Skip to content

Latest commit

 

History

History
1393 lines (1387 loc) · 39.6 KB

17-carol-willing-python.md

File metadata and controls

1393 lines (1387 loc) · 39.6 KB
Raw

Carol Willing: Contributing to Core Python

Key Links

Extra Links

Video

Carol Willing: Contributing to Core Python

Transcript

hello everyone thank you for joining our webinar for today uh thanks for joining data umbrella i'm gonna do a quick introduction uh carol willing is going to do her talk and we'll have a q a session at the end and and this webinar is being recorded a little bit about me i'm a statistician data scientist i'm the founder of data umbrella and i am on twitter linkedin github has raised my s feel free to follow me we have a code of conduct we're dedicated to providing harassment free professional respectful experience for everyone this applies to the chat as well um thank you for helping make this a welcoming and friendly community for all of us about data umbrella we're an inclusive community for underrepresented persons in data science we welcome allies to join us and we are a volunteer run organization so how can you support data umbrella the first and foremost is to follow our code of conduct um the second is we have a discord community chat so feel free to join it the link is on our website and there you can ask questions and answer questions as well for the community we have an open collective feel free to also if you want to donate there to cover our meetup dues and other operational costs and we have a new initiative called you know we're doing transcripts for all of our talks and it is um the transcripts are on github and it requires knowing some mark down and how to submit a pr either via terminal or github um so check out this link github.com data umbrella event transcripts for more information we also have a job board which is under jobs.dataumbrella.org if you are looking for a position or just curious feel free to check it out we have two highlighted jobs today the first is a software engineer position at oscar health which is based in new york city it's a health insurance company and they are developing seamless technology and provide personalized support to members to navigate their health care and they have some other roles open as well so but check it out our next job that we're highlighting is a machine learning engineer by development c that they are based in washington dc or lisbon portugal or it can be a remote position for now and um what they do is they're mapping elections from afghanistan to the us analyzing public data and economic data and leading strategy and development behind data.world bank and other um other social enterprise initiatives um these are just uh um just a sprinkling of what we have available on our website there's a lot of resources on responsibility on accessibility on open source so check that out on your own we have a monthly newsletter it is dataumbrella.substack.com so feel free to sign up for it we only send it out once a month so we promise not to spam you with too much email we are on date umbrella is on a bunch of different platforms under data umbrella so depending on what your preference is um the best place to actually be a member is the meetup group because that's where all the events are posted our website has resources we are on twitter we are on linkedin we are on youtube if you want to subscribe to our channel i will post some of these links once i finish this intro presentation on the chat so you can follow it and before we begin i just want to let you know that next week where our upcoming event is using streamlit for data science with thomas fan thomas is also a core contributor to scikit learn and streamlit is a way to build and share data apps in python and i think it's going to be a great presentation and i'm so glad that um thomas said yes when i invited him and asked him a little bit about today's speaker carol willing carol has an msn management from mit and a bsc in electrical engineering from duke university she is a core developer of core python she is a member of python's steering council for project jupiter jupiter notebook um as many of us have used she's on the steering council and works as a core developer on jupiter health and mybinder she is also co-editor of the journal of open source education and she co-authored an open source book teaching and learning with jupiter there's a lot more that carol has accomplished but i was running out of space so i will let um carol take it over from here oh and and just just one more thing we have a q a tab on this platform so if you have any questions just post them there if you do post them on the chat it's not a problem i can easily move them over to q a and if you want to upvote them um you know i think we'll have time to answer everyone's questions but feel free to upload to see what's really um exciting and important for you and i will hand it over to carol thanks russian thank you all for being here and thank you for the sponsors and everybody who organizes groups um it's really important and it helps us as an ecosystem gets stronger i am going to start sharing my screen hopefully and once roshama lets me know that you guys can see everything then i will start there was a little bit of a lag before but um okay well i'm going to assume you can see my slide deck otherwise um somebody please shout um so today i'm going to talk about contributing to core python but i'm going to do it from the lens of a scientist or a data scientist so it's going to be my opinionated view of working in all of these communities and for those that are advanced users you will find something useful in this for those of you that have never contributed to open source you should also find a lot of really useful stuff as roshama went through my bio the ending part talked about education and using tools and that is really where my heart and passion is less about the technology and more about building tools that empower other people to do good things in the world and um i think our science and data science ecosystem helps us do that really well so let's get started so um yes core python and c python are the same thing you will hear them used interchangeably and for all intents and purposes you can just assume that they are actually the same um so for today's talk we're going to look at this from a data and science perspective so the primary audience is data scientists scientists data engineers but other folks that might get value out of this are computer science compiler engineers operating system experts language geeks but that won't be