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-rw-r--r--doc/source/user/whatisnumpy.rst17
1 files changed, 13 insertions, 4 deletions
diff --git a/doc/source/user/whatisnumpy.rst b/doc/source/user/whatisnumpy.rst
index cd74a8de3..abaa2bfed 100644
--- a/doc/source/user/whatisnumpy.rst
+++ b/doc/source/user/whatisnumpy.rst
@@ -91,6 +91,11 @@ idiom is even simpler! This last example illustrates two of NumPy's
features which are the basis of much of its power: vectorization and
broadcasting.
+.. _whatis-vectorization:
+
+Why is NumPy Fast?
+------------------
+
Vectorization describes the absence of any explicit looping, indexing,
etc., in the code - these things are taking place, of course, just
"behind the scenes" in optimized, pre-compiled C code. Vectorized
@@ -120,9 +125,13 @@ the shape of the larger in such a way that the resulting broadcast is
unambiguous. For detailed "rules" of broadcasting see
`numpy.doc.broadcasting`.
+Who Else Uses NumPy?
+--------------------
+
NumPy fully supports an object-oriented approach, starting, once
again, with `ndarray`. For example, `ndarray` is a class, possessing
-numerous methods and attributes. Many of its methods mirror
-functions in the outer-most NumPy namespace, giving the programmer
-complete freedom to code in whichever paradigm she prefers and/or
-which seems most appropriate to the task at hand.
+numerous methods and attributes. Many of its methods are mirrored by
+functions in the outer-most NumPy namespace, allowing the programmer
+to code in whichever paradigm they prefer. This flexibility has allowed the
+NumPy array dialect and NumPy `ndarray` class to become the *de-facto* language
+of multi-dimensional data interchange used in Python.