As many of you know, pandas released version 0.17.0 on October 9th. In typical pandas fashion there are a bunch of updates, bug fixes and new features which I encourage you to read all about here. I do not plan to go through all of the changes but there are a couple of key things that I think will be useful to me in my daily work that I will explore briefly in this article. In addition, I am including a couple of other tips and tricks for pandas that I use on a frequent basis and hope will be useful to you.

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Using python and pandas in the business world can be a very useful alternative to the pain of manipulating Excel files. While this combination of technologies is powerful, it can be challenging to convince others to use a python script - especially when many may be intimidated by using the command line. In this article I will show an example of how to easily create an end-user-friendly GUI using the Gooey library. This interface is based on wxWindows so it looks like a “native” application on Windows, Mac and Linux. Ultimately, I believe that presenting a simple user interface to your scripts can greatly increase the adoption of python in your place of business.

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Love it or loathe it, PowerPoint is widely used in most business settings. This article will not debate the merits of PowerPoint but will show you how to use python to remove some of the drudgery of PowerPoint by automating the creation of PowerPoint slides using python.

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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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