Getting started¶
This chapter introduces some core concepts of mypy, including function
annotations, the typing
module, stub files, and more.
Anyone familiar with mypy should feel comfortable working with basedmypy.
If you’re looking for a quick intro, see the mypy cheatsheet.
If you’re unfamiliar with the concepts of static and dynamic type checking, be sure to read this chapter carefully, as the rest of the documentation may not make much sense otherwise.
Installing and running basedmypy¶
Basedmypy cannot be installed alongside mypy, which must be uninstalled first:
> pip uninstall mypy -y
Basedmypy requires Python 3.8 or later to run. You can install basedmypy using pip:
$ python3 -m pip install basedmypy
Once basedmypy is installed, run it by using the mypy
tool:
$ mypy program.py
This command makes mypy type check your program.py
file and print
out any errors it finds. Mypy will type check your code statically: this
means that it will check for errors without ever running your code, just
like a linter.
This also means that you are always free to ignore the errors mypy reports, if you so wish. You can always use the Python interpreter to run your code, even if mypy reports errors.
However, if you try directly running mypy on your existing Python code, it will most likely report little to no errors. This is a feature! It makes it easy to adopt mypy incrementally.
In order to get useful diagnostics from mypy, you must add type annotations to your code. See the section below for details.
Dynamic vs static typing¶
Basedmypy has all strictness options enabled by default. This can often be a
little overwhelming, and the baseline functionality can help alleviate this.
For a good compromise of safety and ease of use, we can recommend enabling allow_any_expr
.
A function without type annotations is considered to be dynamically typed by mypy:
def greeting(name):
return 'Hello ' + name
By default, basedmypy will type check dynamically typed functions. This means that with a few exceptions, mypy will report all errors within regular unannotated Python.
This is the case even if you misuse the function!
def greeting(name):
return 'Hello ' + name
# These calls will fail when the program runs, and basedmypy does report an error
# even though "greeting" does not have type annotations.
greeting(123)
greeting(b"Alice")
We can get mypy to detect these kinds of bugs by adding type annotations (also
known as type hints). For example, you can tell mypy that greeting
both accepts
and returns a string like so:
# The "name: str" annotation says that the "name" argument should be a string
# The "-> str" annotation says that "greeting" will return a string
def greeting(name: str) -> str:
return 'Hello ' + name
This function is now statically typed: mypy will use the provided type hints
to detect incorrect use of the greeting
function and incorrect use of
variables within the greeting
function. For example:
def greeting(name: str) -> str:
return 'Hello ' + name
greeting(3) # Argument 1 to "greeting" has incompatible type "int"; expected "str"
greeting(b'Alice') # Argument 1 to "greeting" has incompatible type "bytes"; expected "str"
greeting("World!") # No error
def bad_greeting(name: str) -> str:
return 'Hello ' * name # Unsupported operand types for * ("str" and "str")
Being able to pick whether you want a function to be dynamically or statically typed can be very helpful. For example, if you are migrating an existing Python codebase to use static types, it’s usually easier to migrate by incrementally adding type hints to your code rather than adding them all at once. Similarly, when you are prototyping a new feature, it may be convenient to initially implement the code using dynamic typing and only add type hints later once the code is more stable.
Once you are finished migrating or prototyping your code, you can make mypy warn you
if you add a dynamic function by mistake by using the --disallow-untyped-defs
flag. You can also get mypy to provide some limited checking of dynamically typed
functions by using the --check-untyped-defs
flag.
See The mypy command line for more information on configuring mypy.
Strict mode and configuration¶
Mypy has a strict mode that enables a number of additional checks,
like --disallow-untyped-defs
.
If you run mypy with the --strict
flag, you
will basically never get a type related error at runtime without a corresponding
mypy error, unless you explicitly circumvent mypy somehow.
However, this flag will probably be too aggressive if you are trying to add static types to a large, existing codebase. See Using mypy with an existing codebase for suggestions on how to handle that case.
Mypy is very configurable, so you can start with using --strict
and toggle off individual checks. For instance, if you use many third
party libraries that do not have types,
--ignore-missing-imports
may be useful. See Introduce stricter options for how to build up to --strict
.
See The mypy command line and The mypy configuration file for a complete reference on configuration options.
More complex types¶
So far, we’ve added type hints that use only basic concrete types like
str
and float
. What if we want to express more complex types,
such as “a list of strings” or “an iterable of ints”?
For example, to indicate that some function can accept a list of
strings, use the list[str]
type (Python 3.9 and later):
def greet_all(names: list[str]) -> None:
for name in names:
print('Hello ' + name)
names = ["Alice", "Bob", "Charlie"]
ages = [10, 20, 30]
greet_all(names) # Ok!
greet_all(ages) # Error due to incompatible types
The list
type is an example of something called a generic type: it can
accept one or more type parameters. In this case, we parameterized list
by writing list[str]
. This lets mypy know that greet_all
accepts specifically
lists containing strings, and not lists containing ints or any other type.
