The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

June 04, 2018Pandas Extension Arrays with Tom Augspurger
Summary
Pandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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.
Your host as usual is Tobias Macey and today I’m interviewing Tom Augspurger about the extension interface for Pandas data frames and the use cases that it enables
Interview
Introductions
How did you get introduced to Python?
Most people are familiar with Pandas, but can you describe at a high level the new extension interface?
What is the story behind the implementation of this functionality?
Prior to this interface what was the option for anyone who wanted to extend Pandas?

What are some of the new data types that are available as external packages?
What are some of the unique use cases that they enable?

How is the new interface implemented within Pandas?

What were the most challenging or difficult aspects of building this new functionality?

What are some of the more interesting possibilities that you are aware of for new extension types?

What are the limitations of the interface for libraries that add new array functionality?

What is the next major change or improvement that you would like to add in Pandas?

Keep In Touch
tomaugspurger on GitHub
@TomAugspurger on Twitter
Picks
Tobias
Black Panther

Tom
Dask-ML

Links
Pandas
ExtensionArray
Original IP Address proposal
Mid-implementation blog post
Dataframe
Numpy
Cyberpandas
Geopandas
GIS
Arrow
CuPy
JQ
Wes McKinney
Array ufunc
Matplotlib
Altair
Seaborn
Bokeh
Podcast.__init__ Interview

Dask
Data Engineering Interview

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
34 min
May 27, 2018 Making A Difference Through Software With Eric Schles
Software development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.
0 min
May 27, 2018Making A Difference Through Software With Eric Schles
Summary
Software development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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.
Your host as usual is Tobias Macey and today I’m interviewing Eric Schles about how to get involved with social justice causes as an engineer
Interview
Introductions
How did you get introduced to Python?
What are some ways that engineers can create real-world impact with their skills?
What are some of the common roadblocks to contribution that people should be aware of?
What are some of the types of projects or tools that can provide the most value compared to the amount of effort?
Do you have any advice for picking an organization or cause that will benefit the most from technical expertise?
Many of the tools and systems that get built for public or non-profit organizations require some amount of data for them to be useful. Do you have any advice on methods for identifying, locating, or collecting the necessary information for feeding into these projects?
What are some of the design factors that should be considered when building tools for these organizations to allow them to be maintainable and sustainable in the absense of an experienced engineer?
Keep In Touch
EricSchles on GitHub
@EricSchles on Twitter
Picks
Tobias
Shoes without laces

Eric
Catboost
Pomegranate

Links
USDS
18F
OCW
Python Course

SAS

R

Machine Learning

Version Control

GitHub

Agile

OCR (Optical Character Recognition)

Eric Schles Interview On Podcast.__init__

Excel

ETL (Extract Transform Load)

Automate The Boring Stuff

Web Scraping

Thomas Levine

Elasticsearch

Trello

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
May 21, 2018 Asking Questions From Data Using Active Learning with Tivadar Danka
One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.
28 min
May 21, 2018Asking Questions From Data Using Active Learning with Tivadar Danka
Summary
One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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.
Your host as usual is Tobias Macey and today I’m interviewing Tivadar Danka about modAL, a modular active learning framework for Python3
Interview
Introductions
How did you get introduced to Python?
What is active learning?
How does it differ from other approaches to machine learning?

What is modAL and what was your motivation for starting the project?

For someone who is using modAL, what does a typical workflow look like to train their models?

How do you avoid oversampling and causing the human in the loop to become overwhelmed with labeling requirements?

What are the most challenging aspects of building and using modAL?

What do you have planned for the future of modAL?

Keep In Touch
@TivadarDanka on Twitter
cosmic-cortex on GitHub
https://www.tivadardanka.com?utm_source=rss&utm_medium=rss for anything else
Picks
Tobias
Peter Rabbit Movie

Tivadar
Uri Alon: An Introduction to Systems Biology – Design Principles of Biological Circuits, book and online lectures

Links
modAL homepage
modAL on GitHub
modAL paper
Bioinformatics
Hungary
Phenotypes
Active Learning
Supervised Learning
Unsupervised Learning
Snorkel
Active Feature-Value Acquisition
scikit-learn
Entropy
PyTorch
Tensorflow
Keras
Jupyter Notebooks
Bayesian Optimization
Hyperparameters
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
28 min
May 13, 2018 Great Expectations For Your Data Pipelines with Abe Gong and James Campbell
Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.
51 min
May 13, 2018Great Expectations For Your Data Pipelines with Abe Gong and James Campbell
Summary
Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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]
Your host as usual is Tobias Macey and today I’m interviewing James Campbell and Abe Gong about Great Expectations, a tool for testing the data in your analytics pipelines
Interview
Introduction
How did you first get introduced to Python?
What is Great Expectations and what was your motivation for starting it?
What are some of the complexities associated with testing analytics pipelines?
What types of tests can be executed to ensure data integrity and accuracy?

