I have written several articles about using python and pandas to manipulate data and create useful Excel output. In my experience, no matter how strong the python tools are, there are times when you need to rely on Excel as the vehicle to communicate your message or further analyze the data. This article will walk through some additional improvements you can make to your Excel-based output by:

• Adding Excel tables with XlsxWriter

• Inserting custom VBA into your Excel file

• Using COM for merging multiple Excel worksheets

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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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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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Pandas makes it very easy to output a DataFrame to Excel. However, there are limited options for customizing the output and using Excel’s features to make your output as useful as it could be. Fortunately, it is easy to use the excellent XlsxWriter module to customize and enhance the Excel workbooks created by Panda’s to_excel function. This article will describe how to use XlsxWriter and Pandas to make complex, visually appealing and useful Excel workbooks. As an added bonus, the article will briefly discuss the use of the new assign function that has been introduced in pandas 0.16.0.

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