In the above examples, the type signature is perhaps a little too rigid. After all, there’s no reason why this function must accept specifically a list – it would run just fine if you were to pass in a tuple, a set, or any other custom iterable.
You can express this idea using collections.abc.Iterable
:
from collections.abc import Iterable # or "from typing import Iterable"
def greet_all(names: Iterable[str]) -> None:
for name in names:
print('Hello ' + name)
This behavior is actually a fundamental aspect of the PEP 484 type system: when
we annotate some variable with a type T
, we are actually telling mypy that
variable can be assigned an instance of T
, or an instance of a subtype of T
.
That is, list[str]
is a subtype of Iterable[str]
.
This also applies to inheritance, so if you have a class Child
that inherits from
Parent
, then a value of type Child
can be assigned to a variable of type Parent
.
For example, a RuntimeError
instance can be passed to a function that is annotated
as taking an Exception
.
As another example, suppose you want to write a function that can accept either
ints or strings, but no other types. You can express this using a
union type. For example, int
is a subtype of int | str
:
def normalize_id(user_id: int | str) -> str:
if isinstance(user_id, int):
return f'user-{100_000 + user_id}'
else:
return user_id
Note
If using Python 3.9 or earlier, use typing.Union[int, str]
instead of
int | str
, or use from __future__ import annotations
at the top of
the file (see Annotation issues at runtime).
The typing
module contains many other useful types.
For a quick overview, look through the mypy cheatsheet.
For a detailed overview (including information on how to make your own generic types or your own type aliases), look through the type system reference.
Note
When adding types, the convention is to import types
using the form from typing import <name>
(as opposed to doing
just import typing
or import typing as t
or from typing import *
).
For brevity, we often omit imports from typing
or collections.abc
in code examples, but mypy will give an error if you use types such as
Iterable
without first importing them.
Note
In some examples we use capitalized variants of types, such as
List
, and sometimes we use plain list
. They are equivalent,
but the prior variant is needed if you are using Python 3.8 or earlier.
Local type inference¶
Once you have added type hints to a function (i.e. made it statically typed), mypy will automatically type check that function’s body. While doing so, mypy will try and infer as many details as possible.
We saw an example of this in the normalize_id
function above – mypy understands
basic isinstance
checks and so can infer that the user_id
variable was of
type int
in the if-branch and of type str
in the else-branch.
As another example, consider the following function. Mypy can type check this function
without a problem: it will use the available context and deduce that output
must be
of type list[float]
and that num
must be of type float
:
def nums_below(numbers: Iterable[float], limit: float) -> list[float]:
output = []
for num in numbers:
if num < limit:
output.append(num)
return output
For more details, see Type inference and type annotations.
Types from libraries¶
Mypy can also understand how to work with types from libraries that you use.
For instance, mypy comes out of the box with an intimate knowledge of the
Python standard library. For example, here is a function which uses the
Path
object from the pathlib standard library module:
from pathlib import Path
def load_template(template_path: Path, name: str) -> str:
# Mypy knows that `template_path` has a `read_text` method that returns a str
template = template_path.read_text()
# ...so it understands this line type checks
return template.replace('USERNAME', name)
If a third party library you use declares support for type checking, mypy will type check your use of that library based on the type hints it contains.
However, if the third party library does not have type hints, mypy will complain about missing type information.
prog.py:1: error: Library stubs not installed for "yaml"
prog.py:1: note: Hint: "python3 -m pip install types-PyYAML"
prog.py:2: error: Library stubs not installed for "requests"
prog.py:2: note: Hint: "python3 -m pip install types-requests"
...
In this case, you can provide mypy a different source of type information, by installing a stub package. A stub package is a package that contains type hints for another library, but no actual code.
$ python3 -m pip install types-PyYAML types-requests
Stubs packages for a distribution are often named types-<distribution>
.
Note that a distribution name may be different from the name of the package that
you import. For example, types-PyYAML
contains stubs for the yaml
package.
For more discussion on strategies for handling errors about libraries without type information, refer to Missing imports.
For more information about stubs, see Stub files.
Next steps¶
If you are in a hurry and don’t want to read lots of documentation before getting started, here are some pointers to quick learning resources:
Read the mypy cheatsheet.
Read Using mypy with an existing codebase if you have a significant existing codebase without many type annotations.
Read the blog post about the Zulip project’s experiences with adopting mypy.
If you prefer watching talks instead of reading, here are some ideas:
Carl Meyer: Type Checked Python in the Real World (PyCon 2018)
Greg Price: Clearer Code at Scale: Static Types at Zulip and Dropbox (PyCon 2018)
Look at solutions to common issues with mypy if you encounter problems.
You can ask questions about mypy in the mypy issue tracker and typing Gitter chat.
For general questions about Python typing, try posting at typing discussions.
You can also continue reading this document and skip sections that aren’t relevant for you. You don’t need to read sections in order.