Scale Series.mean(skipna=True)

sdc/datatypes/hpat_pandas_series_functions.py

@@ -3787,7 +3787,7 @@ def hpat_pandas_series_mean_impl(self, axis=None, skipna=None, level=None, numer
             _skipna = skipna
 
         if _skipna:
-            return numpy.nanmean(self._data)
+            return numpy_like.nanmean(self._data)
 
         return self._data.mean()
 

sdc/functions/numpy_like.py

@@ -33,6 +33,7 @@
 
 import numba
 import numpy
+import numpy as np
 
 from numba import types, jit, prange, numpy_support, literally
 from numba.errors import TypingError
@@ -472,3 +473,27 @@ def nanprod_impl(a):
         return c
 
     return nanprod_impl
+
+
+def nanmean(a):
+    pass
+
+
+@sdc_overload(nanmean)
+def np_nanmean(a):
+    if not isinstance(a, types.Array):
+        return
+    isnan = get_isnan(a.dtype)
+
+    def nanmean_impl(a):
+        c = 0.0
+        count = 0
+        for i in prange(len(a)):
+            v = a[i]
+            if not isnan(v):
+                c += v
+                count += 1
+        # np.divide() doesn't raise ZeroDivisionError
+        return np.divide(c, count)
+
+    return nanmean_impl

sdc/tests/test_sdc_numpy.py

@@ -264,6 +264,15 @@ def cases():
             with self.subTest(data=case):
                 np.testing.assert_array_equal(alt_cfunc(case), pyfunc(case))
 
+    def test_nanmean(self):
+        def ref_impl(a):
+            return np.nanmean(a)
+
+        def sdc_impl(a):
+            return numpy_like.nanmean(a)
+
+        self.check_reduction_basic(ref_impl, sdc_impl)
+
     def test_nanmin(self):
         def ref_impl(a):
             return np.nanmin(a)

sdc/tests/test_series.py

@@ -2410,58 +2410,44 @@ def test_impl(S):
         S = pd.Series(['aa', 'bb', np.nan])
         self.assertEqual(hpat_func(S), test_impl(S))
 
+    def _mean_data_samples(self):
+        yield [6, 6, 2, 1, 3, 3, 2, 1, 2]
+        yield [1.1, 0.3, 2.1, 1, 3, 0.3, 2.1, 1.1, 2.2]
+        yield [6, 6.1, 2.2, 1, 3, 3, 2.2, 1, 2]
+        yield [6, 6, np.nan, 2, np.nan, 1, 3, 3, np.inf, 2, 1, 2, np.inf]
+        yield [1.1, 0.3, np.nan, 1.0, np.inf, 0.3, 2.1, np.nan, 2.2, np.inf]
+        yield [1.1, 0.3, np.nan, 1, np.inf, 0, 1.1, np.nan, 2.2, np.inf, 2, 2]
+        yield [np.nan, np.nan, np.nan]
+        yield [np.nan, np.nan, np.inf]
+
+    def _check_mean(self, pyfunc, *args):
+        cfunc = self.jit(pyfunc)
+
+        actual = cfunc(*args)
+        expected = pyfunc(*args)
+        if np.isnan(actual) or np.isnan(expected):
+            self.assertEqual(np.isnan(actual), np.isnan(expected))
+        else:
+            self.assertEqual(actual, expected)
+
     def test_series_mean(self):
         def test_impl(S):
             return S.mean()
-        hpat_func = self.jit(test_impl)
-
-        data_samples = [
-            [6, 6, 2, 1, 3, 3, 2, 1, 2],
-            [1.1, 0.3, 2.1, 1, 3, 0.3, 2.1, 1.1, 2.2],
-            [6, 6.1, 2.2, 1, 3, 3, 2.2, 1, 2],
-            [6, 6, np.nan, 2, np.nan, 1, 3, 3, np.inf, 2, 1, 2, np.inf],
-            [1.1, 0.3, np.nan, 1.0, np.inf, 0.3, 2.1, np.nan, 2.2, np.inf],
-            [1.1, 0.3, np.nan, 1, np.inf, 0, 1.1, np.nan, 2.2, np.inf, 2, 2],
-            [np.nan, np.nan, np.nan],
-            [np.nan, np.nan, np.inf],
-        ]
 
