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nikgrozev.com | ||
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www.ethanrosenthal.com
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| | | | | I make Python packages for everything. Big projects obviously get a package, but so does every tiny analysis. Spinning up a quick jupyter notebook to check something out? Build a package first. Oh yeah, and every package gets its own virtual environment. Let's back up a little bit so that I can tell you why I do this. After that, I'll show you how I do this. Notably, my workflow is set up to make it simple to stay consistent. | |
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vickiboykis.com
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| | | | | When I'm working with Jupyter notebooks, I often want to work with them from within a virtual environment. The general best practice is that you should always use either virtual environments or Docker containers for working with Python, for reasons outlined in this post, or you're gonna have a bad time. I know I have. The workflow is a little long, so I thought I'd document it for future me here. | |
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tomaugspurger.net
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| | | | | This is part 6 in my series on writing modern idiomatic pandas. Modern Pandas Method Chaining Indexes Fast Pandas Tidy Data Visualization Time Series Scaling Visualization and Exploratory Analysis A few weeks ago, the R community went through some hand-wringing about plotting packages. For outsiders (like me) the details aren't that important, but some brief background might be useful so we can transfer the takeaways to Python. The competing systems are "base R", which is the plotting system built into the language, and ggplot2, Hadley Wickham's implementation of the grammar of graphics. For those interested in more details, start with | |
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fharrell.com
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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. | ||
