Jake Vanderplas - Keynote - PyCon 2017

[Applause] all right I think I'm up hi everybody it's great to be here my name is Jake van applause so as I was thinking about this talk I was reflecting on the other PI cons I've been to this is my sixth one in a row and thinking back five years and 2012 I was a kind of scruffy starving PhD student and I came to came to PyCon on a on a scholarship a travel scholarship from the PSF and it was my first experience with with a big conference like this and I was just blown away by the quality of the talk by the people I met you know I'd been using PyCon or using Python for years and I got to shake the hands of the people who wrote the libraries like numpy and Syfy and ipython and all these things that I've been using in my work and I was I was just blown away by this conference and I have to say I never imagined that I'd have the chance to stand up here and address you and it's really really really a privilege for me so I want to thank thank the PSF for giving me a chance five years ago and I want to thank Brandon and the organizers for giving me a chance today to share what I've it is to say with you so thinking about about PyCon one of the one of the things that comes to mind for me and my experience here is that PyCon is kind of a mosaic in some ways you know if you go to a conference like the SyFy conference or Django Khan or PI data or anaconda Khan or Jupiter Khan or all these other ones you you get a specific slice of the Python community but at PyCon you get everybody and it's interesting everybody has each little slice of our Python community has their own way of using the language their own sets of tools that they like to use and their own approach to solving problems and this came to light for me a couple years ago I found myself during one of the PyCon Emile's sitting with someone who was pretty well known in the Python community and I you might know me that I'm pretty I'm a fan of the Jupiter notebook in the ipython notebook and and those types of tools and I was sitting at that table and this person across the way said oh yeah you know ipython it's bloated and it encourages bad software practices and I was thinking you know my first response was to get a little bit defensive and I did the thing that you know it's true true to my family upbringing and I changed the subject and avoided confrontation but thinking about thinking about that later I realized that this this wasn't a moment of attack or defensive 'no sam moment that reflects the fact that that person uses python for different things than i use Python for and jupiter makes sense for me but might not make sense for him and I think that as we go through this weekend it's it's a really nice way to it's a it's a good opportunity to talk to other people who use this language differently than you do so I want you to keep in mind this Python mosaic as we move forward so Who am I I'm Jake vvp if you can find me on most of the internet under that handle and the most important thing about me right now is this little this little one right here so four weeks ago today my wife and I got a call that we'd been waiting for for a long time but nevertheless kind of struck us out of the blue that this this little gal was born that morning and needed a family to take care of her and so I've been I've been since my job is a little more flexible I've at home on paternity leave for the last four weeks like furiously writing PyCon slides between bottles and and poopy diapers which has been just great so huge thanks to my wife for taking a vacation day-to-day to be home with the kids so that I could be here it's awesome yeah in the Python world you might know me I write this blog sometimes I've contributed a lot of code to scifi socket learn Astro PI and some of these types of packages and I also have a couple books that I've written that you can check out if you want to and one of the huge reasons that I'm able to do all that work is that I have an employer who is an amazing and helps me actually supports me in writing code every day and developing these kinds of libraries the University of Washington East Science Institute it's a it's a fun place to work I'm able to spend my days on research on coding on helping researchers around the university to more effectively use their data and use it computational resources and a big reason that I'm able to do that I have to throw a shout out here to the Moore Foundation the sloan Foundation and the Washington Research Foundation because they support pretty much all the work that I do and any of my open source contributions you can kind of you can think that they're behind it so anyway that another thing about me is I'm an astronomer and I'm coming to this talk from the perspective of an astronomer so what I want to do a little bit today is tell you about how I as an astronomer and as a scientist use Python it might be a little bit different from how you use Python and the tools that you use but I want to give some perspective on on what it is that that drew scientists to Python in such big numbers so when I say that I'm an astronomer a lot of people have this kind of image in mind this is this is Edwin Hubble at the Schmidt telescope back in the 1940s you might know Hubble because he has a famous telescope named after him he was one of the people who discovered the expanding universe back in the early 20th century and this is kind of a nice romantic view of astronomy right an aged astronomer with a pipe in his mouth looking through an eyepiece but in my 10 years as a professional astronomer I honestly I think I have never looked through a telescope to be honest the way that professional astronomers look at the world is through database queries of course right because we have all these phenomenal instruments out there that are are measuring data at huge rates and I want to highlight two of these that have been really really big in the lab 20 to 25 years one is the Hubble Space Telescope which you've