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Getting Started

This page is intended to help the beginner get a handle on SciPy and be productive with it as fast as possible.

What are NumPy, SciPy and matplotlib?

  • Python is a general purpose programming language. It is interpreted and dynamically typed and is very suited for interactive work and quick prototyping, while being powerful enough to write large applications in.
  • NumPy is a Python extension module, written mostly in C, that defines the numerical array and matrix types and basic operations on them.
  • SciPy is another Python library that uses NumPy to do advanced math, signal processing, optimization, statistics and much more.
  • matplotlib is a Python library that facilitates publication-quality interactive plotting.

What are NumPy, SciPy, and matplotlib?

SciPy and friends can be used for a variety of tasks:

  • First of all, they are great for performing calculation relying heavily on mathematical and numerical operations. They can work natively with matrices and arrays, perform operations on them, find eigenvectors, compute integrals, solve differential equations.

  • NumPy’s array class (which is used to implement the matrix class) is implemented with speed in mind, so accessing NumPy arrays is faster than accessing Python lists. Further, NumPy implements an array language, so that most loops are not needed. For example, Plain Python (and similarly for C, etc.):

    a = range(10000000)
    b = range(10000000)
    c = []
    for i in range(len(a)):
        c.append(a[i] + b[i])

    This loop can take up to 10 seconds on a several GHz processor. With NumPy:

    import numpy as np
    a = np.arange(10000000)
    b = np.arange(10000000)
    c = a + b

    Not only is this much more compact and readable, it is almost instant by comparison, and even the NumPy import is faster than the loop in plain Python. Why? Python is an interpreted language with dynamic typing. This means that on each loop iteration it needs to check the type of the operands a and b to select the right variant of the ‘+’ operator for those types (in Python, ‘+’ is used for many things, like concatenating strings, and lists can have elements of different types). The NumPy add function, which Python automatically selects when one of the operands of ‘+’ is a NumPy array, does this check only once. It then executes the “real” addition loop in a compiled C function. This is very fast by comparison to the interpreted loop in plain Python.

    There is a sizeable collection of both generic and application-specific numerical code written in or using numpy and scipy. See the Topical Software index for a partial list. Python has many advanced modules to build interactive applications (for instance Traits or wxPython). Using SciPy with these is the quickest way to build a fully-fledged scientific application.

  • Using IPython makes interactive work easy. Data processing, exploration of numerical models, trying out operations on-the-fly allows to go quickly from an idea to a result (see the article on IPython).

  • The matplotlib module produces high quality plots. With it you can turn your data or your models into figures for presentations or articles. No need to do the numerical work in one program, save the data, and plot it with another program.

How to work with SciPy

Python is a language, it comes with several user interfaces. There is no single program that you can start and that gives an integrated user experience. Instead of that there are dozens of way to work with Python.

The most common is to use the advanced interactive Python shell IPython to enter commands and run scripts. Scripts can be written with any text editor, for instance Emacs, Vim or even Notepad.

Neither SciPy nor NumPy provide, by default, plotting functions. They are just numerical tools. The recommended plotting package is matplotlib.

Learning to work with SciPy

To learn more about the Python language, the official Python tutorial is an excellent way to become familiar with the Python syntax and objects. An alternative introduction can be found in the free online book Dive Into Python by Mark Pilgrim.

An example session

Python 2.5.1 (r251:54863, May  2 2007, 16:27:44)
Type "copyright", "credits" or "license" for more information.
IPython 0.7.3 -- An enhanced Interactive Python.
?       -> Introduction to IPython's features.
%magic  -> Information about IPython's 'magic' % functions.
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)'.

IPython offers a great many convenience features, such as tab-completion of python functions and a good help system.

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