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isaacslavitt.com | ||
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tomaugspurger.net
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| | | | | This is part 5 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Reshaping & Tidy Data Structuring datasets to facilitate analysis (Wickham 2014) So, you've sat down to analyze a new dataset. What do you do first? In episode 11 of Not So Standard Deviations, Hilary and Roger discussed their typical approaches. I'm with Hilary on this one, you should make sure your data is tidy. Before you do any plots, filtering, transformations, summary statistics, regressions... Without a tidy dataset, you'll be fighting your tools to get the result you need. With a tidy dataset, it's relatively easy to do all of those. | |
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oslandia.com
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| | | | | At Oslandia, we like working with Open Source tool projects and handling Open (geospatial) Data. In this article series, we will play with the OpenStreetMap (OSM) map and subsequent data. Here comes the seventh article of this series, dedicated to user classification using the power of machine learn | |
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austinrochford.com
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| | | | | I first started working with probabilistic programming about ten years ago (in late 2012 or early 2013) using PyMC2. At the time I was preparing to leave a PhD program in pure math for a data science | |
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blog.demofox.org
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| | | This post explains how to use sliced optimal transport to make blue noise point sets. The plain, well commented C++ code that goes along with this post, which made the point sets and diagrams, is at https://github.com/Atrix256/SOTPointSets. This is an implementation and investigation of "Sliced Optimal Transport Sampling" by Paulin et al (http://www.geometry.caltech.edu/pubs/PBCIW+20.pdf).?They also have... | ||
