The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

January 14, 2019Teaching Digital Archaeology With Jupyter Notebooks
Summary
Computers have found their way into virtually every area of human endeavor, and archaeology is no exception. To aid his students in their exploration of digital archaeology Shawn Graham helped to create an online, digital textbook with accompanying interactive notebooks. In this episode he explains how computational practices are being applied to archaeological research, how the Online Digital Archaeology Textbook was created, and how you can use it to get involved in this fascinating area of research.
Introduction
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Shawn Graham about his work on the Online Digital Archaeology Textbook
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what digital archaeology is?
To facilitate your teaching you have collaborated on the O-DATE textbook and associated Jupyter notebooks. Can you describe what that resource covers and how the project got started?
What have you found to be the most critical lessons for your students to help them be effective archaeologists?
What are the most useful aspects of leveraging computational techniques in an archaeological context?

Can you describe some of the sources and formats of data that would commonly be encountered by digital archaeologists?

The notebooks that accompany the text have a mixture of R and Python code. What are your personal guidelines for when to use each language?

How have the skills and tools of software engineering influenced your views and approach to research and education in the realm of archaeology?

What are some of the most novel or engaging ways that you have seen computers applied to the field of archaeology?

What are your goals and aspirations for the O-DATE project?

Keep In Touch
Blog
@electricarchaeo on Twitter
Picks
Tobias
TaoTronics Noise Cancelling Earbuds

Shawn
Ian Rankin
In A House Of Lies

Links
O-DATE Textbook
Carleton University
Ottawa Canada
Simulation Modeling
Agent Based Modeling
NetLogo
Complexity Theory
Archaeology
Digital Archaeology
The Programming Historian
University of Western Ontario
Historical GIS
ArcGIS
QGIS
Digital Humanities
Project Jupyter
Podcast Episode

Binder – Service for hosting Jupyter notebooks

E-Campus Ontario

Graph Databases

SparQL

OpenContext.org

TDAR (The Digital Archaeology Record)

R Language

R OpenSci

Arrow

Pandas
Podcast Episode

Neural Networks

Generative Adversarial Networks

Computer Vision

Archaeogaming

Alamagordo Atari Excavation

Leiden University

Interactive Pasts Conference

Photogrammetry

LIDAR

Palmyran Arch

Ben Marwick

Matt Harris

Jolene Smith

Sara Perry

Rachel Opitz

Colleen Morgan

Patrick Burns

Ethan Watrall

Andrew Reinhard

Neha Gupta

Katherine Cook

Value Foundation

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
January 07, 2019 Analyzing Satellite Image Data Using PyTroll
Every day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meterological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing the data gathered by satellites. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.
44 min
January 07, 2019Analyzing Satellite Image Data Using PyTroll
Summary
Every day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meteorological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing satellite image data. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Martin Raspaud about PyTroll, a suite of projects for processing earth observing satellite data
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what PyTroll is and how the overall project got started?
What is the story behind the name?
What are the main use cases for PyTroll? (e.g. types of analysis, research domains, etc.)
What are the primary types of data that would be processed and analayzed with PyTroll? (e.g. images, sensor readings, etc.)
When retrieving the data, are you communicating directly with the satellites, or are there facilities that fetch the information periodically which you can then interface with?
How do you locate and select which satellites you wish to retrieve data from?
What are the main components of PyTroll and how do they fit together?
For someone processing satellite data with PyTroll, can you describe the workflow?
What are some of the main data formats that are used by satellites?
What tradeoffs are made between data density/expressiveness and bandwidth optimization?
What are some of the common issues with data cleanliness or data integration challenges?
Once the data has been retrieved, what are some of the types of analysis that would be performed with PyTroll?
Are there other tools that would commonly be used in conjunction with PyTroll?
What are some of the unique challenges posed by working with satellite observation data?
How has the design and capability of the various PyTroll packages evolved since you first began working on it?
What are some of the most interesting or unusual ways that you have seen PyTroll used?
What are some of the lessons that you have learned while building PyTroll that you have found to be most useful or unexpected?
What do you have planned for the future of PyTroll?
Keep In Touch
Martin
mraspaud on GitHub
@MartinRaspaud on Twitter

