The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

March 04, 2017The Update Framework: Securing Your Software Updates with Justin Cappos
Summary
If you write software then there’s a good probability that you have had to deal with installing dependencies, but did you stop to ask whether you’re installing what you think you are? My guest this week is Professor Justin Cappos from the Secure Systems Lab at New York University and he joined me to discuss his work on The Update Framework which was built to guarantee that you never install a compromised package in your systems.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
Your host as usual is Tobias Macey and today I’m interviewing Justin Cappos about The Update Framework, an open spec and reference implementation for mitigating attacks on software update systems.
Interview
Introduction
How did you first get introduced to Python?
Please start by explaining what The Update Framework (TUF) is and the problem that you were trying to solve when you created it.
How is TUF architected and what led you to choose Python for the reference implementation?
TUF addresses the problem of ensuring that the packages that get installed are created by the right developers, but how do you properly establish trust in the first place?
Why are consistent and auditable dependencies important for the security of a system and how does TUF help with that goal?
What are some of the known attack vectors for a software update system and how do Python and other systems attempt to mitigate these vulnerabilities?
One of the perennial problems with any dependency management system is that of transitive dependencies. How does TUF handle this extra complexity of ensuring that all of the secondary, tertiary, etc. dependencies are also properly pinned and trusted?
For someone who wants to start using TUF what are the steps to get it set up with pip?
How would a project that wants to use TUF, do so?
Who is using TUF and when will it be used with PyPI?
Keep In Touch
https://ssl.engineering.nyu.edu/?utm_source=rss&utm_medium=rss
https://ssl.engineering.nyu.edu/personalpages/jcappos/?utm_source=rss&utm_medium=rss
Picks
Tobias
The Enchanted Forest Chronicles

Justin
Hand Pulled Noodles
Lam Zhou

Links
When the Going Gets Tough, Get TUF Going – PyCon 2016
RPM
Apt
Stork Package Manager
Yubikey
Distribution Packages Considered Insecure
Notary
Flynn
Uptane
in-toto
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
38 min
February 26, 2017 Pandas with Jeff Reback
Pandas is one of the most versatile and widely used tools for data manipulation and analysis in the Python ecosystem. This week Jeff Reback explains why that is, how you can use it to make your life easier, and what you can look forward to in the months to come.
50 min
February 26, 2017Pandas with Jeff Reback
Summary
Pandas is one of the most versatile and widely used tools for data manipulation and analysis in the Python ecosystem. This week Jeff Reback explains why that is, how you can use it to make your life easier, and what you can look forward to in the months to come.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
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Visit the site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
Your host as usual is Tobias Macey and today I’m interviewing Jeff Reback about Pandas, the swiss army knife of data analysis in Python.
Interview
Introductions
How did you get introduced to Python?
To start off, what is Pandas and what is its origin story?
How did you get involved in the project’s development?

For someone who is just getting started with Pandas what are the fundamental ideas and abstractions in the library that are necessary to understand how to use it for working with data?

Pandas has quite an extensive API and I noticed that the most recent release includes a nice cheat sheet. How do you balance the power and flexibility of such an expressive API with the usability issues that can be introduced by having so many options of how to manipulate the data?

There is a strong focus for use in science and data analytics, but there are a number of other areas where Pandas is useful as well. What are some of the most interesting or unexpected uses that you have seen or heard of?

What are some of the biggest challenges that you have encountered while working on Pandas?

Do you find the constraint of only supporting two dimensional arrays to be limiting, or has it proven to be beneficial for the success of pandas?

What’s coming for pandas? Pandas 2.0!

Keep In Touch
@jreback on Twitter
jreback on GitHub
Picks
Tobias
http://standards.mousepawgames.com/index.html?utm_source=rss&utm_medium=rss

Jeff
Travis CI
Appveyor
Circle CI

Links
Continuum Analytics
Myths Programmers Believe About Time
Jupyter Notebook
XArray
Dask
Website
Interview

NumFocus

PyLint Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
February 18, 2017 PyTables with Francesc Alted
HDF5 is a file format that supports fast and space efficient analysis of large datasets. PyTables is a project that wraps and expands on the capabilities of HDF5 to make it easy to integrate with the larger Python data ecosystem. Francesc Alted explains how the project got started, how it works, and how it can be used for creating sharable and archivable data sets.
50 min
February 18, 2017PyTables with Francesc Alted
Summary
HDF5 is a file format that supports fast and space efficient analysis of large datasets. PyTables is a project that wraps and expands on the capabilities of HDF5 to make it easy to integrate with the larger Python data ecosystem. Francesc Alted explains how the project got started, how it works, and how it can be used for creating sharable and archivable data sets.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. Linode will has announced new plans, including 1GB for $5 plan, high memory plans starting at 16GB for $60/mo and an upgrade in storage from 24GB to 30GB on our 2GB for $10 plan.
Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
Your host as usual is Tobias Macey and today I’m interviewing Francesc Alted about PyTables
Interview
Introductions
How did you get introduced to Python?
To start with, what is HDF5 and what was the problem that motivated you to wrap Python around it to create PyTables?
Which are the most relevant contributors for PyTables? How you interacted?
How is the project architected and what are some of the design decisions that you are most proud of?
What are some of the typical use cases for PyTables and how does it tie into the broader Python data ecosystem?
How common is it to use an HDF5 file as a data interchange format to be shared between researchers or between languages?
Given the ability to create custom node types, does that inhibit the ability to interact with the stored data using other libraries?
What are some of the capabilities of HDF5 and PyTables that can’t be reasonably replicated in other data storage systems?
One of the more intriguing capabilities that I noticed while reading the documentation is the ability to perform undo and redo operations on the data. How might that be leveraged in a real-world use case?
What are some of the most interesting or unexpected uses of PyTables that you are aware of?
Keep In Touch
@FrancescAlted on Twitter
FrancescAlted on GitHub
Picks
Tobias
The Accountant

