Published

2025-03-31

Modules

Justin Post

Now that we know the basics of how python works and a little bit about how our programming environment functions, we can look at python ‘modules’. If you know R, these are smilar to R packages.

Note: These types of webpages are built from Jupyter notebooks (.ipynb files). You can access your own versions of them by clicking here. It is highly recommended that you go through and run the notebooks yourself, modifying and rerunning things where you’d like!

What is a Module?

A collection of (related) definitions and statements that are grouped together in a single file (a .py file)
- Gives access to additional functionality
Some come standard, others must be installed (i.e. downloaded)
Modules are then imported into your session
- Fully imported with import module_name
- Selective import with from module_name import thing1 thing2

Commonly Used Built-in (Already Downloaded) Modules

These modules are already downloaded but not loaded in when starting python or a Jupyterlab notebook.

math
- Math constants (pi, e, etc.)
- Functions commonly used functions (exp(), sin(), sqrt(), etc.)
random
- Random sampling and random number generation
statistics
- Summary stats (but scipy and pandas have a lot more)
datetime
- Functionality for working with dates

Importing a Module

For built-in modules or modules we’ve downloaded ourselves, we can load the entire module into our session with import module_name

import math
type(math)

module

We can see the functionality using help() on the module but it is usually better to find the documentation on the web!

help(math)

Using a Module’s Function/Objects

Functions contained in our module cannot be called as a built-in function when using import module_name:
For instance, the math module contains the sqrt() function

sqrt(9)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-5-840f67a85afc> in <cell line: 1>()
----> 1 sqrt(9)

NameError: name 'sqrt' is not defined

An error!
We must use the module prefix when calling the function:

print(math.sqrt(9))
math.factorial(5)

3.0

With the math module we could do a common statistical computation such as evaulating the Normal distribution PDF

\[ f(1;\mu = 3, \sigma = 1) = \frac{1}{\sqrt{2\pi}}\exp^{-\frac{1}{2}(1 - 3)^2} \]

This Normal distribution PDF expression can be evaluated using:

(1.0/math.sqrt(2*math.pi))*math.exp(-0.5*(1 - 3.0)**2)

0.05399096651318806

Module Variables

Modules can contain more than functions!

The math module also defines variables like e and pi

print(math.e)
print(math.pi)
radius = 5
print('The area is', math.pi * radius ** 2)

2.718281828459045
3.141592653589793
The area is 78.53981633974483

You can overwrite these values (just like built-in objects) but, you know, don’t do that!

`random` Module

We’ll deal with random sampling from time to time
The random module gives functionality to do so (although we’ll use other modules when we do this later)

import random
help(random)

When we do random number generation, we are really getting pseudo random values
The values are actually generated from some algorithm
We can se the starting point of that algorithm by setting a seed
This allows us to ensure reproducibility of our process!
- Important: A seed sets a starting point for the ‘random’ number generator. This allows you to get the same ‘random’ numbers the next time you run the code.

random.seed(101)
print(random.random())
print(random.random())
print(random.random())

0.5811521325045647
0.1947544955341367
0.9652511070611112

#notice we can get the same 'random value if we set the seed back to the same starting point
random.seed(101)
print(random.random())

0.5811521325045647

We can also randomly obtain integers from a particular range of values
We use the range() function to return an iterator that is able to produce values
Here we obtain random integers between 10 and 25 and use random.sample() to obtain four values from that range

random.sample(range(10, 25), 4)

[13, 18, 15, 17]

Importing `as`

Often we want to use a shorter module name for brevity
We can do so with as when we use import

import random as ran
ran.seed(101)
print(ran.random())
print(ran.random())
print(ran.random())

0.5811521325045647
0.1947544955341367
0.9652511070611112

Obtain random integers between 11 and 26

ran.sample(range(11, 26), 4)

[25, 16, 18, 11]

We’ll see that most of the commonly used modules have common aliases. For instance, numpy as np, pandas as pd, and pyspark as ps.

Selective Import

We can also selectively import functions and variables from a module directly into our main namespace
This allows us to call the functions without the module prefix
Use from module import object1, object2 to do this

from math import sqrt, pi
print(sqrt(9))
pi

3.0

3.141592653589793

Alternatively, if you intend to use a function often you can assign it to a local name

sample = random.random
sample()

0.6634706445300605

Or import everything from a module into the current namespace. Be careful with this as you can overwrite things you rely on!

from math import *

Installing Modules

pip is a package manager for python
Used through the command line usually
We’ll use it through a code cell with ! first
- Can see what modules you have
pip list
- Use pip install module_name to install new modules
pip install scipy

Colab has most everything we need for now but we’ll need to do some installing later in the course!

! pip list

Recap

Modules are just collections of functions, objects, etc.
pip to install them (Colab has most that we need!)
import to bring them in
- Can selectively import

If you are on the course website, use the table of contents on the left or the arrows at the bottom of this page to navigate to the next learning material!

If you are on Google Colab, head back to our course website for our next lesson!