From 1bd0b4e8f176cd80e81b5f50832db5f8ba1ee1e9 Mon Sep 17 00:00:00 2001
From: Allan Haldane <allan.haldane@gmail.com>
Date: Fri, 16 Jan 2015 23:53:41 -0500
Subject: 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.
---
 numpy/add_newdocs.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

(limited to 'numpy/add_newdocs.py')

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)])
-- 
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