We all love Python, it is a multi-paradigm dynamic programming language very popular in Data Science and Machine Learning. Besides some small quirky things in the language, I am quite happy with how it is evolving. However, there are some areas where I thought Python could do better for improving programming productivity in specific contexts:
- While is easy to hack around scripts and get something running, managing a large complex codebase becomes an issue. You can get something working really fast, but maintaining it can become an issue if your code base becomes large enough.
- Many times while reading other people's code (heck, even my own code), and even when documented, it is really hard to figure out what a method or function is doing without a clear knowledge of the types you are working with. In many cases having just the type information (i.e. via a simple comment) would make understanding the code a whole lot faster.
I have also spent a lot of time debugging just because the wrong type was passed to a function/method (e.g. the wrong variable was passed to a method, wrong argument order, etc.). Because of Python's dynamic typing the interpreter and/or linter could not warn me. Plus, some of those errors only were evident at execution time, generally in edge cases.
Although we all like working on greenfield projects, in the real world you will have to work with legacy code and it will generally be ugly and full of issues. Let's take a look at at some Python 2.7 legacy code I have to maintain:
# snnipets.py def get_hotel_type_snippets(self, hotel_type_id, cat_set): snippets = self.get_snippets(hotel_type_id, "pos") snippets += list(it.chain.from_iterable( self.get_snippets( rel_cat, cat_set[rel_cat].sentiment ) for rel_cat in cat_set[hotel_type_id].cat_def.related_cats if rel_cat in cat_set and cat_set[rel_cat].sentiment == "pos" )) return snippets[:self.max_snippets]
Don't focus too much on the fact that it has no documentation and forget about the ugly comprehension inside.
In order to understand this code I have to answer the following questions:
- What type is
hotel_type_id(is it an
- What type is
cat_set, it looks like a dictionary containing something else.
These two issues could be fixed with a proper docstring, however comments sometimes don't contain all the information required, don't include the type of the parameters being passed or can be easily inconsistent as the code might have been changed but the comment not updated.
If I want to understand the code I will have to look for its usage, maybe grepping through the code for something called
sentiment. If you have a large codebase, you might even find many classes implementing the same method name.
I have two choices when I need to modify existing code like this. I can either hack my way around, modifying it enough to make it do what I want, or I can look for a way to make this code better (i.e. the The Boy Scout Rule). Besides adding the needed documentation, it would be cool to have a way to specify the types that could be potentially used by a static linter.
Luckily I was not the only one with this problem (or desire), and that's one of the reasons PEP-484 came to life. The goal is to provide Python with optional type annotations that allow an offline static linter to check for type issues. However I believe making the code easier to understand (via type documentation) is an awesome side-product.
There is an implementation of this PEP called mypy that is in fact the inspiration for the first. Mypy provides a static type checker that works in Python 3 (using type annotations) and Python 2.7 (using specific crafted comments).
At TrustYou we have a lot of Python 2.7 legacy code that suffers many of the issues mentioned above, so I decided to give it a try in a new project I was working on and I have to say it helped catch some issues early in the development stage. I also tried in it in an existing code base that because of its structure was hard to read.
Let's go back to the example code I shared before and let's document the code using type annotations:
from typing import Any, List, Dict from metaprecomp.tops_flops_bake.category import CategorySet def get_hotel_type_snippets(self, hotel_type_id, cat_set): # type: (str, CategorySet) -> List[Dict[str, Any]] snippets = self.get_snippets(hotel_type_id, "pos") # (...) as before
As you might guess,
(str, Category) are the types of the method parameters. What follows
-> is the return type, in this example, a list
of dictionaries from
Any is a catch all-type. It helps when you don't know they type (in this case, i would have had to read the code further, and I was too lazy) or when the function can return literally any type.
Some notes from the code above:
- You might have noticed the
from typing import Any, ..., the typing library brings the required types into Python 2.7, even when used only as comments. So yeah, you will need to add it to your
- You also noticed I had to import explicitly
categorymodel (even if I used it as a comment). I find that good as I am stating there's a relationship or dependency between those modules.
- Finally, you also noticed the
# noqa: F401. This is to avoid
pylintto complain about unused imports. This is not nice, but it is minor annoyance.
