From forcing keyword arguments to anonymous functions
Do you know how to force keyword arguments, create a function decorator, create anonymous functions, or unpack an array or dictionary into a function’s arguments? Here are four advanced tricks regarding Python functions.
1. Forced Keyword Arguments
Keyword arguments have several advantages:
- You’re not forced into a particular order in which you supply your arguments. The name matters — not the position.
- Keyword arguments provide clarity. Without looking up the function itself, you can often guess what the argument is used for by looking at the names.
That’s nice, but you probably already knew these things. What you might not know is that you…
And all the reasons why they are wrong
Python is awesome! So why do people hate Python? After some highly non-scientific, Internet-based research, these are the reasons I encountered most often. I found many more, but most of them were so ridiculous that I wouldn’t even dare to repeat them here.
I won’t link to sources or apply naming and shaming. Consider this read just for fun, but with an educational touch!
Disclaimer: no feelings or programmers were hurt while writing this article, but some feelings might get hurt while reading it. If you have a heart condition and love Python, perhaps you better skip this one.
1. Indentation instead of curly braces
Many…
What is it, when to use, with Python example code
A Bloom filter efficiently tests if an element is a member of a set. It was first proposed by Burton Howard Bloom all the way back in 1970. Although a little unknown, they have become ubiquitous, especially in distributed systems and databases. Bloom filters are an excellent time and memory saver.
After reading this article, you’ll know:
- What Bloom filters are
- Why and when you can use a Bloom filter
I’ll illustrate with practical Python-based example code. The examples will be so easy to understand that non-Python programmers should have no problem following them.
Bloom filter in Python
We’ll dive right in with a…
A handpicked list of the most useful and surprising Python packages from PyPI
Why do we all love Python? For starters, it’s a beautiful and easy-to-learn programming language. Another reason: it comes with batteries included, meaning Python has many excellent libraries included by default. But in my opinion, it’s the 230,000 user-contributed packages that make Python really powerful and popular.
In this article, I handpicked 15 packages that I found most useful during my 10-year career as a Pythonista. Let’s go!
1. Dash
Dash is relatively new. It’s ideal for building data visualization apps in pure Python, so it’s particularly suited for anyone who works with data. Dash is a blend of Flask, Plotly.js, …
JSON in Python is easy; get up and running quickly
JSON, short for JavaScript Object Notation, is an open standard. Although its name doesn’t imply so, it is a language-independent data format. JSON is used to both store and exchange data. It’s a prevalent data format because it is easy to read and write for humans too. Working with JSON in Python is super easy! Python has two data types that, together, form the perfect tool for working with JSON in Python: dictionaries and lists.
Importing the JSON library in Python
Python ships with a powerful and elegant JSON library to help you decode and encode JSON. It can be imported with:
import jsonLearn how to write polished and efficient Python code
In mathematics, there’s a concept called set-builder notation, also called set comprehension. Inspired by this principle, Python offers comprehensions, too. In fact, list comprehensions are one of the defining features of Python. They allow us to create concise, readable code that outperforms the uglier alternatives like for loops or using map().
We’ll first look at the most well-known type: list comprehensions. Once we’ve got a good grasp of how they work, you’ll also learn about set comprehensions and dictionary comprehensions.
What Are List Comprehensions?
A list comprehension is a language construct. It’s used to create a list based on an existing list. Sounds a…
Educational and surprising insights into how Python is used
How is Python being used around the globe and across industries?
This question inspired me to write this piece. I figured a list of the most-used Python packages would give a good indication.
As a starting point, I took a list of the most downloaded Python packages on PyPI over the past 365 days. Let’s dive in and find out what they do, how they’re related, and why they rank so high!
1. Urllib3
1B downloads
Urllib3 is an HTTP client for Python that brings many features that are missing from the Python standard libraries:
- Thread safety.
- Connection pooling.
- Client-side SSL/TLS verification.
- File uploads…
Learn what it is, and how and when to implement it
As published in our tutorial on Python concurrency at python.land. Enjoy!
What is concurrency?
Concurrency is working on multiple things at the same time. In Python, this can be done in several ways:
- With
threading, by letting multiple threads take turns. - By firing off a task and continuing to do other stuff, instead of waiting for an answer from the network or disk. This is how asynchronous IO operates with the
asynciolibrary. - With
multiprocessingwe’re using multiple processes. This way we can truly do more than one thing at a time using multiple processor cores. This is called parallelism.
Concurrency is working…
If you think you mastered the language, you’ve got to read this
There are lots of little tricks that can make life easier for a Python coder. Some of these will be known to you, but I’m sure there are at least a couple that you haven’t seen before.
If you’re done reading, also take a look at our latest article on concurrency:
1. The Python Ellipsis
Learn how iterators work and how to use them, including example code
Iterators make for very elegant for-loops in Python, and also make comprehension possible. Even though it requires some work to understand all the inner workings, they are actually very easy to use in practice!
How a Python iterator works
To understand what a Python iterator is, you need to know two terms:
Iterator — An object that can be iterated, meaning we can keep asking it for a new element until there are no elements left. Elements are requested using a method called __next__.
Iterable — An object that implements another special method, called __iter__. This function returns an iterator.
As stated above, a Python…
