names
         result.index = self.obj.index

diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
@@ -14,7 +14,17 @@ class providing the base-class of operations.
 import inspect
 import re
 import types
-from typing import Dict, FrozenSet, Hashable, Iterable, List, Optional, Tuple, Type, Union
+from typing import (
+    Dict,
+    FrozenSet,
+    Hashable,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+)
 
 import numpy as np
 
@@ -869,10 +879,14 @@ def _cython_transform(self, how, numeric_only=True, **kwargs):
 
         return self._wrap_transformed_output(output, names)
 
-    def _wrap_aggregated_output(self, output: Dict[int, np.ndarray], names: List[Hashable]):
+    def _wrap_aggregated_output(
+        self, output: Dict[int, np.ndarray], names: List[Hashable]
+    ):
         raise AbstractMethodError(self)
 
-    def _wrap_transformed_output(self, output: Dict[int, np.ndarray], names: List[Hashable]):
+    def _wrap_transformed_output(
+        self, output: Dict[int, np.ndarray], names: List[Hashable]
+    ):
         raise AbstractMethodError(self)
 
     def _wrap_applied_output(self, keys, values, not_indexed_same=False):
@@ -893,7 +907,11 @@ def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1):
            if how == "ohlc":
                 assert result.shape[1] == 4
                 result = self._try_cast(result, obj)
-                df = DataFrame(result, index=self.grouper.result_index, columns=["open", "high", "low", "close"])
+                df = DataFrame(
+                    result,
+                    index=self.grouper.result_index,
+                    columns=["open", "high", "low", "close"],
+                )
                 return df
 
             output[index] = self._try_cast(result, obj)