Pytroll
Website
Slack
Mailing List
@PyTroll on Twitter

Picks
Tobias
Tool
A Perfect Circle
Martin
Vulfpeck
Links
PyTroll
Swedish Meteorological and Hydrological Institute
Common Lisp
Danish Meteorological Institute
Trolls in Scandinavian Lore
NumPy
KISS (Keep It Simple Stupid)
Spectroscopy
Radiance
Polar Orbiting Satellite
Geostationary Satellite
EUMETSAT
SatPy
PyResample
Cartographic Projection
Proj4
GOES16
[GOES17](https://en.wikipedia.org/wiki/GOES-17?utm_source=rss&utm_medium=rss
Dask
Data Engineering Podcast Episode
NetCDF
HDF5
PySpectral
PyCoast
SupervisorD
TrollCast
European Space Agency
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
December 31, 2018 Building GraphQL APIs in Python Using Graphene with Syrus Akbary - Episode 192
The web has spawned numerous methods for communicating between applications, including protocols such as SOAP, XML-RPC, and REST. One of the newest entrants is GraphQL which promises a simplified approach to client development and reduced network requests. To make implementing these APIs in Python easier, Syrus Akbary created the Graphene project. In this episode he explains the origin story of Graphene, how GraphQL compares to REST, how you can start using it in your applications, and how he is working to make his efforts sustainable.
53 min
December 31, 2018Building GraphQL APIs in Python Using Graphene with Syrus Akbary
Summary
The web has spawned numerous methods for communicating between applications, including protocols such as SOAP, XML-RPC, and REST. One of the newest entrants is GraphQL which promises a simplified approach to client development and reduced network requests. To make implementing these APIs in Python easier, Syrus Akbary created the Graphene project. In this episode he explains the origin story of Graphene, how GraphQL compares to REST, how you can start using it in your applications, and how he is working to make his efforts sustainable.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Syrus Akbary about Graphene, a python library for building your APIs with GraphQL
Interview
Introductions
How did you get introduced to Python?
What is GraphQL and what is the benefit vs a REST-based API?
How does it compare to specifications such as OpenAPI (formerly Swagger) or RAML?

Can you explain what Graphene is and your motivation for building it?
In addition to the Python implementation there is also a JavaScript library. Is that primarily for use as a client or can it also be used in Node for serving APIs?

What is involved in building a GraphQL API?
What does Graphene do to simplify this process?

How is Graphene implemented and how has that evolved since you first started working on it?
Is there a set of tests for verifying the compliance of Graphene or a specific API with the GraphQL specification?

What are some of the most complex or confusing aspects of building a GraphQL API?

What are some of the unique capabilities that are offered by building an application with GraphQL as the communication interface?

While reading through documentation in preparation for our conversation I noticed the Quiver project. Can you explain what that is and how it fits with the other Graphene projects?
What is it doing under the hood to optimize serving of the API?

For someone who is interested in adding a GraphQL interface to an existing application, what would be involved?

The documentation mentions creation of a schema, as well as defining queries. Is it possible for a client to craft queries that don’t match directly with those defined in the server layer?

What are some of the most interesting or surprising uses of Graphene and GraphQL that you have seeen?

What are some cases where it would be more practical to implement an API using REST instead of GraphQL?

What are some references that you would recommend for anyone who wants to learn more about GraphQL and its ecosystem?

What are your plans for the future of Graphene?

Keep In Touch
syrusakbary on GitHub
Website
@syrusakbary on Twitter
Picks
Tobias
Audible

Syrus
Web Assembly

Links
Graphene
GraphQL
REST (REpresentational State Transfer
OpenAPI
RAML
PHP
Facebook Engineering
Graphene-SQLAlchemy
Graphene-Django
GraphiQL
PyJade
Django Rest Framework
How To GraphQL
Python 3.7 Dataclasses
Graphene GitHub Issue

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
53 min
December 24, 2018 AIORTC: An Asynchronous WebRTC Framework with Jeremy Lainé - Episode 191
Real-time communication over the internet is an amazing feat of modern engineering. The protocol that powers a majority of video calling platforms is WebRTC. In this episode Jeremy Lainé explains why he wrote a Python implementation of this protocol in the form of AIORTC. He also discusses how it works, how you can use it in your own projects, and what he has planned for the future.
41 min
December 24, 2018AIORTC: An Asynchronous WebRTC Framework with Jeremy Lainé
Summary
Real-time communication over the internet is an amazing feat of modern engineering. The protocol that powers a majority of video calling platforms is WebRTC. In this episode Jeremy Lainé explains why he wrote a Python implementation of this protocol in the form of AIORTC. He also discusses how it works, how you can use it in your own projects, and what he has planned for the future.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Jeremy Lainé about AIORTC, an asynchronous implementation of the WebRTC and ObjectRTC protocols in Python
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what the WebRTC and ObjectRTC protocols are?
What are some of the main use cases for these protocols?

What is AIORTC and what was your motivation for creating it?
How does it compare to other implementations of the RTC protocols?
Why do you think there haven’t been any other Python implementations?

What are some of the benefits of having a Python implementation of the RTC protocol?

How is AIORTC implemented?
What have been some of the most difficult or challenging aspects of implementing a WebRTC compliant library?
What are some of the most interesting or useful lessons that you have learned in the process?

