Modules

Overview

Teaching: 30 min
Exercises: 20 min

Questions

• How do I use code defined in other files or libraries?

• What does the import statement do?

• What are the effects of the common import variations?

Objectives

• Learn what a module is.

• Learn how to import symbols from other files and modules.

• Understand the differences between the different forms of import statements.

• Be able to import modules into your code.

Modules and Packages

Modules in Python are simply Python files with the .py extension.

To use the functionality of a module, it has to be imported into your code. To import a module, we use the import command.

The full list of built-in modules can be found in the Python documentation.

For example we can import the Pandas library for Data Analysis:

# import the library
import pandas

# use it
data = pandas.read_csv("data.csv")
data

Following on from the reading files episode, Pandas is great for working with dataframes.

The math module

In the python interpreter, import the math module and print the value of math.pi.

What happens if you don’t import the math module first?

Solution

$ python3
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>> import math
>> print(math.pi)
3.141592653589793

import has many forms

When reading Python code, you may have seen many different types of import statements, such as:

import math
import math as m
from math import pi
from math import *

Let’s look at each one in turn.

import math

The first, import math imports everything from the math module. To access anything in math, you need to prefix with math.. This prevents conflicts with other code, including your own.

Two pi

Either in a file or Python interpreter, try running the following code:

pi = 3.14
import math
print(pi)
print(math.pi)

Do you see that the values of pi and math.pi are different? And how using the module name makes it easy to know which is which?

import math as m

The second form, import math as m is mostly the same, it just changes the name that you need to use to refer to the imported objects. In this case, m. instead of math.. When you start using packages from the SciPy ecosystem, you will see this type of import a lot. Most of the common packages have a conventional abbreviation. This keeps code concise but retains readability. For example, rather than:

import numpy
a = numpy.array([1,2,3])
print(a)

It is more common to write:

import numpy as np
a = np.array([1,2,3])
print(a)

np is the conventional abbreviation (or alias) for numpy.

Three pi

Either in a file or Python interpreter, try running the following code:

Make sure to restart the Jupyter Notebook kernel, or this example won’t work.

pi = 3.14
import math as m
print(pi)
print(m.pi)
print(math.pi)

Solution

$ python3
Python 3.6.7 (default, Oct 22 2018, 11:32:17)
[GCC 8.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>> pi = 3.14
>> import math as m
>> print(pi)
3.14
>> print(m.pi)
3.141592653589793
>> print(math.pi)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'math' is not defined

The last error is because the import math as m changed the alias used to refer to the imported math module.

from math import pi

This one looks a little different. It starts with from rather than import, but it is still an import statement. from math import pi is still importing from the math module, but rather than importing everything and then needing to access those symbols with the math. prefix, it specifically imports just the definition of pi. There is another critical difference. See if you can work out what it is?

The differences between import math and from math import pi

Make sure to restart the Jupyter Notebook kernel, or this example won’t work.

Either in a file or Python interpreter, try running the following code:

from math import pi
print(pi)
print(math.pi)

Solution

When importing specific symbols, nothing else from the module will be available to your code. In this example, we have imported pi but nothing else. Not even the math. prefix is defined.

The other critical difference is that the imported symbol no longer requires a prefix. You need to take care that it doesn’t conflict with your own variables or functions.

from math import *

Exploring from math import *

Make sure to restart the Jupyter Notebook kernel, or this example won’t work.

The built-in dir() function when called without arguments lists the names (variables, functions etc) defined in the local scope. This exercise uses dir() to explore the relative effect of different import statements.

In a new interactive Python session, enter the following commands one at a time in the order shown. The output from dir() will show the defined names at each point.

What items get added to the end of the dir() results at each stage?

dir()

This first call to dir() shows a few standard items that should be ignored. Remember, we are looking for the changes once we start importing from math.

import math
dir()

from math import pi
dir()

from math import *
dir()

Solution

• import math: The only new addition to local symbols is math.
  • This is the namespace object used to access the contents of the math module.
• from math import pi: Adds just one name - pi.
• from math import *: Adds everything from math. Imagine the effect if you did this for a lot of modules?

The code from math import * looks very similar to from math import pi, but the effect on your code is huge. The * is a wildcard that matches everything in the math module. The effect is to import everything without the math. prefix. While this can be very convenient during interactive coding sessions and in small programs, great care must be taken to avoid conflicts. If two modules that you are using both define a function called count_words, then the version you end up with depends on which was imported last. Things might work, but when they fail errors like this can be very hard to find.

For these reasons, it it generally recommended to avoid this type of import. However, it can still be convenient so long as you are aware of the risks.

Key Points

• Modules are simply Python files.

• To use code from a module, it has to be imported.

• The import command is used to import code from a module.

• There are different ways to import from a module. Select the appropriate method based on the required effect.