ENH: Improve histogram bins="auto" for data with little variance (#10739)

Now falls back on sturges estimator when the IQR is zero

1 files changed, 18 insertions, 0 deletions

diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py
index 6777089ab..06daacbdc 100644
--- a/numpy/lib/tests/test_histograms.py
+++ b/numpy/lib/tests/test_histograms.py
@@ -443,6 +443,24 @@ class TestHistogramOptimBinNums(object):
             assert_equal(len(a), numbins, err_msg="{0} estimator, "
                          "No Variance test".format(estimator))
 
+    def test_limited_variance(self):
+        """
+        Check when IQR is 0, but variance exists, we return the sturges value
+        and not the fd value.
+        """
+        lim_var_data = np.ones(1000)
+        lim_var_data[:3] = 0
+        lim_var_data[-4:] = 100
+
+        edges_auto = histogram_bin_edges(lim_var_data, 'auto')
+        assert_equal(edges_auto, np.linspace(0, 100, 12))
+
+        edges_fd = histogram_bin_edges(lim_var_data, 'fd')
+        assert_equal(edges_fd, np.array([0, 100]))
+
+        edges_sturges = histogram_bin_edges(lim_var_data, 'sturges')
+        assert_equal(edges_sturges, np.linspace(0, 100, 12))
+
     def test_outlier(self):
         """
         Check the FD, Scott and Doane with outliers.