first in an occasional series

Last year, my buddy Kevin Thompson and I submitted a talk to Pycon 2015. It didn’t get accepted, so I thought I’d write a few blog posts with some of the material.

When we started working with Python, like a lot of folks, we went through a few code tutorials and then immediately got to work writing ROCK SOLID PRODUCTION CODE. Well, we immediately started writing code, anyway. Along the way, we learned some tricks the hard way. This series explores a few of those tricks. Advanced programmers will already know most of these, but hopefully newer programmers will learn a few things that can make their lives a bit easier.

Python has a lot of data types that make working with data sets a real pleasure. Some specific analysis work, though, can occasionally get a little wonky. And so clever programmers have made our lives even easier by implementing features that keep the simple things simple: specifically, counting.

# collections.Counter

## Use case: count objects (e.g. strings)

So imagine you’re processing some data and you want to count how often you see certain objects (like strings in a list). This gets unwieldy quickly, especially if you have additional associated logic. But collections.Counter() provides a handy Pythonic way to implement this pattern.

# List comprehensions

## Use case: simple for loop

We can simplify this even further with a list comprehension. In general, you place a for loop inside a pair of square brackets, with the expression for each result at the beginning.

# Upcoming tricks

Dictionaries have lots of additional features that can help with complex, nested structures. Every week I find another, so that will definitely be a fun upcoming article. Since a number of my projects involve a good bit of web scraping, I’ll share a lot of things I’ve learned there, too.