add np.nan funcs to _cython_table

Author: tp

doc/source/whatsnew/v0.23.1.txt

@@ -89,3 +89,8 @@ Categorical
 ^^^^^^^^^^^
 
 -
+
+Numeric
+^^^^^^^
+
+- :meth:`~DataFrame.agg` now correctly handles numpy NaN-aware methods like :meth:`numpy.nansum` (:issue:`19629`)

pandas/core/base.py

@@ -22,7 +22,8 @@
 from pandas.core import common as com, algorithms
 import pandas.core.nanops as nanops
 import pandas._libs.lib as lib
-from pandas.compat.numpy import function as nv
+from pandas.compat.numpy import (function as nv, _np_version_under1p10,
+                                 _np_version_under1p12)
 from pandas.compat import PYPY
 from pandas.util._decorators import (Appender, cache_readonly,
                                      deprecate_kwarg, Substitution)
@@ -191,17 +192,31 @@ class SelectionMixin(object):
         np.all: 'all',
         np.any: 'any',
         np.sum: 'sum',
+        np.nansum: 'sum',
         np.mean: 'mean',
+        np.nanmean: 'mean',
         np.prod: 'prod',
         np.std: 'std',
+        np.nanstd: 'std',
         np.var: 'var',
+        np.nanvar: 'var',
         np.median: 'median',
+        np.nanmedian: 'median',
         np.max: 'max',
+        np.nanmax: 'max',
         np.min: 'min',
+        np.nanmin: 'min',
         np.cumprod: 'cumprod',
-        np.cumsum: 'cumsum'
+        np.cumsum: 'cumsum',
     }
 
+    if not _np_version_under1p10:
+        _cython_table[np.nanprod] = 'prod'
+
+    if not _np_version_under1p12:
+        _cython_table[np.nancumprod] = 'cumprod'
+        _cython_table[np.nancumsum] = 'cumsum'
+
     @property
     def _selection_name(self):
         """
@@ -553,8 +568,8 @@ def is_any_frame():
             result = None
 
         f = self._is_cython_func(arg)
-        if f and not args and not kwargs:
-            return getattr(self, f)(), None
+        if f:
+            return getattr(self, f)(*args, **kwargs), None
 
         # caller can react
         return result, True

pandas/tests/test_nanops.py

@@ -994,6 +994,49 @@ def prng(self):
         return np.random.RandomState(1234)
 
 
+@pytest.fixture(params=[
+    pd.Series([1, 2, 3, 4, 5, 6]),
+    pd.DataFrame([[1, 2, 3], [4, 5, 6]])
+])
+def nan_test_object(request):
+    return request.param
+
+
+@pytest.mark.parametrize("standard, nan_method", [
+    (np.sum, np.nansum),
+    (np.mean, np.nanmean),
+    (np.std, np.nanstd),
+    (np.var, np.nanvar),
+    (np.median, np.nanmedian),
+    (np.max, np.nanmax),
+    (np.min, np.nanmin),
+])
+def test_np_nan_functions(standard, nan_method, nan_test_object):
+    tm.assert_almost_equal(nan_test_object.agg(standard),
+                           nan_test_object.agg(nan_method),
+                           check_exact=True)
+
+
+@td.skip_if_no("numpy", min_version="1.10.0")
+def test_np_nanprod(nan_test_object):
+    tm.assert_almost_equal(nan_test_object.agg(np.prod),
+                           nan_test_object.agg(np.nanprod),
+                           check_exact=True)
+
+
+@td.skip_if_no("numpy", min_version="1.12.0")
+def test_np_nancumprod(nan_test_object):
+    # Not using pytest params for methods as will fail at build time
+    methods = [
+        (np.cumprod, np.nancumprod),
+        (np.cumsum, np.nancumsum)
+    ]
+    for standard, nan_method in methods:
+        tm.assert_almost_equal(nan_test_object.agg(standard),
+                               nan_test_object.agg(nan_method),
+                               check_exact=True)
+
+
 def test_use_bottleneck():
 
     if nanops._BOTTLENECK_INSTALLED: