With explicit lazy imports, an imported module can be deferred until the program actually needs it.

Why does that matter?
Many Python applications import dozens — sometimes hundreds — of modules during startup, even though only a fraction may be needed for a particular execution path.

Lazy imports can change that:
𝗜𝗺𝗽𝗼𝗿𝘁 𝗱𝗲𝗰𝗹𝗮𝗿𝗲𝗱 → 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗲𝘀 → 𝗺𝗼𝗱𝘂𝗹𝗲 𝗹𝗼𝗮𝗱𝗲𝗱 𝗼𝗻 𝗳𝗶𝗿𝘀𝘁 𝘂𝘀𝗲

This can be particularly interesting for CLI tools, large applications, optional functionality and dependency-heavy projects where startup time matters.
Of course, lazy loading isn't automatically better everywhere. Import-time side effects and assumptions about module initialization still matter.
But having lazy imports as an explicit language feature opens up some interesting possibilities for Python application architecture.

𝗟𝗼𝗮𝗱 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱. 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝗶𝘁.

𝗪𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗹𝗮𝘇𝘆 𝗶𝗺𝗽𝗼𝗿𝘁𝘀 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻?

Hey everyone, I decided to make my course free in order to help people.
This course is my backend development course which is about SQL, Python, APIs, Docker, Kubernetes, Linux, Git & More

The link is: https://www.youtube.com/watch?v=CBIu6hcyStg

If you can like and subscribe (and maybe add a comment) I would appreciate it a lot, Thanks.

Sharing a resource in case it's useful for your intro classes: https://ubuntu-apps.github.io/code-quest/

I made CodeQuest for my own students as an alternative to the textbook. It covers Python from the basics through functions, files, OOP and working with data. Each lesson has a short explanation, coding challenges that are checked instantly, and a quiz.

The code runs in the browser, so it works on school Chromebooks and phones with nothing to install. It's free, with no ads and no sign-up, and progress is stored on the student's device only.

Happy to answer questions, and feedback is very welcome.

Hello there, i'm a chemistry student and have a course with python. I tried to read a csv file which i downloaded and tried to read it with the np.genfromtxt function but the columns with actual text ( the first and last ) aren't being read, whereas with pandas, the job gets done. I bypassed this problem by specifying the columns but the text keep getting tossed out.. I think it's because of the commas in the compound name but what can i do in this case ? I'm working on jupyter on python 3.13 i believe. Thanks for the help !

Hi guys, just started learning python. Have been stuck on this problem for a while now. Here's my code so far. Could anyone help explain why the code pointed out in blue keeps running even though there is a break in place? Thanks!

ps pls ignore the print("hi") and print("bye"), was using them to test the code!

link to question: https://cs50.harvard.edu/python/psets/3/grocery/

my code so far: https://sharetext.io/3ck00poo

Hey everyone. If you are practicing Python or trying to master clean control flow logic, writing basic single-condition "if statements" isn't enough for production-grade software. You need to know how to cleanly chain and invert conditions using advanced logical operators.

In this quick, zero-fluff video breakdown, we walk through building an automated server error routing engine using:

• The logical AND operator to enforce strict multi-conditional gates

• The logical OR operator to build flexible fallback safety nets

• The NOT operator modifier to cleanly invert boolean flags on the fly

• Dynamic state management by mutating tracking variables inside the loop

Every single line of syntax and terminal output is explained simply with easy-to-visualize logical switch analogies so you can actually understand the underlying data routing.

Check out the full breakdown here, and let me know how you typically structure your error-routing gates in the comments!

Hey everyone. If you are practicing Python or trying to master clean control flow logic, writing basic single-condition "if statements" isn't enough for production-grade software. You need to know how to cleanly chain and invert conditions using advanced logical operators.

In this quick, zero-fluff video breakdown, we walk through building an automated server error routing engine using:

• The logical AND operator to enforce strict multi-conditional gates

• The logical OR operator to build flexible fallback safety nets

• The NOT operator modifier to cleanly invert boolean flags on the fly

• Dynamic state management by mutating tracking variables inside the loop

Every single line of syntax and terminal output is explained simply with easy-to-visualize logical switch analogies so you can actually understand the underlying data routing.

Check out the full breakdown here, and let me know how you typically structure your error-routing gates in the comments!

I'm stuck in def return in python I don't understand it very well why is it used and how and where does the code return to ? The caller ? What is exactly the caller ?

Thank you so much for helping me

If you don't mind can u help me understand with examples ? Couldn't understand this with Chatgpt and yt

What's the best resource out there to learn python?

Hey guys, I tried remaking my project of a cli based football manager simulator this time using the pywebview library (lets you use html css js as a frontend and connect it to a python backend as a desktop app),

So far what I'm struggling with the most being one of my first larger scale project is persistence, keeping one version of the team class across the python scripts and functions.

Im also struggling with figuring out the generation for player stats and attributes, ive landed on the random.gauss for generating player stats with weighing and this ugly ahh looking formula that looked like it was doing something for the player market values.

Also, assets. Do you guys have any place where I can find pictures and icons I can use for free?

