development

How Python Decorators Function

Have you ever seen those “@” tags on top of Python functions and classes? Those are decorators – functions that wrap around other functions. Confusing? At first, but they’re easy with practice. Useful? Very!

The Talk

I delivered a talk on decorators at PyGotham TV 2020, PyCon India 2020, and PyTexas 2020. Here’s the recording from PyTexas:

Of course, I mentioned testing in this talk, too:

Is it even a Pandy Knight talk if testing is not talked about?
“Is it even a Pandy Knight talk if testing is not talked about?”

The Transcript

[Camera]

Hello, PyTexas 2020! It’s Pandy Knight here. I’m the Automation Panda, and I’m a big Python fan, just like y’all.

Have you ever seen those “@” tags on top of Python functions? Maybe you’ve seen them on top of methods and classes, too. Those are decorators, one of Python’s niftiest language features. Decorators are essentially wrappers – they wrap additional code around existing definitions. When used right, they can clean up your code better than OxiClean! Let’s learn how to use them.

[Slide]

So, here’s a regular old “hello world” function. When we run it, …

[Slide]

…It prints “Hello World!” Nothing fancy here.

[Slide]

Now, let’s take that function…

[Slide]

…And BAM! Add a decorator. Using this “@” sign, we just added a decorator named “tracer” to “hello_world”. So, what is this decorator?

[Slide]

“Tracer” is just another function. But, it’s special because it takes in another function as an argument!

[Slide]

Since “tracer” decorates “hello_world”, the “hello_world” function is passed into “tracer” as an argument. Wow!

So, what’s inside “tracer”?

[Slide]

This decorator has an inner function named “wrapper”. Can you even do that? With Python, yes, you can! The “wrapper” function prints “Entering”, calls the function originally passed into the decorator, and then prints “Exiting”.

[Slide]

When “tracer” decorates “hello_world”, that means “hello_world” will be wrapped by “Entering” and “Exiting” print statements.

[Slides]

Finally, the decorator returns the new “wrapper” function. Any time the decorated function is called, it will effectively be replaced by this new wrapper function. 

[Slides]

So, when we call “hello_world”, the trace statements are now printed, too. Wow! That’s amazing. That’s how decorators work.

[Slide] Decorators [Slide] wrap [Slide] functions [Slide] around [Slide] functions!

[Slide]

Think about them like candy bars. The decorator is like the foil wrapper, and the decorated function is like the chocolate inside.

[Slide]

But how is this even possible? That decorator code looks confusing!

[Slide]

Decorators are possible because, in Python, functions are objects. In fancy language, we say functions are “first-order” values. Since functions are just objects, …

[Slide]

…We can pass them into other functions as arguments, …

[Slide]

…define new functions inside existing functions, …

[Slide]

…and return a function from a function.

[Slide]

This is all part of a paradigm called “Functional Programming.” Python supports functional programming because functions can be treated like objects. That’s awesome!

[Slide]

So, using functions as objects, decorators change how functions are called.

[Slide]

Decorators create an “outer” decorator function around an “inner” decorated function. Remember, the outer function is like the foil wrapper, and the inner function is like the chocolate.

[Slide]

Creating an outer function lets you add new code around the inner function. Some people call this “advice.” You can add advice before or after the inner function. You could even skip the inner function!

[Slide]

The best part is, decorators can be applied to any function. They make sharing code easy so you don’t repeat yourself!

[Slide]

Decorators are reminiscent of a paradigm called “Aspect-Oriented Programming,” in which code can be cleverly inserted before and after points of execution. Neat!

[Slide]

So remember, decorators wrap functions around functions, like candy bars!

[Slide]

Hold on, now! We have a problem in that Python code!

[Slide]

If the “wrapper” function effectively replaces “hello_world”, then what identity does “hello_world” report?

[Slide]

Its name is “wrapper”…

[Slide]

And its help is also “wrapper”! That’s not right!

[Slide]

Never fear! There’s an easy solution. The “functools” module provides a decorator named “wraps”. Put “functools.wraps” on the “wrapper” function and pass in the inner function object, and decorated functions once again show the right identity. That’s awesome.

[Slide]

But wait, there’s another problem!

[Slide]

How do decorators work with inputs and outputs? What if we decorate a function with parameters and a return value?

[Slide]

If we try to use the current “tracer”, …

[Slide]

…We get an error! Arguments broke it!

[Slide]

We can fix it! First, add “star args” and “star-star k-w-args” to the “wrapper” function’s parameters, and then pass them through to the inner function. This will make sure all arguments go through the decorator into the decorated function.

[Slide]

Then, capture the inner function’s return value and return it from the “wrapper” function. This makes sure return values also pass through. If the inner function has no return value, don’t worry – the decorator will pass through a “None” value.

[Slide]

When we call the function with the updated “tracer”, …

[Slide]

…we see tracing is now successful again!

[Slide]

When we check the return value, …

[Slide]

…it’s exactly what we expect. It works!

[Slide]

Wow, that’s awesome!

[Slide]

But wait, there’s more!

[Slide]

You can write a decorator to call a function twice!

[Slide]

Start with the decorator template…

[Slide]

…and call the inner function twice! Return the final return value for continuity.

[Slide]

BAM! It works!

[Slide]

But wait, there’s more!

[Slide]

You can write a timer decorator!

[Slide]

Start with the template, …

[Slide]

…call the inner function, …

[Slide]

…and surround it with timestamps using the “time” module!

[Slide]

BAM! Now you can time any function!

