PSSI [Python Software Society of India] is happy to announce that Prabhu Ramachandran, faculty member of Department of Aerospace Engineering, IIT Bombay [and managing director of Enthought India] is the winner of Kenneth Gonsalves Award, 2014.
Prabhu has been active in the Open source and Python community for close to 15 years. He co-founded the Chennai LUG in 1998. He is also well known as the author and lead developer of the award winning Mayavi and TVTK Python packages. He also maintains PySPH, an open source framework for Smoothed Particle Hydrodynamics (SPH) simulations.
Prabhu is also Member of Board, Python Software Foundation since 2010 and is closely involved with the activities of FOSSEE and SciPy India. His research interests are primarily in particle methods and applied scientific computing.
Prabhu will be presented the Award on 27th Sep, the opening day of PyCon India 2014. PSSI and Team PyCon India would like to extend their hearty Congratulations to Prabhu for his achievement and wish him the very best for his future endeavours.
Congratulations Prabu, we’re honored to have you as part of the Enthought team!
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.
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.
After a fairly comprehensive overview of Pandas, Phillip got into the new features. In version 0.11 he covered: Continue reading →
Enthought Canopy 1.4 is now available! Users can easily update to this latest version by clicking on the green “Update available” link at the bottom right of the Canopy intro screen window or by going to Help > Canopy Application Updates within the application.
Key additions in this release are a Canopy-configured command prompt, inclusion of new packages in the full installer utilized by IT groups and users running from disconnected networks, and continued stability upgrades. We’ve also updated or added over 50 supported packages in Canopy’s Package Manager on a continual basis since the v.1.3 release. See the full release notes and the full list of currently available Canopy packages.
New Canopy-Configured Command Prompt
An important usability feature added in Enthought Canopy 1.4 is a Canopy-configured command prompt available from the Canopy Editor window on all platforms via Tools > Command Prompt. When selected, this opens a Command Prompt (Windows) or Terminal (Linux, Mac OS) window pre-configured with the correct environment settings to use Canopy’s Python installation from the command line. This avoids having to modify your login environment variables. In particular, on Windows when using standard (ie, non-administrative) user accounts it can be difficult to override some system settings. Continue reading →
Brian Spector, a technical consultant at NAG, presented “Implied Volatility using Python’s Pandas Library.” He covered a technique and script for calculating implied volatility for option prices in the Black–Scholes formula using Pandas and nag4py. With this technique, you can determine for what volatility the Black–Scholes equation price equals the market price. This volatility is then denoted as the implied volatility observed in the market. Brian fitted varying degrees of polynomials to the volatility curves, then examined the volatility surface and its sensitivity with respect to the interest rate. See the full presentation in the video below:
Today Enthought announced that it is now the worldwide distributor for PyXLL, and we’re excited to offer this key product for deploying Python models, algorithms and code to Excel. Technical teams can use the full power of Enthought Canopy, or another Python distro, and end-users can access the results in their familiar Excel environment. And it’s straightforward to set up and use.
Installing PyXLL from Enthought Canopy
PyXLL is available as a package subscription (with significant discounts for multiple users). Once you’ve purchased a subscription you can easily install it via Canopy’s Package Manager as shown in the screenshots below (note that at this time PyXLL is only available for Windows users). The rest of the configuration instructions are in the Quick Start portion of the documentation. PyXLL itself is a plug-in to Excel. When you start Excel, PyXLL loads into Excel and reads in Python modules that you have created for PyXLL. This makes PyXLL especially useful for organizations that want to manage their code centrally and deploy to multiple Excel users.
Creating Excel Functions with PyXLL
To create a PyXLL Python Excel function, you use the @xl_func decorator to tell PyXLL the following function should be registered with Excel, what its argument types are, and optionally what its return type is. PyXLL also reads the function’s docstring and provides that in the Excel function description. As an example, I created a module my_pyxll_module.py and registered it with PyXLL via the Continue reading →
Enthought Canopy 1.3 is now available and users should see the update notification in the bottom right corner of the Canopy welcome screen (as shown in the image below). This is a fairly small update primarily focused on bug fixing and stability improvement. The biggest change is the move to Python 2.7.6 from 2.7.3.
The bottom right of the Enthought Canopy window notifies users to available updates
Python 2.7.6 rolls up a couple of minor updates to the core Python environment. The most important changes from our perspective are a number of security fixes required by some users as well as fixes for Mac OS “Mavericks.” Details can be found in the Python release notes, but in general the change should be transparent to most users. The only caveat is for users building Python eggs with native C or FORTRAN extensions and publishing those eggs to users who may still be running earlier versions of Canopy or Python 2.7.3 in general. In this case, it is safest to continue building against earlier versions of Canopy.
But isn’t updating Python versions painful you may ask? In the past, yes, updating to a new Python version often required a new Python install and then re-installing all of your custom packages. However, with Canopy’s auto-update mechanism, it’s simply a matter of clicking the “Update available” link and choosing “Install and relaunch” or “Install after quit.” Canopy will automatically update the core Python installation and restart without impacting your environment. Additionally, whether you are running Canopy 1.1, 1.1.1, or 1.2, Canopy will jump straight to 1.3 and get you all of the latest updates.
We encourage all users to update to Canopy 1.3 as the 1.2 and 1.3 versions include a large number of stability fixes as well as cleaning up a lot of other less serious, but still important aspects of the user experience. For those new to Canopy, you can get Canopy here.
Enthought Canopy makes updates convenient with automatic downloads that install without impacting user environments