BUG: Allow nan values in the data when the bins are explicit

Fixes gh-7503
Closes gh-8984

2 files changed, 29 insertions, 2 deletions

diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py
index 9c4ba8efc..ccae9de22 100644
--- a/numpy/lib/histograms.py
+++ b/numpy/lib/histograms.py
@@ -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:]):
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 8319041b8..0986ad16b 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -249,6 +249,31 @@ class TestHistogram(object):
         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):
     """