Python 101: Learn the 5 Must-Know Concepts 

Your timestamps are mislabeled for if_name and function types.

Hi Tim please make a video about GIL in Python and mulithreading in Python.

what do you think about Mojo programming language

please make a video about GIL and why does python not support multithreading

Mojo programming language is super set of python and it is 35000x faster than python

Hello dear sir, You mentioned that 'It's just called something else', and what came up to my mind is that another threading library named _thread which is meant for low level threading and also multiprocess library that allows users to run multiple python clients. Am I correct or did you mean something else?

Thank you, that was a bit misleading. How can you do "multithreading" in python then?

@@Joel-pl6lh The library of choice for actual parallel processing in Python is _multiprocessing_ It has a similar interface, but gives you actual parallel computing on different CPU cores.

​@@TohaBgood2 That's what I found too, thank you because I'd have thought it's not possible. I wonder why he included this in the video?

Agree, in the GIL part of the video there is a lot of confusion since multi-threading is mixed with multi-processing, and not a clear definition has been provided, which contributes to confuse who approaches to these concepts. It simply does not exist a multi-threading code, in all the coding languages, that executes threads at the same time

Thank you so much for correcting this section of the video. I hope enough people read this and try it so they can correct their understanding of the concept.

Isn't Python treat immutable types (strings, numbers, tuples) as literals, while lists and dicts are basically objects? Once you assign different value to a string or number or any other immutable type variable, you're actually creating another literal object, but the one you created previously still resides in memory and will be purged later, no?

Yes, thank you. This kind of sloppy presentation should be career ending.

Actually, Tim was right and was pointing out the possibly-unexpected behavior one can run into with the behavior of mutable types: If you create x, create y with the value of x, and then REPLACE x by creating x again, then x and y will have different values. Try it yourself: x = [1, 2]; y = x; x = [1, 2, 3];print(x, y) If you create x, create y with the value of x, and then CHANGE x by reassigning one of its values, then x and y will have the same new value and the original value will be gone. Try it yourself:: x = [1, 2];y = x;x[0] = 9;print(x, y)

​@@CliffordHeindel-ig5hp your type of comment should be career ending 😂

thanks

I didn't know the kwonly args after *args didn't need a default. The posonly arg names can also be used as kwarg keys when the signature accepts kwargs.

you can also use "*" to force but I the more apt way is to use "/" I guess

Your description is accurate and provides a clear understanding of the use of /, *, and **kwargs in function parameter definitions in Python. Let's break down the key points: / (Forward Slash): When you use / in the function parameter list, it indicates that all parameters before it must be specified as positional arguments when calling the function. This means that parameters before the / cannot have default values and must be passed in the order defined in the parameter list. Parameters after the / can still have default values and can be passed either as keyword arguments or positional arguments. * (Asterisk): When you use * in the function parameter list, it marks the end of positional-only arguments and the start of keyword-only arguments. Parameters defined after * must be passed as keyword arguments when calling the function. They can have default values if desired. **kwargs (Double Asterisks): **kwargs allows you to collect any additional keyword arguments that were not explicitly defined as parameters in the function signature. It must always be the last element in the parameter list if used. Here's an example function that demonstrates these concepts: python Copy code def example_function(a, b, /, c, d, *, e, f=False, **kwargs): """ a and b must be passed as positional arguments. c and d can be passed as positional or keyword arguments. e must be passed as a keyword argument. f is optional and has a default value. Any additional keyword arguments are collected in kwargs. """ print(f"a: {a}, b: {b}, c: {c}, d: {d}, e: {e}, f: {f}") print("Additional keyword arguments:", kwargs) # Valid calls to the function: example_function(1, 2, 3, 4, e=5) example_function(1, 2, c=3, d=4, e=5) example_function(1, 2, 3, 4, e=5, f=True, x=10, y=20) # Invalid calls (will raise TypeError): # example_function(a=1, b=2, c=3, d=4, e=5) # a and b must be positional # example_function(1, 2, 3, 4, 5) # e must be passed as a keyword By using /, *, and **kwargs in your function definitions, you can create more structured and expressive APIs and enforce specific calling conventions for your functions.