probably heard of and another is the Sloan Digital Sky Survey which I think probably fewer people have heard of the Hubble Space Telescope is this instrument that's been flying up orbiting the earth since 1990 and just taking phenomenal pictures of the universe and this is one of my favorites right here this is known as the Ultra Deep Field and what this is is the Hubble looked at a small little slice of sky the field of view is about 1/10 the diameter of the full moon and in that small slice of sky integrated for hours and hours and hours to capture enough photons to create this image and every single blob you see there in that image with the exception of maybe two or three is a distant galaxy it's a collection of billions of stars but sitting out there in the universe up to 13 or more billion light years away so these are some of these blobs that you see in these pictures right now in this picture right now are galaxies that are from from the very beginning of the universe and we can gather that light and we can learn about where where our star came from where our galaxy camp came from and what kind of the overall dynamics of the universe is so this is Hubble has been phenomenal for teaching us about those kinds of things for learning about the early universe and about things in very fine detail a complimentary type of approach is what's what's done by the Sloan Digital Sky Survey and what I'm showing here is the galaxies catalogs in the Sloan Digital Sky Survey slow the Sloan survey rather than looking at just a single little point on the sky like Hubble scanned the entire sky over the course of a decade and was able to do spectroscopic measurements of each of the objects out there and so in this plot right here what you see are the locations the three-dimensional locations of galaxies in our universe are we're right at the origin right at the center and looking out in those arcs is the piece of the sky that the Sloan was able to look at and what scientists have been what astronomers have been able to do with this is to look at this see you know the galaxies are not spread out uniformly there are all these clumps and clusters and filaments and voids and encoded in that information about the spatial distribution of the galaxies is information about the universe as a whole how much matter there is how much dark energy there is how much dark matter there is and so we're able to do that through this this really huge data set that comes from the Sloan survey now these are kind of those are kind of old older telescopes those were launched in the 90s and the modern modern science in the last few years has been driven by some other telescopes what I want to highlight is this Kepler project and you've probably heard about the Kepler project because there have been a lots of press releases lately with images like this right this is the Trappist one exoplanet system it was not actually discovered by Kepler discovered by the Trappist telescope but what's special about this is this is a small star a few light-years away that's that has seven small rocky planets around it and we've been able with the with these telescopes we've been able to discover these other planets orbiting around other stars and start to think about you know what might those planets look like the reason these are so special is because a few of these are in what's called the habitable zone and astronomers define the habitable zone as the region in space where liquid water can exist because we don't have any other good definition of how life can be so we think you know water is a water is a good thing to look for but pictures like this these artist impressions kind of are misleading the actual data that you get on a planet like this or on a system like this looks like that that that's what we've observed with Kepler this is the k2 observation of the Trappist system and let me describe what you're what you're seeing here what you're seeing is these it's a single star that is kind of wandering around between pixels because the one of the one of the stabilizers broke on Kepler so it's sort of wobbles as time goes on and the the signal the planets that you're looking for there are you see those as they eclipse the star so the star has this brightness and the planet goes in front of it and as the planet goes in front it makes the the brightness of the star dipped a little bit and that dip of a sub percent level is what we're looking for to find the planets in in this data and that's really hard because you have instrumental noise it's a faint star you have the star itself varies it has star spots on it as solar flares and all this stuff so the way that you can actually pull out these planets is through this incredibly intricate statistical modeling of the system and the physical parameters of planets as they go around and what we know about how that does so so finding these sorts of systems comes down to writing statistical code and really intricate data processing pipeline and I hope you won't be surprised if I tell you that what Kepler is using for that is Python code that's on github right so if you if you want to see some of the code that's being used for for these data reductions you can actually go and see see this an open source and this is something as I'll talk about later this is something that's come from the scientific community embracing the norms of the Python community so another telescope I want to tell you about this is the James Webb Space Telescope this is not actually the telescope it's a scale model so this is the telescope that's billed is the replacement to Hubble Hubble has been up there 27 years it's had it's had a long run but it's going to wear out soon and we need something else to replace it and James Webb is going to go up in space and it has this huge mirror the Hubble mirror is about the size of a person the James Webb mirror is about three times that diameter and the other thing that's different about the James Webb telescope is once it's