[Slide]

But wait, there’s more!

[Slide]

You can also add more than one decorator to a function! This is called “nesting”. Order matters. Decorators are executed in order of closeness to the inner function. So, in this case, …

[Slide]

…”call_twice” is applied first, and then “timer” is applied.

[Slide]

If these decorators are reversed, …

[Slide]

…then each inner function call is timed. Cool!

[Slide]

But wait, there’s more!

[Slide]

You can scrub and validate function arguments! Check out these two simple math functions.

[Slide]

Create a decorator to scrub and validate inputs as integers.

[Slide]

Add the wrapper function, and make sure it has positional args.

[Slide]

Then, cast all args as ints before passing them into the inner function.

[Slide]

Now, when calling those math functions, all numbers are integers! Using non-numeric inputs also raises a ValueError!

[Slide]

But wait, there’s more!

[Slide]

You can create decorators with parameters! Here’s a decorator that will repeat a function 5 times.

[Slide]

The “repeat” function is a little different. Instead of taking in the inner function object, it takes in the parameter, which is the number of times to repeat the inner function.

[Slide]

Inside, there’s a “repeat_decorator” function that has a parameter for the inner function. The “repeat” function returns the “repeat_decorator” function.

[Slide]

Inside “repeat_decorator” is the “wrapper” function. It uses “functools.wraps” and passes through all arguments. “repeat_decorator” returns “wrapper”.

[Slide]

Finally, “wrapper” contains the logic for calling the inner function multiple times, according to the “repeat” decorator’s parameter value.

[Slide]

Now, “hello_world” runs 5 times. Nifty!

[Slide]

But wait, there’s more!

[Slide]

Decorators can be used to save state! Here’s a decorator that will count the number of times a function is called.

[Slide]

“count_calls” has the standard decorator structure.

[Slide]

Outside the wrapper, a “count” attribute is initialized to 0. This attribute is added to the wrapper function object.

[Slide]

Inside the wrapper, the count is incremented before calling the inner function. The “count” value will persist across multiple calls.

[Slide]

Initially, the “hello_world” count value is 0.

[Slide]

After two calls, the count value goes up! Awesome!

[Slide]

But wait, there’s more!

[Slide]

Decorators can be used in classes! Here, the “timer” decorator is applied to this “hello” method.

[Slider]

As long as parameters and return values are set up correctly, decorators can be applied equally to functions and methods.

[Slide]

Decorators can also be applied directly to classes!

[Slide]

When a decorator is applied to a class, it wraps the constructor.

[Slide]

Note that it does not wrap each method in the class.

[Slide]

Since decorators can wrap classes and methods in addition to functions, it would technically be more correct to say that decorators wrap callables around callables!

[Slide]

So all that’s great, but can decorators be tested? Good code must arguably be testable code. Well, today’s your lucky day, because yes, you can test decorators!

[Slide]

Testing decorators can be a challenge. We should always try to test the code we write, but decorators can be tricky. Here’s some advice:

[Slide]

First, separate tests for decorator functions from decorated functions. For decorator functions, focus on intended outcomes. Try to focus on the “wrapper” instead of the “inner” function. Remember, decorators can be applied to any callable, so cover the parts that make decorators unique. Decorated functions should have their own separate unit tests.

[Slide]

Second, apply decorators to “fake” functions used only for testing. These functions can be simple or mocked. That way, unit tests won’t have dependencies on existing functions that could change. Tests will also be simpler if they use slimmed-down decorated functions.

[Slide]

Third, make sure decorators have test coverage for every possible way it could be used. Cover decorator parameters, decorated function arguments, and return values. Make sure the “name” and “help” are correct. Check any side effects like saved state. Try it on methods and classes as well as functions. With decorators, most failures happen due to overlooked edge cases.

[Slide]

Let’s look at a few short decorator tests. We’ll use the “count_calls” decorator from earlier.

There are two decorated functions to use for testing. The first one is a “no operation” function that does nothing. It has no parameters or returns. The second one is a function that takes in one argument and returns it. Both are very simple, but they represent two equivalences classes of decoratable functions.

[Slide]

The test cases will verify outcomes of using the decorator. For “count_calls”, that means tests will focus on the “count” attribute added to decorated functions.

The first test case verifies that the initial count value for any function is zero.

[Slide]

The second test calls a function three times and verifies that count is three.

[Slide]

The third test exercises the “same” function to make sure arguments and return values work correctly. It calls the “same” function, asserts the return value, and asserts the count value.

This collection of tests is by no means complete. It simply shows how to start writing tests for decorators. It also shows that you don’t need to overthink unit tests for decorators. Simple is better than complex!

[Slide]

Up to this point, we’ve covered how to write your own decorators. However, Python has several decorators available in the language and in various modules that you can use, absolutely free!

[Slide]

Decorators like “classmethod”, “staticmethod”, and “property” can apply to methods in a class. Frameworks like Flask and pytest have even more decorators. Let’s take a closer look.

[Slide]

Let’s start by comparing the “classmethod” and “staticmethod” decorators. We’ll revisit the “Greeter” class we saw before.

[Slide]

The “classmethod” decorator will turn any method into a “class” method instead of an “instance” method. That means this “hello” method here can be called directly from the class itself instead of from an object of the class. This decorator will pass a reference to the class into the method so the method has some context of the class. Here, the reference is named “c-l-s”, and the method uses it to get the class’s name. The method can be called using “Greeter.hello”. Wow!