the focus of the talk it will be the data folks and science fics okay so contributing to core python today i'm going to walk a little bit through how core python is organized today then i'm going to compare it to other open source projects in our ecosystem of data and science scikit-learn is a great example of that and then we'll talk about getting started and how you can go further with your contributions to either see python open source your local community and more so we had an interesting thing happened a couple of years ago gita van rossum who had been the benevolent dictator for life of python stepped down and we were faced with having to create a new governance for core python and what we came up with was a small steering council these are the members currently who sit on the steering council and really we are to sort of do and set the direction of things that guido did by himself in terms of organization in the project and direction of the project and in specific we are tasked with ensuring the quality and stability of the language moving towards contributions that are accessible inclusive and sustainable fostering a stronger relationship with the python software foundation um continuing to facilitate the decision-making process for peps which are python enhancement proposals those are when there are large changes made to the language or proposed to the language or workflow that's something that um is open for everybody to read and comment on and our goal is to seek consensus both with each other but also the community at large because we don't want to be a dictator of the direction of python so as we look at core python what kinds of contributions are needed lots um but i want to first step back and look at the python software foundation which is a sister organization to the core python developers yeah and yeah sorry just we don't we see you but we don't see your slides uh-oh so um maybe if you can just share again maybe let me stop sharing and re-sharing yeah that might that might work ah that might work let's see if escape will do it worked in practice right let's do it again okay now you should see my whole desktop is that correct okay it takes just a couple of seconds for the lag it is loading which is good and now we can see your whole desktop yep and now you should be able to see the talk yep now we can see the talk and we can see you okay so speed version the title of the talk core python opinionated guide for scientists and data scientists yes core python is the same thing as see python i'm targeting this to the scientists and data scientists and data folks in the audience we're going to go through a few different ways of contributing to core python and beyond i started with a quick discussion of governance or introduced the steering council what the steering council is responsible for and now back to you which contributions does see python need and looking at the mission of the python software foundation is a good place to start the python software foundation is a sister organization to the core python group and its goal is to promote protect and advance python the language as well as to grow a diverse and international community of python programmers so as you think of making contributions keep that in the back of your head because those are really going to be the most valuable contributions some ways you can contribute and oftentimes people look at writing new code as the only way to contribute and it is a great way to contribute but in many ways a lot of these other ways to contribute are equally if not more important um when you're adding new code it's got to be with backward compatibility we're a 30 year old language you know people don't are using this in production they don't want things breaking maintaining security anything that you can do to maintain the security of the language or improve core development workflows so mariada has put together a lot of bots that make our core development workflow much easier for folks to get started and also for github and the power of technology to kind of help with some of the more tedious tasks writing and running tests writing and editing documentation both of those are absolutely critical to the success of any open source project python as well um one huge need we have is folks to triage bugs for reproducibility and um in addition to that people to review open prs um right now we're split um our code base is on github but our bug issue tracker is on bugs.python.org so um there's sort of an extra step between the two so um it it's helpful um with uh decreasing the backlog of uh prs and bugs for people to help um triage and review prs and in the last year we've actually started a triaging team which is in many ways a stepping stone to core development um and then really anything that you do to share your knowledge with the community maintain projects give talks write blog posts attend meetups that is a contribution to core python so congratulations you've all made your first contribution to core python or at least the python ecosystem so i'm going to spend a minute comparing the different projects and comparing and contrasting python versus other projects that are younger like jupiter matplotlibs scikit-learn and more so what's similar well most of these projects use a github workflow and with that i mean we host our source code on github or gitlab and we operate with a pull request mindset and what that means is a pull request is hey i would like you to take my code and add it to your code base and then a maintainer will either say yes that sounds like a great idea i accept it or gee could you make these um changes and then resubmit it or in some cases when it's just not an appropriate contribution we might say you know what this would be better as a third party project or um another project all projects have code review and that's when maintainers and core developers look through code that's being submitted or documentation that's being submitted and make suggestions hopefully very kindly about what could be changed or improved and many strong projects have automated testing and continuous integration and it's really valuable to have that as part of your project because it provides sort of an independent view of what's going on so as a contributor it sort of provides you guidance okay i've done like the correct syntax or formatting if i'm submitting code or documentation and one thing that i forgot to mention but is really important is healthy open source projects have not only a code of conduct but also an onboarding guide or a developer guide that helps new