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Using Cookiecutter - a passive code generator

Using a tool to generate a templated result.

In The Pragmatic Programmer Andrew Hunt and David Thomas talk about the importance of code generators when faced with the task of producing the same thing over and over. They further separate code generators into two types: passive and active.

A passive code generator being one that saves on typing. It is run once, the result is placed into version control and then the code is built upon by hand.

Whereas an active code generator is used to produce complete code by converting a source of meta-data into language(s) of interest. Active code generators are run frequently and as the resulting code is reproducible it is also disposable, hence it does not need to be tracked in version control.

In this post I will show you how you can use a passive code generator to create a basic layout for a Python package.

Cookiecutter: a passive code generator

A classic example where passive code generators are useful is in setting up an initial project structure. Let us take the example of creating a Python package, in the simplest case you will want to create a file and a directory with the desired package name containing an file. Scott Torborg has created a great tutorial on How To Package Your Python Code.

Several tools exist to deal with this type of scenario. However, I quite like Audrey Roy’s Cookiecutter. Let us illustrate it’s use by creating a minimal template for a Python package.

Firs of all we install it using pip.

$ sudo pip install cookiecutter

Now we will create a funny looking directory structure. It is funny looking because it uses the Jinja2 templating syntax.

$ mkdir -p mypyproject/{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}

Now create the file myproject/cookiecutter.json and add the code below to it.

  "repo_name": "mypackage",
  "version": "0.0.1",
  "author": "Your Name"

Let us have a look at the directory structure we have created.

$ tree mypyproject/
├── cookiecutter.json
└── {{cookiecutter.repo_name}}
    └── {{cookiecutter.repo_name}}

2 directories, 1 file

We now have enough boilerplate to run cookiecutter. Actually we have more than enough, at this point we do not need the version and author variables.

Let us create an “awesome” Python package to see it in action.

$ cookiecutter mypyproject/
repo_name (default is "mypackage")? awesome
version (default is "0.0.1")? 
author (default is "Your Name")? Tjelvar Olsson

Note that the prompts and default values are the key/value pairs specified in the cookiecutter.json file.

Let us have a look at what was produced.

$ tree awesome/
└── awesome

1 directory, 0 files

Ok, great - let us add an file to the leaf myproject/{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}} directory.

$ touch mypyproject/\{\{cookiecutter.repo_name\}\}/\{\{cookiecutter.repo_name\}\}/

In the above we need to esacape the { and } characters when using bash. If you are not already using tab completion when using bash this may be a good point to try it out (just start typing the name of the file/directory of interest and then press the tab key).

Let’s run cookiecutter again to see what we get now that we have added the file.

$ cookiecutter mypyproject/
repo_name (default is "mypackage")? awesome
version (default is "0.0.1")? 
author (default is "Your Name")? Tjelvar Olsson
$ tree awesome/
└── awesome

1 directory, 1 file

Great we now automatically get an file added to our project when we create it. Now let us add a basic, but all the same templated, file to our project layout. Create the file mypyproject/{{cookiecutter.repo_name}}/ and copy and paste the code below into it.

from setuptools import setup

setup(name="{{ cookiecutter.repo_name }}",
      version="{{ cookiecutter.version }}",
      author="{{ }}"

Let us try this out.

$ cookiecutter mypyproject/
repo_name (default is "mypackage")? awesome
version (default is "0.0.1")? 
author (default is "Your Name")? Tjelvar Olsson
$ tree awesome/
├── awesome
│   └──

1 directory, 2 files
$ cat awesome/ 
from setuptools import setup

      author="Tjelvar Olsson"

Great we now have a basic layout for building up a Python project!

Now that you know the principles you can use them to automate the generation of your boilerplate code.

Making use of GitHub

Once you start building up your template make sure that you save it on GitHub or BitBucket. You are already using version control, right?

A nice feature of Cookiecutter is that it has built in functionality for making use of templates stored in GitHub/Bitbucket. For example to make use of my default Python package layout, which includes:

  • test suite layout using nose and coverage
  • sphinx docs layout using read the docs theme

You can simply use the command below.

$ cookiecutter gh:tjelvar-olsson/cookiecutter-pypackage
Cloning into 'cookiecutter-pypackage'...
remote: Counting objects: 48, done.
remote: Compressing objects: 100% (37/37), done.
remote: Total 48 (delta 13), reused 37 (delta 8), pack-reused 0
Unpacking objects: 100% (48/48), done.
Checking connectivity... done.
repo_name (default is "mypackage")? awesome
version (default is "0.0.1")? 
authors (default is "Tjelvar Olsson")? 

Alternatively, for an even more extensive setup have a look at Audrey Roy’s ultimate python package template.


When you find yourself repeatedly doing the same thing it may be time to start thinking about using a code generator. In this post I have shown you how to use cookiecutter to produce a basic Python package template.

However, it is not limited to Python package projects. You could use it to automate the setup of CMake / HTML / LaTeX files; the world is your oyster.

Happy code generating!