[Slide]

The “staticmethod” decorator works almost the same as the “classmethod” decorator, except that it does not pass a reference to the class into the method.

[Slide]

Notice how the method parameters are empty – no “class” and no “self”. Methods are still called from the class, like here with “Greeter.goodbye”. You could say that “staticmethod” is just a simpler version of “classmethod”.

[Slide]

Next, let’s take a look at the “property” decorator. To show how to use it, we’ll create a class called “Accumulator” to keep count of a tally.

[Slide]

Accumulator’s “init” method initializes a “count” attribute to 0.

[Slide]

An “add” method adds an amount to the count. So far, nothing unusual.

[Slide]

Now, let’s add a property. This “count” method has the “property” decorator on it. This means that “count” will be callable as an attribute instead of a method, meaning that it won’t need parentheses. It is effectively a “getter”. The calls to “count” in the “init” and “add” methods will actually call this property instead of a raw variable.

Inside the “count” property, the method returns an attribute named “underscore-count”. The underscore means that this variable should be private. However, this class hasn’t set that variable yet.

[Slide]

That variable is set in the “setter” method. Setters are optional for properties. Here, the setter validates that the value to set is not negative. If the value is good, then it sets “underscore-count”. If the value is negative, then it raises a ValueError.

“underscore-count” is handled internally, while “count” is handled publicly as the property. The getter and setter controls added by the “property” decorator let you control how the property is handled. In this class, the setter protects the property against bad values!

[Slide]

So, let’s use this class. When an Accumulator object is constructed, its initial count is 0.

[Slide]

After adding an amount to the object, its count goes up.

[Slide]

Its count can be directly set to non-negative values. Attempting to set the count directly to a negative value raises an exception, as expected. Protection like that is great!

[Slide]

Python packages also frequently contain decorators. For example, Flask is a very popular Web “micro-framework” that enables you to write Web APIs with very little Python code.

[Slide]

Here’s an example “Hello World” Flask app taken directly from the Flask docs online. It imports the “flask” module, creates the app, and defines a single endpoint at the root path that returns the string, “Hello, World!” Flask’s “app.route” decorator can turn any function into a Web API endpoint. That’s awesome!

[Slide]

Another popular Python package with decorators is pytest, Python’s most popular test framework.

[Slide]

One of pytest’s best features is the ability to parametrize test functions to run for multiple input combinations. Test parameters empower data driven testing for wider test coverage!

[Slide]

To show how this works, we’ll use a simple test for basic arithmetic: “test addition”. It asserts that a plus b equals c.

[Slide]

The values for a, b, and c must come from a list of tuples. For example, 1 plus 2 equals 3, and so forth.

[Slide]

The “pytest.mark.parametrize” decorator connects the list of test values to the test function. It runs the test once for each tuple in the list, and it injects the tuple values into the test case as function arguments. This test case would run four times. Test parameters are a great way to rerun test logic without repeating test code.

[Slide]

So, act now, before it’s too late!

[Slide]

When should you use decorators in your Python code?

[Slide]

Use decorators for aspects.

[Slide]

An aspect is a special cross-cutting concern. They are things that happen in many parts of the code, and they frequently require repetitive calls.

Think about something like logging. If you want to add logging statements to different parts of the code, then you need to write multiple logging calls in all those places. Logging itself is one concern, but it cross-cuts the whole code base. One solution for logging could be to use decorators, much like we saw earlier with the “tracer” decorator.

[Slide]

Good use cases for decorators include logging, profiling, input validation, retries, and registries. These are things that typically require lots of extra calls inserted in duplicative ways. Ask yourself this:

[Slide]

Should the code wrap something else? If yes, then you have a good candidate for a decorator.

[Slide]

However, decorators aren’t good for all circumstances. You should avoid decorators for “main” behaviors, because those should probably be put directly in the body of the decorated function. Avoid logic that’s complicated or has heavy conditionals, too, because simple is better than complex. You should also try to avoid completely side-stepping the decorated function – that could confuse people!

[Slide]

Ask yourself this: is the code you want to write the wrapper or the candy bar itself? Wrappers make good decorators, but candy bars do not.

[Slide]

I hope you’ve found this infomercial about decorators useful! If you want to learn more, …

[Slide]

…check out this Real Python tutorial by Geir Arne Hjelle named “Primer on Python Decorators”. It covers everything I showed here, plus more.

[Slide]

Thank you very much for listening! Again, my name is Pandy Knight – the Automation Panda and a bona fide Pythonista. Please read my blog at AutomationPanda.com, and follow me on Twitter @AutomationPanda. I’d love to hear what y’all end up doing with decorators!

East Meets West When Translating Django Apps

你好！我叫安迪。 

Don’t understand my Chinese? Don’t feel bad – I don’t know much Mandarin, either! My wife and her mom are from China. When I developed a Django app to help my wife run her small business, I needed to translate the whole site into Chinese so that Mama could use it, too. Thankfully, Django’s translation framework is top-notch.

“East Meets West When Translating Django Apps” is the talk I gave about translating my family’s Django app between English and Chinese. For me, this talk had all the feels. I shared my family’s story as a backdrop. I showed Python code for each step in the translation workflow. I gave advice on my lessons learned. And I spoke truth to power – that translations should bring us all together.

I gave this talk at a few conferences. It was the opener for PyCascades 2020. I also delivered it in person at PyTennessee 2020 and online for PyCon 2020.

