Possible solutions / learning
1. Moving average using PyPI's RSS
My first approach was to take the PyPI New RSS feed and take the average of time between adding packages. The script is here. The problem though is that the RSS feed has only 40 items, not much data. However when I put it in a cronjob and left it running for a week I got pretty similar results: it will happen somewhere at the beginning of March:
# grep Result pypi.log |sed 's/,.* \[Main.*: / /g' 2017-02-09 16:09:13 2017-02-25 13:52:23.701848 2017-02-10 15:57:03 2017-02-26 03:50:38.528795 2017-02-11 15:57:03 2017-03-09 23:10:14.631885 2017-02-12 15:57:03 2017-03-05 22:31:50.575452 2017-02-13 15:57:03 2017-02-27 07:02:47.599206 2017-02-14 15:57:03 2017-02-21 20:41:34.775090 2017-02-15 15:57:03 2017-02-25 00:01:30.304754 2017-02-16 15:57:03 2017-03-01 12:52:38.659931 2017-02-17 08:00:33 2017-03-01 09:38:01.360349
Another source to use if you go this route is PyPI's XML-RPC methods.
2. Using scipy.interpolate on Webarchive data
$ python -m venv venv && source venv/bin/activate $ pip install waybackpack # take 4 years of data (half a GB, delete when done) $ waybackpack https://pypi.python.org/pypi -d pypi-snapshots --from-date 20130214 --to-date 20170214 # few days went by, adjusted end date to 20170217 today # # prep the data $ cd pypi-snapshots # sometimes unix is all you need ;) $ find . -name 'pypi'|xargs grep "<strong>[0-9][0-9]*</strong>"| perl -pe 's/.*?(\d+)\/.*<strong>(\d+)<\/strong>/\1:\2/g' > ../data.txt $ head -2 data.txt 20130214002304:28061 20130216031420:28108 $ tail -2 data.txt 20170215124232:98825 20170216124236:98907
This data (and all scripts) are on our solutions branch.
As you can see from the notebook I am getting: 1st of March 8:37 PM. First of the month, nice date. And consistent with the first method.
What was your solution? Feel free to share in the comments below.
We hope you enjoy these challenges. Please provide us feedback if we can improve anything ...
If you have an interesting challenge you want us to feature, don't hesitate to reach out to us.
Next week we return to the Twitter API to do a sentiment analysis! Stay tuned ...
Update 9th of March 2016
OK so turns out the 1st of March was a bit optimistic, predicting is hard :)
PyBites celebrated closely after hitting this important milestone:
See an error in this post? Please submit a pull request on Github.