Set Up a Python Environment

Import a Python package

As you have already seen, to make use of a Python module in the Python Standard Library you can use the syntax that you are already familiar with:

import <module>

For example, if you want to use the  os  module from the Python Standard Library to print the current working directory of a process, you could create a file called  script.py  with the following contents: 

import os
 
 
print(os.getcwd())

You could then run  script.py  from the terminal using  python script.py  :

~
→ python script.py
/Users/george
~
→

In the script we import the entire  os  module and make use of a piece of its functionality by calling  os.getcwd() .

As an alternative to this, we can instead choose to  import  selected functionality from the  os  package as follows:

from os import getcwd 


print(getcwd()) 

We import third-party Python packages into Python files in the same way as we import modules from the Python Standard Library into Python files. For example, we could make use of the  numpy  package to round numbers up and down.

First, let’s install  numpy  by running  pip install numpy  from the command line terminal:

→ pip install numpy
Collecting numpy
Using cached https://files.pythonhosted.org/packages/5c/f5/0e5e57fa7683cf0e5036320f4676cd7e3dbd9ab8c17ada541c2bb5ebed5e/numpy-1.19.0-cp37-cp37m-macosx_10_9_x86_64.whl
Installing collected packages: numpy
Successfully installed numpy-1.19.0
~
→

Now, we can  import   numpy  in a Python script and use it to round some numbers up and down. Create a Python script called  number_rounding.py  and put the following code into your script: 

import numpy
 
 
print(numpy.ceil(1.2))
print(numpy.ceil(1.933))
print(numpy.floor(1.2))

As you can see, we  import  the third-party  numpy  Python package in the same way as we would  import  modules in the Python Standard Library such as  json  ,  os  ,  datetime  and  re . Running  number_rounding.py  should generate the following output:

→ python number_rounding.py
2.0
2.0
1.0
~
→

Import everything from a Python package

An alternative to using the  import <package>  syntax is to instead  import  everything from a package using  from <package> import *  . This can be useful when you are using the Python shell, because it means you can type less.

For example, let’s start by opening the Python shell by typing  python  in the command line terminal:

→ python
Python 3.7.1 (default, Dec142018, 13:28:58)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

Now, let’s  import  everything from the  numpy  package using  from numpy import *  : 

→ python
Python 3.7.1 (default,Dec142018,13:28:58)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from numpy import *
>>>

Now, we can use the  ceil()  and  floor()  methods without having to prefix them with  numpy  :

→ python
Python 3.7.1 (default, Dec142018, 13:28:58)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from numpy import *
>>> ceil(1.2)
2.0
>>> floor(1.2)
1.0

While using  from <package> import *  in the Python shell is fine, it generally isn’t considered good practice in application source code. If you use  from <package_a> import *  and  from <package_b> import *  and there is a name clash between functionality in  package_a  and functionality in  package_b , then this might result in unintended consequences.

Aliasing packages

When importing packages you also have the option to  import  them with an alias using the command  import <package> as <alias> . Strangely, this is industry standard for some packages, but would be considered unconventional for other packages.

For example, it is in fact industry standard to  import numpy  using  import numpy as np  . If we apply this style of  import  to  number_rounding.py  , it would become:

import numpy as np
 
 
print(np.ceil(1.2))
print(np.ceil(1.933))
print(np.floor(1.2))

It’s functionality remains unchanged, so when we run it using Python the output is exactly the same:

→ python number_rounding.py
2.0
2.0
1.0
~
→

Using the  np  alias in this case means that for large applications with lots of source code, the code becomes slightly easier to read and understand, and thus more maintainable/extendable.

You can choose to use any alias you wish, but it is generally best to use something which could be considered shorthand for the package name. Using import numpy as apples  wouldn’t make much sense, for example!

For a bit more information around importing Python packages, check out this screencast:

Let’s Recap!

Now that you have completed this chapter you should be confident with the following tasks:

• You can  import  Python packages into Python files in the same way as importing modules from the Python Standard Library.

• It is good practice to become familiar with, and accustomed to, a variety of methods for importing modules and packages and then choosing the most appropriate  import  method for your particular use case.

Now that you are more confident with importing packages and modules into Python files, let’s take a look at why versioning of packages is so important in software development.