ENH: Made "isclose()" NA-aware, added release note and versionadded.

3 files changed, 24 insertions, 3 deletions

diff --git a/doc/release/2.0.0-notes.rst b/doc/release/2.0.0-notes.rst
index defdc9c00..7c2bfa8a5 100644
--- a/doc/release/2.0.0-notes.rst
+++ b/doc/release/2.0.0-notes.rst
@@ -94,6 +94,13 @@ A generic sampling function has been added which will generate samples from
 a given array-like. The samples can be with or without replacement, and
 with uniform or given non-uniform probabilities.
 
+New function isclose
+--------------------
+
+Returns a boolean array where two arrays are element-wise equal within a
+tolerance. Both relative and absolute tolerance can be specified. The
+function is NA aware.
+
 Preliminary multi-dimensional support in the polynomial package
 ---------------------------------------------------------------
 
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index e4693431c..846429016 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -2059,6 +2059,8 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
 
     Notes
     -----
+    .. versionadded:: 1.7.0
+
     For finite values, isclose uses the following equation to test whether
     two floating point values are equivalent.
 
@@ -2090,17 +2092,18 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
     y = array(b, copy=False, subok=True, ndmin=1)
     xfin = isfinite(x)
     yfin = isfinite(y)
-    if all(xfin) and all(yfin):
+    if all(xfin, skipna=True) and all(yfin, skipna=True):
         return within_tol(x, y, atol, rtol)
     else:
         finite = xfin & yfin
+        cond = zeros_like(finite, subok=True, maskna=finite.flags.maskna)
         # Because we're using boolean indexing, x & y must be the same shape.
         # Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
         # lib.stride_tricks, though, so we can't import it here.
-        cond = zeros_like(finite, subok=True)
         x = x * ones_like(cond)
         y = y * ones_like(cond)
-        # Avoid subtraction with infinite/nan values...
+        # Avoid subtraction with infinite/nan values and indexing with na...
+        finite[isna(finite)] = False
         cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
         # Check for equality of infinite values...
         cond[~finite] = (x[~finite] == y[~finite])
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
index 174a12c31..5233d0f88 100644
--- a/numpy/core/tests/test_numeric.py
+++ b/numpy/core/tests/test_numeric.py
@@ -1322,6 +1322,17 @@ class TestIsclose(object):
         # Ensure that the mask isn't modified...
         assert_array_equal([True, True, False], y.mask)
 
+    def test_maskna_arrays(self):
+        x = array([NA, 1, 2, 3])
+        y = array([0, 1, 2, NA])
+        assert_array_equal(isclose(x, y), array([NA, True, True, NA]))
+
+        assert_array_equal(isclose(NA, arange(3)), array([NA, NA, NA]))
+
+        x = array([NA, nan, 2, 3])
+        y = array([nan, 1, 2, NA])
+        assert_array_equal(isclose(x, y), array([NA, False, True, NA]))
+
     def test_scalar_return(self):
         assert_(isscalar(isclose(1, 1)))