ENH: Support mask in groupby cumprod

doc/source/whatsnew/v1.6.0.rst

@@ -43,8 +43,32 @@ These are bug fixes that might have notable behavior changes.
 
 .. _whatsnew_160.notable_bug_fixes.notable_bug_fix1:
 
-notable_bug_fix1
-^^^^^^^^^^^^^^^^
+:meth:`.GroupBy.cumsum` and :meth:`.GroupBy.cumprod` overflow instead of lossy casting to float
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In previous versions we cast to float when applying ``cumsum`` and ``cumprod`` which
+lead to incorrect results even if the result could be hold by ``int64`` dtype.
+Additionally, the aggregation overflows consistent with numpy when the limit of
+``int64`` is reached.
+
+*Old Behavior*
+
+.. code-block:: ipython
+
+    In [1]: df = pd.DataFrame({"key": ["b"] * 7, "value": 625})
+    In [2]: df.groupby("key")["value"].cumprod()[5]
+    Out[2]: 5.960464477539062e+16
+
+We return incorrect results with the 6th value.
+
+*New Behavior*
+
+.. ipython:: python
+
+    df = pd.DataFrame({"key": ["b"] * 7, "value": 625})
+    df.groupby("key")["value"].cumprod()
+
+We overflow with the 7th value, but the 6th value is still correct.
 
 .. _whatsnew_160.notable_bug_fixes.notable_bug_fix2:
 
@@ -102,7 +126,8 @@ Deprecations
 
 Performance improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
-- Performance improvement in :meth:`.GroupBy.cumprod` for extension array dtypes (:issue:`37493`)
+- Performance improvement in :meth:`.GroupBy.median` and :meth:`.GroupBy.cumprod` for nullable dtypes (:issue:`37493`)
+- Performance improvement in :meth:`MultiIndex.argsort` and :meth:`MultiIndex.sort_values` (:issue:`48406`)
 - Performance improvement in :meth:`.GroupBy.mean` and :meth:`.GroupBy.var` for extension array dtypes (:issue:`37493`)
 - Performance improvement for :meth:`Series.value_counts` with nullable dtype (:issue:`48338`)
 - Performance improvement for :class:`Series` constructor passing integer numpy array with nullable dtype (:issue:`48338`)

pandas/_libs/groupby.pyx

@@ -226,13 +226,6 @@ def group_median_float64(
                     ptr += size
 
 
-ctypedef fused int64float_t:
-    int64_t
-    uint64_t
-    float32_t
-    float64_t
-
-
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def group_cumprod(

pandas/tests/groupby/test_function.py

@@ -643,13 +643,28 @@ def test_groupby_cumprod():
     df = DataFrame({"key": ["b"] * 100, "value": 2})
     df["value"] = df["value"].astype(float)
     actual = df.groupby("key")["value"].cumprod()
-    # if overflows, groupby product casts to float
-    # while numpy passes back invalid values
     expected = df.groupby("key", group_keys=False)["value"].apply(lambda x: x.cumprod())
     expected.name = "value"
     tm.assert_series_equal(actual, expected)
 
 
+def test_groupby_cumprod_overflow():
+    # GH#37493 if we overflow we return garbage consistent with numpy
+    df = DataFrame({"key": ["b"] * 4, "value": 100_000})
+    actual = df.groupby("key")["value"].cumprod()
+    expected = Series(
+        [100_000, 10_000_000_000, 1_000_000_000_000_000, 7766279631452241920],
+        name="value",
+    )
+    tm.assert_series_equal(actual, expected)
+
+    numpy_result = df.groupby("key", group_keys=False)["value"].apply(
+        lambda x: x.cumprod()
+    )
+    numpy_result.name = "value"
+    tm.assert_series_equal(actual, numpy_result)
+
+
 def test_groupby_cumprod_nan_influences_other_columns():
     # GH#48064
     df = DataFrame(