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Diffstat (limited to 'doc')
| -rw-r--r-- | doc/source/user/whatisnumpy.rst | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/doc/source/user/whatisnumpy.rst b/doc/source/user/whatisnumpy.rst index 1c3f96b8b..cd74a8de3 100644 --- a/doc/source/user/whatisnumpy.rst +++ b/doc/source/user/whatisnumpy.rst @@ -86,14 +86,14 @@ code. In NumPy c = a * b does what the earlier examples do, at near-C speeds, but with the code -simplicity we expect from something based on Python (indeed, the NumPy -idiom is even simpler!). This last example illustrates two of NumPy's +simplicity we expect from something based on Python. Indeed, the NumPy +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. 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 +"behind the scenes" in optimized, pre-compiled C code. Vectorized code has many advantages, among which are: - vectorized code is more concise and easier to read @@ -104,25 +104,25 @@ code has many advantages, among which are: (making it easier, typically, to correctly code mathematical constructs) -- vectorization results in more "Pythonic" code (without - vectorization, our code would still be littered with inefficient and +- vectorization results in more "Pythonic" code. Without + vectorization, our code would be littered with inefficient and difficult to read ``for`` loops. Broadcasting is the term used to describe the implicit element-by-element behavior of operations; generally speaking, in -NumPy all operations (i.e., not just arithmetic operations, but -logical, bit-wise, functional, etc.) behave in this implicit +NumPy all operations, not just arithmetic operations, but +logical, bit-wise, functional, etc., behave in this implicit element-by-element fashion, i.e., they broadcast. Moreover, in the example above, ``a`` and ``b`` could be multidimensional arrays of the same shape, or a scalar and an array, or even two arrays of with -different shapes. Provided that the smaller array is "expandable" to +different shapes, provided that the smaller array is "expandable" to the shape of the larger in such a way that the resulting broadcast is -unambiguous (for detailed "rules" of broadcasting see -`numpy.doc.broadcasting`). +unambiguous. For detailed "rules" of broadcasting see +`numpy.doc.broadcasting`. 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 it's methods mirror -functions in the outer-most NumPy namespace, giving the programmer has +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. |