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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Over the past couple of months, there has been an ongoing discussion about Jupyter Notebooks affectionately called the “Notebook Wars”. The genesis of the discussion is Joel Grus’ presentation I Don’t Like Notebooks and has been followed up with Tim Hopper’s response, aptly titled I Like Notebooks. There have been several follow-on posts on this topic including thoughtful analysis from Yihui Xie.

The purpose of this post is to use some of the points brought up in these discussions as a background for describing my personal best practices for the analysis I frequently perform with notebooks. In addition, this approach can be tailored for your unique situation. I think many new python users do not take the time to think through some of these items I discuss. My hope is that this article will spark some discussion and provide a framework that others can build off for making repeatable and easy to understand data analysis pipelines that fit their needs.

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Pandas offers several options for grouping and summarizing data but this variety of options can be a blessing and a curse. These approaches are all powerful data analysis tools but it can be confusing to know whether to use a groupby, pivot_table or crosstab to build a summary table. Since I have previously covered pivot_tables, this article will discuss the pandas crosstab function, explain its usage and illustrate how it can be used to quickly summarize data. My goal is to have this article be a resource that you can bookmark and refer to when you need to remind yourself what you can do with the crosstab function.

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Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk through a few of the highlights and show how to use the new scatter and line plot functions for quickly creating very useful visualizations of data.

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Python has many options for natively creating common Microsoft Office file types including Excel, Word and PowerPoint. In some cases, however, it may be too difficult to use the pure python approach to solve a problem. Fortunately, python has the “Python for Windows Extensions” package known as pywin32 that allows us to easily access Window’s Component Object Model (COM) and control Microsoft applications via python. This article will cover some basic use cases for this type of automation and how to get up and running with some useful scripts.

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This article is a review of Chris Albon’s book, Machine Learning with Python Cookbook. This book is in the tradition of other O’Reilly “cookbook” series in that it contains short “recipes” for dealing with common machine learning scenarios in python. It covers the full spectrum of tasks from simple data wrangling and pre-processing to more complex machine learning model development and deep learning implementations. Since this is such a fast moving and broad topic, it is nice to get a new book that covers the latest topics and presents them in a compact but very useful format. Bottom line, I enjoyed reading this book and think it will be a useful resource to have on my python bookshelf. Read on for some more details about the book and who will benefit most from reading it.

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In my last article, I presented a flowchart that can be useful for those trying to select the appropriate python library for a visualization task. Based on some comments from that article, I decided to use Bokeh to create waterfall charts and bullet graphs. The rest of this article shows how to use Bokeh to create these unique and useful visualizations.

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This brief article introduces a flowchart that shows how to select a python visualization tool for the job at hand. The criteria for choosing the tools is weighted more towards the “common” tools out there that have been in use for several years. There may be some debate about some of the recommendations but I believe this should be helpful for someone that is new to the python visualization landscape and trying to make a decision about where to invest their time to learn how to use one of these libraries.

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When doing data analysis, it is important to make sure you are using the correct data types; otherwise you may get unexpected results or errors. In the case of pandas, it will correctly infer data types in many cases and you can move on with your analysis without any further thought on the topic.

Despite how well pandas works, at some point in your data analysis processes, you will likely need to explicitly convert data from one type to another. This article will discuss the basic pandas data types (aka dtypes), how they map to python and numpy data types and the options for converting from one pandas type to another.

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