-        for data in data_samples:
+        for data in self._mean_data_samples():
             with self.subTest(data=data):
                 S = pd.Series(data)
-                actual = hpat_func(S)
-                expected = test_impl(S)
-                if np.isnan(actual) or np.isnan(expected):
-                    self.assertEqual(np.isnan(actual), np.isnan(expected))
-                else:
-                    self.assertEqual(actual, expected)
+                self._check_mean(test_impl, S)
 
     @skip_sdc_jit("Series.mean() any parameters unsupported")
     def test_series_mean_skipna(self):
         def test_impl(S, skipna):
             return S.mean(skipna=skipna)
-        hpat_func = self.jit(test_impl)
-
-        data_samples = [
-            [6, 6, 2, 1, 3, 3, 2, 1, 2],
-            [1.1, 0.3, 2.1, 1, 3, 0.3, 2.1, 1.1, 2.2],
-            [6, 6.1, 2.2, 1, 3, 3, 2.2, 1, 2],
-            [6, 6, np.nan, 2, np.nan, 1, 3, 3, np.inf, 2, 1, 2, np.inf],
-            [1.1, 0.3, np.nan, 1.0, np.inf, 0.3, 2.1, np.nan, 2.2, np.inf],
-            [1.1, 0.3, np.nan, 1, np.inf, 0, 1.1, np.nan, 2.2, np.inf, 2, 2],
-            [np.nan, np.nan, np.nan],
-            [np.nan, np.nan, np.inf],
-        ]
 
         for skipna in [True, False]:
-            for data in data_samples:
+            for data in self._mean_data_samples():
                 S = pd.Series(data)
-                actual = hpat_func(S, skipna)
-                expected = test_impl(S, skipna)
-                if np.isnan(actual) or np.isnan(expected):
-                    self.assertAlmostEqual(np.isnan(actual), np.isnan(expected))
-                else:
-                    self.assertAlmostEqual(actual, expected)
+                self._check_mean(test_impl, S, skipna)
 
     def test_series_var1(self):
         def test_impl(S):

sdc/tests/tests_perf/test_perf_numpy.py

@@ -95,6 +95,11 @@ def _test_case(self, cases, name, total_data_length, data_num=1, input_data=test
         CE(type_='Numba', code='np.isnan(data)', jitted=True),
         CE(type_='SDC', code='sdc.functions.numpy_like.isnan(data)', jitted=True),
     ], usecase_params='data'),
+    TC(name='nanmean', size=[10 ** 8], call_expr=[
+        CE(type_='Python', code='np.nanmean(data)', jitted=False),
+        CE(type_='Numba', code='np.nanmean(data)', jitted=True),
+        CE(type_='SDC', code='sdc.functions.numpy_like.nanmean(data)', jitted=True),
+    ], usecase_params='data'),
     TC(name='nansum', size=[10 ** 7], call_expr=[
         CE(type_='Python', code='np.nansum(data)', jitted=False),
         CE(type_='SDC', code='sdc.functions.numpy_like.nansum(data)', jitted=True),

sdc/tests/tests_perf/test_perf_series.py

@@ -103,7 +103,8 @@ def _test_case(self, pyfunc, name, total_data_length, data_num=1, input_data=tes
     TC(name='map', size=[10 ** 7], params='{2.: 42., 4.: 3.14}'),
     TC(name='max', size=[10 ** 8], params='skipna=True'),
     TC(name='max', size=[10 ** 8], params='skipna=False'),
-    TC(name='mean', size=[10 ** 8]),
+    TC(name='mean', size=[10 ** 8], params='skipna=True'),
+    TC(name='mean', size=[10 ** 8], params='skipna=False'),
     TC(name='median', size=[10 ** 8]),
     TC(name='min', size=[10 ** 8], params='skipna=True'),
     TC(name='min', size=[10 ** 8], params='skipna=False'),