7 ms·

Show HN: Intro to Python and Programming for non-CS majors

211 points by webartifex 7y ago

webartifex 7y ago
Hi there, I am a PhD student in Operations Research and developed an Introduction to Python & Programming course for the Bachelor program at a business school. Due to Corona, I got a chance to video tape it thoroughly (should have done that earlier). Maybe someone here finds my lectures (playlist: https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQow...) useful or knows someone who does. The recordings are 25 hours in total. If you do the readings and exercises, you should allocate 90 - 120 hours roughly. The GitHub repo is really an interactive book in Jupyter notebooks: https://github.com/webartifex/intro-to-python https://github.com/webartifex/intro-to-python As I didn't study CS myself, I think I provide another angle for newbies. Pull requests with improvements are highly welcome. The materials contain lots of references to the Python community as well. I love this community: I started without any knowledge of Python in 2014 and the many conference talks and repos really help when learning. Stay safe everybody, Alex
gus_massa 7y ago
Why Chapter 6 has no exercises? (It's not a complain. Just asking in case you forgot to link it.)
- webartifex 7y ago
  They should be ready tomorrow evening. Within the next two weeks, a missing chapter 9 on arrays will also be there. I spent around 50 hours doing the videos in the last couple of days while also holding remote office hours for the 80 students in my class at the university. Crazy situation at the moment, putting everything online due to Corona. We even had an infected student on campus, luckily mild version. Didn't think anyone would comment here as there are so many Python intros out there :)
Waterluvian 7y ago
Forgive me if I missed this, but I would highly recommend talking about virtual environments, pypi, and the existence of other things like pipenv. The environment/dependency management story for Python is such a tire fire and I wish someone introduced me to it from a reasonably high level at the very beginning of my time with Python. It doesn't have to dig deep. Just needs to talk about what these things are, why they exist and why it's such a challenge. Too often I think people skip over these topics, considering them as nothing more than a means to an end. But by far and wide my largest challenges with Python has been environment and dependency management.
- airstrike 7y ago
  I second this. Sure, there's a huge value in the beginner-friendly structure that Jupyter notebooks provide, but it's also a limiting factor as it's just a very small subset of the Python experience (I've personally never edited a single .ipynb file in my life)
  - rahimnathwani 7y ago
    Note, this is intended for people who will use python for data science, not for future software engineers. Data scientists using python but not using jupyter notebooks is the exception.
    - webartifex 7y ago
      That is correct. But even then, I think in the future the data scientist role and the data engineer role will converge and the person doing this must know virtual environments.
      - jonfw 7y ago
        In the future we will likely have different tools to handle dependencies and environments. If these things aren't immediately useful, it might be worth teaching other stuff instead.
santiagobasulto 7y ago
Amazing content, thanks for making it public! For people lurking, it has Notebooks, Quiz (review) questions, AND exercises. This is very good. @webartifex it could help the spread of your course if you could put the Notebooks in a self-hosted Jupyter Notebook environment, so students don't have to install anaconda and all that. Shameless plug here, I'm the creator of Notebooks.ai, which is a hosted Jupyter Lab environment for students, 100% free (we only charge big schools, so teachers and students are free to use). Here's a quick demo of your first lecture: https://notebooks.ai/santiagobasulto/intro-to-python-demo https://notebooks.ai/santiagobasulto/intro-to-python-demo And aside from ours, there are other options like mybinder or Google Collab.
- webartifex 7y ago
  Good tips. I will look at this. I am planning to use Google Collab for a totally remote class next weekend to set up group work.
oman121 7y ago
Is anyone aware of a repo with an introduction to Angular (2+) that's a similar style and equally high quality?
- webartifex 7y ago
  I'd be the first reader :)
ivan_ah 7y ago
Wow this is really thorough and well thought out. It's not just an "intro" but goes into all the language builtins, and common usage patterns (e.g. zip, map, etc), which many "intro-to" style courses skip over. Code in repo is MIT licensed and videos are tagged as Creative Commons (CC BY). Finally a good OER course for people to learn Python!
- webartifex 7y ago
  The Python community is basically one big OER course. Just look at all the recordings from the many PyCons. I have a second half for the "book" planned but "unfortunately" also need to do some research for my PhD. Maybe someone wants to provide some more advanced chapters via a pull request?
idoby 7y ago
Awesome work! I can't wait to test this on my non-programmer friends who want to learn how to code. This seems to have a somewhat different approach to the usual Python tutorial, it might do the trick! Thanks