Here’s the PyCon recording, which is probably the “definitive” version:

Here’s the PyCascades recording:

Here are the power slides from my talk:

Check out my article, Django Admin Translations, to learn how to translate the admin site, too.

Python Program Main Function

This article will show you the best way to handle “main” functions in Python.

Python is like a scripting language in that all lines in a Python “module” (a .py file) are executed whenever the file is run. Modules don’t need a “main” function. Consider a module named stuff.py with the following code:

def print_stuff():
  print("stuff happened!")

print_stuff()

This is the output when it is run:

$ python stuff.py
stuff happened!

The print_stuff function was called as a regular line of code, not in any function. When the module ran, this line was executed.

This will cause a problem, though, if stuff is imported by another module. Consider a second module named more_stuff.py:

import stuff

stuff.print_stuff()
print("more stuff!")

At first glance, we may expect to see two lines printed. However, running more_stuff actually prints three lines:

$ python more_stuff.py
stuff happened!
stuff happened!
more stuff!

Why did “stuff happened!” get printed twice? Well, when “import stuff” was called, the stuff module was loaded. Whenever a module is loaded, all of its code is executed. The print_stuff function was called at line 4 in the stuff module. Then, it was called again at line 3 in the more_stuff module.

So, how can we avoid this problem? Simple: check the module’s __name__. The __name__ variable (pronounced “dunder name”) is dynamically set to the module’s name. If the module is the main entry point, then __name__ will be set to “__main__”. Otherwise, if the module is simply imported, then it will be set to the module’s filename without the “.py” extension.

Let’s rewrite our modules. Here’s stuff:

def print_stuff():
  print("stuff happened!")

if __name__ == '__main__':
  print_stuff()

And here’s more_stuff:

import stuff

if __name__ == '__main__':
  stuff.print_stuff()
  print("more stuff!")

If we rerun more_stuff, then the line “stuff happened!” will print only once:

$ python more_stuff.py
stuff happened!
more stuff!

As a best programming practice, Python modules should not contain any directly-called lines. They should contain only functions, classes, and variable initializations. Anything to be executed as a “main” body should be done after a check for “if __name__ == ‘__main__'”. That way, no rogue calls are made when modules are imported by other modules. The conditional check for __name__ also makes the “main” body clear to the reader.

Some people still like to have a “main” function. That’s cool. Just do it like this:

import stuff

def main():
  stuff.print_stuff()
  print("more stuff!")

if __name__ == '__main__':
  main()

For more information, read this Stack Overflow article:
What does if __name__ == “__main__”: do?

Surviving Without Python


Python is such a popular language for good reason: Its principles are strong. However, if Python is “the second-best language for everything”… that means the first-best is often chosen instead. Oh no! How can Pythonistas survive a project or workplace without our favorite language?

Personally, even though I love Python, I don’t use it daily at my full time job. Nevertheless, Pythonic thinking guides my whole approach to software. I will talk about how the things that make Python great can be applied to non-Python places in three primary ways:

  1. Principles from the Zen of Python
  2. Projects that partially use Python
  3. People who build strong, healthy community

Check out my talk, Surviving Without Python, from PyOhio 2019! It was one of the most meaningful talks I’ve ever given.

Sprint Planning Sucks. Can It Be Fixed?

Warning: This article contains strong opinions that might not be suitable for all audiences. Reader discretion is advised.

It’s Monday morning. After an all-too-short weekend and rush hour traffic, you finally arrive at the office. You throw your bag down at your desk, run to the break room, and queue up for coffee. As the next pot is brewing, you check your phone. It’s 8:44am… now 8:45am, and DING! A meeting reminder appears:

Sprint Planning – 9am to 3pm.

.

What’s your visceral reaction?

.

I can’t tell you mine, because I won’t put profanity on my blog.

Real Talk

In the capital-A Agile Scrum process, sprint planning is the kick-off meeting for the next iteration. The whole team comes together to talk about features, size work items with points, and commit to deliverables for the next “sprint” (typically 2 weeks long). Idealistically, team members collaborate freely as they learn about product needs and give valued input.

Let’s have some real talk, though: sprint planning sucks. Maybe that’s a harsh word, but, if you’re reading this article, then it caught your attention. Personally, my sprint planning experiences have been lousy. Why? Am I just bellyaching, or are there some serious underlying problems?

Sprint planning is a huge time commitment. 9am to 3pm is not an exaggeration. Sprint planning meetings are typically half-day to full-day affairs. Most people can’t stay focused on one thing for that long. Plus, when a sprint is only two weeks long, one hour is a big chunk of time, let alone 3, or 6, or a whole day. The longer the meeting, the higher the opportunity cost, and the deeper the boredom.

Collaboration is a farce. Planning meetings typically devolve into one “leader” (like a scrum master, product owner, or manager) pulling teeth to get info for a pre-determined list of stories. Only two people, the leader and the story-owner, end up talking, while everyone else just stares at their laptops until it’s their turn. Discussions typically don’t follow any routine beyond, “What’s the acceptance criteria?” and, “Does this look right?” with an interloper occasionally chiming in. Each team member typically gets only a few minutes of value out of an hours-long ordeal. That’s an inefficient use of everyone’s time.

No real planning actually happens. These meetings ought to be called “guessing” meetings, instead. Story point sizes are literally made up. Do they measure time or complexity? No, they really just measure groupthink. Teams even play a game called planning poker that subliminally encourages bluffing. Then, point totals are used to guess how much work can be done during the sprint. When the guess turns out to be wrong at the end of the sprint (and it always does), the team berates itself in retro for letting points slip. Every. Time.

