Data Science

Dave Beazley - Episode 72

Summary

Dave Beazley has been using and teaching Python since the early days of the language. He has also been instrumental in spreading the gospel of asynchronous programming and the many ways that it can improve the performance of your programs. This week I had the pleasure of speaking with him about his history with the language and some of his favorite presentations and projects.

Brief Introduction

  • 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. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!
  • Hired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.
  • 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 hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Dave Beazley about his career with Python

Interview with Dave Beazley

  • Introductions
  • How did you get introduced to Python? – Tobias
  • How has Python and its community helped to shape your career? – Tobias
  • What are some of the major themes that you have focused on in your work? – Tobias
  • One of the things that you are known for is doing live-coding presentations, many of which are fairly advanced. What is it about that format that appeals to you? – Tobias
    • What are some of your favorite stories about a presentation that didn’t quite go as planned? – Tobias
  • You have given a large number of talks at various conferences. What are some of your favorites? – Tobias
  • What impact do you think that asynchronous programming will have on the future of the Python language and ecosystem? – Tobias
  • Are there any features that you see in other languages that you would like to have incorporated in Python? – Tobias
  • On the about page for your website you talk about some of the low-level code and hardware knowledge that you picked up by working with computers as a kid. Do you think that people who are getting started with programming now are missing out by not getting exposed to the kinds of hardware and software that was present before computing became mainstream?
  • You have had the opportunity to work on a large variety of projects, both on a hobby and professional level. What are some of your favorites? – Tobias
  • What is it about Python that has managed to hold your interest for so many years? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

GenSim with Radim Řehůřek - Episode 71

Summary

Being able to understand the context of a piece of text is generally thought to be the domain of human intelligence. However, topic modeling and semantic analysis can be used to allow a computer to determine whether different messages and articles are about the same thing. This week we spoke with Radim Řehůřek about his work on GenSim, which is a Python library for performing unsupervised analysis of unstructured text and applying machine learning models to the problem of natural language understanding.

Brief Introduction

  • 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. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit on your account.
  • 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 hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Radim Řehůřek about Gensim, a library for topic modeling and semantic analysis of natural language.

Interview with Radim Řehůřek

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you start by giving us an explanation of topic modeling and semantic analysis? – Tobias
  • What is Gensim and what inspired you to create it? – Tobias
  • What facilities does Gensim provide to simplify the work of this kind of language analysis? – Tobias
  • Can you describe the features that set it apart from other projects such as the NLTK or Spacy? – Tobias
  • What are some of the practical applications that Gensim can be used for? – Tobias
  • One of the features that stuck out to me is the fact that Gensim can process corpora on disk that would be too large to fit into memory. Can you explain some of the algorithmic work that was necessary to allow for this streaming process to be possible? – Tobias
    • Given that it can handle streams of data, could it also be used in the context of something like Spark? – Tobias
  • Gensim also supports unsupervised model building. What kinds of limitations does this have and when would you need a human in the loop? – Tobias
    • Once a model has been trained, how does it get saved and reloaded for subsequent use? – Tobias
  • What are some of the more unorthodox or interesting uses people have put Gensim to that you’ve heard about? – Chris
  • In addition to your work on Gensim, and partly due to its popularity, you have started a consultancy for customers who are interested in improving their data analysis capabilities. How does that feed back into Gensim? – Tobias
  • Are there any improvements in Gensim or other libraries that you have made available as a result of issues that have come up during client engagements? – Tobias
  • Is it difficult to find contributors to Gensim because of its advanced nature? – Tobias
  • Are there any resources you’d like to recommend our listeners explore to get a more in depth understanding of topic modeling and related techniques? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

PyData London with Ian Ozsvald and Emlyn Clay - Episode 48

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Summary

Ian Ozsvald and Emlyn Clay are co-chairs of the London chapter of the PyData organization. In this episode we talked to them about their experience managing the PyData conference and meetup, what the PyData organization does, and their thoughts on using Python for data analytics in their work.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Ian Ozsvald and Emlyn Clay about their work with PyData London, a group within the PyData organization. PyData London represents the largest Python group in London at ~2850 members, they hold regular monthly meetups for ~200 members at AHL near Bank and a yearly conference for around ~300 members. Last year, they and their sponsors raised over £26,000 to sponsor the development of core numerical libraries in Python.
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Interview

