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PyCon: Python for Data Lovers: Explore It, Analyze It, Map It

See the website.

I missed the beginning of this talk, and since I'm not a data lover, I'm afraid my notes may not do it justice.

There is lots of interesting, "open data."

There is a lot of data that is released by cities.

She's a geographer and obviously a real data lover. She gets excited about all this data.

csvkit is an amazing set of utilities for working with CSV. It replaces the csv module.

"Social network analysis is focused on uncovering the patterning of people's interactions."

They used QGIS.

She relies heavily on Google Refine.

PySAL is really great for spatial analysis.

She recommended "Social Network Analysis for Startups" from O'Reilly. Her advisor wrote it.

Comments

Anonymous said…
Python (or at least, pypy) is a really nice tool for processing data.

Unfortunately, with the GIL, its performance is abysmal, especially on large machines (aka recent desktops) with 8+ cores.

Python really needs to get with the times. I've had to abandon it for now, in favor of D. Maybe someday I'll come back to PyPy, if it ever catches up, but I think the damage is already done.
jjinux said…
The correct way to handle I/O bound processing in Python is gevent, which is not limited by the GIL. The correct way to handle CPU bound concurrency in Python is via using multiple processes.

In Guido's keynote, he said, "OS level threads are meant for parallel IO, not for parallel computation." Use separate processes for each core. One team was using Python on a machine with 64,000 cores.

D is a nice language, but I don't believe it's fully open source. Go is probably better. Neither one has the breadth of third-party libraries that Python has.

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