This is part 7 in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

Pandas started out in the financial world, so naturally it has strong time series support. The first half ...

## Modern Pandas (Part 7): Time Series

This is part 7 in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

Pandas started out in the financial world, so naturally it has strong time series support. The first half ...

## Modern Pandas (Part 6): Visualization

This is part 6 in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

A few weeks ago, the R community went through some hand-wringing about plotting packages. For outsiders (like me ...

## Modern Pandas (Part 5): Tidy Data

This is part 5 in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

## Reshaping & Tidy Data

Structuring datasets to facilitate analysis (Wickham 2014)

So, you've sat down to analyze a ...

## Modern Pandas (Part 4): Performance

This is part 4 in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

Wes McKinney, the creator of pandas, is kind of obsessed with performance. From micro-optimizations for element access, to ...

## Modern Pandas (Part 3): Indexes

This is part three in my series on writing modern idiomatic pandas.

This post is available as a Jupyter notebook

Today we're going to be talking about pandas'

`Index`

es. They're essential to pandas, but can ...## Modern Pandas (Part 2): Method Chaining

*Author's Note*Good news everyone, Wes has announced a 2nd edition of

*Python for Data Analysis*!Dear Twitter friends: I have started the process of writing the 2nd edition of Python for Data Analysis -- coming late 2016 or early 2017!

— Wes McKinney (@wesmckinn) March 30, 2016The first edition ...

## Modern Pandas (Part 1)

This is part one in a multipart series on writing idiomatic pandas code.

This post is available as a Jupyter notebook

# Prior Art

There are many great resources for learning pandas. For beginners, I typically recommend Greg Reda ...

## Pipelines and Categoricals

My favorite feature of scikit-learn is its pipelines. These are a nice convenience for putting together a chain of operations from raw data to classifier. More importantly, they help prevent training data from leaking into your validation, so I use them whenever possible.

Pandas somewhat recently added a

`Categorical`

dtype ...## Feature Complete

## Dynamic Programming

Eight months ago, Trey Causey wrote a post about modeling expected points in football, with an emphasis on uncertainty. With my twisted economist's mind, I mentioned that it seemed like dynamic programming could be used in this situation, and indeed it would feature in a future post of Trey ...

Page 1 / 3 »