up there it's not going to be as sensitive to visible light you know the red green and blue that our eyes can see it's more tuned to infrared light so the the light that's just a few wavelengths longer than a little bit longer wavelengths than the red light we can see and one of the reasons that's important is because in the infrared and with the the kind of precision that James Webb will have we might be able to actually image those exoplanets that are going around other stars so rather than just seeing their shadows go in front of the star we might actually be able to see some of those planets themselves and one of the most exciting prospects is looking at the types of spectroscopic measurements that James Webb is going to be able to do its spectra is when you you break light into its component wavelengths and kind of look at the wavelength flux distribution and what you can see what we'll be able to see is as these planets go in front of the star we'll be able to isolate the the light or our fine find the component of the light that is passed through the atmosphere of that planet look for molecular absorption in that light and then infer the molecular content of the atmosphere and this is super exciting this is kind of a holy grail in exoplanet science we're actually going to be able to know what these planets are made of and there's some things that it's a long shot but if you if you look at the types of gases that exist in atmospheres and planets out in the out in the universe there are certain things that never show up like oxygen and ozone and those oxygen and ozone are things that have no known geophysical source they only come from biology they only come from basically bacteria and plants and living things so there's a small possibility that in the next five or 10 years we'll be able to sniff out the chemical composition of a planet around another star and if we see something like an ozone layer it will be a really good indication that there's some sort of at least bacterial life on that planet and I find that to be incredibly exciting right I think exploring the universe in this in this way is huge and again if you want to know how we're processing that data JWST and actually the whole Space Telescope Science Institute has moved to using Python and and Jupiter notebooks to to present their material so it's pretty incredible and and and the last project I want to tell you about briefly is this one that's really going to change every part of astronomy over the next 10 years this is something that my PhD advisor has been intimately involved with from the beginning so as a grad student I was able to work on a lot of aspects of this it's a large synoptic survey telescope so I don't know if you can see but there's a image of a little person standing there right so that's about the size of the telescope this is this is going to be a large ground-based telescope it's not flying up in space here's a picture of the mirror blank a few years back before they started polishing it was the LSP team and what with a mirror this big you need a really large camera right so LSST is constructing the largest digital camera ever created it's a 3 Giga pixel camera that has a has a CCD about the size of a person right and just to give you an idea of its field of view we have an image of the moon there on superimposed on the CCD chips and and this is just absolutely mind-boggling mind-bogglingly huge right 3 Giga pixels if you imagine an HDTV right that's something like 2,000 by 1,000 pixels if you wanted to display a single LSST snapshot in full resolution you would need a bank of 1,500 HD TVs right so it's it's a lot of pixels and the amazing thing is that this these 1500 HD TVs worth of pixels are going to be taken twice every 30 seconds for 10 years right so the the the upshot of LSST is it's basically going to be a decade long video of the entire night sky it's going to take about three days to cover the whole southern sky visible from from Chile where the telescope is going to be located with a final catalogue of hundreds of petabytes of data and I'm not exaggerating when I say this is going to absolutely change the way astronomy is done in every part of astronomy everything from the people studying the dust in the Milky are the dust in the solar system all the way out to the most distant galaxies and the structure of the universe itself and I was going to go through some of these science cases but really there's a 600 page book that talks about everything that we're going to be able to do with LSST and if you're interested I'd suggest at least reading the table of contents because it's pretty it's pretty phenomenal the kinds of things that we're going to do and you're probably sensing a pattern now if you want to know the software that's driving this 100 petabytes of data it's on github and it's in Python with parts of it and C++ so this has been a real revolution in astronomy over the course of the last 10 years and I was curious about this so I went and used a friend script to to mine the astrophysics for physical data system which is the listing of all peer-reviewed papers in the astronomy world and these are the mentions of certain software languages over the course of the last what is a 17 years you can see that Python is just we have this hockey stick thing right Python is having exponential growth in the field of astronomy and some of these older tools like like IDL which was most popular about the time that I started my PhD is sort of tapering off a little bit and that's that's good news because IDL is a closed source system that has licensing fees and doesn't really have you know it students who learn IDL can't go out and get other jobs they only get astronomy jobs right so there's a lot of really nice things about Python and for the rest of the talk I want to I want to focus on this why Python right if you if you went back to 1985 and asked guido if he thought that his little language would be used to manage hundreds of