A Python user is starting a project and thinks to themselves, “Yay, new code! I can use Python 3 for this!”. They install the latest Anaconda for Py3 and get to work. A few days and hundreds of lines of code later they find out that a particular library they need (maybe imposm.parser) only supports Python 2. Our well intentioned user sighs, re-installs Anaconda for Py2, and carries on. Maybe next time (or maybe not). (This is a semi-autobiographical story.)
Elsewhere, a Python library maintainer is excited about Py3’s new asyncio module and could put it to immediate use but doesn’t want to alienate users who are stuck on Py2.
There are many valid reasons to be using Py2 today: a dated dependency, the inertia of existing code, not wanting to break a working setup, not knowing how/why to switch, and lack of time.
There are also many valid reasons for wanting to develop exclusively for Py3: access to new features, reduced support burden, simplified maintenance, wanting to get ahead of the 2020 end-of-support for Py2, and lack of time. These tensions have the potential to create much frustration in the Python community, but I think with some intentional effort on the part of Python developers and leaders it will all be fine. Read More »
One of my upcoming tasks at work is converting Pandana to support both Python 2 and 3. The tricky bit is that Pandana has a C extension written in plain C using the Python 2 C-API, which is not compatible with Python 3.
It seems like the best way to have a C extension that supports both Python 2 and 3 is to not write the extension in C. These days there are a number of alternatives that allow you to write interfaces in Python or something like Python (Cython). I decided to make a sample project with some C functions to wrap so that I could try out CFFI, Cython, and the standard library ctypes module.
You can find the project with examples of all three and a longer writeup at https://github.com/jiffyclub/cext23. Pull requests are welcome on the repo with further examples!
Note: I wrote this to describe agile/scrum development to my colleagues because on our team I have the most experience with it, but I’m not really expert on agile or scrum.
Developing a product involves a number of different roles that all need to coordinate with each other. There are designers, sales people, engineers, writers, managers, and much more. All those people need a way to track what they need to do and what other people are doing. Agile work-flow is a communications system that helps teams broadcast what needs doing and what is being done. Read More »
I work at Autodesk with a team that includes urban planners, architects, and software engineers. Our goal is to make tools for people in regional and urban planning. The tools include a desktop geographic data viewer, statistical modeling of real estate markets, data pipelines, and much more.
The users and collaborators on our data projects are mostly scientists, which sets a high bar for library usability, documentation, and technical communication. With only me doing the bulk of coding and operations things can often take time, but my colleagues are committed to having well tested, well documented code that will work for a long time. (And I wouldn’t have it any other way.)
Day-to-day my brain power goes to things like:
- Asking my colleagues questions about their needs and how they do things
- Thinking about how to make a sensible API or UI
- Thinking about how to actually solve a problem
- Writing tests
- Writing documentation
- Writing code
- Figuring out how to work with a given data source (researching libraries and learning the data format)
- Reviewing code and projects
- Training colleagues in Python and software engineering practices
- Learning new stuff to apply to a task
We’ve got a lot of interesting work coming up, including building several automated data processing pipelines and online services. As always we’ll be working together as a team of diverse expertise to create usable, useful software that has real-world applications and meaningful impact on the citizens of cities around the world.
Edit: 2015/7/1: Sessions will now be held each day of the conference during the afternoon coffee breaks from 3 – 3:30 PM.
Edit: 2015/7/2: Sessions will be held in room 210 on the main level of the conference center.
This year at SciPy 2015 I’d like to run some informal “office hours” help sessions to help people with any questions they might have. I can imagine questions about:
- scientific Python libraries (NumPy, SciPy, Pandas, matplotlib…)
- software installation (Anaconda, conda, pip…)
- software packaging
- Git & GitHub
- the command line (shell)
- web applications
- much more!
The sessions will be during the afternoon coffee breaks 3 – 3:30 PM each day of the conference (Wednesday – Friday). The SciPy organizers have very kindly reserved room 210 for the sessions. Follow me on Twitter for any last minute updates.
If there seems to be significant interest I’ll try to find times for some additional sessions, but that might be hard to do.
Whether you’ve got questions or answers, I hope you’ll join!
In the interest of helping to improve the diversity and beginner friendliness of the SciPy conference, I’m offering to help first-time speakers from underrepresented groups with their talk proposals and potential talk preparations for SciPy 2015. If that sounds like you and you’d like my help editing a proposal and/or preparing a talk, send me an email.
- The deadline for proposals is April 1
- The conference is July 8-10 in Austin, Texas
- SciPy has a Code of Conduct
- SciPy is committed to diversity
- SciPy has some financial aid
- I will be at the conference
- I’m not a conference organizer, but I have in the past helped with talk selection (and may again this year)
- I have never given a talk at SciPy (except lightning talks)
P.S. If you’re looking for some talk ideas, try this post.
The SciPy 2015 call for proposals is open until April 1. In case anyone wants to give a talk but doesn’t have an idea I came up with a few:
- introduction to testing with a focus on numerics
- guide to profiling
- introduction to packaging and distribution
- which tool to use for which job (cover core packages)
- data visualization options
- write a numpy ufunc in Python, Cython, and C
- roundup of high-performance options (C, Cython, Numba, Parakeet, etc.)
Thanks to Rob Story for some suggestions. If you’ve got ideas for talks you’d like to see, leave a comment!
(I will be at SciPy 2015, but I’m organizing a Software Carpentry tutorial so I probably won’t be submitting a talk proposal.)
P.S. If you’re a first-time speaker from an underrepresented group thinking about giving a talk at SciPy 2015, I’m offering to help with proposal editing and talk prep.