I prefer to use miniconda for installing a lightweight python environment on Windows. I also like to create and customize Windows shortcuts for launching different conda environments in specific working directories. This is an especially useful tip for new users that are not as familiar with the command line on Windows.

After spending way too much time trying to get the shortcuts setup properly on multiple Windows machines, I spent some time automating the link creation process. This article will discuss how to use python to create custom Windows shortcuts to launch conda environments.

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This article will discuss several tips and shortcuts for using iloc to work with a data set that has a large number of columns. Even if you have some experience with using iloc you should learn a couple of helpful tricks to speed up your own analysis and avoid typing lots of column names in your code.

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This article is a review of O’Reilly’s Machine Learning Pocket Reference by Matt Harrison. Since Machine Learning can cover a lot of content, I was very interested to see what content a “Pocket Reference” would contain. Overall, I really enjoyed this book and think it deserves a place on many data science practitioner’s book shelves. Read on for more details about what is included in this reference and who should consider purchasing it.

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The other day, I was using pandas to clean some messy Excel data that included several thousand rows of inconsistently formatted currency values. When I tried to clean it up, I realized that it was a little more complicated than I first thought. Coincidentally, a couple of days later, I followed a twitter thread which shed some light on the issue I was experiencing. This article summarizes my experience and describes how to clean up messy currency fields and convert them into a numeric value for further analysis. The concepts illustrated here can also apply to other types of pandas data cleanup tasks.

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When dealing with continuous numeric data, it is often helpful to bin the data into multiple buckets for further analysis. There are several different terms for binning including bucketing, discrete binning, discretization or quantization. Pandas supports these approaches using the cut and qcut functions. This article will briefly describe why you may want to bin your data and how to use the pandas functions to convert continuous data to a set of discrete buckets. Like many pandas functions, cut and qcut may seem simple but there is a lot of capability packed into those functions. Even for more experience users, I think you will learn a couple of tricks that will be useful for your own analysis.

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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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