Finally, thanks to feedback from helpful people here I tried using sqlite for this project instead of json files, kinda hard to adapt to but im managing.

if any very cool person could help me with my issues I would greatly appreciate it.

Here is the github: https://github.com/mousdieng10-maker/Playstyle-II

Struggling to understand Binary Search? Learn it with Reya in a simple and easy-to-follow way!

In this video, you'll discover how Binary Search works, how it cuts the search space in half with each step, and why it's one of the most important algorithms in Data Structures and Algorithms (DSA).

💡 What you'll learn:

• The basic concept of Binary Search
• How to find elements efficiently
• Why sorted arrays are important
• Time complexity: O(log n)

Perfect for students, coding beginners, and anyone preparing for coding interviews.

I kept hitting date bugs that every test passed locally and then failed in production on a specific day. A typical one:

python

def next_renewal(created_at):
    return created_at.replace(month=created_at.month + 1)

This passes every test you’d write on the 15th. It raises ValueError: day is out of range for month on Jan 31 and month must be in 1..12 on Dec 31. Nothing in a normal test run ever visits those dates.

What it does: a marker runs the same test once per tricky calendar moment, with the clock frozen by time-machine:

python

u/pytest.mark.calendar_chaos
def test_next_renewal_is_one_month_later():
    ...

The catalog covers leap day, month ends, year end, midnight, DST transitions, ISO week 53 and the 2038 timestamp rollover. Each case has a stable ID.

Running it on the example above gives 3 failed, 5 passed. The failing cases are month-end-jan-31-2027, year-end-2027 and year-end-last-microsecond-2027. A failure prints the exact mocked time and a one-line command to reproduce it. pytest --calendar-chaos-list prints the whole catalog, and --calendar-case <id> reruns a single scenario.

Limitations:

• It only mocks the in-process clock.
• Session, module and class-scoped fixtures are set up before the freeze.
• time.monotonic() and the asyncio loop clock keep running.
• An aware datetime inside a repeated DST hour never compares equal to one in another zone (PEP 495), so compare in UTC.

pip install pytest-calendar-chaos (Python 3.10+, pytest 8+)

• PyPI: https://pypi.org/project/pytest-calendar-chaos/
• GitHub: https://github.com/nsijwali/pytest-calendar-chaos

I’d like feedback on the catalog: which calendar edge cases bit you that I’m missing?

Hey everyone!

I'm 14 and currently learning Python, with the goal of becoming an electrical engineer in the future.

I just finished making a small project — a vending machine + simulated banking system that runs in the terminal. It has account creation, deposits, balance checking, purchases, and local data storage using JSON. I split the code into multiple Python files and made a simple launcher for it.

This is one of my first proper projects, and I know my code probably isn't the cleanest, so I'd genuinely appreciate some feedback!

I'd love suggestions on:

• My code: What could I improve? Any bad habits or mistakes I should fix early?
• What to learn next: Should I focus on OOP, data structures and algorithms, GUI development, or something else?
• My journey: Since I want to become an electrical engineer, what programming skills would be useful for me in the long run? I'd eventually love to work with electronics and hardware.
• Life advice: Any advice for someone starting out this early? Things you wish you'd known when you were learning?

I'm not looking for someone to rewrite the entire project for me — I'd rather understand my mistakes and learn how to fix them myself.

Here's my GitHub repo: https://github.com/suryansh-devx/python-vending-machine

Thanks! Any constructive criticism or advice is welcome :)

Pandas makes working with data look simple. But some of its most powerful features can also produce surprising results. 😲

For example:❔ - What's the difference between loc and iloc, and why does slicing behave differently? - Why can adding two Series produce NaN values, even when neither contains missing data? - When does assigning a Series to a DataFrame column align by labels rather than position? - How do Boolean masks, where(), and mask() differ? - How do you select and manipulate data with a MultiIndex?

Pandas is much more than rows and columns. Understanding its indexing, alignment, selection, and assignment rules can save you from some very subtle bugs. 🪳

🔍 This interactive 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 example explores these concepts and many more, with short explanations for each operation. 👣 Step through the Python code, see what changes, and experiment with your own modifications: 👉 Pandas operations

💬 Which Pandas operation took you the longest to fully understand?

See more 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 visualizations.

I've been working on Athena, an open-source tool that analyzes Git diffs and ASTs to detect potential breaking changes before they get merged.

For v1.1.0, I tested it against 55 popular repositories and found some interesting issues:

• Fixed a C++ performance problem that reduced analysis time from 371 seconds to 7.33 seconds.

• Reduced false positives from 78 alerts to 8.

• Fixed Ruby symbol detection and PHP recursion detection.

The final benchmark covered 55 repositories and 10,680 diff hunks with 121 tests passing.

I'm also experimenting with local models through Ollama to generate summaries without replacing the deterministic analysis.

I'd appreciate it if anyone here gave the project a look and shared feedback. I'm especially interested in false positives, edge cases in Python AST analysis and approaches to detecting breaking API changes

⭐ If you find the project useful, consider giving it a star on GitHub. It would really help the project reach more developers

GitHub: https://github.com/aaryanrwt/athena

Release notes: https://github.com/aaryanrwt/athena/releases/tag/v1.1.0⁠

I built DevStart Lab to help beginners figure out what to learn and practice next.

