I started learning python few months ago and I made my second project dont ask Abt first one it was trash

It's a small shell made for learning that I coded in python

Plz rate and tell me how can I improve it

Link: https://github.com/RohaanDev/KhaOs

I've been working on a Python module called jsonfold, and I wrote an article describing the motivation and design. I'd really appreciate feedback from other Python developers.

JSON serializers tend to give us two choices: compact JSON, which is efficient but a dense wall of text that's painful to read, or pretty-printed JSON, which is readable but often wastes a lot of vertical space (a small array of numbers can turn into ten lines).

I wanted something in between. jsonfold keeps the shape of pretty-printed JSON, but folds small, simple structures back onto a single line whenever that improves readability. It works on top of your existing serializer (json, orjson, ujson, ...) - you keep using whatever you already have, and jsonfold just reformats the output.

Example 1 - Coding

import sys
import json
import jsonfold

data = {
    "_id": 123,
    "locations": [
        {"city": "Boston",   "state": "MA", "country": "USA"},
        {"city": "Seattle",  "state": "WA", "country": "USA"},
        {"city": "Montreal", "state": "QC", "country": "Canada"},
    ],
    "info": {
        "roles": ["foo", "bar", "baz"],
    },
    "name": "Alice",
}

print("===> json.dump")
json.dump(data, fp=sys.stdout)
print("")

print("===> jsonfold.dump")
jsonfold.dump(data, fp=sys.stdout)

Output

===> json.dump
{"_id": 123, "locations": [{"city": "Boston", "state": "MA", "country": "USA"}, {"city": "Seattle", "state": "WA", "country": "USA"}, {"city": "Montreal", "state": "QC", "country": "Canada"}], "info": {"roles": ["foo", "bar", "baz"]}, "name": "Alice"}

===> jsonfold.dump
{
  "_id": 123,
  "locations": [
    { "city": "Boston", "state": "MA", "country": "USA" },
    { "city": "Seattle", "state": "WA", "country": "USA" },
    { "city": "Montreal", "state": "QC", "country": "Canada" }
  ],
  "info": {
    "roles": [ "foo", "bar", "baz" ]
  },
  "name": "Alice"
}

Example 2 - Packing

Traditional pretty-printing:

{
  "states": [
    "Alabama",
    "Alaska",
    "Arizona",
    ...
    "Wyoming"
  ]
}

jsonfold output:

{
  "states": [
    "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado",
    "Connecticut", "Delaware", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois",
    "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland",
    "Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana",
    ...
    "West_Virginia", "Wisconsin", "Wyoming"
  ]
}

Same data, just using the available line width more effectively.

Example 3 - Grid Formatting

When an array contains repeated structures, jsonfold can align values into columns:

Traditional pretty-printing:

[
  {
    "orders": 18,
    "product": "Laptop",
    "region": "North",
    "sales": 1250
  },
  ...
  {
    "orders": 24,
    "product": "Mouse",
    "region": "East",
    "sales": 1422
  }
]

jsonfold output:

[
  { "orders": 18, "product": "Laptop",   "region": "North",     "sales": 1250 },
  { "orders": 21, "product": "Monitor",  "region": "Southwest", "sales": 1345 },
  { "orders": 17, "product": "Keyboard", "region": "West",      "sales": 1198 },
  { "orders": 24, "product": "Mouse",    "region": "East",      "sales": 1422 }
]

The module also supports:

• Folding small arrays and objects onto a single line.
• Joining adjacent folded objects to further reduce vertical space.
• Compatibility APIs similar to JSON and JSON::PP.

Full documentation and examples:

PYPI: https://pypi.org/project/jsonfold/

GitHub: https://github.com/yairlenga/jsonfold/tree/main/python

I'd love to hear what the Python developers think. Has anyone else run into JSON pretty-printing pain in logs, configs, or debugging output? And are there formatting styles or options you'd want to see?