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is the PyData organization, how does PyData London fit into it and what is your relationship with it? – Tobias
  • In what ways does a PyData conference differ from a PyCon? – Tobias
  • Does PyData do anything in particular to encourage users from disciplines that might not be aware of how much our community has to offer to choose the Python suite of data analysis tools? – Chris
  • You have both spent a good portion of your careers using Python for working with and analyzing data from various domains. How has that experience evolved over the past several years as newer tools have become available? – Tobias
  • For someone who is just getting started in the data analytics space, what advice can you give? – Tobias
  • How can conferences like PyData help strengthen the bonds and synergies between the Python software community and the sciences? – Chris
  • There are a number of different subtopics within the blanket categorization of data science. Is it difficult to balance the subject matter in PyData conferences and meetups to keep members of the audience from being alienated? – Tobias
  • Data science is a young field and we’ve yet to see lots of examples of the successful use of data. How are London-based companies using data with Python? – Ian
  • Is there a Python data science library you think needs a little love? – Emlyn

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Cython with Craig Citro and Robert Bradshaw - Episode 45

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Summary

Do you find yourself reaching for a different language when you need some extra speed? With Cython you can get the best of both worlds by writing your code in Python and executing it as compiled code. In this episode we were joined by Craig Citro and Robert Bradshaw from the Cython project to discuss how and when you might want to incorporate it into your applications.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Craig Citro and Robert Bradshaw

Interview with Craig Citro and Robert Bradshaw

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is Cython and how did the project get started? – Tobias
  • My understanding is that Cython can improve the performance of a Python program without even having to provide any type annotations. How does it manage to do that? – Tobias
  • Can a Cython module be used as a way to sidestep the GIL? What are some of the pitfalls that can be caused by doing so? – Tobias
  • Can you give some examples of how Cython can be used to improve the perfomance of Python programs? – Tobias
  • How does Cython work under the covers? – Tobias
  • What were some of the challenges during the creation of Cython and what design decisions were made to overcome them? – Tobias
  • Does Python’s cross platform nature create any unique challenges when compiling down to the C level? – Chris
  • What processor and system architectures does Cython support and are there plans to expand that support? – Tobias
  • How do generators and list comprehensions map to C, and did those higher level language constructs pose any special challenges in Cython’s design? – Chris
  • Would Rust ever be a potential compile target for performance and safety optimized modules? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Airflow with Maxime Beauchemin - Episode 44

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Summary

Are you struggling with trying to manage a series of related, interdependent batch jobs? Then you should check out Airflow. In this episode we spoke with the project’s creator Maxime Beauchemin about what inspired him to create it, how it works, and why you might want to use it. Airflow is a data pipeline management tool that will simplify how you build, deploy, and monitor your complex data processing tasks so that you can focus on getting the insights you need from your data.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Maxime Beauchemin about his work on the Airflow project.

Interview with Maxime Beauchemin

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is Airflow and what are some of the kinds of problems it can be used to solve? – Chris
  • What are some of the biggest challenges that you have seen when implementing a data pipeline with a workflow engine? – Tobias
  • What are some of the signs that a workflow engine is needed? – Tobias
  • Can you share some of the design and architecture of Airflow and how you arrived at those decisions? – Tobias
  • How does Airflow compare to other workflow management solutions, and why did you choose to write your own? – Chris
  • One of the features of Airflow that is emphasized in the documentation is the ability to dynamically generate pipelines. Can you describe how that works and why it is useful? – Tobias
  • For anyone who wants to get started with using Airflow, what are the infrastructure requirements? – Tobias
  • Airflow, like a number of the other tools in the space, support interoperability with Hadoop and its ecosystem. Can you elaborate on why JVM technologies have become so prevalent in the big data space and how Python fits into that overall problem domain? – Tobias
  • Airflow comes with a web UI for visualizing workflows, as do a few of the other Python workflow engines. Why is that an important feature for this kind of tool and what are some of the tasks and use cases that are supported in the Airflow web portal? – Tobias
  • One problem with data management is tracking the provenance of data as it is manipulated and shuttled between different systems. Does Airflow have any support for maintaining that kind of information and if not do you have recommendations for how practitioners can approach the issue? – Tobias
  • What other kinds of metadata can Airflow track as it executes tasks and what are some of the interesting uses you have seen or created for that information? – Tobias
  • With all the other languages competing for mindshare, what made you choose Python when you built Airflow? – Chris
  • I notice that Airflow supports Kerberos. It’s an incredibly capable security model but that comes at a high price in terms of complexity. What were the challenges and was it worth the additional implementation effort? – Chris
  • When does the data pipeline/workflow management paradigm break down and what other approaches or tools can be used in those cases? – Tobias
  • So, you wrote another tool recently called Panoramix. Can you describe what it is and maybe explain how it fits in the data management domain in relation to Airflow? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Yves Hilpisch on Quantitative Finance - Episode 39

