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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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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This is the second article in a series describing how to use Google Forms to collect information via simple web forms, read it into a pandas dataframe and analyze it. This article will focus on how to use the data in the dataframe to create complex and powerful data visualizations with seaborn.

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In the python world, there are multiple options for visualizing your data. Because of this variety, it can be really challenging to figure out which one to use when. This article contains a sample of some of the more popular ones and illustrates how to use them to create a simple bar chart. I will create examples of plotting data with: Pandas, Seaborn, ggplot, Bokeh, pygal and Plotly.

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