contributors get started and helps existing contributors build their skills so what differs well there's a fair amount of stuff that differs between see python and other data science projects like jupiter and jupiter hub and binder that i've been involved with um and one of the most important or biggest ones is the velocity at which new features are added to the project python's a 30-year language has a long history and a lot of code out in the wild that are being used in production in scientific research and as such we can't just go and change things as quickly as we could in a newer project because we have more embedded users the other thing that's different about core python is it's a language that's used far beyond just data and science so not only do we have to status the data satisfy the data and science community but we also have communities around web development sys admin devops embedded systems teaching and so forth so when we look at python we're looking at okay how do we keep stability and backward compatibility and security while adding new features and in many of these other scientific projects it's a bit flipped we're looking for adding new features while also creating you know a stable um though often changing environment we might uh deprecate things which is stop using things or offering things in a two-year window where python it would be much longer could be five years could be ten years um and then the context in which these projects are used is also different um see python is a tool that we use to build things and it's a tool that is used as a foundation for many of these projects like jupiter or matplotlib to create their own project so the stability of c python is critical to the stability of our entire data and science ecosystem because what we don't want to do when we make a new python release is break a whole bunch of other projects and so um yeah so that should give you a sense of the fact that python's going to move a little slower really emphasize code and quality and security whereas some of these scientific and data science projects are going to move much quicker and you'll see a lot of change in even a year or two years so how do you get started contributing or if you're already contributing how do you continue and perhaps grow your skills well if you're a first time contributor to open source i want to strongly encourage you to consider making your first contribution be a contribution to a project in the scientific data science community and i'm going to go so far since it's my opinionated talk to say i would encourage you to do that over making c python your first contribution and the reason why is you will find going back to the differences between the projects and python language the velocity of change is much higher in these projects like scikit-learn so it actually winds up being a way to learn while doing and make an impact while you're learning which is much harder to do in see python and i highly encourage you to go watch both of these or read the transcripts they're excellent so as you get ready to contribute to see python and this would apply to most open source projects as well you want to kind of get into a mindset that will set you up for success and one of the first things you can do is sort of check your intent or really identify why you want to contribute um and the reason i say that is when things get bumpy along the way um it's easier to persist in the process when you have clear goals of what you're trying to accomplish and why and i would encourage you to think about your initial impact and initial scope keep it small it's much easier to start small and then work up to big and i want to also say that patience is probably one of the best things that you can have along with communication skills in open source most projects are run by volunteers the vast majority of core developers foresee python are volunteers all the stuff i do is as a volunteer on my time notable gives me a lot more latitude than most companies to work on open source but it's not my primary job and there's less than a handful of folks within the core development team where it is their primary job all right so you've got the right mindset you're setting yourself up for success what are some of the common reasons that people contribute to open source and these are just a few of many you're using a project whether it's a scientific python project and you hit a bug and that bug possibly is related to something in c python so you might want to fix something in c python to make your other project run better you might have come across something that perplexed you or was complicated to learn so you might want to improve the documentation so the next person doesn't have to go through the same process a lot of people just think it would be cool to contribute to python which is a totally valid reason um many people want to just understand more about how things work and um to strengthen their development skills so this is just a small subset but um some things that come up time and again so your most important research resource when contributing to core python is what we call the dev guide um and it's located at devguidepython.org it is a comprehensive guide to contributing to python um and it's maintained by the core developers that also maintain the language it is pretty much everything you ever wanted to know about contributing to python and then some as such there is a quick reference guide which is really a great place to start and we'll talk about it um in a little bit but there's also things about how to submit a pull request how to get help how to run tests and many other resources so this is sort of your one stop if you will um towards getting started and many other projects have something similar perhaps not as long but um yeah so some other helpful prerequisites that will improve your contribution experience to see python is to take a little time to understand see python's culture and that can be um there are there's different aspects of the culture because we have people that have you know been with the python language for 30 years versus are relatively new in the last five years to core python you're going to have people with different perspectives and different work styles within the language personally i spend a lot of time looking at discourse which is discussed.python.org and less time looking at mailing lists partially because i find more value in discourse than in the mailing list but there's a lot of core developers that do the reverse so um and then i also spend a lot of time looking at the pull requests