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Summary

Yves Hilpisch is a founder of The Python Quants, a consultancy that offers services in the space of quantitative financial analysis. In addition, they have created open source libraries to help with that analysis. In this episode we spoke with him about what quantitative finance is, how Python is used in that domain, and what kinds of knowledge are necessary to do these kinds of analysis.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus to $4,000.
  • We are recording today on December 30th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Yves Hilpisch about Quantitative Finance
Hired LogoOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

Interview with Yves Hilpisch

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you explain what Quantitative Finance is? – Tobias
  • How common is it for Python to be used in an investment bank or hedge fund? – Tobias
  • What factors contribute to the choice of whether or not to use Python in a Quantitative Finance role? – Tobias
  • Are there any performance bottle necks or other considerations inherent in using Python for quantitative finance? – Chris
  • What kind of background is necessary for getting started in Quantitative Finance? – Tobias
  • What kinds of libraries or algorithms in Python are useful for the day-to-day work of a quant? – Tobias
  • Is Python actually used to enact the trades? What protocols, APis, and libraries are used in this process? – Chris
  • Could you please walk us through how a simple analysis using DXAnalytics might work? – Chris
  • You work for a company called ‘The Python Quants‘. What kinds of services do you provide and what kinds of organizations typically hire you? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Scott Sanderson on Algorithmic Trading - Episode 38

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Summary

Because of its easy learning curve and broad extensibility Python has found its way into the realm of algorithmic trading at Quantopian. In this episode we spoke with Scott Sanderson about what algorithmic trading is, how it differs from high frequency trading, and how they leverage Python for empowering everyone to try their hand at it.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • We are recording today on December 16th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Scott Sanderson on Algorithmic Trading

Interview with Scott Sanderson

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you explain what algorithmic trading is and how it differs from high frequency trading? – Tobias
  • What kinds of algorithms and libraries are commonly leveraged for algorithmic trading? – Tobias
  • Quantopian aims to make algorithmic trading accessible to everyone. What do people need to know in order to get started? Is it necessary to have a background in mathematics or data analysis? – Tobias
  • Does the Quantopian platform build in any safe guards to prevent user’s algorithms from spiraling out of control and creating or contributing to a market crash? – Chris
  • How is Python used within Quantopian and when do you leverage other languages? – Tobias
  • What Pypi packages does Quantopian leverage in its platform? – Chris
  • How do the financial returns compare between algorithmic vs human trading on the stock market? – Tobias
  • Can you speak about any trends you see in the trading algorithms people are creating for the Quantopian platform? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Stuart Mumford on SunPy - Episode 34

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Summary

What is Solar Physics? How does it differ from AstroPhysics? What does this all have to do with Python? In this episode we answer all of those questions when we interview Stuart Mumford about his work on SunPy. So put on your sunglasses and learn about how to use Python to decipher the secrets of our closest star.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are recording today on November 17th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Stuart Mumford about SunPy
Linode Sponsor BannerUse the promo code podcastinit10 to get a $10 credit when you sign up!

Hired LogoOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

Interview with Stuart Mumford

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you explain what the research and applications of solar physics are and how SunPy facilitates those activities? – Tobias
  • What was your inspiration for the SunPy project and what are you using it for in your research? – Tobias
  • Can you tell us what SunPy’s map and light curve classes are and how they might be used? – Chris
  • Are there any considerations that you need to be aware of when writing software libraries for practitioners of the hard sciences that would be different if the target audience were software engineers? – Tobias
  • Can SunPy consume data directly from telescopes and other observational apparatus? – Chris
  • I noticed on the project site that SunPy leverages AstroPy internally. Can you describe the relationship between the two projects and why someone might want to use SunPy in place of or in addition to AstroPy? – Tobias
  • Looking at the documentation I got the impression that there is a fair amount of visual representation of data for analysis. Can you describe some of the challenges that has posed? Is there integrated support for project Jupyter and are there other graphical environments that SunPy supports? – Tobias
  • What are some of the most interesting applications that SunPy has been used for? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Maneesha Sane on Software and Data Carpentry - Episode 33

