Category Archives: NumPy

Webinar: Python for Scientists & Engineers: A Tour of Enthought’s Professional Training Course

What:  A guided walkthrough and Q&A about Enthought’s technical training course Python for Scientists & Engineers with Enthought’s VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for scientific and engineering applications

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“Writing software is not my job…I just have to do it every day.”  
-21st Century Scientist or Engineer

Many scientists, engineers, and analysts today find themselves writing a lot of software in their day-to-day work even though that’s not their primary job and they were never formally trained for it. Of course, there is a lot more to writing software for scientific and analytic computing than just knowing which keyword to use and where to put the semicolon.

Software for science, engineering, and analysis has to solve the technical problem it was created to solve, of course, but it also has to be efficient, readable, maintainable, extensible, and usable by other people — including the original author six months later!

It has to be designed to prevent bugs and — because all reasonably complex software contains bugs — it should be designed so as to make the inevitable bugs quickly apparent, easy to diagnose, and easy to fix. In addition, such software often has to interface with legacy code libraries written in other languages like C or C++, and it may benefit from a graphical user interface to substantially streamline repeatable workflows and make the tools available to colleagues and other stakeholders who may not be comfortable working directly with the code for whatever reason.

Enthought’s Python for Scientists and Engineers is designed to accelerate the development of skill and confidence in addressing these kinds of technical challenges using some of Python’s core capabilities and tools, including:

  • The standard Python language
  • Core tools for science, engineering, and analysis, including NumPy (the fast array programming package), Matplotlib (for data visualization), and Pandas (for data analysis); and
  • Tools for crafting well-organized and robust code, debugging, profiling performance, interfacing with other languages like C and C++, and adding graphical user interfaces (GUIs) to your applications.

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Scientists and Engineers course is the right solution to take your technical skills to the next level, 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 course attendees say about the course

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michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


Python for Scientists & Engineers Training: The Quick Start Approach to Turbocharging Your Work

If you are tired of running repeatable processes manually and want to (semi-) automate them to increase your throughput and decrease pilot error, or you want to spend less time debugging code and more time writing clean code in the first place, or you are simply tired of using a multitude of tools and languages for different parts of a task and want to replace them with one comprehensive language, then Enthought’s Python for Scientists and Engineers is definitely for you!

This class has been particularly appealing to people who have been using other tools like MATLAB or even Excel for their computational work and want to start applying their skills using the Python toolset.  And it’s no wonder — Python has been identified as the most popular coding language for five years in a row for good reason.

One reason for its broad popularity is its efficiency and ease-of-use. Many people consider Python more fun to work in than other languages (and we agree!). Another reason for its popularity among scientists, engineers, and analysts in particular is Python’s support for rapid application development and extensive (and growing) open source library of powerful tools for preparing, visualizing, analyzing, and modeling data as well as simulation.

Python is also an extraordinarily comprehensive toolset – it supports everything from interactive analysis to automation to software engineering to web app development within a single language and plays very well with other languages like C/C++ or FORTRAN so you can continue leveraging your existing code libraries written in those other languages.

Many organizations are moving to Python so they can consolidate all of their technical work streams under a single comprehensive toolset. In the first part of this class we’ll give you the fundamentals you need to switch from another language to Python and then we cover the core tools that will enable you to do in Python what you were doing with other tools, only faster and better!

Additional Resources

Upcoming Open Python for Scientists & Engineers Sessions:

Albuquerque, NM, Sept 11-15, 2017
Washington, DC, Sept 25-29, 2017
Los Alamos, NM, Oct 2-6, 2017
Cambridge, UK, Oct 16-20, 2017
San Diego, CA, Oct 30-Nov 3, 2017
Albuquerque, NM, Nov 13-17, 2017
Los Alamos, NM, Dec 4-8, 2017
Austin, TX, Dec 11-15, 2017

Have a group interested in training? We specialize in group and corporate training. Contact us or call 512.536.1057.

Learn More

Download Enthought’s Machine Learning with Python’s Scikit-Learn Cheat Sheets
Enthought's Machine Learning with Python Cheat Sheets
Additional Webinars in the Training Series:

Python for Data Science: A Tour of Enthought’s Professional Technical Training Course

Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

An Exclusive Peek “Under the Hood” of Enthought Training and the Pandas Mastery Workshop

Download Enthought’s Pandas Cheat SheetsEnthought's Pandas Cheat Sheets

Webinar: Python for Data Science: A Tour of Enthought’s Professional Training Course

View Python for Data Science Webinar
What: A guided walkthrough and Q&A about Enthought’s technical training course “Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications

VIEW


Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

In this webinar, we give you the key information and insight you need to evaluate whether Enthought’s Python for Data Science course is the right solution to advance your professional data science 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 course attendees say about the course

VIEW


michael_connell-enthought-vp-trainingPresenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Webinar – Python for Professionals: The Complete Guide to Enthought’s Technical Training Courses

View the Python for Professionals Webinar

What: Presentation and Q&A with Dr. Michael Connell, VP, Enthought Training Solutions
Who Should Watch: Anyone who wants to develop proficiency in Python for scientific, engineering, analytic, quantitative, or data science applications, including team leaders considering Python training for a group, learning and development coordinators supporting technical teams, or individuals who want to develop their Python skills for professional applications

View Recording  


Python is an uniquely flexible language – it can be used for everything from software engineering (writing applications) to web app development, system administration to “scientific computing” — which includes scientific analysis, engineering, modeling, data analysis, data science, and the like.

