BUG: SeriesGroupBy.value_counts() raising error on an empty Series (#39326)

Author: AnnaDaglis

doc/source/whatsnew/v1.3.0.rst

@@ -345,6 +345,7 @@ Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
 - Bug in :meth:`SeriesGroupBy.value_counts` where unobserved categories in a grouped categorical series were not tallied (:issue:`38672`)
+- Bug in :meth:`SeriesGroupBy.value_counts` where error was raised on an empty series (:issue:`39172`)
 - Bug in :meth:`.GroupBy.indices` would contain non-existent indices when null values were present in the groupby keys (:issue:`9304`)
 - Fixed bug in :meth:`DataFrameGroupBy.sum` and :meth:`SeriesGroupBy.sum` causing loss of precision through using Kahan summation (:issue:`38778`)
 - Fixed bug in :meth:`DataFrameGroupBy.cumsum`, :meth:`SeriesGroupBy.cumsum`, :meth:`DataFrameGroupBy.mean` and :meth:`SeriesGroupBy.mean` causing loss of precision through using Kahan summation (:issue:`38934`)

pandas/core/groupby/generic.py

@@ -730,11 +730,16 @@ def apply_series_value_counts():
         ids, lab = ids[sorter], lab[sorter]
 
         # group boundaries are where group ids change
-        idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
+        idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
+        idx = np.r_[0, idchanges]
+        if not len(ids):
+            idx = idchanges
 
         # new values are where sorted labels change
         lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
         inc = np.r_[True, lchanges]
+        if not len(lchanges):
+            inc = lchanges
         inc[idx] = True  # group boundaries are also new values
         out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts
 

pandas/tests/groupby/test_value_counts.py

@@ -122,6 +122,27 @@ def test_series_groupby_value_counts_with_grouper():
     tm.assert_series_equal(result, expected)
 
 
+def test_series_groupby_value_counts_empty():
+    # GH39172
+    df = DataFrame(columns=["A", "B"])
+    dfg = df.groupby("A")
+
+    result = dfg["B"].value_counts()
+    expected = Series([], name="B", dtype=result.dtype)
+    expected.index = MultiIndex.from_arrays([[]] * 2, names=["A", "B"])
+
+    tm.assert_series_equal(result, expected)
+
+    df = DataFrame(columns=["A", "B", "C"])
+    dfg = df.groupby(["A", "B"])
+
+    result = dfg["C"].value_counts()
+    expected = Series([], name="C", dtype=result.dtype)
+    expected.index = MultiIndex.from_arrays([[]] * 3, names=["A", "B", "C"])
+
+    tm.assert_series_equal(result, expected)
+
+
 def test_series_groupby_value_counts_on_categorical():
     # GH38672