fixed comments

Author: DataInformer

pandas/core/groupby/generic.py

@@ -726,7 +726,6 @@ def value_counts(
         ids = ids[changes]
         cts = np.diff(np.nonzero(np.r_[changes, True]))[0]
         idx = np.r_[0, 1 + np.nonzero(change_ids)[0]]
-        print(idx)
         # how many times each index gets repeated
         rep = partial(np.repeat, repeats=np.add.reduceat(changes, idx))
 
@@ -742,7 +741,6 @@ def value_counts(
 
         if normalize:
             num_repeats = np.diff(idx, append=len(change_ids) + 1)
-            print(num_repeats)
             cts = cts.astype("float") / rep(num_repeats)
             # each divisor is the number of repeats for that index
 

pandas/tests/base/test_value_counts.py

@@ -207,7 +207,7 @@ def test_value_counts_bins_nas(dropna, bins):
 
 
 def test_value_counts_bins_specific_na():
-    # verify specific NA example
+    # GH25970 case where proportions were incorrect for dropna and normalize=True
     s2 = Series([1, 2, 2, 3, 3, 3, np.nan, np.nan, 4, 5])
     intervals = IntervalIndex.from_breaks([0.995, 2.333, 3.667, 5.0])
     expected_dropna = Series([0.375, 0.375, 0.25], intervals.take([1, 0, 2]))

pandas/tests/groupby/test_value_counts.py

@@ -72,12 +72,12 @@ def rebuild_index(df):
     left = gr["3rd"].value_counts(**kwargs)
     left.index.names = left.index.names[:-1] + ["3rd"]
 
-    # gr = df.groupby(keys, sort=isort)
     right = gr["3rd"].apply(Series.value_counts, **kwargs)
     right.index.names = right.index.names[:-1] + ["3rd"]
 
     # have to sort on index because of unstable sort on values
     left, right = map(rebuild_index, (left, right))  # xref GH9212
+
     # have to ignore 0 counts to be consistent with individual column value_counts
     left = left[left.astype(bool)]
     right = right[right.astype(bool)]