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author | Stefan van der Walt <stefanv@berkeley.edu> | 2018-07-25 11:28:33 -0700 |
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committer | Stefan van der Walt <stefanv@berkeley.edu> | 2018-07-25 11:28:33 -0700 |
commit | 530a4ae11a19615626fe9ca52414ec54c9f8ab47 (patch) | |
tree | 9bda6027d4e8c13d7278fe6e235ce4a48ceaf8ae /doc | |
parent | 47904579abb339ca380bf36c4cc72ebfbd0d0643 (diff) | |
download | numpy-530a4ae11a19615626fe9ca52414ec54c9f8ab47.tar.gz |
Clarify section on alternative array types
Diffstat (limited to 'doc')
-rw-r--r-- | doc/neps/scope.rst | 4 |
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 |