- webartifex 7y ago
  I tried to put in everything I myself would have wanted to know when starting in 2014. That is why I spend so much time on the memory diagrams in the videos in particular. It literally took me years to figure that out. I have watched a lot of online lectures (e.g., from OCW), but only rarely do instructors talk about that. Maybe formally studying CS would have done the trick for me as well :)
  - idoby 7y ago
    It's not necessarily covered in a traditional CS program, it really depends on the instructor. This is a good resource. Regarding the videos: they're hard to watch because the text size is very small and it seems to be blurry due to compression/encoding issues and in some of the videos, the audio track has a high-pitched hum in the background that makes it very hard to listen to. If you still have access to the source materials, you should seriously consider cleaning up the sound and re-encoding the videos for better quality (as well as making the text bigger where possible).
    - webartifex 7y ago
      Two videos have the hum. This cannot be changed because the lecture capture system in the lecture room does not allow this (It's Panopto). The quality is actually high enough. Maybe that is due as YouTube is currently lowering the quality due to Corona? My plan is to incorporate the feedback everyone gives in the next weeks and do the recordings again in studio at the end of summer for the next semester. The videos are actually intended to only support the chapters in the Jupyter notebooks. The funny thing is this: Once I started doing the memory diagrams in the lecture, the students' work exhibited a lot more quality. Further, absolute beginners understand, for example, the concept of an iterator vs. iterable. BTW: chapters 9 and 10 will be released soon on GitHub.
qubex 7y ago
Can I suggest adjusting it for some other consumer platforms, such as, for example, Pyhtonista on the iPad? That would make it easier to divulge in these turbulent times when many people are stuck using somewhat unusual instruments and platforms.
- webartifex 7y ago
  Does Pythonista for iPad support Jupyter notebooks?
  - qubex 7y ago
    I don’t think so, no. But I might be wrong.
vcsilva 7y ago
That's a really cool project! I will probably be recommending this to beginner Python programmers I know. As a side note, I think sharing lessons as Jupyter notebooks is a really great idea for programming education. I would like to see more of this style of course for other programming languages as well.
- webartifex 7y ago
  I agree. Although at some point, any beginner must be exposed to the command line :) My observation about myself when studying is this: Nothing, no video course, beats a well written book. And, such books are often "slow" reads as you have to think a lot when reading. I found Jupyter notebooks a good instrument to put a lot of info in. My guess is that there is three times the info in my course's notebooks as there is in the 25 hour video recordings.
ipnon 7y ago
Is it really this easy to become a data scientist today? Can you go from 0 to 1 with online programming tutorials like the original post and fast.ai?
- OJFord 7y ago
  Well, OP says [0] they're a PhD candidate, so maybe take 'non-CS majors' quite literally, in terms of where it's hyphenated. 0 - https://news.ycombinator.com/item?id=22669091 https://news.ycombinator.com/item?id=22669091
  - webartifex 7y ago
    I consider my course a thorough course. You really learn a lot of theory. This is usually what makes students at my university drop the course after two weeks of auditing. Just because the course assumes no prior CS contents (anything really, except high school math), does not mean they don't get introduced.
    - OJFord 7y ago
      Oh I wasn't suggesting anything against the quality or content of your course - perhaps I misunderstood the comment I replied to, but I took it as: > High schoolers are really just doing some web course and then getting data scientist jobs? so my response was: > No - or at least this isn't evidence of that - the author is a postgraduate student and the target audience appears to be other graduates/university students.
      - webartifex 7y ago
        I hope there are no companies that hire a "data scientist" that only speaks HTML :) The course aims at students of business administration. What do they usually do? Maximize some profit function. So, my background is really more traditional Operations Research.
- webartifex 7y ago
  It depends on who wants to hire you. For the top jobs, this is for sure not enough. I always tell people to please make sure they know all the contents from here https://www.youtube.com/watch?v=ZK3O402wf1c&list=PL49CF3715CB9EF31D&index=1 https://www.youtube.com/watch?v=ZK3O402wf1c&list=PL49CF3715C... (Gilbert Strang's Linear Algebra course) before they make any claims about being a data scientist. I bet I can teach a monkey to open a CSV in pandas and call .fit() in sklearn. But do the people really understand the underlying assumptions. Most self-proclaimed data scientists don't I am sure. I still have a hard time calling myself a data scientist. And I am three years into a relevant PhD. The more I study, the less I feel I truly know.
