Webinar: Work Better, Smarter, and Faster in Python with Enthought Training on Demand

Jul 22 2014 Published by under Enthought Training on Demand, NumPy, Python, SciPy, Training, Webinars

 

Join Us For a Webinar

We’ll demonstrate how Enthought Training on Demand can help both new Python users and experienced Python developers be better, smarter, and faster at the scientific and analytic computing tasks that directly impact their daily productivity and drive results.

Enthought Training on Demand Webinar

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

  • Tues, August 5, 2014, 1:00-1:45 PM CDT (REGISTER)
  • Wed, August 6, 2014, 8:00-8:45 AM CDT (REGISTER)
  • Wed, August 13, 2014 11:00-11:45 AM CDT (REGISTER)

 

What You’ll Learn

Whether you’re new to the language or looking to expand your existing capabilities, you’ll see examples of how this innovative training can help you:

    • Work Better by using the most effective approaches to problems, reducing time spent on trial and error; we’ll show you not just the “what” but the “how” and “why”
    • Work Smarter by building and refining your use of the language and deepening your skillset with new tools and techniques proven to make you more efficient and deliver more insightful results
    • Work Faster by accelerating your learning through courses tailored to the particular tasks and needs of your role; you’ll focus your learning time on the 20% of topics you’ll put to use 80% of the time

See FREE course preview videos

See the course catalog / buy a course(s) here 


 

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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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