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authorStefan van der Walt <stefanv@berkeley.edu>2018-07-25 11:28:33 -0700
committerStefan van der Walt <stefanv@berkeley.edu>2018-07-25 11:28:33 -0700
commit530a4ae11a19615626fe9ca52414ec54c9f8ab47 (patch)
tree9bda6027d4e8c13d7278fe6e235ce4a48ceaf8ae /doc
parent47904579abb339ca380bf36c4cc72ebfbd0d0643 (diff)
downloadnumpy-530a4ae11a19615626fe9ca52414ec54c9f8ab47.tar.gz
Clarify section on alternative array types
Diffstat (limited to 'doc')
-rw-r--r--doc/neps/scope.rst4
1 files changed, 2 insertions, 2 deletions
diff --git a/doc/neps/scope.rst b/doc/neps/scope.rst
index 20dc0f9b8..440f6256e 100644
--- a/doc/neps/scope.rst
+++ b/doc/neps/scope.rst
@@ -18,10 +18,10 @@ Here, we describe aspects of N-d array computation that are within scope for Num
- **Python API and a C API** to the ndarray's methods and attributes.
-- Other **specialized types of N-dimensional arrays**:
+- Other **specialized types or uses of N-dimensional arrays**:
- Masked arrays
- - Structured arrays
+ - Structured arrays (informally known as record arrays)
- Memory mapped arrays
- Historically, NumPy has included the following **basic functionality