pbreit 7y ago
If looking for more of a "crash course" to be up and programming a web-based project rapidly, have a look at: http://www.web2py.com/books/default/chapter/29/02/the-python-language http://www.web2py.com/books/default/chapter/29/02/the-python...
starpilot 7y ago
> The course's main goal is to prepare the student for further studies in the "field" of data science. Does the material seem to have major gaps for DS to anyone else? There's no pandas, no matplotlib, no ML. It seems like more a tutorial with a computer science focus, with recursion and bit manipulation. Those are great programming topics but rarely used by data scientists. For that I would probably use .py files, not jupyter notebooks. It just doesn't align with my experience as a Python-based data scientist.
- webartifex 7y ago
  Pandas and numpy & friends will be in Chapter 9 that I had not yet time to finish (the semester runs until end of April and is currently at Chapter 5). Furthermore, it is a programming (!) course, not (!) a data science course. Please define data science to me. Is it "only" curve fitting to you? Or also optimization (e.g., in logistics)? Then, dynamic programming (and because of that recursion) is super important. I rely on Jupyter notebooks mainly because it is a course for people with absolutely no prior experience that also do not major in CS. .py files are actually explained in Chapter 2 and will be used in a follow-up course.
rob_austin 7y ago
At this point, I feel like there are too many "Intro to Python" courses floating around. It will take a newbie, multiple hours of reading reviews and recommendations to find a course. There are many good ones out there. I wish there was more effort spent on creating intermediate courses. It would be great if there were more people trying to write books like this Nicolas Rougier is trying (https://github.com/rougier/scientific-visualization-book https://github.com/rougier/scientific-visualization-book). Take a specific library and help people become proficient in them. Usually, the core contributors to a library are not always the best people to teach people how to use that library.
- webartifex 7y ago
  What would be contents you expect from an intermediate Python course?
  - rob_austin 7y ago
    Some example topics that took me a lot of reading from different sources to understand. - how to write a python library that you can host for public/private use - adding test coverage to data science python projects - learning libraries like matplotlib, seaborn beyond what you see in tutorials I think material for all fo this exists in different sources like documentation/stack over flow but either it's too detailed or too superfluous. The middle (intermediate) layer is often missing.
    - webartifex 7y ago
      I get you. The plotting tools I would actually consider in an "Intro to Data Science" course, not in an "Intro to Programming". I started to write a library implementing Gilbert Strang's Linear Algebra book assuming nothing but core Python. What inspired me to do that was reading Philip N. Klein's book "Coding the Matrix". I thought writing a LA library for fun and study purposes makes sense if you want to go on and study data science. However, that project is not yet ready to be published. Would that be what you are looking for?
AmericanChopper 7y ago
What I never understand about intros like this (even intro courses in college), is why not start with types? Reading the content, it’s obvious there’s something different between a string and a number, and a list (or a dict) is obviously very different again. You quickly figure out that these things are also capable of doing completely different operations, but why? And what things (methods) are they capable of? I was helping a friend who decided to start a CompSci degree recently (the 101 course was taught in python), and she was massively struggling with type coercion and understanding type methods. Looking at the course forum, so was everybody else. I helped her understand what types were, why they were important and what the built in type methods were (and how to read the python documentation), and it was a major breakthrough for her. She’s now getting close to 100% on her tests. I was also blown away by how archaic some of the content was, like using .format() instead of fStrings, but that’s a seperate topic.
- webartifex 7y ago
  I introduce the idea of a type right in chapter 1 because of exactly the argument you make. As Python is really more about the behavior of objects and not so much their type, I introduce these already from chapter 4 onward, for example, iterable vs. container, and many more. I actually would say that this is the essence of any dynamic language (duck typing). "and how to read the python documentation" -> that is an important point you raise!!! I found that beginners have real trouble reading the docs because they are screening for words like "list" instead of "iterable". However, as I teach abstract behaviors early, they actually understand the docs. "like using .format() instead of fStrings" -> I mention .format() but mainly use f-strings and tell the students right away that they are both faster and easier to read and that they should default to them.

