numpy
diff --git a/‎doc/release/1.15.0-notes.rst
Lines changed: 10 additions & 0 deletions b/‎doc/release/1.15.0-notes.rst
Lines changed: 10 additions & 0 deletions
diff --git a/‎numpy/lib/histograms.py
Lines changed: 4 additions & 2 deletions b/‎numpy/lib/histograms.py
Lines changed: 4 additions & 2 deletions
diff --git a/‎numpy/lib/tests/test_histograms.py
Lines changed: 25 additions & 0 deletions b/‎numpy/lib/tests/test_histograms.py
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@@ -64,6 +64,16 @@ to maintain compatibility, aliased at ``np.lib.function_base.histogram(dd)``.
 Code that does ``from np.lib.function_base import *`` will need to be updated
 with the new location, and should consider not using ``import *`` in future.
 
+``histogram`` will accept NaN values when explicit bins are given
+-----------------------------------------------------------------
+Previously it would fail when trying to compute a finite range for the data.
+Since the range is ignored anyway when the bins are given explcitly, this error
+was needless.
+
+Note that calling `histogram` on NaN values continues to raise the
+`RuntimeWarning`s typical of working with nan values, which can be silenced
+as usual with `errstate`.
+
 ``np.r_`` works with 0d arrays, and ``np.ma.mr_` works with ``np.ma.masked``
 ----------------------------------------------------------------------------
 0d arrays passed to the `r_` and `mr_` concatenation helpers are now treated as
 
@@ -259,8 +259,6 @@ def _get_bin_edges(a, bins, range, weights):
         The upper bound, lowerbound, and number of bins, used in the optimized
         implementation of `histogram` that works on uniform bins.
     """
-    first_edge, last_edge = _get_outer_edges(a, range)
-
     # parse the overloaded bins argument
     n_equal_bins = None
     bin_edges = None
@@ -276,6 +274,8 @@ def _get_bin_edges(a, bins, range, weights):
             raise TypeError("Automated estimation of the number of "
                             "bins is not supported for weighted data")
 
+        first_edge, last_edge = _get_outer_edges(a, range)
+
         # truncate the range if needed
         if range is not None:
             keep = (a >= first_edge)
@@ -304,6 +304,8 @@ def _get_bin_edges(a, bins, range, weights):
         if n_equal_bins < 1:
             raise ValueError('`bins` must be positive, when an integer')
 
+        first_edge, last_edge = _get_outer_edges(a, range)
+
     elif np.ndim(bins) == 1:
         bin_edges = np.asarray(bins)
         if np.any(bin_edges[:-1] > bin_edges[1:]):
 
@@ -249,6 +249,31 @@ def test_object_array_of_0d(self):
         np.histogram([np.array([0.5]) for i in range(10)] + [.500000000000001])
         np.histogram([np.array([0.5]) for i in range(10)] + [.5])
 
+    def test_some_nan_values(self):
+        # gh-7503
+        one_nan = np.array([0, 1, np.nan])
+        all_nan = np.array([np.nan, np.nan])
+
+        # the internal commparisons with NaN give warnings
+        sup = suppress_warnings()
+        sup.filter(RuntimeWarning)
+        with sup:
+            # can't infer range with nan
+            assert_raises(ValueError, histogram, one_nan, bins='auto')
+            assert_raises(ValueError, histogram, all_nan, bins='auto')
+
+            # explicit range solves the problem
+            h, b = histogram(one_nan, bins='auto', range=(0, 1))
+            assert_equal(h.sum(), 2)  # nan is not counted
+            h, b = histogram(all_nan, bins='auto', range=(0, 1))
+            assert_equal(h.sum(), 0)  # nan is not counted
+
+            # as does an explicit set of bins
+            h, b = histogram(one_nan, bins=[0, 1])
+            assert_equal(h.sum(), 2)  # nan is not counted
+            h, b = histogram(all_nan, bins=[0, 1])
+            assert_equal(h.sum(), 0)  # nan is not counted
+
 
 class TestHistogramOptimBinNums(object):
     """