Does It Spark Joy?

I’ve long wondered to myself if sprint planning is a good concept just implemented poorly, or if it’s conceptually flawed at its root. I’m pretty sure it’s just flawed. The meetings don’t facilitate efficient collaboration relative to their time commitments, and estimates are based on poor models. Retros can’t fix that. And gut reactions don’t lie.

So, what should we do? Should we Konmari our planning meetings to see if they spark joy? Should we get rid of our ceremonies and start over? Is this an indictment of the whole Agile Scrum process? But then, how will we know what to do, and when things can get done?

I think we can evolve our Agile process with more effective practices than sprint planning. And I don’t think that evolution would be terribly drastic.

Behavior-Driven Planning

What we really want out of a planning meeting is planning, not pulling and not predicting. Planning is the time to figure out what will be done and how it will be done. The size of the work should be based on the size of the blueprint. Enter Example Mapping.

Example Mapping is a Behavior-Driven Development practice for clarifying and confirming stories. The process is straightforward:

  1. Write the story on a yellow card.
  2. Write each rule that the story must satisfy on a blue card.
  3. Illustrate each rule with examples written on green cards.
  4. Got stuck on a question? Write it on a red card and move on.

One story should take about 20-30 minutes to map. The whole team can participate, or the team can split up into small groups to divide-and-conquer. Rules become acceptance criteria, examples become test cases, and questions become spikes.

Here’s a good walkthrough of Example Mapping.

What about story size? That’s easy – count the cards. How many cards does a story have? That’s a rough size for the work to be done based on the blueprint, not bluffing. More cards = more complexity. It’s objective. No games. Frankly, it can’t be any worse that made-up point values.

This is real planning: a blueprint with a course of action.

So, rather than doing traditional sprint planning meetings, try doing Example Mapping sessions. Actually plan the stories, and use card counts for point sizes. Decisions about priority and commitments can happen between rounds of story mapping, too. The Scrum process can otherwise remain the same.

If you want to evolve further, you could eliminate the time boxes of sprints in favor of Kanban. Two-week work item boundaries can arbitrarily fall in the middle of progress, which is not only disruptive to workflow but can also encourage bad responses (like cramming to get things done or shaming for not being complete.) Kanban treats work items as a continuous flow of prioritized work fed to a team in bite-sized pieces. When a new story comes up, it can have its own Example Mapping “planning” meeting. Now, Kanban is not for everyone, but it is popular among post-Agile practitioners. What’s important is to find what works for your team.

Rant Over

I know I expressed strong, controversial opinions in this article. And I also recognize that I’m arguing against bad examples of Agile Scrum. Nevertheless, I believe my points are fair: planning itself is not a waste of time, but the way many teams plan their sprints uses time inefficiently and sets poor expectations. There are better ways to do planning – let’s give them a try!

Frameworks: All-in-One or Piece-by-Piece?

Software frameworks are great because they apply the principle of Separation of Concerns. A framework’s tools and code handle a specific need in a standard way for developers to write other code more easily. For example:

  • Web frameworks support receiving requests and sending responses.
  • Test frameworks include test case structure, runners, and reporting mechanisms.
  • Logging frameworks control how messages are gathered and stored.
  • Dependency injection frameworks create and manage object instances.

Recently, a question hit me: How far should a framework go to separate concerns? Should a framework try to do everything all-in-one, or should it behave more like a library that focuses on doing one thing well?

Let’s look at Python Web frameworks as an example. Django, the “Web framework for perfectionists with deadlines,” provides everything a developer could want out of the box. Flask, on the other hand, is a “microframework” that prides itself on minimalism: any extras must be handled by extensions or other packages. The differences between the two become clear when comparing some of their features:

Feature Django Flask
HTTP Requests and Routing Included Werkzeug (bundled)
Templates Included Jinja2 (bundled)
Forms Included None (Flask-WTF)
Object-Relational Mapping (ORM) Included None (SQLAlchemy)
Security Included None (Flask-Security)
Localization Included None (Flask-Babel)
Admin Interface Included None

Clearly, Django is all-in-one, while Flask is piece-by-piece. To make a serious Flask app, developers must pull in many extra pieces. There are many other frameworks with similar competitions:

  • JavaScript testing: Jasmine vs. Mocha
  • JavaScript development: Angular vs. React
  • Java BDD testing: Serenity vs. Cucumber-JVM

I think each approach has its merits. All-in-one frameworks are more convenient to use, especially for beginners. For developers who are new to a domain or just need to get something up fast, all-in-ones are the better choice. They come with all units already integrated together, and they often have good documentation. However, developing an all-in-one framework takes much more work because it covers multiple concerns. Developers may also feel shoehorned into the framework’s way of doing things. All-in-ones typically dictate what they believe to be the “best” solution.

Piece-by-piece frameworks require more expertise but offer greater flexibility. Developers can pick and choose the pieces they need, and they can change the packages used by the solution more easily. Found a better ORM? Not a problem. Need to localize the site in Chinese? Add it! Solutions can avoid excess weight and stay nimble for the future. The big challenge is successful integration. Furthermore, a library or framework for a singular concern tends to solve the concern in better ways simply because project contributors give it exclusive focus. The more I learn about a space, the more I lean towards a piece-by-piece approach.