petabytes of data for a global system of astronomers he'd probably say some Dutch word for you're crazy and and if you look back you know what python was developed for back in the 80s it was developed as a teaching language kind of to bridge the gap between the shell and C and he even said it was never intended to be the primary leg which for programmers so if any of you consider yourself a programmer in here you're using the wrong language right and you know if you go further in that interview it's really interesting he said I thought we'd write programs ten lines fifty maybe 500 lines but here we are using using thousands or millions of lines of Python to to store and analyze some of the largest data sets in the world and I think that's incredible so why is it that Python is such an effective tool in science I think the first thing that comes up is that it's the interoperability with other language languages that really drove this if you go back long ways back and you you look at some of the great scientists one of them was Isaac Newton and he has this famous line where he said if I've seen further it's by standing on the shoulders of giants basically saying that his science doesn't exist in a vacuum he needed all the science that came before him and I think if Isaac Newton were alive today he'd say something like this it's by importing from the code of giants right so whenever scientists are doing something today they're using some sort of computation that's the nature of the data that we're working with and if you have to reinvent the wheel every time you do something every time you extend the study you're never going to get anything done so we're constantly using the code of the people who came before us if you want to get a glimpse into kind of the first steps of Python and science Dave Beasley wrote this interesting paper in 2000 you probably know Dave if you've been around you've been around PyCon you probably know him as a person who teaches you to do diabolical things with co-routines or whatever right but back in the day 20 years ago he was working in a National Lab and he was pushing some of those scientists some of the first scientists to use Python in the scientific fields and he said scientists work with a wide variety of systems ranging from simulation codes data analysis packages databases visualization tools homegrown software each of which presents the user with a different set of interfaces and file formats as a result the scientists may spend considerable amount of time and to get all these components to work together in some manner and so he in 2000 was advocating Python as the solution to that kind of glue together all these tools and if you look at some of the other know patriarchs of the of scientific Python world like John Hunter he was the creator of Matt Matt plot lid and he said I had a hodgepodge of work processes I'd have Perl scripts that called C++ I'd load them into Matt Matt lab and plot them and then I started using new plot and this is what inspired him to work on matplotlib is sort of a Python replacement for that whole mismatched pipe pipeline that he used a similar Fernando Perez he started the ipython project which has grown into Jupiter and he had a similar thing my advisor had a heavily customized oxide bash workflow to manage job submissions post-processing of C codes he so used her scripts and on top of that added a layer of Perl gnuplot IDL in Mathematica so this sounds absolutely horrible to me right and this is what inspired him to create ipython as a way to use Python to wrap all these together so really fundamentally in science Python is glue more than anything else it started out as a way to glue together all these hodgepodge of scientific tools that people were working with and that the high-level Python syntax wraps these low-level C and Fortran libraries so another reason that Python has become such an effective tool is this whole notion of batteries included right Python can out-of-the-box can do so so many things if you compare it to something like C or C++ out-of-the-box Python has this built in standard library that can do everything from you know launch a web server to read JSON to working with the file system to manipulating strings and that's that sort of thing is incredibly valuable and for the for the libraries that aren't built-in there's been this ecosystem of software built around Python that has has turned out to be really useful and so if you look at the genesis of scientific Python there were a number of people that worked on it but one of the one of the faces of this is Travis olifant he started the numpy project and parts of the sci-fi project and he was saying when I discovered Python I really liked the language but it was very nascent and lacked a lot of libraries and I felt like I could add value to the world by connecting these low level libraries to high level usage in Python so this is moving from Python as glue to Python as applications that kind of glue together these underlying tools and the scientific ecosystem has really really ballooned and exploded over the last few years you have Python on the ground and then you have these various packages that that work with Python and extend it in ways that are useful to scientists of course numpy gives you array computing ipython on jupiter give you kind of a IDE on top of on top of Python you have the syphon project which lets you basically wrap C code and write C code in Python which it's really interesting actually aside my favorite language in the world the program in is psyphon you should you should try it sometime it's really fun if tools like numba and ask that addressed scalability and computation and built on top of this layer you have some things that are slightly more specialized tools like bouquet matplotlib for plotting you have pandas for data frames and x-ray for for multi-dimensional labelled structures and then of course SCI Pi which wraps all the scientific routines like integration and