He definitely should've included this info in the video. I've learned this before but am not a professional programmer so haven't used it, so seeing it in this video would help non-experts like me. Also, this can be inferred from the explanations above, but maybe still worth stating explicitly: Parameters between / and * can be positional OR named. And the function won't work if * comes before /, since the parameters in between would be required positional and required keyword, which creates a contradiction.

I'm glad I stuck around; I had no idea about some of those other tips like in the function calls.

this is just what ive been searching, please elaborate on python interpretor and how does it differ from C compiler, noting that python is developed in C.

Please do this man!!!

​@@adrianoros4083 c compiler is very fast than python interpreter due to the defining of type of variable before compiling

​@@xxd1167bro you clearly have no clue what you're talking about

@@xxd1167 If you don’t know what you’re talking about, please don’t post anything. Stuff like this hurts those who are here to learn.

I might be dumb but don't you mean "x" instead of "y" here: "After that, you change what identifier y is referring to"

Came here to say the same. The point can be illustrated with this code, x and y point to the same thing: >>> x = 1 >>> y = x >>> print(hex(id(x)), hex(id(y))) 0x7f82aa9000f0 0x7f82aa9000f0

@@illusionofquality979 Yes, indeed you are correct. I have edited my comment.

thanks, just fixed it :)

Should I be worried learning these concepts if I'm thoroughly learning Python as my first language? What should I look out for since I plan to move on to C++?

Can you clarify what you mean with a code example? I thought you meant this, but the default value doesn't change in this case (luckily, that would have been disastrous...) >>> def f(x,a=[]): ... print(a) ... a=[3,4,5] ... print(a) ... pass ... >>> f(9) [] [3, 4, 5] >>> f(9) [] [3, 4, 5] How would you make the default value change?

@@HerrNilssonOmJagFarBe yes, here you setting the value with the statement, , a=[3, 4, 5],which as far as I know is now stored at a different place in the memory but try instead by having your default value as say a = [1] and then in the function append a value to the list, something like, def add_item(a = [1] ): a.append(2) print(a)

@@ireonus >>> def f(x,a=[]): ... a.append(2) ... print(a) ... pass ... >>> f(1) [2] >>> f(1) [2, 2] >>> f(1) [2, 2, 2] Oh. Well, that's truly weird...! I also tried a recursing version of f() which made the issue even more spectacular. So 'a' is local to each particular invocation of the function, but the default value itself is the same across calls? What happens to the memory that's claimed by the default value once I've called the function too many times. There is no way to directly reference it outside the function. Can I ever reclaim it (short of redefining the function)?

f.__kwdefaults__['a'] I use this like f(x, *, _cache={}) but have not tested it across imports. Should probably fully understand the implications with good tests before using these for personal projects.

@@kmn1794 Clever. It also made me understand the behaviour. Thanks! But such code seems obscure and abusive of that particular language quirk. How many would understand such code? I certainly wouldn't have until I saw this youtube vid.

Hey Lina, i also have a django function on which request lands, it was giving timeout error when 2users were hitting the same fn using url, then i increased the gunicorn worker and now it's working fine. So my qn is, was that a good idea or there is any other way to handle concurrent request on prod. Fyi that fn involve hitting different tables, and storing bulk data in one of tables using orm. So if you can comment over this about the best way to handle these things. Kindly share.

@@shubhamjha5738 Gunicorn is actually running your Django application on two different instances.

least obvious chatgpt user

Certainly!

@@excessreactant9045 Yes i using ChatGPT to translate from my Native Language in Englisch + I Used it to put more information in it

😂❤

Yeah I was doing this on one of my projects and I'm surprised Tim didn't mention it in this video. Made it seem like you just can't do it at all.

Mojo will solve multi-thread problems in Python. Do you need something fast and it is Python? Mojo is the answer for you.

Exactly. Additionally, assignment to x just lets x point to another thing and doesn't modify what x pointed to. That whole part was conceptionally wrong.

Ya tu sabe

There are a few good answers to the in the comments 👍🏻

Threads occur concurrently, while processes occur in parallel. Threads are better for I/O bound operations while processes are better for CPU bound operations.

Yes in the case where you are waiting for responses certainly this is a good use of threads!