Remove breakpoints

Author: MarcoGorelli

pandas/core/groupby/generic.py

@@ -1141,17 +1141,11 @@ def nunique(self, dropna=True):
             Number of unique values within each group.
         """
         ids, _, _ = self.grouper.group_info
-        # breakpoint()
 
         val = self.obj._internal_get_values()
-        # breakpoint()
 
-        # # GH 27951
-        # breakpoint()
+        # GH 27951
         val[isna(val)] = np.datetime64("NaT")
-        #     mask = notna(val)
-        #     ids = ids[mask]
-        #     val = val[mask]
 
         try:
             sorter = np.lexsort((val, ids))
@@ -1163,29 +1157,22 @@ def nunique(self, dropna=True):
             _isna = lambda a: a == -1
         else:
             _isna = isna
-        # breakpoint()
 
         ids, val = ids[sorter], val[sorter]
-        # breakpoint()
 
         # group boundaries are where group ids change
         # unique observations are where sorted values change
-        # idx: ids at which groups change
         idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
-        # inc: 1 if it's a new value, 0 else
         inc = np.r_[1, val[1:] != val[:-1]]
-        # breakpoint()
 
         # 1st item of each group is a new unique observation
         mask = _isna(val)
         if dropna:
-            inc[idx] = 1  # 1st itme of each group is defo unique!
-            inc[mask] = 0  # Nas should be excluded, we don't like them!
+            inc[idx] = 1
+            inc[mask] = 0
         else:
-            inc[mask & np.r_[False, mask[:-1]]] = 0  # only set it to
-            # zero if there are two consecutive nans?
-            inc[idx] = 1  # As before, first of each group is defo unique
-        # breakpoint()
+            inc[mask & np.r_[False, mask[:-1]]] = 0 
+            inc[idx] = 1
 
         out = np.add.reduceat(inc, idx).astype("int64", copy=False)
         if len(ids):
@@ -1199,13 +1186,11 @@ def nunique(self, dropna=True):
         else:
             res = out[1:]
         ri = self.grouper.result_index
-        # breakpoint()
 
         # we might have duplications among the bins
         if len(res) != len(ri):
             res, out = np.zeros(len(ri), dtype=out.dtype), res
             res[ids[idx]] = out
-        # breakpoint()
 
         return Series(res, index=ri, name=self._selection_name)