Installing and running mypy
So far we have used
mypy syntax (actually PEP 484 - Type Hints) to do some annotation, but all this hassle should bring something to the table besides a nifty documentation. So let's install
mypy and try the command line.
mypy requires a Python 3 environment so if your main Python environment is 2.7 you will need to install it in a separate one. Luckly you can
call the binary directly (even when your Py27 environment is activated). I you use Anaconda you can easily create a dedicated environment for
[miguelc@machine]$ conda create -n mypy python=3.6 (...) [miguelc@machine]$ source activate mypy (mypy)[miguelc@machine]$ pip install mypy # to get the latest mypy (mypy)[miguelc@machine]$ ln -s `which mypy` $HOME/bin/mypy # I have $HOME/bin in my $PATH (mypy)[miguelc@machine]$ source deactivate [miguelc@machine]$ mypy --help # this should work
With that out of the way, we can start using
mypy executable for checking our source code. I run
mypy the following way:
[miguelc@machine]$ mypy --py2 --ignore-missing-imports --check-untyped-defs [directory or files]
--py2: indicates that the code to check is a Python 2 codebase.
mypyto ignore error messages when imports cannot be resolved, e.g. when they don't exist on the env mypy is running.
--check-untyped-defs: checks functions but does not fail if the arguments are not typed.
The command line tool provides a lot of options and the documentation is very good. An interesting feature is that it allows you to generate reports that can be displayed using CI tools like Jenkins.
Checking for type errors
Let's take a look at another method I annoated for the purpose of exemplifying the type of errors you can find using
mypy after adding type annotations:
from typing import Any, List, Dict, FrozenSet # noqa: F401 def get_snippets( self, category_id, sentiment, pos_contradictory_subcat_ids=frozenset(), neg_contradictory_subcat_ids=frozenset()): # type: (str, str, FrozenSet[str], FrozenSet[str]) -> List[Dict[str, str]] # (...) not relevant code...
Indeed, another method with no documentation whatsoever. So I had to read a little bit of the code to figure out what are the input and return types. Now let's imagine that somewhere in the code something like this happens:
# bake_reduce.py cat = 13 # (...) snippets_generator = SnippetsGenerator( snippets_by_cat_sent, self.metacategory_bundle[lang] ) snippets_generator.get_snippets(cat, "pos")
If I run
mypy I would get the following error:
[miguelc@machine]$ mypy --ignore-missing-imports --check-untyped-defs --py2 metaprecomp/tops_flops_bake/bake_reduce.py metaprecomp/tops_flops_bake/bake_reduce.py:238: error: Argument 1 to "get_snippets" of "SnippetsGenerator" has incompatible type "int"; expected "str"
If you come from the static typed language world this should look really normal to you, but for Python developers finding an error like this (in particular in large code bases) requires to spend quite a bit of time debugging (and sometimes the use of Voodoo magic).
When to use mypy
Optional type annotations are that, optional. You can start hacking as normal using the speed that Python dynamic typing gives you and once your code is stable enough you can gradually add type annotations to help avoid bugs and to document the code. The
mypy FAQ contains some scenarios in which a project will benefit from using static type annotations:
- Your project is large or complex.
- Your codebase must be maintained for a long time.
- Multiple developers are working on the same code.
- Running tests takes a lot of time or work (type checking may help you find errors early in development, reducing the number of testing iterations).
- Some project members (devs or management) don’t like dynamic typing, but others prefer dynamic typing and Python syntax. Mypy could be a solution that everybody finds easy to accept.
- You want to future-proof your project even if currently none of the above really apply.
In the particular case of my team, a lot of the code we write ends up running for quite a long time inside of MapReduce (Hadoop) jobs, so being able to detect bugs ahead of time would save precious developer time and make everyone happier.
Adding support to Emacs
By now you might be thinking that it would be cool to integrate
mypy checks into your editor. Some, like PyCharm, already support this.
For Emacs you can integrate
mypy into Flycheck via flycheck-mypy. You can install it via
M-x package-install flycheck-mypy.
Configuring it is a matter of setting a couple of variables:
(set-variable 'flycheck-python-mypy-executable "/Users/miguel/anaconda2/envs/py35/mypy/mypy") (set-variable 'flycheck-python-mypy-args '("--py2" "--ignore-missing-imports" "--check-untyped-defs"))
Mypy recommends disabling all other linters/checkers like
flake8 and others when using it, however I wanted to keep both at the same time (call me paranoid). In Emacs, you can accomplish this with the following configuration:
(flycheck-add-next-checker 'python-flake8 'python-mypy)
Final words and references
mypy won't magically find errors in your code, it will be as good as the type annotations you add and the way you structure the code. Also, it is not a replacement for proper documentation. Sometimes there are methods/functions that become easier to read just by adding type annotations, but documenting key parts of the code is vital for ensuring code maintainability and extensibility.
I did not mention all the features of
mypy so please check official documentation to learn more.
There are a couple of talks that can serve as a nice introduction to the topic:
- Introducing Type Annotations for Python - by Guido, Greg Price and David Fisher
- Static Types for Python PyCon 2017 - by Jukka Lehtosalo and David Fisher
The first one of them is given by Guido, who's pushing the project a lot. Thus, I expect
mypy to become more popular in the following years. Happy hacking.