As always, pick frameworks based on the needs at hand. For example, I like to use Django to make websites for my wife’s small businesses because the admin interface is just so convenient for her, even though I could get away with Flask. However, I’ll probably pick Mocha (piece-by-piece) over Jasmine (all-in-one) whenever I return to JavaScript testing.

Speaking Pythonese

The Python community, like many groups, has its own language – and I don’t mean just Python. There are many words and phrases thrown around that may confuse people new to Python. I originally shared some terms in my article, Which Version of Python Should I Use?, but below are some more of those colloquialisms for quick reference:

Word or Phrase Meaning
Anaconda
  • Open-source implementation of Python (and R)
  • Meant for data scientists
  • Uses the conda package manager
Benevolent Dictator for Life (BDFL)
  • Guido van Rossum
  • The inventor of Python
  • Resigned in July 2018 but remains BDFL Emeritus
The CheeseShop
  • a fun code name for the Python Package Index
Class
  • A programming definition for creating objects
  • Combines attributes (variables) and behaviors (methods)
  • Useful for reusing code
Conda
  • Package manager for Python and other languages
  • Part of the Anaconda project
Core Developer
  • A developer who has commit privilege to the CPython codebase
  • Very few Pythonistas are core developers
CPython
  • The default and most widely used implementation of the Python language
  • Implemented in C
Django
  • A batteries-included Python Web framework for perfectionists with deadlines
  • Offers many features out of the box
  • The most popular Python framework in 2017
  • Size: Flask < Pyramid < Django
Flask
  • A microframework for Python Web development
  • Uses Werkzeug and Jinja2
  • Super-minimalist
  • Size: Flask < Pyramid < Django
Function
  • A definition for a callable subroutine
  • May take inputs
  • May return outputs
  • Great for code reuse
  • Functions are first-order values
The Hitchhiker’s Guide to Python
  • A popular online guide to Python
  • Opinionated
  • Shares many good best practices
Jinja2
  • A Python template engine
  • Inspired by Django’s templates
Jupyter
  • A project for interactive Python development
  • “Jupyter Notebooks” allow programmers to dynamically rewrite and rerun Python code, and then share code easily with others
  • Popular with data scientists
Module
  • A Python source code file containing definitions and statements
  • Every .py file is a module
  • May be imported by other modules to reuse code
NumPy
  • A popular Python package for scientific computing
Pandas
  • A popular Python package for data analysis
pip
  • The PyPA-recommended tool for installing Python packages
  • Command: “pip install “
  • Recursive acronym for “pip installs packages”
PyBites
  • A community of Pythoneers who improve their skills through code challenges
PyCharm
  • A popular Python IDE developed by JetBrains
  • Offers great development features
  • Has a free Community Edition and a paid Professional Edition
PyCon
  • The annual Python conference held in North America
  • GO – it will change your life!
  • Several other conferences are held worldwide
PyPy
  • An alternative Python implementation
  • Known for speed, memory usage, and compatibility
  • Good alternative to CPython for high performance workloads
Pyramid
  • A Python Web framework
  • Start small, finish big, stay finished
  • Provides many parts but not everything (such as ORM)
  • Size: Flask < Pyramid < Django
pytest
  • A lightweight-yet-powerful Python test framework (and arguably the best)
Python 2
  • The old version of Python
  • Will reach end-of-life in 2020
  • Final version will be 2.7.x
  • Please upgrade to version 3
Python 3
  • The current version of Python
  • Most packages now support Python 3
  • Has incompatibilities with Python 2
  • Please don’t use Python 2
Python Enhancement Proposal (PEP)
  • Official proposals for enhancing the Python language
Python Package Index (PyPI)
The Python Software Foundation (PSF)
  • Non-profit organization
  • Keeps Python going strong
  • Support them!
Pythoneer
  • A programmer who uses Python to solve problems
  • Styled off the word “engineer”
Pythonic
  • Describes idiomatic code for Python
  • Closely related to conciseness, readability, and elegance
  • Highly recommended
  • Follow style guidelines to learn how to be Pythonic
Pythonista
  • Someone who loves the Python language
  • Often an advanced Python programmer
Sphinx
  • A popular Python tool to generate documentation
virtualenv
  • Tool to create isolated Python environments
  • Enables programmers to use different versions of Python and packages for different projects
  • Also see venv and pipenv
Web Server Gateway Interface (WSGI)
  • A specification for how Web servers forward requests to Web applications and frameworks
  • A core piece of Python Web development
  • See PEP-333 and PEP-3333
The Zen of Python
  • The list of guiding principles for Python’s design
  • Run “import this” to see them
  • See PEP-20

Ignoring Files with Git

Git is one of the most popular version control systems (VCS) available, especially thanks to hosting vendors like GitHub. It keeps code safe and shareable. Sometimes, however, certain files should not be shared, like local settings or temporary configs. Git provides a few ways to make sure those files are ignored.

.gitignore

The easiest and most common way to ignore files is to use a gitignore file. Simply create a file named .gitignore in the repository’s root directory. Then, add names and patterns for any files and directories that should not be added to the repository. Use the asterisk (“*”) as a wildcard. For example, “*.class” will ignore all files that have the “.class” extension. Remember to add the .gitignore file to the repository so that it can be shared. As a bonus, Git hosting vendors like GitHub usually provide standard .gitignore templates for popular languages.

