Pandas Cookbook

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 entails.

It uses 3 datasets:

  • 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

The easiest way is to try it out instantly online using Binder's awesome service. Start by clicking here, wait for it to launch, then click on "cookbook", and you'll be off to the races! It will let you run all the code interactively without having to install anything on your computer.

To install it locally, you'll need Jupyter notebook and pandas on your computer.

You can get these using pip (you may want to do this inside a virtual environment to avoid conflicting with your other libraries).

  pip install -r requirements.txt

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 Jupyter, you can get going!

git clone
cd pandas-cookbook/cookbook
jupyter notebook

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

Happy pandas!

Running the cookbook inside a Docker container.

This repository contains a Dockerfile and can be built into a docker container. To build the container run following command from inside of the repository directory:

docker build -t jvns/pandas-cookbook -f Dockerfile-Local .

run the container:

docker run -d -p 8888:8888 -e "PASSWORD=MakeAPassword" <IMAGE ID>

you can find out about the id of the image, by checking

docker images

After starting the container, you can access the Jupyter notebook with the cookbook on port 8888.


Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


There's a translation into Chinese of this repo.

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