is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not.

Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not.

The latest version is 4.0a5, released 16 February 2015. It is supported on Python versions 2.6, 2.7, 3.3, 3.4, and 3.5a1, as well as PyPy 2.4, and PyPy3 2.4. This is a pre-release build. The usual warnings about possible bugs apply. The latest stable version is 3.7.1, described here.

Quick start

Getting started is easy:

  1. Install from the coverage page on the Python Package Index, or by using “pip install coverage”. For a few more details, see Installation.

  2. Use coverage run to run your program and gather data:

    $ coverage run arg1 arg2
    blah blah ..your program's output.. blah blah
  3. Use coverage report to report on the results:

    $ coverage report -m
    Name                      Stmts   Miss  Cover   Missing
    my_program                   20      4    80%   33-35, 39
    my_other_module              56      6    89%   17-23
    TOTAL                        76     10    87%
  4. For a nicer presentation, use coverage html to get annotated HTML listings detailing missed lines:

    $ coverage html

    Then visit htmlcov/index.html in your browser, to see a report like this one.


There are a few different ways to use The simplest is the command line, which lets you run your program and see the results. If you need more control over how your project is measured, you can use the API.

Some test runners provide coverage integration to make it easy to use coverage while running tests. For example, nose has a cover plug-in.

You can fine-tune coverage’s view of your code by directing it to ignore parts that you know aren’t interesting. See Specifying source files and Excluding code from coverage for details.

Getting help

If the FAQ doesn’t answer your question, you can discuss or get help using it on the Testing In Python mailing list.

Bug reports are gladly accepted at the Bitbucket issue tracker. Bitbucket also hosts the code repository. There is a mirrored repo on GitHub.

I can be reached in a number of ways. I’m happy to answer questions about using