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Summary

The Software and Data Carpentry organizations have a mission of making it easier for scientists and data analysts in academia to replicate and review each others work. In order to achieve this goal they conduct training and workshops that teach modern best practices in software and data engineering, including version control and proper data management. In this episode we had the opportunity to speak with Maneesha Sane, the program coordinator for both organizations, so that we could learn more about how these projects are related and how they approach their mission.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • This episode is sponsored by Zato – Microservices, ESB, SOA, REST, API, and Cloud Integrations in Python. Visit zato.io to learn more about how to integrate smarter in the modern world.
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are recording today on November 10th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Maneesha Sane about Software Carpentry and Data Carpentry

Interview with Maneesha Sane

  • Introductions
  • How did you get introduced to Python?
  • Can you explain what the Software and Data Carpentry organizations are and what their respective goals are?
  • What is the history of these organizations and how are they related?
  • What does a typical Software Carpentry or Data Carpentry workshop look like?
  • What is the background of your instructors?
  • Can you explain why Python was chosen as the language for your workshops and why it is such a good language to use for teaching proper software engineering practices to scientists?
  • In what ways do the lessons taught by both groups differ and what parts are common between the two organizations?
  • What are some of the most important tools and lessons that you teach to scientists in academia?
  • Do you tend to focus mostly on procedural development or do you also teach object oriented programming in Software Carpentry?
  • What is the target audience for Data Carpentry and what are some of the most important lessons and tools taught to them?
  • Do you teach any particular method of pre-coding design like flowcharting, pseudocode, or top down decomposition in software carpentry?
  • What scientific domains are most commonly represented among your workshop participants for Software Carpentry?
  • What are some specific things the Python community and the Python core team could do to make it easier to adopt for your students?
  • What are the most common concepts students have trouble with in software & data carpentry?
  • How can our audience help support the goals of these organizations?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Erik Tollerud on AstroPy - Episode 32

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Summary

Erik Tollerud is an astronomer with a background in software engineering. He leverages these backgrounds to help build and maintain the AstroPy framework and its associated modules. AstroPy is a set of Python libraries that provide useful mechanisms for astronomers and astrophysicists to perform analyses on the data that they receive from observational equipment such as the mountain observatory that Erik was preparing to visit when we talked to him about his work. If you like Python and space then you should definitely give this episode a listen!

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
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  • We are recording today on November 2nd, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Erik Tollerud about AstroPy
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Interview with Erik Tollerud

  • Introductions
  • How did you get introduced to Python?
  • What was the inspiration to create AstroPy and what kinds of astronomical research can it be used for?
  • Can you tell us what AstroPy’s modeling functions are and give us examples of where they might be used?
  • Are there any considerations that you need to be aware of when writing software libraries for practitioners of the hard sciences that would be different if the target audience were software engineers?
  • What are some of the most interesting applications that AstroPy has been used for?
  • Are there open data sets that are available for people outside of academia to do analysis of astronomical data using AstroPy?
    • Have there been any useful discoveries made in this way?
  • Could you please tell us about AstroPy’s Virtual Observatory capabilities?
  • What are some interesting use cases for AstroPy’s Cosmological calculations?
  • Are there other libraries available that provide similar capabilities, perhaps in other languages? What makes AstroPy unique among them?
  • Can AstroPy consume data directly from telescopes and other observational apparatus?
  • The amount of data generated from observing astronomical phenomena must be immense. What are some of the tools used to manage that data and how does AstroPy interface with them?
  • How might AstroPy be used to prove or disprove the cold dark matter hypothesis?
  • What are some of the architectural choices that have been made to allow for the AstroPy library to serve as the core for a number of other add-ons?
    • Does AstroPy provide a common data format to allow for easy interoperability between the various addons?
  • I noticed that AstroPy adheres to the PSF code of conduct, as well as having adopted an enhancement proposal process modelled after PEPs. Can you explain why that is important and what kind of an impact it has had on the community around AstroPy?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA