Math

Learning Objectives

• Basic functions of the math library
• In place operators
• array math with numpy

The math library

The math library is part of the Standard library, and provides many mathematical functions that extend the basic operators we saw previously.

We need to import the math library before we can use it:

import math
print("sin pi/2:", math.sin(math.pi/2))

('sin pi/2:', 1.0)

In place operators.

A Python sytax that can be a little confusing to begin with is the use of in-place-operators. Compare:

a = 2.
a = a +  2
print(a)

4.0

a = 2.
a += 2
print(a)

4.0

This will work for all mathematical operators. The syntax It also extends to other data types, specifically, numpy arrays:

a = np.array([1, 2, 3], float)
b = a
print(a is b)
a += 4
print(a is b)
print(a, b)

True
True
(array([ 5.,  6.,  7.]), array([ 5.,  6.,  7.]))

The behaviour is different here than if we had used the non-in-place operation:

a = np.array([1, 2, 3], float)
b = a
print(a is b)
a = a + 4
print(a is b)
print(a, b)

True
False
(array([ 5.,  6.,  7.]), array([ 1.,  2.,  3.]))

Math with arrays

We cannot apply maths library functions, element wise, to lists or numpy arrays:

a = np.array([1, 2, 3], float)
print(math.exp(a))

TypeError                                 Traceback (most recent call last)
<ipython-input-389-4f48d5ba7274> in <module>()
      1 a = np.array([1, 2, 3], float)
----> 2 math.exp(a)

TypeError: only length-1 arrays can be converted to Python scalars

Instead, we use the same mathematical functions directly from the numpy library:

a = np.array([1, 2, 3], float)
print(numpy.exp(a))

[  2.71828183   7.3890561   20.08553692]