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Programming Throwdown

139: Scientific Python with Guido Imperiale

Programming Throwdown

Patrick Wheeler and Jason Gauci

Objective C, Java, Programming Throwdown, Education, News, Programming Languages, How To, Tech News, C, Python

4.6604 Ratings

🗓️ 25 July 2022

⏱️ 84 minutes

🧾️ Download transcript

Summary

Coming off the heels of the scientific python conference, we are delighted to have Guido Imperiale, OSS Engineer at Coiled, to talk with us about this exciting space, including his own work on Dask, remote work benefits, and how distributed computing with the latest Python tools has allowed him to manage large data science projects with ease.

Transcript

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0:00.0

Programming Throwdown Episode 139, Scientific Python with Guido Imperial. Take it away, Jason.

0:24.2

Hey, everybody. So if you're following, you know, hacker news or you're following these

0:29.2

various sources, you've probably heard about scientific Python. It's becoming a really important

0:34.4

way for people to try and do numerical computation and solve really difficult

0:39.2

problems. There's a bunch of really awesome libraries for that, and we'll definitely cover that in

0:45.0

detail. And we have Guido Imperale here from Coiled to tell us kind of more about it. So Guido's

0:51.8

an open source software engineer for Coiled, and, you know,

0:55.4

I think we're all really grateful for having Guido on the show to lend us his, you know,

0:59.9

experience and expertise. So thanks for coming on the show, Guido. Hi, everyone. Cool. So,

1:05.1

you know, before we dive into what is Scientific Python, why don't you give us some background about,

1:11.3

you know,

1:16.7

what have you been up to? What kind of led you to Coiled? And how did you get involved in,

1:25.4

you know, in Dask and in the overall SciPi community? Well, I have 10 something years in risk management, in banking and insurances in Italy and UK.

1:30.7

And what risk management is about is simulate all financial instruments or the whole

1:38.4

balance sheet many, many times. And a lot of financial institutions use obscure close source software which tends to be very,

1:48.5

very slow and inefficient and really unwieldy. So in one of my previous jobs, I was faced

1:58.1

with this software and I decided, nope, I'm going to rewrite it from zero.

2:03.1

And I needed a really, really big hammer to break down the problem.

2:10.5

And I realized that Dask was just right.

2:14.8

Cool.

2:15.3

So why don't you dive into a little bit? So I've heard stories of banks

2:20.0

running their own version of Python. I think Patrick, you mentioned this in an earlier episode.

...

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