What are some examples of the potential impact of pipeline debt?

What is Great Expectations and how does it simplify the process of building and executing pipeline tests?

What are some examples of the types of tests that can be built with Great Expectations?

For someone getting started with Great Expectations what does the workflow look like?

What was your reason for using Python for building it?
How does the choice of language benefit or hinder the contexts in which Great Expectations can be used?

What are some cases where Great Expectations would not be usable or useful?

What have been some of the most challenging aspects of building and using Great Expectations?

What are your hopes for Great Expectations going forward?

Contact Info
James
jpcampb2 on GitHub

Abe
abegong on GitHub
Website
@AbeGong on Twitter

Picks
Tobias
Fitbit Versa

James
Unplug and spend some time away from the computer

Abe
Superconductive Health
Slack: Getting Past Burnout, Busy Work, and the Myth of Total Efficiency

Links
Superconductive Health
Laboratory for Analytical Sciences
Great Expectations
Medium Post
DAG (Directed Acyclic Graph)
SLA (Service Level Agreement)
Integration Testing
Data Engineering
Histogram
Pandas
SQLAlchemy
Tutorial Videos
Jupyter Notebooks
Dataframe
Airflow
Luigi
Spark
Oozie
Azkaban
JSON
XML
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
51 min
May 06, 2018 Exploring Color Theory In Python With Thomas Mansencal
We take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.
58 min
May 06, 2018Exploring Color Theory In Python With Thomas Mansencal
Summary
We take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.
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 you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
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])
Your host as usual is Tobias Macey and today I’m interviewing Thomas Mansencal about Colour, a python library for working with algorithms and transformations to explore color theory
Interview
Introductions
How did you get introduced to Python?
What is color theory?
How does Colour assist in the process of working with some of the practical applications of colour science?

What was your motivation for creating Colour?

What are some example use cases for colour?

One of the aspects of color in digital environments that is often confusing is the number of different ways that it can be represented. What are the relative benefits of things like RGB, HSV, CMYK, etc.?

How is the Colour library architected and how has that evolved over time?
Are there new developments in the area of color theory that need to be periodically incorporated into the library?

What have you found to be some of the most often misunderstood aspects of color?

What have been some of the most difficult or frustrating aspects of building, maintaining, and promoting Colour?

What are some of the most interesting or unexpected uses of Colour that you have seen?

What are your plans for the future of Colour?

Keep In Touch
Website
Picks
Tobias
Beasts of Olympus by Lucy Coates

Thomas
Coursera Mathematics Machine Learning Course

Links
Colour
Color Theory
Color Science
Weta Digital
Wingnut AR
Visual Effects Artist
Allegro
AutoDesk Maya
PyQT
Isaac Newton
Color Wheel
Colorimetry
CIE
VY Canis Majoris (Red Hypergiant)
Rigel (Blue-White Supergiant)
Kelvin Temperature Scale
Black Body Radiation
HDRI (High Dynamic Range Imaging)
Adobe DNG SDK
ICC
OpenColorIO
MERCK Group
Color Space
RGB
HSV
CMYK
CIE XYZ
CIE RGB
CIE Lab
CIE Luv
sRGB
Gamma Correction
Additive Color Space
Subtractive Color Space
Color Blindness
Gustavo Machado
Rods and Cones
Dichromacy
Color Appearance Model
Uniform Color Spaces
JOSS
ArXiv
CIECAM02 Color Appearance Model
Cinematic Color
Jeremy Selan (Author of OpenColorIO)
Academy Color Encoding System
Color Appearance Models by Mark D. Fairchild
The Reproduction of Colour by Dr. R.W.G. Hunt
Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition by Günther Wyszecki and W. S. Stiles
Katherine Crowson
Google Colab
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
58 min
April 30, 2018 Destroy All Software With Gary Bernhardt
Many developers enter the market from backgrounds that don't involve a computer science degree, which can lead to blind spots of how to approach certain types of problems. Gary Bernhardt produces screen casts and articles that aim to teach these principles with code to make them approachable and easy to understand. In this episode Gary discusses his views on the state of software education, both in academia and bootcamps, the theoretical concepts that he finds most useful in his work, and some thoughts on how to build better software.
53 min