http://www.pophealthmetrics.com/content/13/1/29

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In [1]: import glob In [2]: lines = '' for fname in glob.glob('*.py'): with file(fname) as f: lines += f.read() lines += '\n' In [3]: import re # Find top np.* calls In [9]: np_calls = re.findall('np\.[\w\.]+', lines) np_calls[:5] Out[9]: ['np.linspace', 'np.random.random', 'np.random.normal', 'np.sqrt', 'np.random.normal'] In [10]: import pandas as pd In [12]: pd.Series(np_calls).value_counts().head(20) Out[12]: np.array 219 np.random.normal 170 np.mean 130 np.random.seed 126 np.round 124 np.log 119 np.exp 114 np.linspace 96 np.random.choice 84 np.where 84 np.zeros 78 np.ones 65 np.dot 62 np.empty 62 np.sum 52 np.absolute 49 np.nan 47 np.arange 45 np.inf 38 np.sqrt 37

Number one thing: `np.array`! I wonder why I use that.

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A fun online appendix includes probabilistic combinatorial calculations of the sort that I was actually trained in: http://www.pophealthmetrics.com/content/supplementary/s12963-015-0061-1-s2.zip

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I meant to have a picture of it in my hands to go here, but I didn’t get it in time. Here is a multiscale rainbow smudge instead:

http://www.washington.edu/uwpress/search/books/FLAINT.html

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Here is my answer: http://stats.stackexchange.com/questions/172163/making-a-single-decision-tree-from-a-random-forest/172811#172811

It made me ask another question: http://stats.stackexchange.com/questions/172810/are-there-any-new-approaches-to-generating-random-examples-in-combined-multiple

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My 3 year old and I modded it, and may we now share “castle smudge” with you: http://bl.ocks.org/aflaxman/raw/26fc5aa0e14b01754b0f/

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