and the code itself so understanding the culture and where to find information and how the pace at which things happen is really important also understanding the difference between the core language and the standard library the core language is a smaller subset of what you would think of as core python and then the standard library is actually many other smaller libraries that provide additional functions that gave python the batteries included um name and um core python core lang the core language is really things like data types and really the fundamentals that you would have in any software development language again i can't reiterate enough that core developers are volunteers and um be kind um many of us are wearing many different hats on the flip side the core developers should be kind to you as well so we do have a code of conduct and you know i encourage you if you're seeing behavior that is not professional please let folks know um understanding git and github workflow is i think very important for contributing to core python at the point where people are contributing to core python the general assumption in the community is that you have um basic git and github workflow experience and understanding and if you jump back to where i said oh i i am encouraging you to also consider contributing to other data science and science libraries partially it's because those projects tend to be a little bit gentler with new contributors that are still learning git and github and there's lots of information out there about doing it um software carpentry or the carpentries have a great guide and um there's a talk which maybe we can link to in the future unfortunately it wasn't recorded but a slide deck that i put together gosh probably 2016 for complete beginners at writespeak code who are learning git and github and it really is a very gentle introduction to both but um has been very popular and then the other prerequisite is some familiarity of python um it's not necessary to know c it's not necessarily to know c plus so most of the language is written in python so you can be very effective without really understanding much if at all from c or c plus plus so that's a lot to digest and i wanna um for those of you that want a great exercise um later or when you're ready um i wanna just give you the brief directions on how to build core python from source and um people often think it's a really super complicated process and um the quick reference guide in the dev guide actually runs through these steps there may be some subtleties based on the operating system that you're running but essentially what you're going to do is fork and clone the source code from github which is at python c python then you're going to use a c compiler and most of the time c compiler is kind of available with your operating system to configure and build python um it's one command to configure and build it with unix linux and mac um make is what will actually do the actual building and then windows there's a bat file that combines those same steps so to configure and build python is one step so what would you want to do as you're starting well a good place is just to run the tests and again like building and config and configuring python it's one line of code and there are this should look pretty familiar with folks that have done python it's just basically executing the test library so um there you go you have now learned how to build configure and run tests for python um the dev guide will definitely give you additional questions and answers on how to contribute um again remember changes to the language are submitted as github pull requests our continuous integration will run the automated tests just like you're running them locally it will run them in an automated way across all operating systems and a number of different versions of python the next step in the process if you submit a pr would be to wait for some review from a core developer or a contributor address the feedback as appropriate and hopefully then you will have a final core developer review and if luck will have it um it'll be merged into the code base and um folks can use it from then on for those of you that want to go deeper in your understanding of see python there is a wonderful blog post which is now a book by anthony shaw about a guide to see python source code it is the most accessible yet highly technical um explanation of how see python works um you know down to the lowest levels um in fact it was so good that when it first came out i took the entire uh blog post copied it all had it bound um in a spiral you know thing so that i could refer to it on a day-to-day basis and i encouraged anthony to write a book based on it and he did so we're very lucky that there's many ways of accessing his materials and he's also a very prolific speaker so there's lots of stuff on youtube as well another great resource um for learning about python i don't know if you can see me or if you're just seeing my slides but there is a book called high performance python by misha gorlach and ian oswald and it is um i think an outstanding book for uh both learning python and how it is built and comes together but also the things you can do to improve the performance of python um one of the things you might hear in the media is python is slow python you know isn't performant because there is this global interpreter lock or gill and the global interpreter lock or gill what it does is it limits at certain points the processing to one thread if you will at a time and that tends to be a bottleneck because right now we have many multi-core processors and things like that you'd want to use all that and not have to bottleneck into things but this high performance python it runs through things like how to profile your code how to use multi-processing how to use cython which is a great project in our ecosystem in terms of that allows us to do a lot of more cpu intensive stuff i believe it also covers number which is also another great project that kind of uh uses just entire time compilation to get around the gill so yeah there's lots of ways you can improve your performance and these two resources are just some of the many that are out there in addition to those resources um there are many core developers keep websites that have a lot of um technical content victor stinner brett cannon and guido a lot of historical information from guido as well much like the sprints that have been held for scikit-learn see python typically runs sprints when we have in-person conferences which sadly um 2020 but you know the hope is that