A Python toolbox

I started learning Python four years ago and have been heavily programming with it for near than a year now. In this post I’ll share some tools I use to ease and speed-up my workflow, either in the Python code or in the development environment.

These tips should be OS- and editor-independent. I have some useful Vim plugins to work with Python, but that’ll be for another post. You might have to adapt commands if you work on Windows.

Setting Things Up

Let’s say you’re starting a Python library. You have a couple dependencies, and hopefully you’d like it to work on multiple versions, like 2.6, 2.7, and 3.x. How can you test that? You have to (a) manage your dependencies to not mess up with your user environment, and (b) test with multiple Python versions.

Introducing virtualenv.

Virtualenv lets you create isolated Python environments. That means you get a pristine environment where you’ll install your library’s dependencies, and nothing else. It’ll be independent of your user space. It’s important to work with an isolated environment because you don’t know which environment will your users have, so you shouldn’t make any assumption besides your own requirements. Working with your user environment means you might forgot a dependency because it just works since it’s installed on your computer but it’ll broke if run on another computer without this dependency.

You should be able to install it with pip :

$ pip install virtualenv

It needs a directory to store the environment. I usually use venv , but you can choose whatever you want:

$ virtualenv venv

You can then either “activate” the environment, which adds its bin directory in your PATH :

$ source venv/bin/activate $ python # that's your virtualenv's Python $ deactivate $ python # that's your Python

or prefix your commands with venv/bin/ (replace venv with your directory’s name):

$ venv/bin/python # that's your virtualenv's Python $ python # that's your Python

I usually do the later. Install dependencies in the environment:

$ venv/bin/pip install your-dependency

Don’t forget to tell Git or any tool you’re using for versioning to ignore this venv directory. It can take some place (from 12MB to more than 40MB) depending on the number of dependencies you’re relying on.

To remove an environment, just delete its directory:

$ rm -rf venv

It can be convenient to save place on your computer if you have dozen of environments for different projects, especially if can quickly re-create any environment with its dependencies.

If you’re using pip to manage them, you should know you can install dependencies not only from the command line, but also from a file, with one dependency per line. Each one of them is processed as if it were given on the command-line.

For example, you could have a file containing this:

colorama==0.2.7 coverage==3.7.1 pep8==1.4.6 py==1.4.20 tox==1.7.0 argparse>=1.1 virtualenv==1.11.4

This file is usually called requirements.txt , but here again you can call it whatever you want. You call install all these at once with this command:

$ pip install -r requirements.txt

But we’re programmers and we’re lazy, we don’t want to track down each installed library to include it in this file.

Here comes pip freeze .

pip freeze outputs all installed libraries with their version. It can be put in our requirements.txt for later use:

$ pip freeze > requirements.txt

This requirements.txt file becomes handy when used with virtualenv because you’re now able to fire up a new environment and install all required libraries, with two commands:

$ virtualenv venv $ venv/bin/pip install -r requirements.txt

Note that these are libraries used in the environment, not necessarily your library’s dependencies. In the above example you can see we’re installing coverage and pep8 , which respectively are a code coverage test tool and a lint one we’ll talk about later in this post, not libraries we’re depending on here.

You should thus add this file in your public repository, because it provides anyone with the informations they need to have in order to mirror your environment and be able to contribute to your project.

Coding

Now that your local environment is ready, you can start coding your library. You’ll often have to fire an interpreter to test some things, use help() to check a function’s arguments, etc. Having to type the same things over and over takes time, and remember: we’re lazy.

Like Bash and some other tools, the Python interpreter can be configured with an user file, namely $PYTHONSTARTUP . It allows you to add autocompletion, import common modules, and execute pretty much any code you want.

Start by setting PYTHONSTARTUP in your ~/.bashrc (if you’re using Bash):

export PYTHONSTARTUP = " $HOME /.pythonrc.py"

Here we’re telling the interpreter to look for the $HOME/.pythonrc.py file and executing it before giving us a prompt.

Let’s initialize this file with autocompletion support:

try : import readline except ImportError : pass else : import rlcompleter readline . parse_and_bind ( "tab: complete" )

You can add a lot more stuff in this file, like history support, colored prompts or common imports. For example, if you use sys and re a lot, you can save time by adding them in your startup file:

import sys import re

You won’t need to type these two lines anymore in your interpreter. It doesn’t change how Python executes files, just the interactive interpreter.

Testing

Four different kinds of tests are covered by this part: style checkers to ensure your code’s consistency, static analysis tools to detect problems before executing the code, unit tests to actually test your library, and code coverage tests to ensure you do test all the code.

Style Checking

These are tools which help you maintaining a consistent coding style in your whole codebase. Most of these tools are easy to use, the hardest part being to choose which one fits your requirements.

Python has a PEP ( Python Enhancement Proposal , sort of RFC), the PEP 8, dedicated to its coding conventions. If you want to follow it, a command-line tool, rightly named pep8 , is available.

$ venv/bin/pip install pep8 $ venv/bin/pep8 your/lib/root/directory ... foo/mod.py:84:80: E501 line too long (96 > 79 characters) foo/mod.py:85:6: E203 whitespace before ':' foo/mod.py:86:80: E501 line too long (87 > 79 characters) foo/mod.py:87:4: E121 continuation line indentation is not a multiple of four ...

It’ll check each file and print a list of warnings. You can choose to hide some of them, or use a white list to decide which ones you want. It’s a good tool if you want to follow the PEP 8 conventions.