211 points by webartifex 7y ago

Hi there, I am a PhD student in Operations Research and developed an Introduction to Python & Programming course for the Bachelor program at a business school. Due to Corona, I got a chance to video tape it thoroughly (should have done that earlier). Maybe someone here finds my lectures (playlist: https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQow...) useful or knows someone who does. The recordings are 25 hours in total. If you do the readings and exercises, you should allocate 90 - 120 hours roughly. The GitHub repo is really an interactive book in Jupyter notebooks: https://github.com/webartifex/intro-to-python https://github.com/webartifex/intro-to-python As I didn't study CS myself, I think I provide another angle for newbies. Pull requests with improvements are highly welcome. The materials contain lots of references to the Python community as well. I love this community: I started without any knowledge of Python in 2014 and the many conference talks and repos really help when learning. Stay safe everybody, Alex

gus_massa 7y ago

Why Chapter 6 has no exercises? (It's not a complain. Just asking in case you forgot to link it.)

webartifex 7y ago
They should be ready tomorrow evening. Within the next two weeks, a missing chapter 9 on arrays will also be there. I spent around 50 hours doing the videos in the last couple of days while also holding remote office hours for the 80 students in my class at the university. Crazy situation at the moment, putting everything online due to Corona. We even had an infected student on campus, luckily mild version. Didn't think anyone would comment here as there are so many Python intros out there :)

Waterluvian 7y ago

Forgive me if I missed this, but I would highly recommend talking about virtual environments, pypi, and the existence of other things like pipenv. The environment/dependency management story for Python is such a tire fire and I wish someone introduced me to it from a reasonably high level at the very beginning of my time with Python. It doesn't have to dig deep. Just needs to talk about what these things are, why they exist and why it's such a challenge. Too often I think people skip over these topics, considering them as nothing more than a means to an end. But by far and wide my largest challenges with Python has been environment and dependency management.

airstrike 7y ago
I second this. Sure, there's a huge value in the beginner-friendly structure that Jupyter notebooks provide, but it's also a limiting factor as it's just a very small subset of the Python experience (I've personally never edited a single .ipynb file in my life)
- rahimnathwani 7y ago
  Note, this is intended for people who will use python for data science, not for future software engineers. Data scientists using python but not using jupyter notebooks is the exception.
  - webartifex 7y ago
    That is correct. But even then, I think in the future the data scientist role and the data engineer role will converge and the person doing this must know virtual environments.
    - jonfw 7y ago
      In the future we will likely have different tools to handle dependencies and environments. If these things aren't immediately useful, it might be worth teaching other stuff instead.

santiagobasulto 7y ago

Amazing content, thanks for making it public! For people lurking, it has Notebooks, Quiz (review) questions, AND exercises. This is very good. @webartifex it could help the spread of your course if you could put the Notebooks in a self-hosted Jupyter Notebook environment, so students don't have to install anaconda and all that. Shameless plug here, I'm the creator of Notebooks.ai, which is a hosted Jupyter Lab environment for students, 100% free (we only charge big schools, so teachers and students are free to use). Here's a quick demo of your first lecture: https://notebooks.ai/santiagobasulto/intro-to-python-demo https://notebooks.ai/santiagobasulto/intro-to-python-demo And aside from ours, there are other options like mybinder or Google Collab.

webartifex 7y ago
Good tips. I will look at this. I am planning to use Google Collab for a totally remote class next weekend to set up group work.

oman121 7y ago

Is anyone aware of a repo with an introduction to Angular (2+) that's a similar style and equally high quality?

webartifex 7y ago
I'd be the first reader :)

ivan_ah 7y ago

Wow this is really thorough and well thought out. It's not just an "intro" but goes into all the language builtins, and common usage patterns (e.g. zip, map, etc), which many "intro-to" style courses skip over. Code in repo is MIT licensed and videos are tagged as Creative Commons (CC BY). Finally a good OER course for people to learn Python!

webartifex 7y ago
The Python community is basically one big OER course. Just look at all the recordings from the many PyCons. I have a second half for the "book" planned but "unfortunately" also need to do some research for my PhD. Maybe someone wants to provide some more advanced chapters via a pull request?

