BUG: Check min_periods before applying the function (#58886)

* Check min_periods before calling the function

* Update whatsnew

Author: rob-sil

doc/source/whatsnew/v3.0.0.rst

@@ -536,7 +536,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
 - Bug in :meth:`DataFrameGroupBy.apply` with ``as_index=False`` that was returning :class:`MultiIndex` instead of returning :class:`Index`. (:issue:`58291`)
 - Bug in :meth:`DataFrameGroupby.transform` and :meth:`SeriesGroupby.transform` with a reducer and ``observed=False`` that coerces dtype to float when there are unobserved categories. (:issue:`55326`)
-
+- Bug in :meth:`Rolling.apply` where the applied function could be called on fewer than ``min_period`` periods if ``method="table"``. (:issue:`58868`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/numba_.py

@@ -227,10 +227,10 @@ def roll_table(
             stop = end[i]
             window = values[start:stop]
             count_nan = np.sum(np.isnan(window), axis=0)
-            sub_result = numba_func(window, *args)
             nan_mask = len(window) - count_nan >= minimum_periods
+            if nan_mask.any():
+                result[i, :] = numba_func(window, *args)
             min_periods_mask[i, :] = nan_mask
-            result[i, :] = sub_result
         result = np.where(min_periods_mask, result, np.nan)
         return result
 

pandas/tests/window/test_numba.py

@@ -67,6 +67,21 @@ def f(x, *args):
         )
         tm.assert_series_equal(result, expected)
 
+    def test_numba_min_periods(self):
+        # GH 58868
+        def last_row(x):
+            assert len(x) == 3
+            return x[-1]
+
+        df = DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]])
+
+        result = df.rolling(3, method="table", min_periods=3).apply(
+            last_row, raw=True, engine="numba"
+        )
+
+        expected = DataFrame([[np.nan, np.nan], [np.nan, np.nan], [5, 6], [7, 8]])
+        tm.assert_frame_equal(result, expected)
+
     @pytest.mark.parametrize(
         "data",
         [