Over time you have probably developed a set of python scripts that you use on a frequent basis to make your daily work more effective. However, as you start to collect a bunch of python files, the time you take take to manage them can increase greatly. Your once simple development environment can become an unmanageable mess; especially if you do not try to have some consistency and common patterns for your development process. This article will discuss some best practices to manage your python code base so that you can sustain and maintain it over the years without pulling your hair out in the process.

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This is the second article in a series describing how to use Google Forms to collect information via simple web forms, read it into a pandas dataframe and analyze it. This article will focus on how to use the data in the dataframe to create complex and powerful data visualizations with seaborn.

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Google Forms is a service that allows you to collect information via simple web forms. One of the useful features is that the forms will automatically save your data to a Google Sheet. This article will walk through how to create a form, authenticate using OAuth 2 and read all the responses into a pandas dataframe. Because the initial setup and authentication process is a little time consuming, this article will be the first in a two part series.

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In case you missed it, github recently announced that Jupyter notebooks will be natively rendered by github. This useful new feature will make it easier for followers of pbpython to view notebooks through github as well as download them to your local system and follow along.

I have moved over 4 notebooks to github and set up the associated files so that it should be pretty straightforward for anyone to checkout the pbpython repo and work with the notebooks. This will also make it easier for others to follow along and help spot issues and make this collection of tips and tricks even more robust.

This post also contains a couple of helpful links I wanted to pass on and keep record of because I think they are really useful.

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The combination of IPython + Jupyter + Pandas makes it easy to interact with and display your data. Not surprisingly, these tools are easy to customize and configure for your own needs. This article summarizes some of the most useful and interesting options.

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