Any files covered by the .gitignore file will not be added to the repository. This approach is ideal for local IDE settings like .idea or .vscode, compiler output files like *.class or *.pyc, and test reports. For example, here’s GitHub’s .gitignore template for Java:

# Compiled class file
*.class
# Log file
*.log
# BlueJ files
*.ctxt
# Mobile Tools for Java (J2ME)
.mtj.tmp/
# Package Files #
*.jar
*.war
*.nar
*.ear
*.zip
*.tar.gz
*.rar
# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
hs_err_pid*

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GitHub_Java.gitignore
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.git/info/exclude

As a best practice, .gitignore should be committed to the repository, which means all team members will share the same set of ignored files. However, some files should be ignored locally and not globally. Those files could be added to .gitignore, but large .gitignore files become cryptic and more likely to break other people’s setup. Thankfully, git provides a local-only solution: the .git/info/exclude file (under the repository’s hidden .git directory). Simply open it with a text editor and add new entries using the same file pattern format as .gitignore.

# Append a new file to ignore locally
echo "my_private_file" >> .git/info/exclude

skip-worktree

A .gitignore file prevents a file from being added to a repository, but what about preventing changes from being committed to an existing file? For example, developers may want to safely override settings in a shared config file for local testing. That’s where skip-worktree comes in: it allows a developer to “skip” any local changes made to a given file. Changes will not appear under “git status” and thus will not be committed.

Use the following commands:

# Ignore local changes to an existing file
git update-index --skip-worktree path/to/file

# Stop ignoring local changes
git update-index --no-skip-worktree path/to/file

Warning: The skip-worktree setting applies only to the local repository. It is not applied globally! Each developer will need to run the skip-worktree command in their local repository.

assume-unchanged

Another option for ignoring files is assume-unchanged. Like skip-worktree, it makes Git ignore changes to files. However, whereas skip-worktree assumes that the user intends to change the file, assume-unchanged assumes that the user will not change the file. The intention is different. Large projects using slow file systems may gain significant performance optimizations by marking unused directories as assume-unchanged. This option also works with other update-index options like really-refresh.

Use the following commands:

# Assume a file will be unchanged
git update-index --assume-unchanged path/to/file

# Undo that assumption
git update-index --no-assume-unchanged path/to/file

Again, this setting applies only to the local repository – it is not applied globally.

Comparison Table

Which is the best way to ignore files?

Method Description Best Use Cases Scope
.gitignore file Prevents files from being added to the repository. Local settings, compiler output, test results, etc. Global
.git/info/exclude file Prevents local files from being added to the repository. Local settings, compiler output, test results, etc. Local
skip-worktree setting Prevents local changes from being committed to an existing file. Shared files that will have local overwrites, like config files. Local
assume-unchanged setting Allows Git to skip files that won’t be changed for performance optimization. Files and folders that a developer won’t touch. Local

Resources

NuGet Quick Reference

What is NuGet?

NuGet is a package manager for Microsoft .NET. It installs packages and manages dependencies for .NET projects. It is like Maven (Java) or pip (Python). The NuGet Gallery hosts thousands of popular packages like Json.NET, NUnit, and jQuery. If you develop .NET applications (like in C#), then you probably need to use NuGet.

Installing Packages

The easiest way to use NuGet is through Visual Studio, which includes NuGet features by default. Packages are managed per project. Right-click on a project in Solution Explorer and select “Manage NuGet Packages…” to open the project’s package manager page.

  • The Browse tab lets you search and install new packages.
  • The Installed tab shows which packages are installed and can uninstall them.
  • The Updates tab lets you update packages to their latest versions.

Nuget Package Manager Page

The NuGet Package Manager page for a project in Visual Studio

When packages are installed and updated, NuGet also pulls any dependencies they require. Visual Studio also creates a packages.config file for all dependencies. Then, just build and run!

<?xml version="1.0" encoding="utf-8"?>
<packages>
<package id="FluentAssertions" version="5.4.1" targetFramework="net461" />
<package id="Newtonsoft.Json" version="11.0.2" targetFramework="net461" />
<package id="SpecFlow" version="2.4.0" targetFramework="net461" />
<package id="SpecRun.Runner" version="1.8.0" targetFramework="net461" />
<package id="SpecRun.SpecFlow" version="1.8.0" targetFramework="net461" />
<package id="SpecRun.SpecFlow.2-4-0" version="1.8.0" targetFramework="net461" />
<package id="System.ValueTuple" version="4.5.0" targetFramework="net461" />
</packages>

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example-nuget-quick-ref-packages-config.xml
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NuGet Configuration

NuGet can be configured using a NuGet.Config file. This file can be placed under a project directory, a solution directory, or a system-wide location. One of the most common settings is the package sources: NuGet uses the public nuget.org repository by default, but others (like private company repos) can also be added. Check the nuget.config reference online for docs on all options. (Package sources can also be configured through Visual Studio under Tools > NuGet Package Manager > Package Manager Settings.)

<?xml version="1.0" encoding="utf-8"?>
<configuration>
<packageSources>
<add key="nuget.org" value="https://www.nuget.org/api/v2/" />
<add key="my.company.com" value="https://my.company.com/httpAuth/app/nuget/v1/FeedService.svc/" />
</packageSources>
</configuration>

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example-nuget-quick-ref-nuget-config.xml
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NuGet Package Manager Console

Sometimes, it’s helpful to control NuGet directly through the Package Manager Console. From the menu bar: Tools > NuGet Package Manager > Package Manager Console. For example, when packages get messed up, I’ll run “Update-Package -Reinstall” to reinstall everything. (Right-clicking the solution and selecting “Restore NuGet Packages” never seems to work for me.) Check the help command or the official guide for more info.

