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, matplotlib, ...?¶
SciPy and friends can be used for a variety of tasks:
- NumPy‘s array type augments the Python language with an efficient data structure useful for numerical work, e.g., manipulating matrices. NumPy also provides basic numerical routines, such as tools for finding eigenvectors.
- SciPy contains additional routines needed in scientific work: for example, routines for computing integrals numerically, solving differential equations, optimization, and sparse matrices.
- 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.
- 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 IPython site for many examples.
- There is a sizeable collection of both generic and application-specific numerical and scientific code, written using Python, NumPy and SciPy. Don’t reinvent the wheel, there may already be a pre-made solution for your problem. See Topical Software for a partial list.
- As Python is a popular general-purpose programming language, it has many advanced modules for building for example interactive applications (see e.g. wxPython and Traits) or web sites (see e.g. Django). Using SciPy with these is a quick way to build a fully-fledged scientific application.
How to work with SciPy¶
Python is a programming language, and there are several ways to approach it. There is no single program that you can start and that gives an integrated user experience. Instead, there are several possible ways 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. Some of the packages such as Python(x,y) mentioned in Installing the SciPy Stack also offer an integrated scientific development environment.
Neither SciPy nor NumPy provide plotting functions. There are several plotting packages available for Python, the most commonly used one being 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.
One way of getting a handle on the scientific computation tools in Python is to take a look at the following online resources:
- Python Scientific Lecture Notes
- NumPy User Guide
- SciPy Tutorial contains examples for each submodule in the SciPy library
- Matplotlib beginner’s guide
- Pandas tutorials
- Sympy tutorial
In addition, a number of books have been written on numerical computation in Python, see for example a Google search on books related to SciPy.
An example session¶
To give a simple example of typical interactive use, we find and plot the maximum of a Bessel function. If you have worked with numerical computation environments before, what follows looks very familiar.
This assumes you have installed the SciPy stack, for example following the instructions in Installing the SciPy Stack.
$ ipython --pylab Python 2.7.4 (default, Apr 19 2013, 18:28:01) Type "copyright", "credits" or "license" for more information. IPython 0.13.2 -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details. Welcome to pylab, a matplotlib-based Python environment [backend: Agg]. For more information, type 'help(pylab)'. In : from scipy import special, optimize In : f = lambda x: -special.jv(3, x) In : sol = optimize.minimize(f, 1.0) In : x = linspace(0, 10, 5000) In : x Out: array([ 0.00000000e+00, 2.00040008e-03, 4.00080016e-03, ..., 9.99599920e+00, 9.99799960e+00, 1.00000000e+01]) In : plot(x, special.jv(3, x), '-', sol.x, -sol.fun, 'o') In : savefig('plot.png', dpi=96)
An example script¶
The above example session can be written as a non-interactive script as follows. Here, we don’t give the simplest example possible, but follow what is considered good practice on command-line scripts.
Contents of a file
"""example.py Compute the maximum of a Bessel function and plot it. """ import argparse import numpy as np from scipy import special, optimize import matplotlib.pyplot as plt def main(): # Parse command-line arguments parser = argparse.ArgumentParser(usage=__doc__) parser.add_argument("--order", type=int, default=3, help="order of Bessel function") parser.add_argument("--output", default="plot.png", help="output image file") args = parser.parse_args() # Compute maximum f = lambda x: -special.jv(args.order, x) sol = optimize.minimize(f, 1.0) # Plot x = np.linspace(0, 10, 5000) plt.plot(x, special.jv(args.order, x), '-', sol.x, -sol.fun, 'o') # Produce output plt.savefig(args.output, dpi=96) if __name__ == "__main__": main()