8000 ENH: When histogramming data with integer dtype, force bin width >= 1. · numpy/numpy@36596ed · GitHub
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ENH: When histogramming data with integer dtype, force bin width >= 1.
Bins of width < 1 don't make sense for integer data, they just add a bunch of spurious, unpopulated bins. (Perhaps an even better improvement would be to make sure that, when using integer data, the binwidth is also integer, so that each bin always covers the same number of possible values, but I guess that's possibly a more domain-specific issue.) Before the PR: In [1]: np.histogram_bin_edges(np.tile(np.arange(10), 1000), "auto") Out[1]: array([0. , 0.45, 0.9 , 1.35, 1.8 , 2.25, 2.7 , 3.15, 3.6 , 4.05, 4.5 , 4.95, 5.4 , 5.85, 6.3 , 6.75, 7.2 , 7.65, 8.1 , 8.55, 9. ]) After: In [1]: np.histogram_bin_edges(np.tile(np.arange(10), 1000), "auto") Out[1]: array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
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‎doc/release/upcoming_changes/12150.improvement.rst

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``histogram`` auto-binning now returns bin sizes >=1 for integer input data
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---------------------------------------------------------------------------
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For integer input data, bin sizes smaller than 1 result in spurious empty
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bins. This is now avoided when the number of bins is computed using one of the
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algorithms provided by `histogram_bin_edges`.

‎numpy/lib/_histograms_impl.py

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@@ -410,6 +410,8 @@ def _get_bin_edges(a, bins, range, weights):
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# Do not call selectors on empty arrays
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width = _hist_bin_selectors[bin_name](a, (first_edge, last_edge))
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if width:
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if np.issubdtype(a.dtype, np.integer) and width < 1:
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width = 1
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n_equal_bins = int(np.ceil(_unsigned_subtract(last_edge, first_edge) / width))
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else:
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# Width can be zero for some estimators, e.g. FD when
@@ -625,6 +627,9 @@ def histogram_bin_edges(a, bins=10, range=None, weights=None):
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The simplest and fastest estimator. Only takes into account the
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data size.
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Additionally, if the data is of integer dtype, then the binwidth will never
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be less than 1.
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Examples
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--------
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>>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5])

‎numpy/lib/tests/test_histograms.py

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@@ -469,7 +469,7 @@ def test_small(self):
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'doane': 3, 'sqrt': 2, 'stone': 1}}
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for testlen, expectedResults in small_dat.items():
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testdat = np.arange(testlen)
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testdat = np.arange(testlen).astype(float)
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for estimator, expbins in expectedResults.items():
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a, b = np.histogram(testdat, estimator)
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assert_equal(len(a), expbins, err_msg="For the {0} estimator "
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assert_array_equal(hist, hist32)
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assert_array_equal(edges, edges32)
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@pytest.mark.parametrize("bins", ['auto', 'fd', 'doane', 'scott',
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'stone', 'rice', 'sturges'])
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def test_integer(self, bins):
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"""
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Test that bin width for integer data is at least 1.
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"""
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assert_equal(
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np.histogram_bin_edges(np.tile(np.arange(9), 1000), bins),
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np.arange(9))
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def test_integer_non_auto(self):
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"""
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Test that the bin-width>=1 requirement *only* applies to auto binning.
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"""
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assert_equal(
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np.histogram_bin_edges(np.tile(np.arange(9), 1000), 16),
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np.arange(17) / 2)
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assert_equal(
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np.histogram_bin_edges(np.tile(np.arange(9), 1000), [.1, .2]),
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[.1, .2])
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def test_simple_weighted(self):
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"""
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Check that weighted data raises a TypeError

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