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.
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
crosstab to build a summary table.
Since I have previously covered pivot_tables, this article will discuss the
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
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.
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.