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author | Mark Wiebe <mwiebe@enthought.com> | 2011-07-05 12:25:50 -0500 |
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committer | Charles Harris <charlesr.harris@gmail.com> | 2011-07-06 16:24:13 -0600 |
commit | f2f7bd6510b24f6f1c12642afb888f82da0a353e (patch) | |
tree | 937d9076054ec9571793e860022b95642b6b5ea3 /doc | |
parent | bc0a86219f62088e108a9464ce928890f2525084 (diff) | |
download | numpy-f2f7bd6510b24f6f1c12642afb888f82da0a353e.tar.gz |
NEP: missing-data: Fix copy/paste/edit typo for np.all example
Diffstat (limited to 'doc')
-rw-r--r-- | doc/neps/missing-data.rst | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/doc/neps/missing-data.rst b/doc/neps/missing-data.rst index 66d70ae48..0dde5bb0f 100644 --- a/doc/neps/missing-data.rst +++ b/doc/neps/missing-data.rst @@ -176,8 +176,8 @@ provides a starting point. For example,:: - >>> np.array([1.0, 2.0, np.NA, 7.0], masked=True) - array([1., 2., NA, 7.], masked=True) + >>> np.array([1.0, 2.0, np.NA, 7.0], namasked=True) + array([1., 2., NA, 7.], namasked=True) >>> np.array([1.0, 2.0, np.NA, 7.0], dtype='NA[f8]') array([1., 2., NA, 7.], dtype='NA[<f8]') @@ -189,14 +189,14 @@ It may be worth overloading the np.NA __call__ method to accept a dtype, returning a zero-dimensional array with a missing value of that dtype. Without doing this, NA printouts would look like:: - >>> np.sum(np.array([1.0, 2.0, np.NA, 7.0], masked=True)) - array(NA, dtype='float64', masked=True) + >>> np.sum(np.array([1.0, 2.0, np.NA, 7.0], namasked=True)) + array(NA, dtype='float64', namasked=True) >>> np.sum(np.array([1.0, 2.0, np.NA, 7.0], dtype='NA[f8]')) array(NA, dtype='NA[<f8]') but with this, they could be printed as:: - >>> np.sum(np.array([1.0, 2.0, np.NA, 7.0], masked=True)) + >>> np.sum(np.array([1.0, 2.0, np.NA, 7.0], namasked=True)) NA('float64') >>> np.sum(np.array([1.0, 2.0, np.NA, 7.0], dtype='NA[f8]')) NA('NA[<f8]') @@ -446,7 +446,7 @@ their behavior is through a series of examples:: True >>> np.all(np.array([True, True, True], namasked=True)) - False + True >>> np.all(np.array([True, NA, True], namasked=True)) NA >>> np.all(np.array([False, NA, True], namasked=True)) |