Recipes for using Python's pandas library
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Pandas cookbook

pandas is a Python library for doing data analysis. It's really fast and lets you do exploratory work incredibly quickly.

The goal of this cookbook is to give you some concrete examples for getting started with pandas. The docs are really comprehensive. However, I've often had people tell me that they have some trouble getting started, so these are examples with real-world data, and all the bugs and weirdness that that entails.

I'm working with 3 datasets right now

  • 311 calls in New York
  • How many people were on Montréal's bike paths in 2012
  • Montreal's weather for 2012, hourly

It comes with batteries (data) included, so you can try out all the examples right away.

Table of Contents

How to use this cookbook

You'll need an up-to-date version of IPython Notebook (>= 1.0) and pandas (>=0.13) for this to work properly

You can get these using pip:

pip install ipython pandas numpy tornado pyzmq jinja2 matplotlib

This can be difficult to get set up and require you to compile a whole bunch of things. I instead use and recommend Anaconda, which is a Python distribution which will give you everything you need. It's free and open source.

Once you have pandas and IPython, you can get going!

git clone
cd pandas-cookbook/cookbook
ipython notebook

A tab should open up in your browser at http://localhost:8888

Happy pandas!


If you see something wrong, or there's something you'd like to learn that I haven't explained here, or there's something you know about that you would like to share, create an issue! Send me email! Send a pull request!


  • Joining dataframes
  • Using stack/unstack
  • ???