Answered Apr 21, 2016 · 226 votes
Python is strongly typed because every object has a type, every object knows its type, it's impossible to accidentally or deliberately use an object of a type "as if" it was an object of a different type, and all elementary operations on the object are delegated to its type.
This has nothing to do with names. A name in Python doesn't "have a type": if and when a name's defined, the name refers to an object, and the object does have a type (but that doesn't in fact force a type on the name: a name is a name).
A name in Python can perfectly well refer to different objects at different times (as in most programming languages, though not all) -- and there is no constraint on the name such that, if it has once referred to an object of type X, it's then forevermore constrained to refer only to other objects of type X. Constraints on names are not part of the concept of "strong typing", though some enthusiasts of static typing (where names do get constrained, and in a static, AKA compile-time, fashion, too) do misuse the term this way.
Answered Jan 08, 2022 · 1130 votes
The other answers have done a good job at explaining duck typing and the simple answer by tzot:
Python does not have variables, like other languages where variables have a type and a value; it has names pointing to objects, which know their type.
However, one interesting thing has changed since 2010 (when the question was first asked), namely the implementation of PEP 3107 (implemented in Python 3). You can now actually specify the type of a parameter and the type of the return type of a function like this:
def pick(l: list, index: int) -> int: return l[index]-
Here we can see that pick takes 2 parameters, a list l and an integer index. It should also return an integer.
So here it is implied that l is a list of integers which we can see without much effort, but for more complex functions it can be a bit confusing as to what the list should contain. We also want the default value of index to be 0. To solve this you may choose to write pick like this instead:
def pick(l: "list of ints", index: int = 0) -> int: return l[index]-
Note that we now put in a string as the type of l, which is syntactically allowed, but it is not good for parsing programmatically (which we'll come back to later).
It is important to note that Python won't raise a TypeError if you pass a float into index, the reason for this is one of the main points in Python's design philosophy: "We're all consenting adults here", which means you are expected to be aware of what you can pass to a function and what you can't. If you really want to write code that throws TypeErrors you can use the isinstance function to check that the passed argument is of the proper type or a subclass of it like this:
def pick(l: list, index: int = 0) -> int: if not isinstance(l, list): raise TypeError return l[index]-
More on why you should rarely do this and what you should do instead is talked about in the next section and in the comments.
PEP 3107 does not only improve code readability but also has several fitting use cases which you can read about here.
Type annotation got a lot more attention in Python 3.5 with the introduction of PEP 484 which introduces a standard module typing for type hints.
These type hints came from the type checker mypy (GitHub), which is now PEP 484 compliant.
The typing module comes with a pretty comprehensive collection of type hints, including:
- [" List ",", Tuple ",", Set ",", Dict "," - for list ",", tuple ",", set "," and dict "," respectively."]
- [" Iterable "," - useful for generators."]
- [" Any "," - when it could be anything."]
- [" Union "," - when it could be anything within a specified set of types, as opposed to Any ","."]
- [" Optional "," - when it might be None. Shorthand for Union[T, None] ","."]
- [" TypeVar "," - used with generics."]
- [" Callable "," - used primarily for functions, but could be used for other callables."]
These are the most common type hints. A complete listing can be found in the documentation for the typing module.
Here is the old example using the annotation methods introduced in the typing module:
from typing import List-def pick(l: List[int], index: int) -> int: return l[index]-
One powerful feature is the Callable which allows you to type annotate methods that take a function as an argument. For example:
from typing import Callable, Any, Iterable-def imap(f: Callable[[Any], Any], l: Iterable[Any]) -> List[Any]: """An immediate version of map, don't pass it any infinite iterables!""" return list(map(f, l))-
The above example could become more precise with the usage of TypeVar instead of Any, but this has been left as an exercise to the reader since I believe I've already filled my answer with too much information about the wonderful new features enabled by type hinting.
Previously when one documented Python code with for example Sphinx some of the above functionality could be obtained by writing docstrings formatted like this:
def pick(l, index): """ :param l: list of integers :type l: list :param index: index at which to pick an integer from *l* :type index: int :returns: integer at *index* in *l* :rtype: int """ return l[index]-
As you can see, this takes a number of extra lines (the exact number depends on how explicit you want to be and how you format your docstring). But it should now be clear to you how PEP 3107 provides an alternative that is in many (all?) ways superior. This is especially true in combination with PEP 484 which, as we have seen, provides a standard module that defines a syntax for these type hints/annotations that can be used in such a way that it is unambiguous and precise yet flexible, making for a powerful combination.
In my personal opinion, this is one of the greatest features in Python ever. I can't wait for people to start harnessing the power of it. Sorry for the long answer, but this is what happens when I get excited.
An example of Python code which heavily uses type hinting can be found here.
Answered Mar 22, 2010 · 24 votes
You don't specify a type. The method will only fail (at runtime) if it tries to access attributes that are not defined on the parameters that are passed in.
def no_op(param1, param2): pass-
... will not fail no matter what two args are passed in.
def call_quack(param1, param2): param1.quack() param2.quack()-
... will fail at runtime if param1 and param2 do not both have callable attributes named quack.