Another highly customizable tool is pylint . It reads its configuration from a file in your project, which can inherit from global and user configurations. It’ll warn you about bad naming, missing docstrings, functions which take too many arguments, duplicated code, etc. It also gives you some statistics about your code. It’s really powerful but can be a pain if you don’t configure it. For example, it warns you about one-letter variables while you might find them ok.

Enters prospector .

Prospector is built on top of pep8 and pylint and comes with sane defaults regarding the pickiness of both tools. You can tell it to only print important problems about your code:

$ venv/bin/pip install prospector $ venv/bin/prospector --strictness high

You’ll get a much shorter output, which will hopefully help you find potential problems in your code.

Static Analysis

Here, we’re talking about analysing the code without executing it. Compiled languages benefit from this at compilation time, but interpreted languages like Python have no way to have it.

One of the most popular tools for static analysis on Python code is Pyflakes. It doesn’t check your coding style like pep8 or pylint , but warns you about missing imports, dead code, unused variables, redefined functions and more. You can work without style checkers, but static analysis is really helpful to detect potential bugs before actually running the code.

Pyflakes can be integrated in editors like Vim with Syntastic, but its command-line usage is as easy as the previous tools:

$ venv/bin/pip install pyflakes $ venv/bin/pyflakes your/directory

Prospector, mentioned in the previous section, also includes pyflakes . You might also want to try Flake8, which combines pyflakes and pep8 .

Unit Tests

When talking about testing , we usually think of unit testing, which is testing small pieces of our code at a time, to make sure everything is working correctly. The goal is to test only one feature at a time, to quickly be able to find which parts of the code are not working. There are a lot of great testing frameworks, and Python comes with a built-in one, unittest, which I personnally use. I won’t cover these, and I’ll instead cover the case when you need to test on multiple Python versions, which is often the case when you plan to release a public library. You obviously don’t want to manually switch to each Python version, install your dependencies then run your tests suit each time.

This is a job for tox .

Tox uses Virtualenv, which I talked about earlier, to create standalone Python environments for different Python versions, and test your code in each one of them.

$ venv/bin/pip install tox

Like some previous tools, it needs a configuration file. Here is a basic tox.ini to test on Python 2.6, 2.7, 3.3 and 3.4:

[tox] envlist = py26, py27, py33, py34 downloadcache = {toxworkdir}/_download/ [testenv] sitepackages = False deps = colorama commands = {envpython} {toxinidir}/tests/test.py

It declares one dependency, colorama , and tells tox to run tests by executing tests/test.py . That’s all. We can then run our tests:

$ venv/bin/pip/tox

It’ll takes some time on the first run to fetch dependencies and create environments, but all the following times will be faster.

Like virtualenv , tox uses a directory to store these environments. You can safely delete it if you need more space, it’ll be re-created by tox the next time:

$ rm -rf .tox

Code Coverage

This last part about testing talks about code coverage tests. These are tests about tests. The goal here is to ensure your tests cover all your code, and you don’t leave some parts untested. Most tools tell you how many lines where executed when running your tests suit, and give you an overall coverage percentage.

One of them is coverage .

$ venv/bin/pip install coverage

Give it your project’s root directory as well as a file to run your tests:

$ venv/bin/coverage run --source=your/directory tests/test.py

It’ll run them, and give you a nice coverage report:

$ coverage report -m Name Stmts Miss Cover Missing ----------------------------------------------- foobar/__init__ 5 0 100% foobar/base 46 2 96% 5-6 foobar/cli 159 159 0% 3-280 foobar/config 66 66 0% 3-150 foobar/session 64 14 78% 9-10, 106-124 foobar/barfooqux 22 0 100% foobar/helloworld 25 20 20% 14-41 ... ----------------------------------------------- TOTAL 459 313 32%

It can also give you an HTML version:

$ venv/bin/coverage html

Getting to 100% is the ultimate goal, but you’ll quickly find that’s the first 80% are easy and the remaining 20% are the hardest part, especially when you have I/O, external dependencies like databases, and/or complicated corner-cases.

Check Coverage’s doc for more info.

Debugging

There are a lot of ways to debug, including logs, but the simplest debugging tool is the good old print . It becomes really impracticable when you have to restart your server every time you add or remove one of them from your code. What if you could fire a Python interpreter right from your code and inspect it when it’s running? Well, you can do that with Python’s code module! This trick is really handy, and I’ve been using it heavily instead of these print s we write everywhere since I’ve discovered it.

The code module provides you with an interact function, which takes a dict of variables to inject in the interpreter. You’ll be able to print them, play with them, but these changes won’t be reflected in the program you’re debugging.

Remember that Python lets you get all global variables as a dict with globals() and all local ones with locals() . We thus start by creating a mirror of the local environment:

vars = globals () . copy () vars . update ( locals ())

These two lines get all global variables (including imported modules) in a dict called vars and add local variables in it. This can then be passed directly to the interpreter:

import code code . interact ( local = vars )

This will start an interpreter with all these variables already available in it. There’s nothing to install, this is a standard Python module.

You can even inline the code and add it in your favorite snippets manager:

g = globals () . copy (); g . update ( locals ()); import code ; code . interact ( local = g )

Don’t forget to remove it when you’re done!

TL;DR

Use virtualenv to isolate your Python environment, pip freeze and a requirements.txt file to keep track of your dependencies.

to isolate your Python environment, and a file to keep track of your dependencies. Write a pythonrc.py file to add autocomplete support to your interpreter

file to add autocomplete support to your interpreter Use pep8 , pylint and pyflakes to keep a high code quality

, and to keep a high code quality Use tox to test on multiple Python versions

to test on multiple Python versions Fire a local interpreter instead of print ing variables

That was all. Please comment on this post if you think of any tool you use to speed-up your Python development!