"Python" entries

A software engineer’s role traversal

Software engineer and author Jason Myers on changing roles in a changing market.

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We often hear about how the tech job market is booming and has space for newcomers, but what does that mean for the developers already in the market? In December of 2014, Fortune.com predicted that 2015 would be an excellent year for developers to change jobs. Citing Dice.com, they note that jobs are popping up all over the country. In fact, Dice’s survey also reports 40% of hiring managers seeing voluntary departures, a higher number than was seen just six months earlier.

These are all large, general numbers. What does a job change, and the changing market, look like for individual developers? To get a better sense, I spoke with Jason Myers, who is working on our upcoming Essential SQLAlchemy, 2e title. Jason recently went from working for the email marketing service Emma, Inc., to working for networking giant Cisco. Here, he talks about how a change like that feels, and how the market looks to him.

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Four short links: 23 January 2015

Four short links: 23 January 2015

Investment Themes, Python Web Mining, Code Review, and Sexist Brilliance

  1. 16 Andreessen-Horowitz Investment Areas — I’m struck by how they’re connected: there’s a cluster around cloud development, there are two maybe three on sensors …
  2. Patterna web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization.
  3. Code Review — FogCreek’s code review checklist.
  4. Expectations of Brilliance Underlie Gender Distributions Across Academic Disciplines (Science) — Surveys revealed that some fields are believed to require attributes such as brilliance and genius, whereas other fields are believed to require more empathy or hard work. In fields where people thought that raw talent was required, academic departments had lower percentages of women. (via WaPo)
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Four short links: 26 November 2014

Four short links: 26 November 2014

Metastable Failures, Static Python Analysis, Material Desktop, and AWS Scale Numbers

  1. Metastable Failure State (Facebook) — very nice story about working together to discover the cause of one of those persistently weird problems.
  2. Bandit — static security analysis of Python code.
  3. Quantum OS — Linux desktop based on Google’s Material Design. UI guidelines fascinate me: users love consistency, designers and brands hate that everything works the same.
  4. Inside AWSEvery day, AWS installs enough server infrastructure to host the entire Amazon e-tailing business from back in 2004, when Amazon the retailer was one-tenth its current size at $7 billion in annual revenue. “What has changed in the last year,” Hamilton asked rhetorically, and then quipped: “We have done it 365 more times.” That is another way of saying that in the past year AWS has added enough capacity to support a $2.55 trillion online retailing operation, should one ever be allowed to exist.
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Python tuples: immutable but potentially changing

Think labels, not boxes

Python tuples have a surprising trait: they are immutable, but their values may change. This may happen when a tuple holds a reference to any mutable object, such as a list. If you need to explain this to a colleague who is new to Python, a good first step is to debunk the common notion that variables are like boxes where you store data.

In 1997 I took a summer course about Java at MIT. The professor, Lynn Andrea Stein — an award-winning computer science educator — made the point that the usual “variables as boxes” metaphor actually hinders the understanding of reference variables in OO languages. Python variables are like reference variables in Java, so it’s better to think of them as labels attached to objects.

Here is an example inspired by Lewis Carroll’s Through the Looking-Glass, and What Alice Found There.

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Tweedledum and Tweedledee are twins. From the book: “Alice knew which was which in a moment, because one of them had ‘DUM’ embroidered on his collar, and the other ‘DEE’.”

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Python 3: threat or menace?

Prepare for the future of computing with Python.

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I wish I still had my copy of this: a Harvard Lampoon parody of Life magazine from the ’60s, displaying a picture of a flying saucer and the ominous headline: “Flying Saucers: Threat or Menace?”.

I was reminded of this when reading some recent articles worrying about the slow transition from Python 2 to Python 3, such as Python 3 is Killing Python. The authors of such articles, and Python developers in general, really like Python, and for the most part like Python 3. Their main concern is that the protracted 2-3 straddle will hurt Python’s popularity.

About five years ago, I started writing an introductory Python book for O’Reilly. It featured Python 2, which was dominant then. Unfortunately, the tides of business went out and took the book with them. Two years ago, the tides returned and the book was revived. Introducing Python: Modern Computing in Simple Packages is finally in production and early release.

When we rebooted the book, there was now a serious question of whether to feature Python 2 or 3. The other version might merit some sidebars or an appendix, but we really needed to pick just a single base for the code examples. And by now it seemed that Python 3 had become the right choice. If you’re wondering why the editors and I thought Python 3 was best for this book, let me give some of the reasons, more or less in order of importance.

