NumPy 1.5 Beginner's Guide

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By Ivan Idris
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  1. NumPy Quick Start

About this book

In today's world of science and technology, the hype is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy is the fundamental package needed for scientific computing with Python. NumPy will give you both speed and high productivity. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favourite programming language.

NumPy 1.5 Beginner's Guide will teach you about NumPy from scratch. It includes everything from installation, functions, matrices, and modules to testing, all explained with appropriate examples.

Numpy 1.5 Beginner's Guide will teach you about installing and using NumPy and related concepts.

This book will give you a solid foundation in NumPy arrays and universal functions. At the end of the book, we will explore related scientific computing projects such as Matplotlib for plotting and the SciPy project through examples.

NumPy 1.5 Beginner's Guide will help you be productive with NumPy and write clean and fast code.

Publication date:
November 2011


Chapter 1. NumPy Quick Start

Let's get started. We will install NumPy on different operating systems and have a look at some simple code that uses NumPy. The IPython interactive shell is introduced briefly. As mentioned in the preface, SciPy is closely related to NumPy, so you will see the SciPy name appearing here and there. At the end of this chapter, you will find pointers on how to find additional information online if you get stuck or are uncertain about the best way to solve problems.

In this chapter, we shall:

  • Install Python and NumPy on Windows

  • Install Python and NumPy on Linux

  • Install Python and NumPy on Macintosh

  • Write simple NumPy code

  • Get to know IPython

  • Browse online documentation and resources



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.


Time for action – installing Python on different operating systems

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:

  1. Debian and Ubuntu : Python might already be installed on Debian and Ubuntu but the development headers are usually not. On Debian and Ubuntu install python and python-dev with the following commands:

    sudo apt-get install python
    sudo apt-get install python-dev
  2. Windows : The Windows Python installer can be found at On this website, we can also find installers for Mac OS X and source tarballs for Linux, Unix, and Mac OS X.

  3. Mac: Python comes pre-installed on Mac OS X . We can also get Python through MacPorts, Fink, or similar projects.

    We can install, for instance, the Python 2.6 port by running the following command:

    sudo port install python26

    LAPACK does not need to be present but, if it is, NumPy will detect it and use it during the installation phase. It is recommended to install LAPACK for serious numerical analysis.

What just happened?

We installed Python on Debian, Ubuntu, Windows, and the Mac.


Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at If you purchased this book elsewhere, you can visit and register to have the files e-mailed directly to you.



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


Time for action – installing NumPy on Windows

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

  1. Download the NumPy installer: Download a NumPy installer for Windows from the SourceForge website

    Choose the appropriate version. In this example, we chose numpy-1.5.1-win32-superpack-python2.6.exe.

  2. Open the installer: Open the EXE installer by double clicking on it.

  3. NumPy features: Now, we see a description of NumPy and its features. Click Next.

  4. Install Python: If you have Python installed, it should automatically be detected. If it is not detected, maybe your path settings are wrong. At the end of this chapter, resources are listed in case you have problems with installing NumPy:

  5. Finish the installation: In this example, Python 2.6 was found. Click Next if Python is found; otherwise, click Cancel and install Python (NumPy cannot be installed without Python). Click Next. This is the point of no return. Well, kind of, but it is best to make sure that you are installing to the proper directory and so on and so forth. Now the real installation starts. This may take a while:

What just happened?

We installed NumPy on Windows.



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).


Time for action – installing NumPy on Linux

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

  1. Installing NumPy on Red Hat: Run the following instructions from the command line:

    yum install python-numpy
  2. Installing NumPy on Mandriva : To install NumPy on Mandriva, run the following command line instruction:

    urpmi python-numpy
  3. Installing NumPyon Gentoo : To install NumPy on Gentoo run the following command line instruction:

    sudo emerge numpy
  4. Installing NumPyon Debian andUbuntu : On Debian or Ubuntu, we need to type the following:

    sudo apt-get install python-numpy

The following table gives an overview of the Linux distributions and corresponding NumPy package names.

Linux distribution

Package name

Arch Linux









python-numpy, python-numpy-devel



What just happened?

We installed NumPy on various Linux distributions.


Mac OS X

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.


Time for action – installing NumPy on Mac OS X with a GUI installer

We will install NumPy with a GUI installer.

