diff options
Diffstat (limited to 'numpy/doc/glossary.py')
| -rw-r--r-- | numpy/doc/glossary.py | 15 |
1 files changed, 9 insertions, 6 deletions
diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py index f591a5424..dc7c75a0a 100644 --- a/numpy/doc/glossary.py +++ b/numpy/doc/glossary.py @@ -181,7 +181,7 @@ iterable [1, 4, 9] It is often used in combintion with ``enumerate``:: - + >>> keys = ['a','b','c'] >>> for n, k in enumerate(keys): ... print "Key %d: %s" % (n, k) ... @@ -242,13 +242,16 @@ masked array >>> x = np.ma.masked_array([np.nan, 2, np.nan], [True, False, True]) >>> x masked_array(data = [-- 2.0 --], - mask = [ True False True], - fill_value=1e+20) + mask = [ True False True], + fill_value = 1e+20) + <BLANKLINE> >>> x + [1, 2, 3] masked_array(data = [-- 4.0 --], - mask = [ True False True], - fill_value=1e+20) + mask = [ True False True], + fill_value = 1e+20) + <BLANKLINE> + Masked arrays are often used when operating on arrays containing missing or invalid entries. @@ -366,7 +369,7 @@ tuple This is often used when a function returns multiple values: >>> def return_many(): - ... return 1, 'alpha' + ... return 1, 'alpha', None >>> a, b, c = return_many() >>> a, b, c |