black

Author: smithto1

pandas/tests/groupby/test_categorical.py

@@ -1259,45 +1259,40 @@ def test_get_nonexistent_category():
         )
 
 
-def test_dataframe_groupby_on_2_categoricals_when_observed_is_true(
-        reduction_func:str):
+def test_dataframe_groupby_on_2_categoricals_when_observed_is_true(reduction_func: str):
 
-    if reduction_func == 'ngroup':
+    if reduction_func == "ngroup":
         pytest.skip("ngroup does not return the Categories on the index")
 
     res, unobserved_cats = _dataframe_groupby_on_2_categoricals(
-        reduction_func, observed=True)
-    
+        reduction_func, observed=True
+    )
+
     for cat in unobserved_cats:
-        assert cat not in res.index   
-    
-
-def _dataframe_groupby_on_2_categoricals(reduction_func:str, observed:bool):
-    
-    df = pd.DataFrame({
-        "cat_1": pd.Categorical(list("AABB"), categories=list("ABC")),
-        "cat_2": pd.Categorical(list("1111"), categories=list("12")),
-        "value": [.1, .1, .1, .1]
-    })
-    unobserved_cats = [ 
-        ('A', '2'), 
-        ('B', '2'), 
-        ('C', '1'), 
-        ('C', '2')
-    ]
-    
-    df_grp = df.groupby(['cat_1', 'cat_2'], observed=observed)
-    
-    args = {
-        'nth' : [0],
-        'corrwith' : [df]
-    }.get(reduction_func, [])        
+        assert cat not in res.index
+
+
+def _dataframe_groupby_on_2_categoricals(reduction_func: str, observed: bool):
+
+    df = pd.DataFrame(
+        {
+            "cat_1": pd.Categorical(list("AABB"), categories=list("ABC")),
+            "cat_2": pd.Categorical(list("1111"), categories=list("12")),
+            "value": [0.1, 0.1, 0.1, 0.1],
+        }
+    )
+    unobserved_cats = [("A", "2"), ("B", "2"), ("C", "1"), ("C", "2")]
+
+    df_grp = df.groupby(["cat_1", "cat_2"], observed=observed)
+
+    args = {"nth": [0], "corrwith": [df]}.get(reduction_func, [])
     res = getattr(df_grp, reduction_func)(*args)
-    
+
     return res, unobserved_cats
 
 
-_results_for_groupbys_with_missing_categories = dict([
+_results_for_groupbys_with_missing_categories = dict(
+    [
         ("all", np.NaN),
         ("any", np.NaN),
         ("count", 0),
@@ -1321,33 +1316,38 @@ def _dataframe_groupby_on_2_categoricals(reduction_func:str, observed:bool):
         ("std", np.NaN),
         ("sum", np.NaN),
         ("var", np.NaN),
-])
+    ]
+)
 
 
-@pytest.mark.parametrize('observed', [False, None])
+@pytest.mark.parametrize("observed", [False, None])
 def test_dataframe_groupby_on_2_categoricals_when_observed_is_false(
-        reduction_func:str, observed:bool, request):
-    
-    if reduction_func == 'ngroup':
+    reduction_func: str, observed: bool, request
+):
+
+    if reduction_func == "ngroup":
         pytest.skip("ngroup does not return the Categories on the index")
-        
-    if reduction_func == 'count':
+
+    if reduction_func == "count":
         mark = pytest.mark.xfail(
-            reason=("DataFrameGroupBy.count returns np.NaN for missing "
-                    "categories, when it should return 0"))
+            reason=(
+                "DataFrameGroupBy.count returns np.NaN for missing "
+                "categories, when it should return 0"
+            )
+        )
         request.node.add_marker(mark)
 
     res, unobserved_cats = _dataframe_groupby_on_2_categoricals(
-        reduction_func, observed)
-    
+        reduction_func, observed
+    )
+
     expected = _results_for_groupbys_with_missing_categories[reduction_func]
-    
+
     if expected is np.nan:
         assert res.loc[unobserved_cats].isnull().all().all()
     else:
         assert (res.loc[unobserved_cats] == expected).all().all()
-    
-      
+
 
 def test_series_groupby_on_2_categoricals_unobserved(
     reduction_func: str, observed: bool, request
@@ -1379,17 +1379,18 @@ def test_series_groupby_on_2_categoricals_unobserved(
 
 
 def test_series_groupby_on_2_categoricals_unobserved_zeroes_or_nans(
-        reduction_func:str, request):
+    reduction_func: str, request
+):
     # GH 17605
     # Tests whether the unobserved categories in the result contain 0 or NaN
-    
+
     if reduction_func == "ngroup":
         pytest.skip("ngroup is not truly a reduction")
-    
+
     if reduction_func == "corrwith":  # GH 32293
         mark = pytest.mark.xfail(reason="TODO: implemented SeriesGroupBy.corrwith")
         request.node.add_marker(mark)
-    
+
     df = pd.DataFrame(
         {
             "cat_1": pd.Categorical(list("AABB"), categories=list("ABC")),
@@ -1403,7 +1404,7 @@ def test_series_groupby_on_2_categoricals_unobserved_zeroes_or_nans(
     series_groupby = df.groupby(["cat_1", "cat_2"], observed=False)["value"]
     agg = getattr(series_groupby, reduction_func)
     result = agg(*args)
-    
+
     zero_or_nan = _results_for_groupbys_with_missing_categories[reduction_func]
 
     for idx in unobserved: