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author | Allan Haldane <ealloc@gmail.com> | 2017-11-18 19:39:29 +0100 |
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committer | GitHub <noreply@github.com> | 2017-11-18 19:39:29 +0100 |
commit | 365fc9d19b52325bd5618aac1a3fa7b8a6be3b3c (patch) | |
tree | fba9b1e900840c057cea1f7bdc872c477e0aa60c /numpy/lib/arraysetops.py | |
parent | 2d140f11857fc1353d6b2dcbb801e693128fd09a (diff) | |
parent | ac6b1a902b99e340cf7eeeeb7392c91e38db9dd8 (diff) | |
download | numpy-365fc9d19b52325bd5618aac1a3fa7b8a6be3b3c.tar.gz |
Merge pull request #10021 from eric-wieser/no-dtype-bool-repr
ENH: Don't show the boolean dtype in array_repr
Diffstat (limited to 'numpy/lib/arraysetops.py')
-rw-r--r-- | numpy/lib/arraysetops.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index f3301af92..a9426cdf3 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -435,12 +435,12 @@ def in1d(ar1, ar2, assume_unique=False, invert=False): >>> states = [0, 2] >>> mask = np.in1d(test, states) >>> mask - array([ True, False, True, False, True], dtype=bool) + array([ True, False, True, False, True]) >>> test[mask] array([0, 2, 0]) >>> mask = np.in1d(test, states, invert=True) >>> mask - array([False, True, False, True, False], dtype=bool) + array([False, True, False, True, False]) >>> test[mask] array([1, 5]) """ @@ -552,13 +552,13 @@ def isin(element, test_elements, assume_unique=False, invert=False): >>> mask = np.isin(element, test_elements) >>> mask array([[ False, True], - [ True, False]], dtype=bool) + [ True, False]]) >>> element[mask] array([2, 4]) >>> mask = np.isin(element, test_elements, invert=True) >>> mask array([[ True, False], - [ False, True]], dtype=bool) + [ False, True]]) >>> element[mask] array([0, 6]) @@ -568,13 +568,13 @@ def isin(element, test_elements, assume_unique=False, invert=False): >>> test_set = {1, 2, 4, 8} >>> np.isin(element, test_set) array([[ False, False], - [ False, False]], dtype=bool) + [ False, False]]) Casting the set to a list gives the expected result: >>> np.isin(element, list(test_set)) array([[ False, True], - [ True, False]], dtype=bool) + [ True, False]]) """ element = np.asarray(element) return in1d(element, test_elements, assume_unique=assume_unique, |