idoby 7y ago

Awesome work! I can't wait to test this on my non-programmer friends who want to learn how to code. This seems to have a somewhat different approach to the usual Python tutorial, it might do the trick! Thanks

webartifex 7y ago
I tried to put in everything I myself would have wanted to know when starting in 2014. That is why I spend so much time on the memory diagrams in the videos in particular. It literally took me years to figure that out. I have watched a lot of online lectures (e.g., from OCW), but only rarely do instructors talk about that. Maybe formally studying CS would have done the trick for me as well :)
- idoby 7y ago
  It's not necessarily covered in a traditional CS program, it really depends on the instructor. This is a good resource. Regarding the videos: they're hard to watch because the text size is very small and it seems to be blurry due to compression/encoding issues and in some of the videos, the audio track has a high-pitched hum in the background that makes it very hard to listen to. If you still have access to the source materials, you should seriously consider cleaning up the sound and re-encoding the videos for better quality (as well as making the text bigger where possible).
  - webartifex 7y ago
    Two videos have the hum. This cannot be changed because the lecture capture system in the lecture room does not allow this (It's Panopto). The quality is actually high enough. Maybe that is due as YouTube is currently lowering the quality due to Corona? My plan is to incorporate the feedback everyone gives in the next weeks and do the recordings again in studio at the end of summer for the next semester. The videos are actually intended to only support the chapters in the Jupyter notebooks. The funny thing is this: Once I started doing the memory diagrams in the lecture, the students' work exhibited a lot more quality. Further, absolute beginners understand, for example, the concept of an iterator vs. iterable. BTW: chapters 9 and 10 will be released soon on GitHub.

qubex 7y ago

Can I suggest adjusting it for some other consumer platforms, such as, for example, Pyhtonista on the iPad? That would make it easier to divulge in these turbulent times when many people are stuck using somewhat unusual instruments and platforms.

webartifex 7y ago
Does Pythonista for iPad support Jupyter notebooks?
- qubex 7y ago
  I don’t think so, no. But I might be wrong.

vcsilva 7y ago

That's a really cool project! I will probably be recommending this to beginner Python programmers I know. As a side note, I think sharing lessons as Jupyter notebooks is a really great idea for programming education. I would like to see more of this style of course for other programming languages as well.

webartifex 7y ago
I agree. Although at some point, any beginner must be exposed to the command line :) My observation about myself when studying is this: Nothing, no video course, beats a well written book. And, such books are often "slow" reads as you have to think a lot when reading. I found Jupyter notebooks a good instrument to put a lot of info in. My guess is that there is three times the info in my course's notebooks as there is in the 25 hour video recordings.

ipnon 7y ago

Is it really this easy to become a data scientist today? Can you go from 0 to 1 with online programming tutorials like the original post and fast.ai?

OJFord 7y ago
Well, OP says [0] they're a PhD candidate, so maybe take 'non-CS majors' quite literally, in terms of where it's hyphenated. 0 - https://news.ycombinator.com/item?id=22669091 https://news.ycombinator.com/item?id=22669091
- webartifex 7y ago
  I consider my course a thorough course. You really learn a lot of theory. This is usually what makes students at my university drop the course after two weeks of auditing. Just because the course assumes no prior CS contents (anything really, except high school math), does not mean they don't get introduced.
  - OJFord 7y ago
    Oh I wasn't suggesting anything against the quality or content of your course - perhaps I misunderstood the comment I replied to, but I took it as: > High schoolers are really just doing some web course and then getting data scientist jobs? so my response was: > No - or at least this isn't evidence of that - the author is a postgraduate student and the target audience appears to be other graduates/university students.
    - webartifex 7y ago
      I hope there are no companies that hire a "data scientist" that only speaks HTML :) The course aims at students of business administration. What do they usually do? Maximize some profit function. So, my background is really more traditional Operations Research.
webartifex 7y ago
It depends on who wants to hire you. For the top jobs, this is for sure not enough. I always tell people to please make sure they know all the contents from here https://www.youtube.com/watch?v=ZK3O402wf1c&list=PL49CF3715CB9EF31D&index=1 https://www.youtube.com/watch?v=ZK3O402wf1c&list=PL49CF3715C... (Gilbert Strang's Linear Algebra course) before they make any claims about being a data scientist. I bet I can teach a monkey to open a CSV in pandas and call .fit() in sklearn. But do the people really understand the underlying assumptions. Most self-proclaimed data scientists don't I am sure. I still have a hard time calling myself a data scientist. And I am three years into a relevant PhD. The more I study, the less I feel I truly know.

