Taking care of business, one python script at a time
Posted by Chris Moffitt in articles
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.
Posted by Chris Moffitt in articles
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.
Posted by Chris Moffitt in articles
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.
Posted by Chris Moffitt in articles
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.
Posted by Chris Moffitt in articles
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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