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Four short links: 1 September 2014

Four short links: 1 September 2014

Sibyl, Bitrot, Estimation, and ssh

  1. Sibyl: Google’s System for Large Scale Machine Learning (YouTube) — keynote at DSN2014 acting as an intro to Sibyl. (via KD Nuggets)
  2. Bitrot from 1997That’s 205 failures, an actual link rot figure of 91%, not 57%. That leaves only 21 URLs as 200 OK and containing effectively the same content.
  3. What We Do And Don’t Know About Software Effort Estimation — nice rundown of research in the field.
  4. fabric — simple yet powerful ssh library for Python.
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Four short links: 28 August 2014

Four short links: 28 August 2014

Visual Python, Scraping and Screenshotting, Un-free Speech, IP Law Textbook

  1. PlotDeviceA Python-based graphics language for designers, developers, and tinkerers. More in the easy-to-get-started + visual realm, like Processing. (via Andy Baio)
  2. Scumblr and Sketchy Search — Netflix open sourcing some scraping, screenshot, and workflow tools their security team uses to monitor discussion of themselves.
  3. Should Twitter, Facebook and Google Executives be the Arbiters of What We See and Read? (Glenn Greenwald) — In the digital age, we are nearing the point where an idea banished by Twitter, Facebook and Google all but vanishes from public discourse entirely, and that is only going to become more true as those companies grow even further. Whatever else is true, the implications of having those companies make lists of permitted and prohibited ideas are far more significant than when ordinary private companies do the same thing.
  4. Intellectual Property: Law and the Information Society; Cases and Materials (PDF) — James Boyle and Jennifer Jenkins’ open law textbook on IP (which even explores the question of whether that’s a valid and meaningful term). (via James Boyle)
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Four short links: 10 July 2014

Four short links: 10 July 2014

Journalism Security, Inclusive Technology, Network Magic, and Python Anti-Patterns

  1. Ex-Google Hacker Taking On The World’s Spy Agencies (Wired) — profile of the security expert working on protecting reporters.
  2. Meet Google’s Security Princess (Elle) — would have preferred to see her story in Wired. Much is good here, but this is pithy and strong: “If you have ambitions to create technology for the whole world, you need to represent the whole world, and the whole world is not just white men.”
  3. snabb switch — open source Linux userspace executable for making network appliances. Processes millions of ethernet packets per second per core. Suitable for ISPs. Speaks natively to Ethernet hardware, Hypervisors, and the Linux kernel. You can program it with LuaJIT extensions to do anything you want.
  4. Anti-Patterns in Python Programming — gold.
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Open teaching stacks help us teach at scale

Use teaching stacks to drive growth.

Elliott Hauser is CEO of Trinket, a startup focused on creating open sourced teaching materials. He is also a Python instructor at UNC Chapel Hill.

Well-developed tools for teaching are crucial to the spread of open source software and programming languages. Stacks like those used by the Young Coders Tutorial and Mozilla Software Carpentry are having national and international impact by enabling more people to teach more often.

The spread of tech depends on teaching

Software won’t replace teachers. But teachers need great software for teaching. The success and growth of technical communities are largely dependent on the availability of teaching stacks appropriate to teaching their technologies. Resources like try git or interactivepython.org not only help students on their own but also equip instructors to teach these topics without also having to discover the best tools for doing so. In that way, they play the same function as open source Web stacks: getting us up and running quickly with time-tested and community-backed tools. Thank goodness I don’t need to write a database just to write a website; I can use open source software instead. As an instructor teaching others to code websites, what’s the equivalent tool set? That’s what I mean by Teaching Stack: a collection of open tools that help individual instructors teach technology at scale.

Elements of a great teaching stack

Here are some of the major components of a teaching stack for a hands-on technology course:

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Theming in Kivy

Adding consistency to Kivy's Python UI tools

Kivy has a wonderful set of built-in widgets that can be extended in numerous ways. They have very useful behaviors, but their look and feel may not integrate well with your App or the platforms you are targeting. Kivy doesn’t support theming out of the box right now, but if you poke around enough, there are a range of options you can use to customize the default look of widgets without having to define your own inherited versions of them.

I’ll first introduce you to Kivy’s image atlases, which are less mysterious than they sound, and are important groundwork for understanding theming in Kivy. Then you’ll learn two different ways to do manual theming in Kivy, with an eye to future automation.

Introducing Atlases

To understand theming, you must first understand atlases. An atlas is essentially a collection of distinct images combined into a single image file for loading efficiency. A JSON file describes the location of the separate images inside that master image file so that Kivy can access them directly. If you’ve ever worked with CSS sprites, you know exactly what I’m talking about. If you haven’t, the following example should explain everything.

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