pbreit 7y ago

If looking for more of a "crash course" to be up and programming a web-based project rapidly, have a look at: http://www.web2py.com/books/default/chapter/29/02/the-python-language http://www.web2py.com/books/default/chapter/29/02/the-python...

starpilot 7y ago

> The course's main goal is to prepare the student for further studies in the "field" of data science. Does the material seem to have major gaps for DS to anyone else? There's no pandas, no matplotlib, no ML. It seems like more a tutorial with a computer science focus, with recursion and bit manipulation. Those are great programming topics but rarely used by data scientists. For that I would probably use .py files, not jupyter notebooks. It just doesn't align with my experience as a Python-based data scientist.

webartifex 7y ago
Pandas and numpy & friends will be in Chapter 9 that I had not yet time to finish (the semester runs until end of April and is currently at Chapter 5). Furthermore, it is a programming (!) course, not (!) a data science course. Please define data science to me. Is it "only" curve fitting to you? Or also optimization (e.g., in logistics)? Then, dynamic programming (and because of that recursion) is super important. I rely on Jupyter notebooks mainly because it is a course for people with absolutely no prior experience that also do not major in CS. .py files are actually explained in Chapter 2 and will be used in a follow-up course.

rob_austin 7y ago

At this point, I feel like there are too many "Intro to Python" courses floating around. It will take a newbie, multiple hours of reading reviews and recommendations to find a course. There are many good ones out there. I wish there was more effort spent on creating intermediate courses. It would be great if there were more people trying to write books like this Nicolas Rougier is trying (https://github.com/rougier/scientific-visualization-book https://github.com/rougier/scientific-visualization-book). Take a specific library and help people become proficient in them. Usually, the core contributors to a library are not always the best people to teach people how to use that library.

webartifex 7y ago
What would be contents you expect from an intermediate Python course?
- rob_austin 7y ago
  Some example topics that took me a lot of reading from different sources to understand. - how to write a python library that you can host for public/private use - adding test coverage to data science python projects - learning libraries like matplotlib, seaborn beyond what you see in tutorials I think material for all fo this exists in different sources like documentation/stack over flow but either it's too detailed or too superfluous. The middle (intermediate) layer is often missing.
  - webartifex 7y ago
    I get you. The plotting tools I would actually consider in an "Intro to Data Science" course, not in an "Intro to Programming". I started to write a library implementing Gilbert Strang's Linear Algebra book assuming nothing but core Python. What inspired me to do that was reading Philip N. Klein's book "Coding the Matrix". I thought writing a LA library for fun and study purposes makes sense if you want to go on and study data science. However, that project is not yet ready to be published. Would that be what you are looking for?

AmericanChopper 7y ago

What I never understand about intros like this (even intro courses in college), is why not start with types? Reading the content, it’s obvious there’s something different between a string and a number, and a list (or a dict) is obviously very different again. You quickly figure out that these things are also capable of doing completely different operations, but why? And what things (methods) are they capable of? I was helping a friend who decided to start a CompSci degree recently (the 101 course was taught in python), and she was massively struggling with type coercion and understanding type methods. Looking at the course forum, so was everybody else. I helped her understand what types were, why they were important and what the built in type methods were (and how to read the python documentation), and it was a major breakthrough for her. She’s now getting close to 100% on her tests. I was also blown away by how archaic some of the content was, like using .format() instead of fStrings, but that’s a seperate topic.

webartifex 7y ago
I introduce the idea of a type right in chapter 1 because of exactly the argument you make. As Python is really more about the behavior of objects and not so much their type, I introduce these already from chapter 4 onward, for example, iterable vs. container, and many more. I actually would say that this is the essence of any dynamic language (duck typing). "and how to read the python documentation" -> that is an important point you raise!!! I found that beginners have real trouble reading the docs because they are screening for words like "list" instead of "iterable". However, as I teach abstract behaviors early, they actually understand the docs. "like using .format() instead of fStrings" -> I mention .format() but mainly use f-strings and tell the students right away that they are both faster and easier to read and that they should default to them.