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PyCon 2015 Pandas tutorial materials

License: MIT License

Jupyter Notebook 98.96% Python 0.88% Shell 0.05% Makefile 0.03% CSS 0.07%

pycon-pandas-tutorial's Introduction

Pandas Practice Problems

Practice problems from a PyCon 2015 talk.

Quick Start

If you have both conda and git on your system (otherwise, read the next section for more detailed instructions):

$ conda install --yes ipython-notebook matplotlib pandas
$ git clone https://github.com/brandon-rhodes/pycon-pandas-tutorial.git
$ cd pycon-pandas-tutorial
$ build/BUILD.sh
$ ipython notebook

Detailed Instructions

You will need Pandas, the Jupyter Notebook, and Matplotlib installed before you can successfully run the tutorial notebooks. The Anaconda Distribution is a great way to get up and running quickly without having to install them each separately — running the conda command shown above will install all three.

Note that having git is not necessary for getting the materials. Simply click the “Download ZIP” button over on the right-hand side of this repository’s front page at the following link, and its files will be delivered to you as a ZIP archive:

Once you have unpacked the ZIP file, download the following four IMDB data files and place them in the tutorial’s build directory:

  • ftp://ftp.fu-berlin.de/pub/misc/movies/database/actors.list.gz
  • ftp://ftp.fu-berlin.de/pub/misc/movies/database/actresses.list.gz
  • ftp://ftp.fu-berlin.de/pub/misc/movies/database/genres.list.gz
  • ftp://ftp.fu-berlin.de/pub/misc/movies/database/release-dates.list.gz

To convert these into the CSV files that the tutorial needs, run the BUILD.py script with either Python 2 or Python 3. It will create the three CSV files in the data directory that you need to run all of the tutorial examples. It should take about 5 minutes to run on a fast modern machine:

$ python build/BUILD.py

You can then start up the IPython Notebook and start looking at the notebooks:

$ ipython notebook

I hope that the recording and the exercises in this repository prove useful if you are interested in learning more about Python and its data analysis capabilities!

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