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A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair)

Regress to Impress

Why Even Try, Man?

I recently came upon Brian Granger and Jake VanderPlas’s Altair, a promising young visualization library. Altair seems well-suited to addressing Python’s ggplot envy, and its tie-in with JavaScript’s Vega-Lite grammar means that as the latter develops new functionality (e.g., tooltips and zooming), Altair benefits — seemingly for free!

Indeed, I was so impressed by Altair that the original thesis of my post was going to be: “Yo, use Altair.”

But then I began ruminating on my own Pythonic visualization habits, and — in a painful moment of self-reflection — realized I’m all over the place: I use a hodgepodge of tools and disjointed techniques depending on the task at hand (usually whichever library I first used to accomplish that task1).

This is no good. As the old saying goes: “The unexamined plot is not worth exporting to aPNG.” 

Thus, I’m using my discovery…

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Video Conference Part 1: These Things Suck

Ben Garney

What I cannot create, I do not understand. – Richard Feynman

I do a lot of video chat for work. If it’s not a one on one, it’s pair programming. If it’s not pair programming, it’s a client meeting. I use a lot of Skype and Hangouts.

Sometimes they don’t work for unclear reasons. Sometimes file transfers fail. Sometimes screenshare breaks, or when it’s active you don’t get webcam, too. Or the connection lags or drops even though everything is running fast.

Every time I experience such a failure, I get really angry and think, “I could do this better!” But I never quite got angry enough… until now. I guess the weight of years of frustration finally got to me.

I wrote my own (prototype) video conferencing app. It turned out pretty well. And that’s what these posts are about.

Conventions & Caveats

We will be referencing a 640×480…

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The 100:10:1 method – the heart of my game design process

Nick Bentley Games


This is the second post of a series on practical game-design techniques. Here’s the first

In my years designing games, my methods have evolved from Games-Randomly-Emerging-from-the-Inchoate-Chaos-of-my-Brain-Area to something resembling an honest-to-goodness, write-downable process. I’ve decided to share this process here, for four reasons:

1. I’ve used it to create 3 of my 4 favorites among my own designs (Catchup, Stinker, and Cat Herders – Odd is the exception), which suggests it might have value.

2. I haven’t seen anything exactly like it.

3. Writing about it will give me ideas for improving it.

4. Pondering game design is one of the two great pleasures of my life (the other is spending time with my ladylove, who’s just sort of discombobulatingly great to be around)

Recombobulation Area …so thank heaven for this

I call it the 100:10:1 method. I’ll start by describing it, then discuss why it helps me.

The 100:10:1…

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C++ in Competitive Programming: I/O

Growing up

Welcome back to my series about Competitive Programming. Here is the introduction in case you missed it.

In this post I’ll explain some common idioms to deal with input and output.

In C++ a simple task like reading an integer from the standard input can be done in different ways: using streams, C functions or OS-dependant calls. The streams model offers a pretty high level interface, but it is generally slower than using native operating system calls. However, in different cases, it is acceptable.

I have solved a lot of challenges and very rarely I had to switch to C functions (e.g. scanf) or turn off the synchronization between C++ streams and standard C streams after each input/output operation (by using std::ios_base::sync_with_stdio). Most of the time the I/O is not the point of the exercise then we can use the convenient streams model. This point seems irrelevant but it brings about simplification, enabling us to write not only simpler but also safer idioms. We’ll…

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The 50 Startups That Launched At Y Combinator Summer 2015 Demo Day 1


Hardware took the spotlight at today’s Y Combinator Demo Day, reflecting a major shift of the accelerator beyond the cliche mobile app startup. Out of the 50 companies from the Summer 2015 batch that demoed on the record today, 20 featured hardware. What was formally the Demo Day lunchroom has become an expo hall for all manners of robots and gadgets.

Tomorrow, another 50 or so startups will present. Soon, we’ll have our selections made for our favorites. But for now, here’s a look at all 50 that strutted the stage today:

TeaBOT — An automated beverage vendor

TeaBOT is a robot that makes grab’n’go tea. You enter up to three types of its dozen teas, pay via iPad or credit card, and the bot automatically mixes you a hot cup of tea. The company says the bots can earn $100,000 a year. The startup licenses the TeaBOTs to retailers and colleges, and…

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The Mystery Money Creating The Unicorn Herd


[tc_contributor_byline slug=”jeff-grabow”]

Varied explanations for the growing herd of unicorns — and predictions of their imminent demise — abound. But all can agree: Those who invest in unicorns are chasing returns they can’t get elsewhere. And while venture capital is widely perceived to be fueling the unicorns’ growth, a closer look reveals that not all venture capital is really venture capital.

Data on VC fundraising shows a long-term decline in capital to VC funds, a trend I covered here. If less money goes in, less is available to be deployed.

image009 Declining fundraising in the U.S. since 2007. 2009 marks the fewest funds raised in 16 years. 2009 was the low point in terms of dollars raised since 2003.

In 2014, Dow Jones reported that the amount of venture capital invested in the United States soared to $52 billion, from $35 billion in 2013, an increase of almost 50 percent…

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How Amazon Could Drive Blended Reality Into The Living Room


[tc_dropcap]A[/tc_dropcap]mazon has been amassing computer vision expertise for a long time. And continues to do so. A LinkedIn search for computer vision jobs at the company currently returns more than 50 posts — mostly split across research and software engineering roles. But is there more than meets the eye to the ecommerce giant’s interest in technologies that can sense the world around them?

Amazon’s nascent drone delivery program, Prime Air, is one clear driver to hire scientists with a grounding in machine learning and computer vision, since drones need to navigate in real world environments. And if you’re chasing a dream of autonomous delivery drones, as Amazon is, then accurate object detection and dynamic collision avoidance are essential.

Likewise there’s Amazon’s thus far ill-fated foray into smartphones, with last year’s debut Fire Phone. The handset’s flagship feature was a 3D interface that uses data from four front-facing cameras to generate three dimensional effects on screen…

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