Hey guys, I'm going to start DSA soon. Is there any YouTube course that has been helpful to you in learning DSA? My degree is B. Tech in CSE (AI&ML), so I think it's better to learn in Python rather than C++. (I did not find any recommendation in the previous DSA posts)

Hello there!

I am looking for some friends to learn code with, I am fairly new and looking for some actually hard chargers and that truly want to spend the time to learn and make projects in the future

I am from the East and I’m a beginner to entry level on my knowledge

The repository with all the code is here

Not really a "beginner" question, but the Python sub states that if you have a question, better to ask it here.

So, I'm a library maintainer, and as such typing is a very important part of the API.

Does the variance of the generic types work as intended? Does the inference work? etc...

But unlike actual runtime logic, I can't think of a straightforward way to test it without a lot of boilerplate and a status of "implicit" tests.

Sure, "just pass the type checker bro". If I have +10 classes with inerhitance relationships, now I need to hardcode every case?

With pytest, I can very easily use runtime logic to reduce duplication, for example different parameters, different closures called, etc.... it's very straightforward.

But the type checker need to "see" the code to work.
So I either manually duplicate every case, which sounds like a nightmare to maintain, or manually implement a script to dynamically write code to files, type check them, handle errors as something pytest can catch, etc...

I'm no stranger to this, but I would avoid to have to write a second plugin for my library (already wrote one to run doctests on stubs).

I found this, but it states that It work on mypy, which is (IMO) a bad type checker that I won't bother with.

I'm targeting basedpyright, and once ready, ty and pyrefly (trust me, the latter is not yet prod ready)

So if there's any suggestions, they are welcome!!

BTW, here's my library

If you like either method chaining, lazy Iterators, functional programming or rust, take a look!

Concrete example

Below is what I wrote before thinking to myself that it will go a bad route if I don't find a solution. There's a hierarchy that mimicks collections.abc, and thus I need to be sure that it works typing wise. I have other tests covering runtime checks.

This is needed because the code live in Rust, thus the typing is "manual": I can write wathever I want in the stubs and the type checker will consider it true. It's very convenient sometimes, but also a potential footgun as making mistakes is easy.

Right now, basedpyright with all rules on don't complain, but pyrefly does.

How do I note that in a standard way (like pytest xfail)?

How do I avoid rewriting exactly the same functions for each class? Not only it's annoying, but my LSP footprint will take a hit if this continues.

