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.
This article will discuss several tips and shortcuts for using
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.
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.
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
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,
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.
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.
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
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.
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.
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.
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.
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.