data science; research collaboration

We'll open with an installment of our Pythonic Pitfalls series: a common dictionary access gotcha.
Mark can demo the jupyter notebook, matplotlib, pandas, and numpy project he used for a recent data science interview.
And we have another batch of cool links from around the web gathered by Mike; last month's list fueled a really fun meeting!
All happening at 7pm Monday 11april at
And check out our sister group, the AI Study Group, meeting virtually every first Sunday at 2pm out of, lately hosting provocative talks by nvidia's Ben Firner on why neural networks fail!
And introducing...
Collaboration corner: a new announcement slot where folks can solicit volunteer* collaborators for their open* projects and members get hands-on experience with real-world applications!
Min Cao [mincao at gmail dot com], an assistant professor of accounting, is looking for a pythonista to help with a talk on using high frequency trading data (NASDAQ ITCH).
The data is used in the paper "How is Earnings News Transmitted to Stock Prices?" at
The python code is available at
Min Cao will present the paper and the data but needs help regarding the python code.  Feel free to contact Min directly if you're interested.
*Princeton Python encourages collaboration to be volunteer (not paid) and any resulting code, talks, and publications to be open (not proprietary); we urge any exceptions to be negotiated before undertaking any work.
Future meetings will include:
- data visualization in Pandas
- crash course in deep learning
- learn python from scratch
And more in our ongoing series:
- Auto-mania explores automated transformation of source code.
- Demysti-py looks at how familiar constructs are implemented.
- Pythonic pitfalls: error fu deepens your understanding of fundamentals.
- Deep reading explores and applies academic articles on deep learning.
- New tool roundup by our resident code hound keeps you updated.