The Latest and Greatest Pandas Features (since v 0.11)

Jun 20 2014 Published by under Finance, General, Python

On May 28, 2014 Phillip Cloud, core contributor for the Pandas data analytics Python library, spoke at a joint meetup of the New York Quantitative Python User’s Group (NY QPUG) and the NY Finance PUG. Enthought hosted and about 60 people joined us to listen to Phillip present some of the less-well-known, but really useful features that have come out since Pandas version 0.11 and some that are coming soon. We all learned more about how to take full advantage of the Pandas Python library, and got a better sense of how excited Phillip was to discover Pandas during his graduate work.

Pandas to MATLAB

After a fairly comprehensive overview of Pandas, Phillip got into the new features. In version 0.11 he covered:

  • indexers loc/at, iloc/iat,
  • dtypes,
  • using numexpr to evaluate arithmetic expressions for large objects, focusing mainly on numexpr. Then in version 0.12 he went into some depth on read_html. In the process he read data from a website and re-created a plot from the website. His examples are valuable as a way to see how an expert uses the Pandas package. He also goes over read_json and others new features as well, again with some really interesting examples.

Phillip covered some experimental features in version 0.13 including query/eval, msgpack IO and Google BigQuery IO. He then wrapped up with a sneak peak at some version 0.14 (soon to be released) features including MultiIndex slicing. His MultiIndex slicing example comes from his work on neuroscience (his cool data collection system is in the figure below).

You can watch his presentation below (thank you to Aaron Watters for holding up my iPhone for close to 30min from the second row to get shots of Phillip speaking), and you can get his iPython Notebooks from the talk as well.

The Latest and Greatest Pandas Features (since v 0.11) from NYQPUG.

 

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New Webinar: Supercharging Excel Analytics with Python

May 05 2014 Published by under PyXLL, Webinars

Python in Excel allows for interactive graphical data selection and cleansingJoin us for a webinar to see how the PyXLL Python for Excel add-in makes it easy to solve your biggest data analysis challenges with advanced Python tools and analytic engines.

Space is limited! Click a session link below to reserve your spot today:

Tuesday, May 13, 2014, 1:00-1:45 PM CDT
Wednesday, May 14, 2014, 8:00-8:45 AM CDT / 2:00-2:45 PM BST
Wednesday, May 21, 2014, 1:00-1:45 PM CDT

Who Should Attend

Anyone who wants a user-friendly solution to leverage the full power of Python’s data analysis libraries and parallel processing capabilities within Excel.

What You’ll Learn

Extending the native analytic capabilities of Excel or implementing Excel-backend analytics on a cluster or in the cloud with VBA, C++, and other legacy languages is challenging and time-consuming. Python’s elegant syntax and extensive ecosystem of analytic packages can greatly simplify the development of advanced analytic tools and cluster/cloud-based backend capabilities in Excel.

We’ll demonstrate how to:

  1. Implement Excel backend parallel computations on local clusters or cloud-based platforms such as Microsoft Azure with Python
  2. Use Python functions to implement advanced, interactive graphical analytic tools in ExcelGitHub_Logo
  3. Reduce risk by storing code in GitHub version control instead of embedded VBA in Excel files

Click a session link below to reserve your spot today:

Tuesday, May 13, 2014, 1:00-1:45 PM CDT
Wednesday, May 14, 2014, 8:00-8:45 AM CDT / 2:00-2:45 PM BST
Wednesday, May 21, 2014, 1:00-1:45 PM CDT

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