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| author | Allan Haldane <allan.haldane@gmail.com> | 2015-01-16 23:53:41 -0500 |
|---|---|---|
| committer | Allan Haldane <allan.haldane@gmail.com> | 2015-01-22 17:36:43 -0500 |
| commit | 1bd0b4e8f176cd80e81b5f50832db5f8ba1ee1e9 (patch) | |
| tree | fce876400e049c7927cfe4b62ee4d1ca00a8ed7b /numpy/add_newdocs.py | |
| parent | b69035e8ea28bd759b929822aaba544d3c5f8c30 (diff) | |
| download | numpy-1bd0b4e8f176cd80e81b5f50832db5f8ba1ee1e9.tar.gz | |
DOC: improve record/structured array nomenclature & guide
This update adds a section better describing record arrays in the user
guide (numpy/doc/structured_arrays.py).
It also corrects nomenclature, such that "structured array" refers to
ndarrays with structured dtype, "record array" refers to modified
ndarrays as created by np.rec.array, and "recarray" refers to ndarrays
viewed as np.recarray. See the note at the end of the structured
array user guide.
Diffstat (limited to 'numpy/add_newdocs.py')
| -rw-r--r-- | numpy/add_newdocs.py | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 73efdb6a9..66b889cc9 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -4629,7 +4629,7 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('view', >>> print x [(1, 20) (3, 4)] - Using a view to convert an array to a record array: + Using a view to convert an array to a recarray: >>> z = x.view(np.recarray) >>> z.a @@ -5875,17 +5875,18 @@ add_newdoc('numpy.core.multiarray', 'dtype', >>> np.dtype(np.int16) dtype('int16') - Record, one field name 'f1', containing int16: + Structured type, one field name 'f1', containing int16: >>> np.dtype([('f1', np.int16)]) dtype([('f1', '<i2')]) - Record, one field named 'f1', in itself containing a record with one field: + Structured type, one field named 'f1', in itself containing a structured + type with one field: >>> np.dtype([('f1', [('f1', np.int16)])]) dtype([('f1', [('f1', '<i2')])]) - Record, two fields: the first field contains an unsigned int, the + Structured type, two fields: the first field contains an unsigned int, the second an int32: >>> np.dtype([('f1', np.uint), ('f2', np.int32)]) |