Unlike some “generalist” providers who teach generic Python to the lowest common denominator across all these roles, Enthought specializes in Python training for professionals in scientific and analytic fields. In fact, that’s our DNA, as we are first and foremost scientists, engineers, and data scientists ourselves, who just happen to use Python to drive our daily data wrangling, modeling, machine learning, numerical analysis, simulation, and more.

If you’re a professional using Python, you’ve probably had the thought, “how can I be better, smarter, and faster in using Python to get my work done?” That’s where Enthought comes in – we know that you don’t just want to learn generic Python syntax, but instead you want to learn the key tools that fit the work you do, you want hard-won expert insights and tips without having to discover them yourself through trial and error, and you want to be able to immediately apply what you learn to your work.

Bottom line: you want results and you want the best value for your invested time and money. These are some of the guiding principles in our approach to training.

In this webinar, we’ll give you the information you need to decide whether Enthought’s Python training is the right solution for your or your team’s unique situation, helping answer questions such as:

  • What kinds of Python training does Enthought offer? Who is it designed for? 
  • Who will benefit most from Enthought’s training (current skill levels, roles, job functions)?
  • What are the key things that make Enthought’s training different from other providers and resources?
  • What are the differences between Enthought’s training courses and who is each one best for?
  • What specific skills will I have after taking an Enthought training course?
  • Will I enjoy the curriculum, the way the information is presented, and the instructor?
  • Why do people choose to train with Enthought? Who has Enthought worked with and what is their feedback?

We’ll also provide a guided tour and insights about our our five primary course offerings to help you understand the fit for you or your team:

View Recording  


michael_connell-enthought-vp-training

Presenter: Dr. Michael Connell, VP, Enthought Training Solutions

Ed.D, Education, Harvard University
M.S., Electrical Engineering and Computer Science, MIT


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Plotting in Excel with PyXLL and Matplotlib

Author: Tony Roberts, creator of PyXLL, a Python library that makes it possible to write add-ins for Microsoft Excel in Python. Download a FREE 30 day trial of PyXLL here.


Plotting in Excel with PyXLL and MatplotlibPython 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

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

Join Us For a Webinar

Enthought Training on Demand 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.

View a recording of the Work Better, Smarter, and Faster in Python with Enthought Training on Demand webinar here.

What You’ll Learn

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PyQL and QuantLib: A Comprehensive Finance Framework

Authors: Kelsey Jordahl, Brett Murphy

Earlier this month at the first New York Finance Python User’s Group (NY FPUG) meetup, Kelsey Jordahl talked about how PyQL streamlines the development of Python-based finance applications using QuantLib. There were about 30 people attending the talk at the Cornell Club in New York City. We have a recording of the presentation below.

FPUG Meetup Presentation Screenshot

QuantLib is a free, open-source (BSD-licensed) quantitative finance package. It provides tools for financial instruments, yield curves, pricing engines, creating simulations, and date / time management. There is a lot more detail on the QuantLib website along with the latest downloads. Kelsey refers to a really useful blog / open-source book by one of the core QuantLib developers on implementing QuantLib. Quantlib also comes with different language bindings, including Python.

So why use PyQL if there are already Python bindings in QuantLib? Well, PyQL provides a much more Pythonic set of APIs, in short. Kelsey discusses some of the differences between the original QuantLib Python API and the PyQL API and how PyQL streamlines the resulting Python code. You get better integration with other packages like NumPy, better namespace usage and better documentation. PyQL is available up on GitHub in the PyQL repo. Kelsey uses the IPython Notebooks in the examples directory to explore PyQL and QuantLib and compares the use of PyQL versus the standard (SWIG) QuantLib Python APIs.

PyQL remains a work in progress, with goals to make its QuantLib coverage more complete, the API even more Pythonic, and getting a successful build on Windows (works on Mac OS and Linux now). It’s open source, so feel free to step up and contribute!

For the details, check out the video of Kelsey’s presentation (44 minutes).

And here are the slides online if you want to check the links in the presentation.

If you are interested in working on either QuantLib or PyQL, let the maintainers know!

Exploring NumPy/SciPy with the “House Location” Problem

Author: Aaron Waters

I created a Notebook that describes how to examine, illustrate, and solve a geometric mathematical problem called “House Location” using Python mathematical and numeric libraries. The discussion uses symbolic computation, visualization, and numerical computations to solve the problem while exercising the NumPy, SymPy, Matplotlib, IPython and SciPy packages.

I hope that this discussion will be accessible to people with a minimal background in programming and a high-school level background in algebra and analytic geometry. There is a brief mention of complex numbers, but the use of complex numbers is not important here except as “values to be ignored”. I also hope that this discussion illustrates how to combine different mathematically oriented Python libraries and explains how to smooth out some of the rough edges between the library interfaces.

http://nbviewer.ipython.org/urls/raw.github.com/awatters/CanopyDemoArchive/master/misc/house_locations.ipynb

Advanced Cython Recorded Webinar: Typed Memoryviews

Author: Kurt SmithWebinar_screenshot

Typed memoryviews are a new Cython feature for accessing memory buffers, such as NumPy arrays, without any Python overhead. This makes them very useful for manipulating blocks of memory in Cython directly without calling into the Python-C API.  Typed memoryviews have a clean declaration syntax and have a NumPy-like look and feel, supporting slicing, striding and indexing.

I go into more detail and provide some specific examples on how to use typed memoryviews in this webinar: “Advanced Cython: Using the new Typed Memoryviews”.

If you would like to watch the recorded webinar, you can find a link below (the different formats will play directly in different browsers so check to see which one works for you, and you won’t have to download the whole recording ahead of time):