Introducing Python, 2nd Edition

Chapter 22. Py Sci

In her reign the power of steam On land and sea became supreme, And all now have strong reliance In fresh victories of science.

James McIntyre, Queen’s Jubilee Ode 1887

In the past few years, largely because of the software you’ll see in this chapter, Python has become extremely popular with scientists. If you’re a scientist or student yourself, you might have used tools like MATLAB and R, or traditional languages such as Java, C, or C++. Now you’ll see how Python makes an excellent platform for scientific analysis and publishing.

Math and Statistics in the Standard Library

First, let’s take a little trip back to the standard library and visit some features and modules that we’ve ignored.

Math Functions

Python has a menagerie of math functions in the standard math library. Just type import math to access them from your programs.

It has a few constants such as pi and e:

>>> import math
>>> math.pi
>>> 3.141592653589793
>>> math.e
2.718281828459045

Most of it consists of functions, so let’s look at the most useful ones.

fabs() returns the absolute value of its argument:

>>> math.fabs(98.6)
98.6
>>> math.fabs(-271.1)
271.1

Get the integer below (floor()) and above (ceil()) some number:

>>> math.floor(98.6)
98
>>> math.floor(-271.1)
-272
>>> math.ceil(98.6)
99
>>> math.ceil(-271.1)
-271

Calculate the factorial (in math, n !) by using factorial():

>>> math.factorial(0)
1
>>> math.factorial(1)
1
>>> math.factorial(2)
2
>>> math.factorial(3)
6
>>> math.factorial ...