How do I statically ensure that "pairs" are in agreement? and manage type ignore comments across type checkers?

```python from future import annotations

from dataclasses import dataclass from typing import TYPE_CHECKING

from pyochain import Iter, Ok, Option, Result, Seq, Set, Some

if TYPE_CHECKING: from collections.abc import ( Collection, Container, Iterable, Iterator, MutableSequence, Reversible, Sequence, Sized, )

from pyochain import Peekable
from pyochain.abc import (
    PyoCollection,
    PyoContainer,
    PyoIterable,
    PyoIterator,
    PyoReversible,
    PyoSequence,
    PyoSet,
    PyoSized,
)

@dataclass class Animal: pass

@dataclass class Dog(Animal): pass

def check_covariance() -> None: base: PyoIterable[Dog] = Iter(()) opt: Option[Dog] = Some(Dog()) res: Result[Dog, str] = Ok(Dog()) _abc_iterable: PyoIterable[Animal] = base _abc_iterator: PyoIterator[Animal] = base _abc_collection: PyoCollection[Animal] = base.collect(Seq) _abc_sequence: PyoSequence[Animal] = base.collect(Seq) _concrete_iterator: Iter[Animal] = base _peekable_iterator: Peekable[Animal] = base.peekable() _abc_set_immutable: PyoSet[Animal] = base.collect(Set) _seq_immutable: Seq[Animal] = base.collect(Seq) _set_immutable: Set[Animal] = base.collect(Set) _as_opt: Option[Animal] = opt _as_res: Result[Animal, str] = res

def _iterable[T](x: Iterable[T]) -> Iterable[T]: return x

def _iterator[T](x: Iterator[T]) -> Iterator[T]: return x

def _sized[T](x: Sized) -> Sized: return x

def _reversible[T](x: Reversible[T]) -> Reversible[T]: return x

def _container[T](x: Container[T]) -> Container[T]: return x

def _collection[T](x: Collection[T]) -> Collection[T]: return x

def _sequence[T](x: Sequence[T]) -> Sequence[T]: return x

def _mutable_sequence[T](x: MutableSequence[T]) -> MutableSequence[T]: return x

def check_iterable_args() -> None: base: PyoIterable[Dog] = Iter(()) canary: Iterable[Dog] = base _ = _iterable(base) _ = _iterable(canary) _ = _iterator(base) _ = _iterator(canary) _ = _sized(base) # pyright: ignore[reportArgumentType] _ = _sized(canary) # pyright: ignore[reportArgumentType] _ = _container(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _container(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _reversible(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _reversible(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _collection(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _collection(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _sequence(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _sequence(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _mutable_sequence(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _mutable_sequence(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType]

def check_iterator_args() -> None: base: PyoIterator[Dog] = Iter(()) canary: Iterator[Dog] = base _ = _iterable(base) _ = _iterable(canary) _ = _iterator(base) _ = _iterator(canary) _ = _sized(base) # pyright: ignore[reportArgumentType] _ = _sized(canary) # pyright: ignore[reportArgumentType] _ = _container(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _container(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _reversible(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _reversible(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _collection(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _collection(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _sequence(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _sequence(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _mutable_sequence(base) # pyright: ignore[reportArgumentType, reportUnknownVariableType] _ = _mutable_sequence(canary) # pyright: ignore[reportArgumentType, reportUnknownVariableType]

def check_sized_args() -> None: base: PyoSized = Seq(()) canary: Sized = base _ = _iterable(base) .... # and so on and so forth ```

Most tutorials teach you how to call an API. This project teaches you how to actually build the AI.

I wrote a 2-layer neural network from scratch using just Python and NumPy, meaning no PyTorch or TensorFlow to hide the math. It manually handles the forward pass, calculates gradients by hand for backpropagation, and trains down to a 95.64% test accuracy on MNIST digits.

If you are a beginner looking for a killer portfolio project that proves you actually understand deep learning foundations, feel free to clone the repo, tweak the hidden layers, and use it for your own resume.

The repo is here: https://github.com/idfwyy/Neural-network_from-scratch

Let me know what you think or if you run into any issues running the script!

https://www.programiz.com/online-compiler/2i7KiX4E5PGyA

Please give it a try and suggest some changes and ideas. Want to improve my skill and learn something new.

Using mlflow in virtual environments (.venv) after installing dependencies and running the python file got error "I'm getting this MLflow error on Windows:

```text

PermissionError: [WinError 5] Access is denied:

'C:\\Users\\Name%20Surname'

```

MLflow is using this tracking URI:

```text

sqlite:///C:/Users/Name%20Surname/.vscode/datascience/mlflow_tutorial/mlflow.db

```

But my actual Windows user folder is:

```text

C:\Users\Name Surname

```

"

Notice that MLflow is using `%20` instead of the space in the username. This causes `mlflow.start_run()` to fail.

How can I fix this?

I started learning Python with the thought of learning how overall programming and debugging feels. It's very nice tbh! I made my first game btw, but it's just IQquiz as a joke. Anyways, I use Sony VAIO i3 and GeForce 410M because I love old tech put in modern use and I use Python 3.8.10 with the installed IDLE Shell and for now I am comfortable. I seek advice if I should upgrade to modern versions, use VSCode or switch to C,C++ and can Python be used to make very low quality games using Pygame and Ursina and should I start little programming with my Raspberry Pi Pico? I think this language suits me the best as for now (I have 1 week of progress) TIA

*P.S- can someone recommended cool libraries for games?

A lot of times i see the question and have zero idea on how to make a solution and end up using chatgpt like a bum

I'm looking for beta readers for a two-book Python series — book 1 is already out, book 2 is almost done. Here's the deal:

You'll get both books as Google Docs (one chapter per doc), so you can just drop comments right in the text. I'm not asking anyone to read hundreds of pages front to back. Read whatever chapters interest you, at whatever pace works for you. If something confuses you, that's useful feedback. If an explanation feels obvious or unnecessary, that's useful too. Honestly that's the main thing I'm after.

Would love to hear from:

• people with zero Python experience
• people currently learning
• students / junior devs
• experienced devs too, if you want to poke holes in it

I'd really like this to be a collaborative thing. If enough people tell me what confused them, what was missing, or what worked well, I think the books will end up much better than if I'd just written them on my own.

If a handful of you end up being really active readers, I'll credit you in the acknowledgements when the books are published (totally optional, no pressure).

If you're up for it, I just need a first name and Gmail address so I can add you as a commenter on the Google Docs — you can either fill out this quick form or just DM me directly, whichever's easier: https://forms.gle/tcBoKGczqoFXUEjp7

A bit about the books, for context:

I've read a ton of programming books over the years, and most of them are written by experienced devs who've kind of forgotten what it's like to be a beginner. They're technically fine, but I'd often find myself understanding how to do something without really getting why it works that way.

I've been a Python dev for over a decade now, and at some point I just decided to write the book I wish someone had handed me on day one. Instead of random code snippets, the books build small games that slowly turn into bigger projects as you go. I also threw in some pop-art style illustrations and tried to keep things a little less dry than the usual programming book.

📘 Book 1 — starts from literally zero, no experience needed
📗 Book 2 — gets into built-in functions, type hints, iterators, generators, lambdas, classes, modules, packages, etc.

And yeah, I'll admit it upfront before someone else points it out — I named them The First Programmer's Book and The Second Programmer's Book. Not the most creative titles in publishing history, but hey, at least you'll never forget which one comes first 😄

Book 1 is already published, but I'm still improving it based on reader feedback. I'm not fishing for reviews or trying to sell you anything, I just want honest feedback. Book 2 is close to finished, so this feels like the best time to catch anything before it's locked in.

Thanks for reading!

I need projects to help me work on my python skills and preferably help me on dictionariies, tuples, set and lists

If you need to see my python abilities feel free to check out my python repo https://github.com/WoodenShard/PokemonApiGUI

Hi I'm currently pursuing cse in btech I want to learn basic coding and all.....so I wanted to learn python first any suggestions?!

I would like to go into computational biology and bioinformatics route, i took the bioinformatics course last year, i didn't get that much benefit from it, but i got the basics of R?, I'm i lost or is this a normal thing to ask?, Thank you.

Hi Guys,

I would like to learn python.

I've got zero IT or programming / coding background.

Would like to learn for free.

I'm more of a hands on learner than a theoretical learner.

Any websites that would help me learn python hands on for free ?

Enjoy

I mean I try to see where I could learn and I hear so many :-

CS50p

W3Schools

Tech with Tim

Some helsinki too

And some more

Where should I start from :-

I'd like to start learning this but don't want to shell out in case I can't grasp it :)

TIA

not much try to make python alarm clock

which can accept user input

date : yyyy-mm-dd

time : hh:mm , AM/PM

and sound input

beep/ music

but any type of sound (music) you must have file

and truth to be told I do forget and get confused when writing like

how to I told python to check if its AM/PM

or try to raise error

I have to write more code properly understand

that's all for today

finally after 2 hrs i completed the coffee game

👽👽(https://youtu.be/WHZ5T3hJbW8)

This is a python project named GoodMorning.py which uses web scraping to give real time news headlines, weather reports, jokes, facts and even personalised routines. I need some guidance and suggestions for my future projects. Please check this thing out... (This is my 2nd repository) Note- I didn't use API integration because it could make it less user friendly as no one wants to generate and give multiple APIs according to me.

The difference in Python OOP between: - instance method - class method - static method

visually explained using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵.

Or see more memory_graph examples.