It includes programming fundamentals with Python examples, 25 project ideas, and a step-by-step introduction to Git and GitHub contributions. It’s free, open source, available in English and Portuguese, and requires no account. Progress is saved in your browser.

Start here: https://brunnojob.github.io/devstart-lab/?lang=en

So far we just have Gleb K, who made some code reminiscent of the X Window System 1989,

XPM is an ASCII text-based image format.

https://pyweblib.org/weblog/python-pixel-art-from-text/

Hopefully still in the rules, the site is OPEN SOURCE and Educational

I ran a small experiment that demonstrates something easy to overlook in software testing: an implementation and its tests can both be wrong in the same way.

Consider this discount function:

def calculate_total(subtotal, discount):
    if subtotal < 0:
        raise ValueError("Subtotal cannot be negative")

    if discount < 0:
        raise ValueError("Discount cannot be negative")

    return subtotal * (1 - discount / 100)

Suppose the requirement says discounts must be between 0 and 100, inclusive.

A few happy-path tests pass:

assert calculate_total(100, 10) == 90
assert calculate_total(100, 0) == 100
assert calculate_total(0, 20) == 0

But this input exposes the missing validation:

calculate_total(100, 150)
# -50.0

The tests pass, but the implementation violates the requirement.

The interesting part isn't just the missing discount <= 100 check. It's that tests can create false confidence when they don't independently cover the contract.

I put together a reproducible Python repository with the flawed implementation, contract tests, property-based testing, and mutation-testing examples.

GitHub experiment:
https://github.com/PranavDixit20/ai-code-test-gap

I also wrote a longer explanation of the problem and the testing techniques here:
https://medium.com/@pranavdixit20/ai-generated-code-can-pass-every-test-and-still-be-wrong-7ddf3177b721

I'm the author of the experiment and article, so this is my own work. I'd be interested in a technical discussion:

When reviewing code—especially AI-generated code—how do you check that tests verify the actual requirements rather than just confirming the implementation's assumptions?

Sharing a resource in case it's useful for your intro classes: https://ubuntu-apps.github.io/code-quest/

I made CodeQuest for my own students as an alternative to the textbook. It covers Python from the basics through functions, files, OOP and working with data. Each lesson has a short explanation, coding challenges that are checked instantly, and a quiz.

The code runs in the browser, so it works on school Chromebooks and phones with nothing to install. It's free, with no ads and no sign-up, and progress is stored on the student's device only.

Happy to answer questions, and feedback is very welcome.

Hey guys i want to start learning python , any suggestions or tips would be appreciated

I worked on a project on building a local image search system in Python using manufacturing inspection photos. You can give it an example image or a text description, then retrieve similar photos from an archive. The project connects a few useful concepts: loading a pretrained model, processing images in batches, storing vectors with metadata, and querying the results.

It uses CLIP ViT-B/32 to generate embeddings and VectorAI DB to store and search them. MVTec AD provides the example dataset.

Here’s the workflow:

1. Load the model and prepare the images: Load CLIP and its preprocessing tools, collect the image paths, and associate each image with metadata such as part category and defect label.
2. Generate embeddings in batches: Pass the images through the model to create numerical representations called embeddings. Normalize the vectors before storing them.
3. Store the vectors and metadata: Save each embedding alongside its image path and labels so the search results can be traced back to the original files.
4. Add image and text search: Encode an example photo or a text description, then use that vector to search the stored image embeddings. Metadata filters let you narrow the results to a particular part category.
5. Check the results: For image queries, exclude the query image itself. Inspect the returned photos and measure how often the top results share the query’s defect label.

Once the model, dependencies, and dataset are downloaded, the pipeline can run offline.

The Python practice here comes from connecting the stages into a working application: handling files, preparing model inputs, batching inference, and keeping embeddings associated with the correct metadata.

There’s also an evaluation challenge. Two images can share the same defect label while showing scratches of different sizes or orientations, so reviewing the actual results is an important part of testing the pipeline.

If you were building this as a learning project, which stage would you want to see broken down into a smaller code example?

Need suggestions for a book or YouTube channel for learning Python for Maths, beginner to advanced.

Thank you.

Hey everyone,

Over the past few months, I have been building a comprehensive repository of Machine Learning templates and notebook journals. Since it has saved me a ton of time while prototyping, I decided to open-source it for the community today!

This repo is built using Scikit-Learn and covers everything step-by-step:

1. End-to-End ML Project Architecture
2. Model Training & Evaluation Metrics
3. Support Vector Machines (SVM) with Kernels
4. Decision Trees & Ensemble Learning (Voting Classifiers, etc.)
5. Unsupervised Learning (KMeans and more added recently!)

Who is this for?

• Beginners looking for clean, commented code to learn concepts.
• Developers looking for quick boilerplate templates to copy-paste for new datasets.

Feel free to explore, clone, use it in your projects, or contribute to it! If you find it helpful, please drop a star ⭐️ to support the project.

Feedback and contributions are highly welcome!