NumPy 1.5 Beginner's Guide

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 corresponds with the NumPy version you want to install.

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. We will go through the various steps required to install Python on the following operating systems:

Installing NumPy on Windows is straightforward. You only need to download an installer, and a wizard will guide you through the installation steps.

Installing NumPy on Windows is necessary but, fortunately, a straightforward task. The actions we will take are as follows:

Installing NumPy 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).

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 NumPy package names.

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

We will install NumPy with a GUI installer.

Alternatively we can install NumPy through the MacPorts route. It is shown as follows:

We can retrieve the source code for NumPy with git. This is shown as follows:

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

Install /usr/local with the following command:

python setup.py buildsudo 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.

NumPy arrays are more efficient than Python lists, when it comes to numerical operations. NumPy code requires less explicit loops than equivalent Python code.

Imagine that we want to add two vectors called a and b. Vector a holds the squares of integers 0 to n, for instance, if n = 3, then a = (0, 1, 4). Vector b holds the cubes of integers 0 to n, so if n = 3, then b = (0, 1, 8). How would you do that using plain Python? After we come up with a solution, we will compare it with the NumPy equivalent.

Notice that numpysum() does not need a for loop. Also, we used the arange function from NumPy that creates a NumPy array for us with integers 0 to n. The arange function was imported; that is why it is prefixed with numpy.

Now comes the fun part. Remember that it is mentioned in the preface that NumPy is faster when it comes to array operations. How much faster is Numpy, though? The following program will show us by measuring the elapsed time in microseconds, for the numpysum and pythonsum functions. It also prints the last two elements of the vector sum. Let's check that we get the same answers by using Python and NumPy:

import sys
from datetime import datetime
import numpy

def numpysum(n):
  a = numpy.arange(n) ** 2
  b = numpy.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

The output of the program for 1000, 2000, and 3000 vector elements is as follows:

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

Scientists and engineers are used to experimenting. IPython was created by scientists with experimentation in mind. The interactive environment that IPython provides is viewed by many as a direct answer to Matlab, Mathematica, and Maple. You can find more information, including installation instructions, at: http://ipython.org/

IPython is free, open source, and available for Linux, Unix, Mac OS X, and Windows. The IPython authors only request that you cite IPython in scientific work where IPython was used. Here is the list of features of IPython:

The Pylab switch imports all the Scipy, NumPy, and Matplotlib packages. Without this switch, we would have to import every package we need ourselves.

All we need to do is enter the following instruction on the command line:

$ ipython -pylab
Python 2.6.1 (r261:67515, Jun 24 2010, 21:47:49) 
Type "copyright", "credits" or "license" for more information.

IPython 0.10 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object'. ?object also works, ?? prints more.

  Welcome to pylab, a matplotlib-based Python environment.
  For more information, type 'help(pylab)'.

In [1]: quit()

quit() or Ctrl + D quits the IPython shell. We might want to be able to go back to our experiments. In IPython, it is easy to save a session for later.

In [1]: %logstart
Activating auto-logging. Current session state plus future input saved.
Filename       : ipython_log.py
Mode           : rotate
Output logging : False
Raw input log  : False
Timestamping   : False
State          : active

Let's say we have the vector addition program that we made in the current directory. We can run the script as follows:

In [1]: ls
README        vectorsum.py

In [2]: %run -i vectorsum.py 1000

As you probably remember, 1000 specifies the number of elements in a vector. The -d switch of %run starts an ipdb debugger with 'c' the script is started. 'n' steps through the code. Typing quit at the ipdb prompt exits the debugger.

In [2]: %run -d vectorsum.py 1000
*** Blank or comment
*** Blank or comment
Breakpoint 1 at /Users/ivanidris/Documents/numpyBeginnersGuide/book/ch1code/vectorsum.py:3

><string>(1)<module>()

ipdb> c
> /Users/ivanidris/Documents/numpyBeginnersGuide/book/ch1code/vectorsum.py(3)<module>()
      2 
1---> 3 import sys
      4 from datetime import datetime

ipdb> n
> /Users/ivanidris/Documents/numpyBeginnersGuide/book/ch1code/vectorsum.py(4)<module>()
1     3 import sys
----> 4 from datetime import datetime
      5 import numpy

ipdb> n
> /Users/ivanidris/Documents/numpyBeginnersGuide/book/ch1code/vectorsum.py(5)<module>()
      4 from datetime import datetime
----> 5 import numpy
      6 

ipdb> quit

We can also profile our script by passing the -p option to %run.

