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.
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