Francesc
Blosc a high speed compressor, specially meant for binary data
The Lego Batman Movie

Links
PyTables
PyTables – Optimization
Presentations and Videos about PyTables
Part of the story behind PyTables
HDF5
Pandas
SIMD
NumFOCUS
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
February 11, 2017 SKIDL with Dave Vandenbout
As circuits and electronic components become more complex, visual circuit building tools are more difficult to use effectively. If you wish that you could just write your circuits in Python then you're in luck! Dave Vandenbout created a library called SKIDL that brings the power and flexibility of Python to the realm of Electrical Engineering and he tells us all about it in this weeks show.
41 min
February 11, 2017SKIDL with Dave Vandenbout
Summary
As circuits and electronic components become more complex, visual circuit building tools are more difficult to use effectively. If you wish that you could just write your circuits in Python then you’re in luck! Dave Vandenbout created a library called SKIDL that brings the power and flexibility of Python to the realm of Electrical Engineering and he tells us all about it in this weeks show.
Preamble
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
Your host as usual is Tobias Macey and today I’m interviewing Dave Vandenbout about SKIDL, a library for designing and validating circuit layouts.
Interview
Introductions
How did you get introduced to Python?
Can you describe what SKIDL is and the problem that you were trying to solve when you first started it?
Most of my experience designing circuits has been done using a graphical tool. If you are using Python for the entire layout does it become difficult to understand the overall circuit without the visual representation?
Is there a way to generate a circuit diagram from the SKIDL code for a visual reference?

It seems that there is a substantial amount of electrical knowledge required to be able to design and build schematics in code. For someone who is more of a hobbyist or is just starting to work with circuit design are there any facilities of SKIDL to assist with that understanding?

What does the testing and validation process of a generated circuit look like?

What does the internal architecture of SKIDL look like and what are some of the biggest challenges that you have faced while building it?

For the generated netlist does SKIDL take into account voltage losses due to the lengths of the traces in the final PCB and does it have any facilities to optimize the overall layout for space and efficiency?

Sometimes a circuit board is meant to be accessible for maintenance or even display purposes. Is it possible to specify the arrangement of components to make them more aesthetically pleasing or to space them so that they are easier to access physical interface ports (e.g. GPIO pins or I2C buses)?

What are some of the most interesting or surprising uses of SKIDL that you have seen?

Keep In Touch
Website
Documentation
Picks
Tobias
Samsonite Tectonic Backpack

Dave
Ball 4 by Jim Bouton

Links
KiCad
Gerber Files
ASIC
FPGA
PHDL
MyHDL
VHDL
SPICE Simulator
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
41 min
February 04, 2017 Parsing and Parsers with Dave Beazley and Erik Rose
If you have ever found yourself frustrated by a complicated regular expression or wondered how you can build your own dialect of Python then you need a parser. Dave Beazley and Erik Rose talk about what parsers are, how some of them work, and what you can do with them in this episode.
50 min
February 04, 2017Parsing and Parsers with Dave Beazley and Erik Rose
Summary
If you have ever found yourself frustrated by a complicated regular expression or wondered how you can build your own dialect of Python then you need a parser. Dave Beazley and Erik Rose talk about what parsers are, how some of them work, and what you can do with them in this episode.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
Your host as usual is Tobias Macey and today I’m interviewing Erik Rose and Dave Beazley about what parsing is, why you might want to use it, and how their respective libraries Parsimonious and PLY make it easy.
Interview
Introductions
How did you get introduced to Python?
Can you each start by talking a bit about your respective libraries and what problem you were trying to solve when they were first created?
In what ways does a full-fledged parser differ from what a regular expression engine is capable of?
What are some of the different high-level approaches to building a parser and when might you want to choose one over the others?
I’m sure that when most people hear the term parsing they associate it with reading in a data interchange format such as JSON or CSV. What are some of the more interesting or broadly applicable uses of parsing that might not be as obvious?
One term that kept coming up while I was doing research for this interview was “Grammars”. How would you explain that concept for someone who is unfamiliar with it?
Once an input has been parsed, what does the resulting data look like and how would a developer interact with it to do something useful?
For someone who wants to build their own domain specific language (DSL) what are some of the considerations that they should be aware of to create the grammar?
What are some of the most interesting or innovative uses of parsers that you have seen?
Keep In Touch
Dave Beazley
@dabeaz on Twitter
Website

Erik Rose
@ErikRose on Twitter
Website

Picks
Tobias
Terminix

Erik
Riven
ScummVM

Dave
iTerm2
Kerbal Space Program

Links
Python Cookbook
Python Essential Reference
Fathom
SWIG
Windows Scripting Host
PEG (Brian Foord)
Parsing Techniques by Grune and Jacobs
The Dragon Book
Stack Overflow HTML regex parsing
Earley parsing
SPARK
Hy-lang
Docs
Interview

Trampolining

Lisp

NLTK

SLY

DXR

LLVM

Numba

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
51 min
January 28, 2017 Home Assistant with Paulus Schoutsen
Don't you wish you could make all of your devices talk to each other? Check out Home Assistant, the Python 3 platform for unified automation. Paulus Schoutsen shares the story of how the project got started, what makes it tick, and how you can use it today!
42 min