We’ve had a number of major product development efforts underway over the last year, and we’re pleased to share a lot of new announcements for 2017:
A New Chapter for the Enthought Python Distribution (EPD):
Python 3 and Intel MKL 2017
In 2004, Enthought released the first “Python: Enthought Edition,” a Python package distribution tailored for a scientific and analytic audience. In 2008 this became the Enthought Python Distribution (EPD), a self-contained installer with the "enpkg" command-line tool to update and manage packages. Since then, over a million users have benefited from Enthought’s tested, pre-compiled set of Python packages, allowing them to focus on their science by eliminating the hassle of setting up tools.
Fast forward to 2017, and we now offer over 450 Python packages and a new era for the Enthought Python Distribution; access to all of the packages in the new EPD is completely free to all users and includes packages and runtimes for both Python 2 and Python 3 with some exciting new additions. Our ever-growing list of packages includes, for example, the 2017 release of the MKL (Math Kernel Library), the fruit of an ongoing collaboration with Intel.
The New Enthought Deployment Server:
Secure, Onsite Access to EPD and Private Packages
Enthought’s Pandas Mastery Workshop is designed to accelerate the development of skill and confidence with Python’s Pandas data analysis package — in just three days, you’ll look like an old pro! This course was created ground up by our training experts based on insights from the science of human learning, as well as what we’ve learned from over a decade of extensive practical experience of teaching thousands of scientists, engineers, and analysts to use Python effectively in their everyday work.
In this webinar, we’ll give you the key information and insight you need to evaluate whether the Pandas Mastery Workshop is the right solution to advance your data analysis skills in Python, including:
Who will benefit most from the course
A guided tour through the course topics
What skills you’ll take away from the course, how the instructional design supports that
What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
What previous workshop attendees say about the course
Data exploration, manipulation, and visualization start with loading data, be it from files or from a URL. Pandas has become the go-to library for all things data analysis in Python, but if your intention is to jump straight into data exploration and manipulation, the Canopy Data Import Tool can help, instead of having to learn the details of programming with the Pandas library. Continue reading →
For example, the Data Import Tool lets you delete rows and columns containing Null values or replace the Null values in the DataFrame with a specific value. It also allows you to create new columns from existing ones. All operations are logged and are reversible in the Data Import Tool so you can experiment with various workflows with safeguards against errors or forgetting steps. Continue reading →
Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:
Acquire > Wrangle > Analyze and Model > Share and Refine > Publish
Since PyXLL was first released back in 2010 it has grown hugely in popularity and is used by businesses in many different sectors.
The original motivation for PyXLL was to be able to use all the best bits of Excel combined with a modern programming language for scientific computing, in a way that fits naturally and works seamlessly.
Since the beginning, PyXLL development focused on the things that really matter for creating useful real-world spreadsheets; worksheet functions and macro functions. Without these all you can do is just drive Excel by poking numbers in and reading numbers out. At the time the first version of PyXLL was released, that was already possibly using COM, and so providing yet another API to do the same was seen as little value add. On the other hand, being able to write functions and macros in Python opens up possibilities that previously were only available in VBA or writing complicated Excel Addins in C++ or C#.
With the release of PyXLL 3, integrating your Python code into Excel has become more enjoyable than ever. Many things have been simplified to get you up and running faster, and there are some major new features to explore.
If you are new to PyXLL have a look at the Getting Started section of the documentation.
All the features of PyXLL, including these new ones, can be found in the Documentation
NEW FEATURES IN PYXLL V. 3.0
1. Ribbon Customization
Ever wanted to write an add-in that uses the Excel ribbon interface? Previously the only way to do this was to write a COM add-in, which requires a lot of knowledge, skill and perseverance! Now you can do it with PyXLL by defining your ribbon as an XML document and adding it to your PyXLL config. All the callbacks between Excel and your Python code are handled for you.
Python has a broad range of tools for data analysis and visualization. While Excel is able to produce various types of plots, sometimes it’s either not quite good enough or it’s just preferable to use matplotlib.
Users already familiar with matplotlib will be aware that when showing a plot as part of a Python script the script stops while a plot is shown and continues once the user has closed it. When doing the same in an IPython console when a plot is shown control returns to the IPython prompt immediately, which is useful for interactive development.
Something that has been asked a couple of times is how to use matplotlib within Excel using PyXLL. As matplotlib is just a Python package like any other it can be imported and used in the same way as from any Python script. The difficulty is that when showing a plot the call to matplotlib blocks and so control isn’t returned to Excel until the user closes the window.
This blog shows how to plot data from Excel using matplotlib and PyXLL so that Excel can continue to be used while a plot window is active, and so that same window can be updated whenever the data in Excel is updated. Continue reading →
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 →