/explore
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www.pythoncharts.com | ||
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tomaugspurger.net
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| | | | | Welcome back. As a reminder: In part 1 we got dataset with my cycling data from last year merged and stored in an HDF5 store In part 2 we did some cleaning and augmented the cycling data with data from http://forecast.io. You can find the full source code and data at this project's GitHub repo. Today we'll use pandas, seaborn, and matplotlib to do some exploratory data analysis. For fun, we'll make some maps at the end using folium. | |
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andrewpwheeler.com
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| | | | | Here are some notes (for myself!) about how to format histograms in python using pandas and matplotlib. The defaults are no doubt ugly, but here are some pointers to simple changes to formatting to make them more presentation ready. First, here are the libraries I am going to be using. import pandas as pd import... | |
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erikbern.com
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| | | | | I made a New Year's resolution: every plot I make during 2018 will contain uncertainty estimates. Nine months in and I have learned a lot, so I put together a summary of some of the most useful methods. | |
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hbiostat.org
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| | | In this article I provide much more extensive simulations showing the near perfect agreement between the odds ratio (OR) from a proportional odds (PO) model, and the Wilcoxon two-sample test statistic. The agreement is studied by degree of violation of the PO assumption and by the sample size. A refinement in the conversion formula between the OR and the Wilcoxon statistic scaled to 0-1 (corcordance probability) is provided. | ||