Nuget Package Manager Console

The NuGet Package Manager Console in Visual Studio

NuGet CLI

The NuGet CLI nuget.exe provides the full extent of NuGet features, including the ability to make packages. It is more powerful than the Package Manager Console. It must be installed independently – it does not come with Visual Studio. Check the NuGet CLI reference online for full details. The .NET Core CLI dotnet.exe can also be used for managing packages. See the feature comparison for the differences.

nuget CLI

The NuGet CLI

Creating a NuGet Package

A NuGet package is basically a ZIP file with a .nupkg extension. It typically contains an assembly DLL and maybe other related files. Creating a NuGet package is pretty easy:

  1. Install the NuGet CLI.
  2. Create a .nuspec file for the project.
  3. Add appropriate settings to the .nuspec file.
  4. Run the “nuget pack” command to create the .nupkg file.
  5. Publish the .nupkg file to the desired destination.

The .nuspec file can be created by running the “nuget spec” command in the project’s directory. The generated <project-name>.nuspec file will contain replacement tokens that will be substituted with values from the project’s AssemblyInfo when the package is built. Make sure to set AssemblyInfo values appropriately for the substitution. The version is especially important, and the automatic version format may be useful for guaranteeing uniqueness. Be sure to add any packages upon which the project depends as dependencies, too. (The .nuspec file can also be created manually.) Refer to the .nuspec reference for full details.

The standard package creation command is “nuget pack <project-name>.nuspec”. However, if the .nuspec file contains replacement tokens, then use “nuget pack <project-name>.csproj” instead. Once the package is created, it can be published publicly to nuget.org or to a private NuGet feed.

Below is an example .nuspec file with replacement tokens:

<?xml version="1.0"?>
<package >
<metadata>
<id>$id$</id>
<version>$version$</version>
<title>$title$</title>
<authors>$author$</authors>
<owners>$author$</owners>
<requireLicenseAcceptance>false</requireLicenseAcceptance>
<description>$description$</description>
<copyright>$copyright$</copyright>
<tags>tag1 tag2</tags>
<dependencies>
<dependency id="Newtonsoft.Json" version="11.0.2" />
<dependency id="RestSharp" version="106.3.1" />
<dependency id="Selenium.Support" version="3.14.0" />
<dependency id="Selenium.WebDriver" version="3.14.0" />
</dependencies>
</metadata>
</package>

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example-nuget-quick-ref-nuspec.xml
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Resources

 

The Panda’s Dozen: Top PyCon 2018 Talks

There were tons of great talks at PyCon 2018 – more than I could attend in person – that are now available on the PyCon 2018 YouTube channel. This post has links to my favorites. Enjoy!

Check out PyCon 2018 Reflections to read my personal reflections, too. Watch my talk, Behavior-Driven Python, too!

By the Numbers: Python Community Trends in 2017/2018 (Dmitry Filippov, Ewa Jodlowska) – At the end of 2017, the Python Software Foundation teamed up with JetBrains to conduct an official Python Developers Survey. Data science is taking Python by storm, and Python 3 now has majority adoption. There are tons of other cool statistics, too!

How Netflix does failovers in 7 minutes flat (Amjith Ramanujam) – That speed at that scale is mind-blowing. This is a fascinating talk, even for non-engineers!

Solve Your Problem With Sloppy Python (Larry Hastings) – “If you ever start writing a shell script, delete it and write a Python script instead.” This talk is a jovial reminder that Python is a powerful tool, even for hack-n-slash jobs.

The AST and Me (Emily Morehouse-Valcarcel) – Emily gives a great overview of the inner workings of the Python language. This talk is a must-see for anyone into compiler theory.

Dataclasses: The code generator to end all code generators (Raymond Hettinger) – Dataclasses are new data structures to Python to generate classes based on specs.

Pipenv: the Future of Python Dependency Management (Kenneth Reitz) – Pipenv is a new tool that makes pip, Pipfile, and virtualenv easier to use together. Kenneth gives a good overview of Python packaging and why pipenv is awesome.

Type-checked Python in the real world (Carl Meyer) – Sometimes, I wish Python had static typing. Now, it can! Facebook has done some innovative things to make it possible.

Beyond Unit Tests: Taking Your Testing to the Next Level (Hillel Wayne) – Property tests + contracts = integration tests. Hillel gives a fantastic strategy for making tests smarter.

“WHAT IS THIS MESS?” – Writing tests for pre-existing code bases (Justin Crown) – This is a pragmatic guide to adding new tests to old code. Now, you’ll never procrastinate on your tests again!

Demystifying the Patch Function (Lisa Roach) – Mocking is a great practice for limiting scope in unit tests. Whether using unittest.mock or other patching/mocking packages, this is a great talk for learning why and how to do mocking in unit tests!

Automating Code Quality (Kyle Knapp) – One of Python’s most beloved traits is its elegance. Maintaining high standards of code quality can be challenging for large projects, though. Kyle shows how to use existing tools to drive higher quality.

Keynote (Ying Li) – [35:07] – Ying is a software security engineer at Docker. In her keynote, she urged all people involved in technology to learn the basics of security. Definitely watch the video recording – she used a fun story to illustrate her points.

Keynote: The People and Python (Qumisha Goss) – [1:07:35] – “Q” is a librarian at the Detroit Public Library who taught herself Python so she could teach coding classes to kids. She shared the highs and lows of her experiences, especially in light of many disadvantages her students had. My favorite takeaway was to “cultivate greatness in others.”