On September 17th, 2014, I published my first article which means that today is the 5th birthday of Practical Business Python. Thank you to all my readers and all those that have supported me through this process! It has been a great journey and I look forward to seeing what the future holds.

This 5 year anniversary gives me the opportunity to reflect on the blog and what will be coming next. I figured I would use this milestone to walk through a few of the stats and costs associated with running this blog for the past 5 years. This post will not be technical but I am hopeful that my readers as well as current and aspiring bloggers going down this path will find it helpful. Finally, please use the comments to let me know what content you would like to see in the future.

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One of the most commonly used pandas functions is read_excel. This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command.

For those of you that want the TLDR, here is the command:

df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True)

Read on for an explanation of when to use this and how it works.

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This guest post is the second article about using python tools to automate a report generation pipeline. If you have not read the first post you should probably review before continuing with this article.

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This guest post that walks through a great example of using python to automate a report generating process. I think PB Python readers will enjoy learning from this real world example using python, jupyter notebooks, papermill and several other tools.

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This article describes how to to use Microsoft Azure’s Cognitive Services Face API and python to identify, count and classify people in a picture. In addition, it will show how to use the service to compare two face images and tell if they are the same person. We will try it out with several celebrity look-alikes to see if the algorithm can tell the difference between two similar Hollywood actors. By the end of the article, you should be able to use these examples to further explore Azure’s Cognitive Services with python and incorporate them in your own projects.

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On May 30th, I had the pleasure of presenting at the MinneAnalytics Data Tech Conference with @KatieKodes. Our talk was on “Evangelizing Python for Business”. Here is the summary of the talk:

Python’s simple structure has been vital to the democratization of data science. But as the field rushes forward, making splashy headlines about specialized new jobs, everyday Excel users remain unaware of the value that elementary building blocks of Python for data science can bring them at the office.

Join us for a conversation about bringing Python out of IT and into the business. We’ll share challenges and successes from writing tutorials, teaching classes, and advocating adoption among new users.

I really enjoyed the presentation and received a lot of positive feedback. As a result, I wanted to capture some of the ideas in a post so that the broader community could see it and generate some dialog on tips and techniques that have worked for you. The actual content in this blog is closely tied to our presentation but contain some additional idea and thoughts that I may want to expand on in future posts.

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I have been working on a side project so I have not had as much time to blog. Hopefully I will be able to share more about that project soon.

In the meantime, I wanted to write an article about styling output in pandas. The API for styling is somewhat new and has been under very active development. It contains a useful set of tools for styling the output of your pandas DataFrames and Series. In my own usage, I tend to only use a small subset of the available options but I always seem to forget the details. This article will show examples of how to format numbers in a pandas DataFrame and use some of the more advanced pandas styling visualization options to improve your ability to analyze data with pandas.

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There are many sophisticated models people can build for solving a forecasting problem. However, they frequently stick to simple Excel models based on average historical values, intuition and some high level domain-specific heuristics. This approach may be precise enough for the problem at hand but there are alternatives that can add more information to the prediction with a reasonable amount of additional effort.

One approach that can produce a better understanding of the range of potential outcomes and help avoid the “flaw of averages” is a Monte Carlo simulation. The rest of this article will describe how to use python with pandas and numpy to build a Monte Carlo simulation to predict the range of potential values for a sales compensation budget. This approach is meant to be simple enough that it can be used for other problems you might encounter but also powerful enough to provide insights that a basic “gut-feel” model can not provide on its own.

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Several years ago, I wrote an article about using pandas to creating a diff of two excel files. Ovet the years, the pandas API has changed and the diff script no longer works with the latest pandas releases. Through the magic of search engines, people are still discovering the article and are asking for help in getting it to work with more recent versions of pandas. Since pandas is closing in on a 1.0 release, I think this is a good time to get an updated version out there.

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In my previous article, I wrote about pandas data types; what they are and how to convert data to the appropriate type. This article will focus on the pandas categorical data type and some of the benefits and drawbacks of using it.

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