This is part 4 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Wes McKinney, the creator of pandas, is kind of obsessed with performance. From micro-optimizations for element access, to embedding a fast hash table inside pandas, we all benefit from his and others’ hard work. This post will focus mainly on making efficient use of pandas and NumPy. ...

This is part 2 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Method Chaining Method chaining, where you call methods on an object one after another, is in vogue at the moment. It’s always been a style of programming that’s been possible with pandas, and over the past several releases, we’ve added methods that enable even more chaining. ...

This is part 1 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Effective Pandas Introduction This series is about how to make effective use of pandas, a data analysis library for the Python programming language. It’s targeted at an intermediate level: people who have some experience with pandas, but are looking to improve. Prior Art There are many great resources for learning pandas; this is not one of them. For beginners, I typically recommend Greg Reda’s 3-part introduction, especially if they’re familiar with SQL. Of course, there’s the pandas documentation itself. I gave a talk at PyData Seattle targeted as an introduction if you prefer video form. Wes McKinney’s Python for Data Analysis is still the goto book (and is also a really good introduction to NumPy as well). Jake VanderPlas’s Python Data Science Handbook, in early release, is great too. Kevin Markham has a video series for beginners learning pandas. ...

This notebook compares pandas and dplyr. The comparison is just on syntax (verbage), not performance. Whether you’re an R user looking to switch to pandas (or the other way around), I hope this guide will help ease the transition. We’ll work through the introductory dplyr vignette to analyze some flight data. I’m working on a better layout to show the two packages side by side. But for now I’m just putting the dplyr code in a comment above each python call. ...

Welcome back. As a reminder: In part 1 we got dataset with my cycling data from last year merged and stored in an HDF5 store In part 2 we did some cleaning and augmented the cycling data with data from http://forecast.io. You can find the full source code and data at this project’s GitHub repo. Today we’ll use pandas, seaborn, and matplotlib to do some exploratory data analysis. For fun, we’ll make some maps at the end using folium. ...

This is Part 2 in the Practical Pandas Series, where I work through a data analysis problem from start to finish. It’s a misconception that we can cleanly separate the data analysis pipeline into a linear sequence of steps from data acqusition data tidying exploratory analysis model building production As you work through a problem you’ll realize, “I need this other bit of data”, or “this would be easier if I stored the data this way”, or more commonly “strange, that’s not supposed to happen”. ...

This is the first post in a series where I’ll show how I use pandas on real-world datasets. For this post, we’ll look at data I collected with Cyclemeter on my daily bike ride to and from school last year. I had to manually start and stop the tracking at the beginning and end of each ride. There may have been times where I forgot to do that, so we’ll see if we can find those. ...

Last time, we got to where we’d like to have started: One file per month, with each month laid out the same. As a reminder, the CPS interviews households 8 times over the course of 16 months. They’re interviewed for 4 months, take 8 months off, and are interviewed four more times. So if your first interview was in month $m$, you’re also interviewed in months $$m + 1, m + 2, m + 3, m + 12, m + 13, m + 14, m + 15$$. ...

In part 2 of this series, we set the stage to parse the data files themselves. As a reminder, we have a dictionary that looks like id length start end 0 HRHHID 15 1 15 1 HRMONTH 2 16 17 2 HRYEAR4 4 18 21 3 HURESPLI 2 22 23 4 HUFINAL 3 24 26 ... ... ... ... giving the columns of the raw CPS data files. This post (or two) will describe the reading of the actual data files, and the somewhat tricky process of matching individuals across the different files. After that we can (finally) get into analyzing the data. The old joke is that statisticians spend 80% of their time munging their data, and 20% of their time complaining about munging their data. So 4 posts about data cleaning seems reasonable. ...

Hadley Whickham wrote a famous paper (for a certain definition of famous) about the importance of tidy data when doing data analysis. I want to talk a bit about that, using an example from a StackOverflow post, with a solution using pandas. The principles of tidy data aren’t language specific. A tidy dataset must satisfy three criteria (page 4 in Whickham’s paper): Each variable forms a column. Each observation forms a row. Each type of observational unit forms a table. In this StackOverflow post, the asker had some data NBA games, and wanted to know the number of days since a team last played. Here’s the example data: ...