there will be some being done virtually and um you know if anybody wants to run the c python sprint let me know i'd be happy to kind of help guide you with that um many of the python python talks both um from pycon us and beyond um explain how to contribute to core python i think my python talk from 2015 was how to contribute to core python when you're not a core developer mariada's given great talks about contributing both to the language and to the workflow of victor stinner and for those of you that have any interest in asynchronous programming lucas has a great series on youtube about async io i think it's about seven parts and um it's a really great introduction so we can always use help from folks that are interested in asynchronous and this is just a small subset maybe a third of the core developers as volunteers our time is limited but the community is key and it's what lets python both as a language and ecosystem thrive so i want to encourage you to go forward and contribute join the discussion on discourse which is discuss python.org and whether you're contributing to python or any of the projects in the ecosystem you are creating real change and helping others solve important progress problems in the world so i want to thank you for listening to me and um thank you to roshama and the organizers and i'm going to shut off my screen this is already available on speaker deck and it will be available through data umbrella as well and i am happy to take any questions that might have come up okay so um there's a question about the slide so i will find all right speaker deck for that um speaker deck okay um so the next question is is the need to reproduce old bugs against the newest version of three nine in need of the project depends on how old the bug is um if the bug is like within two years old i'd say yeah it's probably useful to reproduce those if in general my view and it's strictly my view it's not a official view in any way or shape or form personally i would close the vast majority of issues that are over let's say three years old and because they're still accessible to people to find if needed but the likelihood of them being worked on i think is fairly low um uh we are going to move the issue tracker to github um there is work in process to do so um and that should make the whole process more streamlined and it will let us do some things with notebooks and some of the tools that we have in our ecosystem to help surface issues that haven't been looked at to help recognize contributors there's many different things we can do with the data that is available within the repo hopefully that answers the question okay uh the next question is g-i-l is a problem but the advice i've seen is avoid being limited to one core is to use multi-processing over threads okay so there's a lot of different perspectives and um the gil or gill which is the global interpreter lock there are different ways of getting performance that um gets around the limitations of the global interpreter lock multi-processing is a great way i would say threads would not be my first choice in how to do that but things like cython number you know depending on the use case async io but yeah it's a great question and the resources that i mention particularly high performance python will give you excellent advice on how to get the most out of your deployments in a safe and efficient way okay and the next question is what is the best way to attract a mentor from the core team does the project have a formal process for being mentored so there is a core mentorship mailing list which used to be more active right now mentors you know because we're all volunteers people mentor as they are able to i typically my mentoring is basically being welcoming and answering questions that people directly ask me um i just don't have the time and bandwidth to mentor individuals but um one of the things that we do have is the triage team so if you've been involved for a while somebody may ask you hey do you want to join the triage team and the triage team you know it has some additional capabilities they can do things like um make comments uh or attach labels to different issues and that's really a great way that recently folks have found mentors as a result of the work that they were doing within triage and then victor stenner has been really great at mentoring folks as well as you know barry raymond hedger you know there's a lot of people that do eric snow um pablo salgado who's our release manager for 310 and 311 have also mentored fix and victor's got a lot of writing on it so hopefully that helps and feel free to reach out to me directly and i can probably provide additional resource what's the best way to reach you carol uh reshma knows this i stink at email um github is really the best way to at mention me on something but um you know my dms are open on twitter and you know i will get to it when i can get to it um it's not that i don't want to respond but the volume of stuff that comes in is i could spend my whole day doing email and nothing else so um you know i tell folks that i meet you know be persistent if you don't hear from me once or twice do not hesitate to email me a third time and i will do my best to answer okay the next question is from your viewpoint what does the future hold for python for instance for the next 30 years wow that's 30 years i hope that python is as vibrant today as 30 years 30 years from now as it is today i think in many ways you know the whole data science scientific python ecosystem has been one way that has really revitalized the language over the last five years another place that we're seeing a lot of growth is in embedded systems things like micro python and circuit python and uh you know that's really exciting to me from an open hardware and education standpoint and and getting young folks or folks that maybe aren't computer scientists to start with involved i would love to see us um can improve the performance of the language um and what that will look like i'm not entirely certain um there's definitely going to be efforts over the next five years to do that some of it is time some of it is funding um but i the one thing i hope stays the same 30 years from now is the readability of the language and i think because python is so readable that actually makes it much more accessible for folks as well as um tools like the notebook kind of break down and provide a good education tool for learning more about python so the community is going to be the one though that drives where python goes and truth be told there's another language that