Learning NumPy Array

NumPy is based on Python, so it is required to have Python installed. On some operating systems, Python is already installed. You, however, need to check whether the Python version is compatible with the NumPy version you want to install. There are many implementations of Python, including commercial implementations and distributions. In this book, we will focus on the standard CPython implementation, which is guaranteed to be compatible with NumPy.

NumPy has binary installers for Windows, various Linux distributions, and Mac OS X. There is also a source distribution, if you prefer that. You need to have Python 2.4.x or above installed on your system. Python 2.7.6 is currently the best Python version to have because most scientific Python libraries support it.

Installing NumPy on Windows is a necessary but, fortunately, straightforward task that we will cover in detail. You only need to download an installer, and a wizard will guide you through the installation steps. It is recommended that Matplotlib, SciPy, and IPython be installed. However, this is not required to enjoy this book. The actions we will take are as follows:

Installing NumPy and related recommended software on Linux depends on the distribution you have. We will discuss how you would install NumPy from the command line although you could probably use graphical installers; it depends on your distribution (distro). The commands to install Matplotlib, SciPy, and IPython are the same—only the package names are different. Installing Matplotlib, SciPy, and IPython is recommended, but optional.

Most Linux distributions have NumPy packages. We will go through the necessary steps for some of the popular Linux distros:

The following table gives an overview of the Linux distributions and corresponding package names for NumPy, SciPy, Matplotlib, and IPython:

You can install NumPy, Matplotlib, and SciPy on the Mac with a graphical installer or from the command line with a port manager, such as MacPorts or Fink, depending on your preference.

We will install NumPy with a GUI installer using the following steps:

Alternatively, we can install NumPy, SciPy, Matplotlib, and IPython through the MacPorts route or with Fink. The following installation steps install all these packages. We only need NumPy for the tutorials in this book, so please omit the packages you are not interested in.

As a last resort or if we want to have the latest code, we can build from source. In practice it shouldn't be that hard although, depending on your operating system, you might run into problems. As operating systems and related software are rapidly evolving, the best you can do is search online or ask for help. In this chapter, we give pointers on good places to look for help.

The steps to install NumPy from source are straightforward and given here. We can retrieve the source code for NumPy with .git as follows:

git clone git://github.com/numpy/numpy.git numpy

Install on /usr/local with the following command:

python setup.py build
sudo python setup.py install --prefix=/usr/local

To build, we need a C compiler such as GCC and the Python header files in the python-dev or python-devel package.

After going through the installation of NumPy, it's time to have a look at NumPy arrays. NumPy arrays are more efficient than Python lists when it comes to numerical operations. NumPy arrays are in fact specialized objects with extensive optimizations. NumPy code requires less explicit loops than the equivalent Python code. This is based on vectorization.

If we go back to high school mathematics, then we should remember the concepts of scalars and vectors. The number 2 for instance is a scalar. When we add 2 and 2, we are performing scalar addition. We can form a vector out of a group of scalars. In Python programming terms, we will then have a one-dimensional array. This concept can of course be extended to higher dimensions. Performing an operation on two arrays such as addition can be reduced to a group of scalar operations. In straight Python, we will do that with loops going through each element in the first array and adding it to the corresponding element in the second array. However, this is more verbose than the way it is done in mathematics. In mathematics, we treat the addition of two vectors as a single operation. That's the way NumPy arrays do it too and there are certain optimizations using low-level C routines, which make these basic operations more efficient. We will cover NumPy arrays in more detail in the next chapter.

def pythonsum(n):
   a = range(n)
   b = range(n)
   c = []

   for i in range(len(a)):
       a[i] = i ** 2
       b[i] = i ** 3
       c.append(a[i] + b[i])

   return c

def numpysum(n):
  a = numpy.arange(n) ** 2
  b = numpy.arange(n) ** 3
  c = a + b
  return c

#!/usr/bin/env/python

import sys
from datetime import datetime
import numpy as np

"""
 This program demonstrates vector addition the Python way.
 Run from the command line as follows
     
  python vectorsum.py n
 
 where n is an integer that specifies the size of the vectors.

 The first vector to be added contains the squares of 0 up to n.
 The second vector contains the cubes of 0 up to n.
 The program prints the last 2 elements of the sum and the elapsed time.
"""

def numpysum(n):
   a = np.arange(n) ** 2
   b = np.arange(n) ** 3
   c = a + b

   return c

def pythonsum(n):
   a = range(n)
   b = range(n)
   c = []

   for i in range(len(a)):
       a[i] = i ** 2
       b[i] = i ** 3
       c.append(a[i] + b[i])

   return c
   
size = int(sys.argv[1])

start = datetime.now()
c = pythonsum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "PythonSum elapsed time in microseconds", delta.microseconds

start = datetime.now()
c = numpysum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "NumPySum elapsed time in microseconds", delta.microseconds

$ python vectorsum.py 1000
The last 2 elements of the sum [995007996, 998001000]
PythonSum elapsed time in microseconds 707
The last 2 elements of the sum [995007996 998001000]
NumPySum elapsed time in microseconds 171
$ python vectorsum.py 2000
The last 2 elements of the sum [7980015996, 7992002000]
PythonSum elapsed time in microseconds 1420
The last 2 elements of the sum [7980015996 7992002000]
NumPySum elapsed time in microseconds 168
$ python vectorsum.py 4000
The last 2 elements of the sum [63920031996, 63968004000]
PythonSum elapsed time in microseconds 2829
The last 2 elements of the sum [63920031996 63968004000]
NumPySum elapsed time in microseconds 274

The main documentation website for NumPy and SciPy is at http://docs.scipy.org/doc/. On this web page, we can browse the NumPy reference at http://docs.scipy.org/doc/numpy/reference/ and the user guide, as well as several tutorials.

NumPy has a wiki with lots of documentation at http://docs.scipy.org/numpy/Front%20Page/.

The NumPy and SciPy forum can be found at http://ask.scipy.org/en.

The popular Stack Overflow software development forum has hundreds of questions tagged as numpy. To view them, go to http://stackoverflow.com/questions/tagged/numpy.

If you are really stuck with a problem or you want to be kept informed of NumPy's development, you can subscribe to the NumPy discussion mailing list. The e-mail address is <numpy-discussion@scipy.org>. The number of e-mails per day is not too high, and there is almost no spam to speak of. Most importantly, developers actively involved with NumPy also answer questions asked on the discussion group. The complete list can be found at http://www.scipy.org/Mailing_Lists.

For IRC users, there is an IRC channel on irc://irc.freenode.net. The channel is called #scipy, but you can also ask NumPy questions since SciPy users also have knowledge of NumPy, as SciPy is based on NumPy. There are at least 50 members on the SciPy channel at all times.

In this chapter, we installed NumPy and other recommended software that we will be using in some tutorials. We got a vector addition program working and convinced ourselves that NumPy has superior performance. In addition, we explored the available NumPy documentation and online resources.

In the next chapter, we will take a look under the hood and explore some fundamental concepts, including arrays and data types.