interpolation and a lot of these depend on the layer below them and of course scientists you know these are higher level tools but there there's an even higher level from there you know people want to do machine learning so scikit-learn was built that depends on Syfy and numpy simpie does this symbolic manipulation similar to if you've used Mathematica or Wolfram Alpha you can you can solve equations symbolically in Python Network X I could image PI MC for Markov chain Monte Carlo stats models and then this even more specialized than that on top of that you have a lot of different fields that have developed these fields specific packages for Python so we're really at the at the point here within the Python community in the science community be that if you have a problem you want to solve in Python someone has written a package for it you can go out there and it's most likely on github out there and this has been this has been absolutely huge in the scientific world and that's why we're seeing that hockey stick growth in in astronomy so another reason Python is so so effective for science is just its simplicity and dynamic nature you know you've probably seen this xkcd before if you type import antigravity into a Python interpreter this will pop up and it gets set a thing that a lot of people have noticed about Python Python is just fun all right if you if you go from using another language and then start using Python it's it's kind of a freeing thing you you just you just write down what you want to happen and it happens for it Python is kind of like executable pseudo code and it just works it's great and Perri greenfield he's a one of the project leads at the Space Telescope Science Institute these are the people behind the Hubble Space Telescope and James Webb Space Telescope he gave an interesting talk at a PI data conference a couple years ago about why Python has been adopted in astronomy and he's one of the people if you want to know why Python is in astronomy he's one of the people that's really been in the background making it happen getting it adopted by some of the large organizations and what he said is Python is a language that's very powerful for developers but also accessible to astronomers getting those two classes of people using the same tools I think provides a huge benefit that's not always noticed or mentioned so really the the key here is that Python is what the people writing the libraries are using and it's also the tool that the people using the libraries are using and that simplicity really makes it makes it tick because astronomers are not computer scientists a lot of us don't have CS training so we're used to writing scripting languages and Python fits that bill pretty well so for day-to-day scientific exploration it's really the speed of development that is primary and the speed of execution is secondary so you sometimes get people especially when you're working in a in an area with lots of data you get people a little bit incredulous that you're using Python to manage these terabytes or petabytes of data right and they start saying things like you know why don't why don't you you see it's so much faster right and my favorite answer is you know why don't you commute by airplane and sort of car it's so much faster right the nice thing about Python is there's very little startup cost you don't have to go to the airport and go through TSA and pack your bags and squeeze into a little tube and then get to the wrong place of the city that you're going to you just hop you just hop in and start going right so um another piece of this this puzzle is that scientific coding is is really really nonlinear and exploratory so this is a clip from you know I'm I spent a lot of time with kids these days this is a clip from a from a kids book called ADA twist scientist you should really check it out if you have have little ones but it says in one place ADA Murray did what scientists do she asks a small question and then she asked two and each of those led her to three questions more and some of those questions resulted in four and this is really the process of science you know we're not software developers who have a road map and sit down and write a module and do test-driven development until the tests pass we have a data set and we start playing around with it and we maybe plot it one way maybe plotted another way I don't know if my videos going to play because of the web web connectivity oh well this is a video the Jupiter notebook of me kind of doing some data exploration trying some things out going back to the beginning trying it again and the and and the thing is that that the way scientists use Python is really very much like that we're doing we're doing all sorts of kind of back-and-forth exploratory work very nonlinear and it means that a system like jupiter notebook is actually ideal it might not work for people who are kind of used to classic software development and looking for an IDE and it might feel a little bloated but for what we're doing it's it's exactly what we need right sorry about that video not working so the last piece of why I think Python is such an effective tool is because it has this open ethos you know I pointed a while ago to the fact that all these big telescope projects now have github repositories with their processing pipeline in Python and other languages open on there that was not the case um ten years ago and if you if you read a lot about in the media about science these days you'll see all these articles about these replication crisis's right you know everyone's talking about how science was broken the replication crisis has begun we can't replicate studies by our peers like that the core foundations of science are sort of crumbling because we're not doing a good job of making our studies replicable and the the people who are thinking deeply about how to solve this mainly what they're landing on is the idea of open science there's this great quote from Buckhead and Donahoe and a paper about 20 years ago an article about computational result is advertising not scholarship the actual scholarship is the full software environment the code and data that produced the result so basically what people are saying is in order for science to progress we need to start doing things the way the open source world has been doing things for decades right we need to start putting our code out there on the web so anyone can download it and run it we need to start licensing it in a way so that other people can use it and build on it and this is starting to happen one of the one of the great examples of this is this incredible event that was detected last year by the LIGO the laser interferometer fer AMA tree gravitational observatory they managed to detect the ripples and space-time that were caused by two black holes spiraling in and merging together black holes are so massive that they actually bend the fabric of space-time and as they spiral in you get these spiral waves of gravity waves ripples in space-time is heading out and when LIGO released that a result this is a result that I'm almost certain will get a Nobel Prize sometime in the next few years when they release that result they also released you can see if you if you recognize matplotlib these are the the graphs that they have and they made them in that pileup they also released notebooks and you can go from the raw data all the way to this plot that they published just by following their notebooks and so this really is the the way forward for science out of this replication crisis making sure that everything we publish is out there with the code and following what the open source world has done so this is something that I've tried to do in my own projects these are two books that I mentioned and you can buy them from O'Reilly if you want to or you can go on my github and you can look at the whole book in the form of notebooks you can download it and execute it and confirm what the code runs the way I said it runs so I've been trying to walk the walk as far as this this openness in my own work whether it's writing books or writing my research papers and things like that incidentally if you if you want a copy of this book I'm going to be signing it tomorrow the Riley boys at 10:00 a.m. so come on by so the Python world really in this in the sense of openness is really influencing science you know the Astro PI library that I mentioned previously this is something that astronomers are rallying around and there's so many papers these days that are doing their analysis based on tools and Astro PI and and the way Astro PI is structured is modeled off the the Python the Python world the way that Python has developed and I'm going back to Harry Greenfield this is a adapted from a slide that he gave at that same keynote and where he was talking about what Python has done for astronomy you know traditionally astronomy has been that we haven't really shared software been possessive about it and Python it's cooperative and it's all about sharing then fragmented efforts in the past and now it's more unified under this Astro PI umbrella rather than being plans top down by an institution it's planned bottom up by the people who are using and creating the software and rather than read the rest I'll let you take a look at that but the point is here that by having scientists come into the world of Python into the world of Pike on a lot of you folks do things differently than we do as scientists but but we've been able to learn from you and really improve the way science is done and I'm very hopeful that these things like this replication crisis is going to be behind us soon because we as scientists are adapting the tools of the open-source world so why is python such an effective tool in science since these I think it's these four key things we have this interoperability where it can it can serve as glue for everything else that's out there we have this batteries included and third-party modules that have all been developed in order to make Python as useful as possible and as few keystrokes as possible the simplicity and dynamic nature it allows an intro astronomy student who's never coded before to start writing scripts by the end of a week and writing applications by the end of the semester right it's really really phenomenal and this open source ethos is is such a good fit to science and I think science and astronomy in particular owes the Python world a big thank-you for kind of getting us on the right track so that I want to bring back this this idea of the the mosaic so I gave you a little bit of a perspective of how Python is used in science you know this is kind of one of those tiles in the mosaic and everyone else out here has a different experience you know you might you might be a person versus a condo person or you might be a pycharm person versus a Jupiter person or you might be what else pie pie versus cpython and everyone depending on depending on what you're doing in your day to day life you have a really different approach and a different set of tools that work for you and I'd encourage you while you're here at PyCon this weekend to keep that in mind you know the real opportunity here is that you can interface with people who are using this language ways that are completely different than you right so I want you to try to seek out talks from people who are in other fields and seek out conversations with folks who are using Python in different ways and you know if you do you never know you might end up completely changing the way your field is done so that's the end and thank you very much I'd love to take some questions [Applause] [Music] it looks by my clock like we're unfortunately at time so if you have questions for dr. van der PLAs come up front or find him and one of the other meeting rooms of the conference as we head into our first break before our first round of big talks let's give another hand to dr. van der PLoS thank you for talking [Applause]

