User guide

NumPy User Guide, Release 1.9.0
This saves all the overhead involved in interpreting the Python code and manipulating Python objects, but at the
expense of the benefits gained from coding in Python. Furthermore, the coding work required increases with the
dimensionality of our data. In the case of a 2-D array, for example, the C code (abridged as before) expands to
for (i = 0; i < rows; i++): {
for (j = 0; j < columns; j++): {
c[i][j] = a[i][j]
*
b[i][j];
}
}
NumPy gives us the best of both worlds: element-by-element operations are the “default mode” when an ndarray is
involved, but the element-by-element operation is speedily executed by pre-compiled C 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 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 code has many advantages, among
which are:
• vectorized code is more concise and easier to read
• fewer lines of code generally means fewer bugs
• the code more closely resembles standard mathematical notation (making it easier, typically, to correctly code
mathematical constructs)
• 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, 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 the shape of the larger in such a way that the resulting broadcast is 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 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.
1.2 Building and installing NumPy
1.2.1 Binary installers
In most use cases the best way to install NumPy on your system is by using an installable binary package for your
operating system.
Windows
Good solutions for Windows are, Enthought Canopy (which provides binary installers for Windows, OS X and Linux)
and Python (x, y). Both of these packages include Python, NumPy and many additional packages.
4 Chapter 1. Introduction