What is involved in building an application on top of AIORTC?
What would be required to integrate AIORTC into an existing application built with something such as Flask or Django?

What are some of the most interesting uses of AIORTC that you have seen?

What are some of the projects that you would like to build with AIORTC?

What are some cases where it would make more sense to use a different library or framework for your WebRTC projects?

What are your plans for the future of AIORTC?

Keep In Touch
jlaine on GitHub
Website
@JeremyLaine on Twitter
Picks
Tobias
Tengger Cavalry

Jeremy
PyAV
Mike Boers

Links
AIORTC
WebRTC
Electrical Engineering
[C](https://en.wikipedia.org/wiki/C_(programming_language)?utm_source=rss&utm_medium=rss
C++
PHP
Ruby
STUN (Session Traversal Utilities for NAT)
TURN (Traversal Using Relays around NAT)
ICE (Internet Connectivity Establishment)
TLS (Transport Layer Security)
RTP (Real-time Transport Protocol)
Zencastr
Jitsi
RawRTC
AsyncIO
AIOICE
Cryptography
Podcast.init Episode

OpenCV

PyAV

FFMPEG

Edge Detection

Asterisk

Raspberry Pi

Datagram Transport Security

Mozilla

Augmented Reality

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
41 min
December 17, 2018 Polyglot: Multi-Lingual Natural Language Processing with Rami Al-Rfou - Episode 190
Using computers to analyze text can produce useful and inspirational insights. However, when working with multiple languages the capabilities of existing models are severely limited. In order to help overcome this limitation Rami Al-Rfou built Polyglot. In this episode he explains his motivation for creating a natural language processing library with support for a vast array of languages, how it works, and how you can start using it for your own projects. He also discusses current research on multi-lingual text analytics, how he plans to improve Polyglot in the future, and how it fits in the Python ecosystem.
44 min
December 17, 2018Polyglot: Multi-Lingual Natural Language Processing with Rami Al-Rfou
Summary
Using computers to analyze text can produce useful and inspirational insights. However, when working with multiple languages the capabilities of existing models are severely limited. In order to help overcome this limitation Rami Al-Rfou built Polyglot. In this episode he explains his motivation for creating a natural language processing library with support for a vast array of languages, how it works, and how you can start using it for your own projects. He also discusses current research on multi-lingual text analytics, how he plans to improve Polyglot in the future, and how it fits in the Python ecosystem.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Your host as usual is Tobias Macey and today I’m interviewing Rami Al-Rfou about Polyglot, a natural language pipeline with support for an impressive amount of languages
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Polyglot is and your reasons for starting the project?
What are the types of use cases that Polyglot enables which would be impractical with something such as NLTK or SpaCy?
A majority of NLP libraries have a limited set of languages that they support. What is involved in adding support for a given language to a natural language tool?
What is involved in adding a new language to Polyglot?
Which families of languages are the most challenging to support?

What types of operations are supported and how consistently are they supported across languages?

How is Polyglot implemented?

Is there any capacity for integrating Polyglot with other tools such as SpaCy or Gensim?

How much domain knowledge is required to be able to effectively use Polyglot within an application?

What are some of the most interesting or unique uses of Polyglot that you have seen?

What have been some of the most complex or challenging aspects of building Polyglot?

What do you have planned for the future of Polyglot?

What are some areas of NLP research that you are excited for?

Keep In Touch
Picks
Tobias
Duolingo

Rami
The Wizard and the Prophet: Two Remarkable Scientists and Their Dueling Visions to Shape Tomorrow’s World by Charles C. Mann

Links
Polyglot
Polyglot-NER
Jordan
NLP (Natural Language Processing)
Stony Brook University
Arabic
Sentiment Analysis
Assembly Language
C
.NET
Stack Overflow
Deep Learning
Word Embedding
Wikipedia
Word2Vec
NLTK (Python Natural Language Toolkit)
SpaCy
Podcast Episode

Gensim
Podcast Episode

Morphology

Morpheme

Transfer Learning

Read The Docs

BERT (Bidirectional Encoder Representations from Transformers)

FastText

data.world
Data Engineering Podcast Episode

Quilt package management for data
Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
December 10, 2018 Gnocchi: A Scalable Time Series Database For Your Metrics with Julien Danjou - Episode 189
Do you know what your servers are doing? If you have a metrics system in place then the answer should be "yes". One critical aspect of that platform is the timeseries database that allows you to store, aggregate, analyze, and query the various signals generated by your software and hardware. As the size and complexity of your systems scale, so does the volume of data that you need to manage which can put a strain on your metrics stack. Julien Danjou built Gnocchi during his time on the OpenStack project to provide a time oriented data store that would scale horizontally and still provide fast queries. In this episode he explains how the project got started, how it works, how it compares to the other options on the market, and how you can start using it today to get better visibility into your operations.
40 min