  1. Download the GUI installer: We can get a NumPy installer from the SourceForge website Download the appropriate DMG file. Usually the latest one is the best:

  2. Open the DMG file : Open the DMG file (in this example,

    • Double-click on the icon of the opened box, the one having a subscript that ends with .mpkg. We will be presented with the welcome screen of the installer.

    • Click on the Continue button to go to the ReadMe screen, where we will be presented with a short description of NumPy:

    • Continue to the License screen.

  3. Accept the license: Read the license, click Continue and then the Accept button, when prompted to accept the license. Continue through the next screens and click Finish at the end.

What just happened?

We installed NumPy on Mac OS X with a GUI installer.


Time for action – installing NumPy with MacPorts or Fink

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

  1. Installing with MacPorts: Type the following command:

    sudo port install py-numpy
  2. Installing with Fink: Fink also has packages for NumPy—scipy-core-py24, scipy-core-py25, and scipy-core-py26. We can install the one for Python 2.6 with the following package:

    fink install scipy-core-py26

What just happened?

We installed NumPy on Mac OS X with MacPorts and Fink.


Building from source

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

git clone git:// numpy

Install /usr/local with the following command:

python buildsudo python 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.


Time for action – adding vectors

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.

  1. Adding vectors using pure Python: The following function solves the vector addition problem using pure Python without NumPy:

    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
  2. Adding vector susing NumPy: Following is a function that achieves the same with NumPy.

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

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

What just happened?

Clearly, NumPy is much faster than the equivalent normal Python code. One thing is certain; we get the same results whether we are using NumPy or not. However, the result that is printed differs in representation. Notice that the result from the numpysum function does not have any commas. How come? Obviously we are not dealing with a Python list but with a NumPy array. It was mentioned in the preface that NumPy arrays are specialized data structures for numerical data. We will learn more about NumPy arrays in the next chapter.

Pop Quiz - functioning of arange function

  1. What does arange(5) do?

    • Creates a Python list of 5 elements with values 1 to 5.

    • Creates a Python list of 5 elements with values 0 to 4.

    • Creates a NumPy array with values 1 to 5.

    • Creates a NumPy array with values 0 to 4.

    • None of the above.

Have a go hero – continue the analysis

The program we used here to compare the speed of NumPy and regular Python is not very scientific. We should at least repeat each measurement a couple of times. It would be nice to be able to calculate some statistics such as average times, and so on. Also, you might want to show plots of the measurements to friends and colleagues.


Hints to help you can be found throughout this book and in the online documentation and resources listed at the end of this chapter. NumPy has, by the way, statistical functions that can calculate averages for you. I recommend using matplotlib to produce plots.


IPython—an interactive shell

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:

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:

  • Tab completion

  • History mechanism

  • Inline editing

  • Ability to call external Python scripts with %run

  • Access to system commands

  • Pylab switch

  • Access to Python debugger and profiler

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       :
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

In [2]: %run -i 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 1000
*** Blank or comment
*** Blank or comment
Breakpoint 1 at /Users/ivanidris/Documents/numpyBeginnersGuide/book/ch1code/


Enter c at the ipdb> prompt to start your script.


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

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

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

ipdb> quit

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

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

   Ordered by: internal time

        1    0.001    0.0010.0010.001
        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<module>)
        1    0.000    0.0000.0000.000
        3    0.000    0.0000.0000.000 {range}
        1    0.000    0.0000.0000.000
      3/1    0.000    0.0000.0000.000
        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
        2    0.000    0.0000.0000.000 {numpy.core.multiarray.arange}
        1    0.000    0.0000.0000.000
      3/1    0.000    0.0000.0000.000
        1    0.000    0.0000.0000.000
        2    0.000    0.0000.0000.000
        1    0.000    0.0000.0000.000
        2    0.000    0.0000.0000.000<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!


Online resources and help

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>
arctan        arctan2       arctanhargmaxargmin
argsortargwhere      around        array2string  array_equal
array_equivarray_reprarray_splitarray_str     arrow         

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 Through this webpage, we can browse the NumPy reference at and the user guide as well as several tutorials.

NumPy has a wiki with lots of documentation at

The NumPy and SciPy forum can be found at

The popular Stack Overflow software development forum has hundreds of questions tagged "numpy". To view them, go to

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 . 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

For IRC users, there is an IRC channel on 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.

About the Author

  • Ivan Idris

    Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

    Browse publications by this author

Latest Reviews

(1 reviews total)
Good book, good introduction to Numpy
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