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# Using mypy with an existing codebase

This section explains how to get started using mypy with an existing, significant codebase that has little or no type annotations. If you are a beginner, you can skip this section.

## Start small

If your codebase is large, pick a subset of your codebase (say, 5,000 to 50,000 lines) and get mypy to run successfully only on this subset at first, before adding annotations. This should be doable in a day or two. The sooner you get some form of mypy passing on your codebase, the sooner you benefit.

You’ll likely need to fix some mypy errors, either by inserting annotations requested by mypy or by adding `# type: ignore` comments to silence errors you don’t want to fix now.

We’ll mention some tips for getting mypy passing on your codebase in various sections below.

## Run mypy consistently and prevent regressions

Make sure all developers on your codebase run mypy the same way. One way to ensure this is adding a small script with your mypy invocation to your codebase, or adding your mypy invocation to existing tools you use to run tests, like `tox`.

- Make sure everyone runs mypy with the same options. Checking a mypy configuration file into your codebase is the easiest way to do this.

- Make sure everyone type checks the same set of files. See Specifying code to be checked for details.

- Make sure everyone runs mypy with the same version of mypy, for instance by pinning mypy with the rest of your dev requirements.

In particular, you’ll want to make sure to run mypy as part of your Continuous Integration (CI) system as soon as possible. This will prevent new type errors from being introduced into your codebase.

A simple CI script could look something like this:

    python3 -m pip install mypy==1.8
    # Run your standardised mypy invocation, e.g.
    mypy my_project
    # This could also look like `scripts/run_mypy.sh`, `tox run -e mypy`, `make mypy`, etc

## Ignoring errors from certain modules

By default mypy will follow imports in your code and try to check everything. This means even if you only pass in a few files to mypy, it may still process a large number of imported files. This could potentially result in lots of errors you don’t want to deal with at the moment.

One way to deal with this is to ignore errors in modules you aren’t yet ready to type check. The `ignore_errors` option is useful for this, for instance, if you aren’t yet ready to deal with errors from `package_to_fix_later`:

    [mypy-package_to_fix_later.*]
    ignore_errors = True

You could even invert this, by setting `ignore_errors = True` in your global config section and only enabling error reporting with `ignore_errors = False` for the set of modules you are ready to type check.

The per-module configuration that mypy’s configuration file allows can be extremely useful. Many configuration options can be enabled or disabled only for specific modules. In particular, you can also enable or disable various error codes on a per-module basis, see Error codes.

## Fixing errors related to imports

A common class of error you will encounter is errors from mypy about modules that it can’t find, that don’t have types, or don’t have stub files:

    core/config.py:7: error: Cannot find implementation or library stub for module named 'frobnicate'
    core/model.py:9: error: Cannot find implementation or library stub for module named 'acme'
    ...

Sometimes these can be fixed by installing the relevant packages or stub libraries in the environment you’re running `mypy` in.

See Missing imports for a complete reference on these errors and the ways in which you can fix them.

You’ll likely find that you want to suppress all errors from importing a given module that doesn’t have types. If you only import that module in one or two places, you can use `# type: ignore` comments. For example, here we ignore an error about a third-party module `frobnicate` that doesn’t have stubs using `# type: ignore`:

    import frobnicate  # type: ignore
    ...
    frobnicate.initialize()  # OK (but not checked)

But if you import the module in many places, this becomes unwieldy. In this case, we recommend using a configuration file. For example, to disable errors about importing `frobnicate` and `acme` everywhere in your codebase, use a config like this:

    [mypy-frobnicate.*]
    ignore_missing_imports = True

    [mypy-acme.*]
    ignore_missing_imports = True

If you get a large number of errors, you may want to ignore all errors about missing imports, for instance by setting `--disable-error-code=import-untyped`. or setting `ignore_missing_imports` to true globally. This can hide errors later on, so we recommend avoiding this if possible.

Finally, mypy allows fine-grained control over specific import following behaviour. It’s very easy to silently shoot yourself in the foot when playing around with these, so this should be a last resort. For more details, look here.

## Prioritise annotating widely imported modules

Most projects have some widely imported modules, such as utilities or model classes. It’s a good idea to annotate these pretty early on, since this allows code using these modules to be type checked more effectively.

Mypy is designed to support gradual typing, i.e. letting you add annotations at your own pace, so it’s okay to leave some of these modules unannotated. The more you annotate, the more useful mypy will be, but even a little annotation coverage is useful.

## Write annotations as you go

Consider adding something like these in your code style conventions:

1. Developers should add annotations for any new code.

2. It’s also encouraged to write annotations when you modify existing code.

This way you’ll gradually increase annotation coverage in your codebase without much effort.

## Automate annotation of legacy code

There are tools for automatically adding draft annotations based on simple static analysis or on type profiles collected at runtime. Tools include monkeytype:index, autotyping and PyAnnotate.

A simple approach is to collect types from test runs. This may work well if your test coverage is good (and if your tests aren’t very slow).

Another approach is to enable type collection for a small, random fraction of production network requests. This clearly requires more care, as type collection could impact the reliability or the performance of your service.

## Introduce stricter options

Mypy is very configurable. Once you get started with static typing, you may want to explore the various strictness options mypy provides to catch more bugs. For example, you can ask mypy to require annotations for all functions in certain modules to avoid accidentally introducing code that won’t be type checked using `disallow_untyped_defs`. Refer to The mypy configuration file for the details.

An excellent goal to aim for is to have your codebase pass when run against `mypy --strict`. This basically ensures that you will never have a type related error without an explicit circumvention somewhere (such as a `# type: ignore` comment).

The following config is equivalent to `--strict` (as of mypy 1.0):

    # Start off with these
    warn_unused_configs = True
    warn_redundant_casts = True
    warn_unused_ignores = True

    # Getting these passing should be easy
    strict_equality = True
    strict_concatenate = True

    # Strongly recommend enabling this one as soon as you can
    check_untyped_defs = True

    # These shouldn't be too much additional work, but may be tricky to
    # get passing if you use a lot of untyped libraries
    disallow_subclassing_any = True
    disallow_untyped_decorators = True
    disallow_any_generics = True

    # These next few are various gradations of forcing use of type annotations
    disallow_untyped_calls = True
    disallow_incomplete_defs = True
    disallow_untyped_defs = True

    # This one isn't too hard to get passing, but return on investment is lower
    no_implicit_reexport = True

    # This one can be tricky to get passing if you use a lot of untyped libraries
    warn_return_any = True

Note that you can also start with `--strict` and subtract, for instance:

    strict = True
    warn_return_any = False

Remember that many of these options can be enabled on a per-module basis. For instance, you may want to enable `disallow_untyped_defs` for modules which you’ve completed annotations for, in order to prevent new code from being added without annotations.

And if you want, it doesn’t stop at `--strict`. Mypy has additional checks that are not part of `--strict` that can be useful. See the complete The mypy command line reference and Error codes for optional checks.

## Speed up mypy runs

You can use mypy daemon to get much faster incremental mypy runs. The larger your project is, the more useful this will be. If your project has at least 100,000 lines of code or so, you may also want to set up remote caching for further speedups.