Channel: PyCon 2017

Views: 20,332

Rating: 4.9270515 out of 5

Keywords:

Id: ZyjCqQEUa8o

Length: 38min 51sec (2331 seconds)

Published: Sat May 20 2017

Reddit Comments

[u/tmt_game]

/u/8rouillard explained the video stealing practice by youtube channel 'Coding Tech'

I've seen a lot of Pycon talks reposted by other youtube channels who make $$ on them through ads. I feel like we should redirect traffic to the original pycon video (in this case: https://www.youtube.com/watch?v=ZyjCqQEUa8o) since Pycon is an awesome event every year, and should be supported.

For example, in this repost, they cut out the first ~5 minutes, where he explains what Pycon is and why it's great. The original video has like 6x fewer views than this one.

Plus, the Pycon channel doesn't have ads (that I've seen).

Therefore I decide to delete my old post and put up the pycon video. I think repost is justified because my last post has incurred some 3.1k views. The talk is interesting and deserved to be seen by more.

[u/Astrokiwi]

I'm an astronomer and yeah, everybody in my department uses python all the time, (except for numerical simulations, where we use C, C++, and Fortran).

[u/[deleted]]

Is it just me, or are the PyCon 2017 videos less watchable due to only using half the screen area for the video? The videos certainly need the small picture of the presenter, and the sponsors deserve that their generosity be noted, but I find the videos less watchable on even large-format mobile.

This is no criticism of the quality of the talks. Wish I had been there.

Edit: After viewing the video I noted that the sponsor logos were removed for the bulk of the video. So the static ~50% of the screen showed only that the video was from "Portland, Oregon, PyCon 2017" - twice. I kind of knew that and didn't need reminding. The only reason I can think for so much wasted screen real-estate is that it makes editing of the video much easier, as the producer doesn't have to move the presenter picture-in-picture around to ensure the slide isn't obsured in important ways.