Data Cleaning 101 Save

Data Cleaning 101

Welcome to the code repository for Practical Data Cleaning with Python! This is a two-day training offered through Safari with O'Reilly media. You can sign up by searching for the course on Safari.

This course aims to give you a practical overview of data cleaning and validation libraries and methods in Python. Since we only have 6 hours, it can't go massively in-depth into any one library or tool, but I have tried to include useful tools I have found in my work and incorporate a mixture of the munging and testing I have seen in my own and others workflows.

If you have a suggestion for another library or additional topic, feel free to drop me a line :)

Installation

These lessons has been tested for Python 3.4 and Python 3.6 and primarily uses the latest release of each library, except where versions are pinned. You likely can run most of the code with older releases, but if you run into an issue, try upgrading the library in question first.

pip install -r install_reqs.txt

I believe this will also work with Conda, although I am less familiar with Conda so please report issues! (special thanks to @blue_hacker for this fix!)

$ conda create -n dataclean --copy python=3.6
$ source activate dataclean
$ pip install -r install_reqs.txt

In addition, you will need to install sqlite3 or make changes to the second day case study with a connection string to your database of choice. more info

If you want to visualize graphs using Dask, you will need to install Graphviz, which has special requirements on all platforms. For linux, it is usually available via the system package library (apt, yum). For other platforms, you might need to use a special installer. It is also available via conda install graphviz and pip install graphviz, but these might not include all necessary dependencies for your OS. For best results, search for your OS and "install graphviz and dependencies" and follow a recent article on setup.

Repository structure

Each day coincides with a particular notebook folder. For day one, we will use cleaning-notebooks. Day two will focus on validation-notebooks. The data folder holds data we will use throughout the course. The queue_example.py file is used in the day two case study.

Python2 v. Python3

This repository has been built with Python 3. If you are using Python 2 and need help porting some logic or finding alternatives, please let me know and I will try and help. :)

Corrections?

If you find any issues in these code examples, feel free to submit an Issue or Pull Request. I appreciate your input!

Questions?

Reach out to @kjam on Twitter or GitHub. @kjam is also often on freenode. :)