A look at the world wine market using Python, Pandas, and Seaborn

In this article we want to have a look at present wine market prices by region and appellation from the point of view of the Wine.com website catalog. We will use Python-based libraries such as Pandas and Seaborn.

Exploring geographical data with SparkR and ggplot2

The present analysis will make use of SparkR’s power to analyse large datasets in order to explore the 2013 American Community Survey dataset, more concretely its geographical features. For that purpose, we will aggregate data using the different tools introduced in the SparkR documentation and our series of notebooks, and then use ggplot2 mapping capabilities to put the different aggregations into a geographical context.

A visual on tuberculosis evolution using Python and Bokeh

In this second approach to the World situation of infectious tuberculosis from 1990 to 2007, we want to make a point about how a simple visual representation of tabular data, a Bokeh heatmap in this case, can provide a lot of information that, although is already there in the tabular data, might be more difficult to percieve.

World differences in infectious tuberculosis prevalence 1990-2007

In this first approach to the world situation regarding infectious tuberculosis we want to have a look at how different countries have been affected by the disease in the period from 1990 to 2007. By doing so we want to better understand different trends in the prevalence of this important disease. Which countries are getting better and worse? Are there more or less clear groups of countries based on how much are the affected and how their situation is changeing?