i also find really interesting that probably is relevant to many members in this community and that is julia it's still a very young language but i think it has a lot of potential and promise and um is very similar to python in a lot of ways um but gets around issues like the gill and relies more on c c plus and should you ever want to compare julia code with python code tom sargent's quant econ website is excellent for doing that because even if you're not an economics person it has economics code written in python and then similar economics code written in julia and it's a good compare and contrast when you're learning so i don't know that's about all i can say for the future that's there's been a lot of discussion i see going on because o'reilly published a report too about python and i guess speed is the thing that's most under discussion right um it these days you know it it's interesting because oftentimes and and this has been historically you know i'm 54 i've been in this industry a long time speed has always been at the key like is this faster than the other is vim faster than emacs is emacs faster than vim um you know speed is relative and it depends on what you're measuring i would say one of the things that is not often discussed and really should be is the speed to create a project and python is a really efficient language for going from no code to prototype to production and there's value in that beyond just pure processing power and now that said there are you know certain things that if you're cpu bound um like high cpu operations versus io operations and you know web is going to be different than pure number crunching you know your performance is going to vary and one of the things i think that would be really useful is to try out like profile your code look at the other tools for increasing the speed you know there's trade-offs regardless of what language you use and i encourage you to try other languages as well there's another question about um you mentioned the global reach of the tech community aside from events like this what is the role of the python steering committee in making the community more inclusive so um the steering council has been a part of some of the code of conduct um decisions and discussions related to core developers and we have this year taking some actions to redirect or remove uh folks that were probably not contributing constructively to the community um the so you know adopting the code of conduct throughout the project was one way uh the steering council also looks at efforts um whether it is the core language summit or the core developer sprints those used to be exclusive to only core developers and in more recent years we've um invited more people to it that to both increase the diversity on a number of dimensions as well as just even use cases my hope has long been that diversity will benefit from the move to github i was an early proponent of moving the code base to github and have been a strong advocate for moving the bug tracker to github or or it could have been gitlab but something where the tooling is modern and is more accessible to more people because one of the things i personally feel when you're an underrepresented group your time is more limited than if you are in the majority so you have to pick and choose what you work on much more carefully and by having a roadblock like having to learn an issue tracker versus using uh tooling that you're used to from your day-to-day work i think that adds a barrier to inclusion so um [Music] yeah so it's really trying to shift the culture towards more inclusive culture mariata has done an amazing job with that and you know the steering council is really there to support the efforts within the community and the core developers that are interested in moving that effort forward also just to add you know pi ladies which is um you know under python software foundation is also connected to um improving inclusivity in the python space yes yes that's a great suggestion or comment because we also have um within pi ladies there's the ability to ask questions um just like with our ladies there's an ability to ask questions on core development within those channels another question here is what will be the impact of the new peg parser in future python tools and libraries and maybe you could explain what peg means to someone like me who doesn't know um so it's we affectionately call it the peg parser and when you're creating a language um you have a grammar you know your syntax and the language has to have a way to sort of know okay this is a keyword this is a variable this is an operation things like that and that is the job of the parser um i believe that the peg parser will give us more flexibility over time it is probably equally as performant as the prior parser and there was a lot of extensive testing done during an entire release cycle to make sure that you know it wasn't introducing weird regressions and things like that but one of the things it is much easier to write grammar rules now with the peg parser than it was with um the old parser that we have so hopefully that ans answered a little bit what it is and uh why it is and and someone who can provide far more answers than i would be pablo salgado and um do reach out to him he's probably got some talks on the peg parser as well and one last question which is i heard a talk that is a twist on the speed demand instead of focusing developer time expertise on on optimizing to a capital o one go with n squared performance because developer time is more expensive than ram is what are your thoughts on that um i i think it really comes back to use cases and um i think the difficulty with python is because we serve so many different communities what is optimal for one community may not be optimal for other communities um so you know it's an approach is it the approach i don't know to be really honest it wouldn't be my first approach i guess is what i'm saying okay so with that we are reaching the top of the hour and so um thank you so much carol for taking the time to join us and sharing about core python um i have a link to the slides i put them in the chat and i'll include them in the video when it's uploaded to youtube and there's a bunch of links to that you've mentioned which i've been putting in chat and i will link to it in the you know youtube description or somewhere very convenient um yeah thank you so much great thank you and thank you everybody for attending i hope you learned something about contributing to open source and i hope you you do so as you're ready to do it thank you