In [4]: %run -p vectorsum.py 1000
         1058 function calls (1054 primitive calls) in 0.002 CPU seconds

   Ordered by: internal time

ncallstottimepercallcumtimepercallfilename:lineno(function)
        1    0.001    0.0010.0010.001 vectorsum.py:28(pythonsum)
        1    0.001    0.001    0.002    0.002 {execfile}
     1000    0.000    0.0000.0000.000 {method 'append' of 'list' objects}
        1    0.000    0.000    0.002    0.002 vectorsum.py:3(<module>)
        1    0.000    0.0000.0000.000 vectorsum.py:21(numpysum)
        3    0.000    0.0000.0000.000 {range}
        1    0.000    0.0000.0000.000 arrayprint.py:175(_array2string)
      3/1    0.000    0.0000.0000.000 arrayprint.py:246(array2string)
        2    0.000    0.0000.0000.000 {method 'reduce' of 'numpy.ufunc' objects}
        4    0.000    0.0000.0000.000 {built-in method now}
        2    0.000    0.0000.0000.000 arrayprint.py:486(_formatInteger)
        2    0.000    0.0000.0000.000 {numpy.core.multiarray.arange}
        1    0.000    0.0000.0000.000 arrayprint.py:320(_formatArray)
      3/1    0.000    0.0000.0000.000 numeric.py:1390(array_str)
        1    0.000    0.0000.0000.000 numeric.py:216(asarray)
        2    0.000    0.0000.0000.000 arrayprint.py:312(_extendLine)
        1    0.000    0.0000.0000.000 fromnumeric.py:1043(ravel)
        2    0.000    0.0000.0000.000 arrayprint.py:208(<lambda>)
        1    0.000    0.000    0.002    0.002<string>:1(<module>)
       11    0.000    0.0000.0000.000 {len}
        2    0.000    0.0000.0000.000 {isinstance}
        1    0.000    0.0000.0000.000 {reduce}
        1    0.000    0.0000.0000.000 {method 'ravel' of 'numpy.ndarray' objects}
        4    0.000    0.0000.0000.000 {method 'rstrip' of 'str' objects}
        3    0.000    0.0000.0000.000 {issubclass}
        2    0.000    0.0000.0000.000 {method 'item' of 'numpy.ndarray' objects}
        1    0.000    0.0000.0000.000 {max}
        1    0.000    0.0000.0000.000 {method 'disable' of '_lsprof.Profiler' objects}

This gives us a bit more insight in the workings of our program. In addition, we can now identify performance bottlenecks. The %hist command shows the commands history.

In [2]: a=2+2

In [3]: a
Out[3]: 4

In [4]: %hist
1: _ip.magic("hist ")
2: a=2+2
3: a

I hope you agree that IPython is a really useful tool!

When we are in IPython's pylab mode, we can open manual pages for NumPy functions with the help command. It is not necessary to know the name of a function. We can type a few characters and then let tab completion do its work. Let's, for instance, browse the available information for the arange function.

In [2]: help ar<Tab>
arangearccosarccosharcsinarcsinh
arctan        arctan2       arctanhargmaxargmin
argsortargwhere      around        array2string  array_equal
array_equivarray_reprarray_splitarray_str     arrow         
array

In [2]: help arange

Another option is to put a question mark behind the function name.

In [3]: arange?

The main documentation website for NumPy and SciPy is at http://docs.scipy.org/doc/. Through this webpage, 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 "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 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.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. We got a vector addition program working and convinced ourselves that NumPy has superior performance. We were introduced to the IPython interactive shell. 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.