Understanding Python Functions: Defining, Calling, and Using

Python is often praised for its simplicity and developer productivity, but what makes it particularly interesting is how much of its core actually runs on C through the CPython implementation. That design introduces a set of tradeoffs that are easy to overlook but important to understand. At a high level, Python gives you a clean, expressive syntax while delegating heavy lifting—such as memory management, object handling, and built-in data structures—to optimized C code. This is why operations on lists, dictionaries, and built-in functions often perform much better than equivalent logic written in pure Python loops. However, this abstraction comes at a cost. When you write Python code, especially iterative or CPU-bound logic, it is still interpreted and does not benefit from the same level of optimization as compiled C code. This creates a noticeable gap between “Python-level” performance and “C-backed” operations within the same program. The Global Interpreter Lock (GIL) is another direct consequence of this design. It simplifies memory management and ensures thread safety within CPython, but it also prevents true parallel execution of CPU-bound threads. As a result, developers often have to rely on multiprocessing or external libraries to fully utilize multi-core systems. On the positive side, Python’s tight integration with C makes it highly extensible. Performance-critical components can be offloaded to C or leveraged through libraries like NumPy and Pandas, which internally use optimized native code. In practice, many high-performance Python applications are structured as orchestration layers in Python, with execution-intensive parts handled elsewhere. The key takeaway is that Python is not inherently “slow” or “fast”—it depends on where and how the work is being done. Understanding the boundary between Python and its underlying C implementation allows you to make better architectural decisions, balancing readability, maintainability, and performance.

🐍 If you can’t handle files in Python, you’re missing real-world skills. Most tutorials focus on theory… But real projects? They read and write files constantly. Here’s what you actually need: 🔹 open() Open a file → First step to any file operation 🔹 read() Read file content → Extract data for processing 🔹 write() Write to a file → Overwrites existing content 🔹 append ("a") Add to a file → Keeps existing data, adds new lines 🔹 close() Close the file → Prevents memory leaks 🔹 with (best practice) Auto-manages files → No need to manually close 💡 Pro insight: Most beginners forget this: 👉 "r" = read 👉 "w" = overwrite 👉 "a" = append 👉 "r+" = read + write And one more thing… 👉 Always prefer with open() It’s cleaner, safer, and production-ready. 🎯 Want to build real Python skills? Start here: 💻 Python Automation 🔗 https://lnkd.in/dyJ4mYs9 📊 Data + Python 🔗 https://lnkd.in/dTdWqpf5 🚀 Python isn’t just about syntax. It’s about solving real problems. 👉 What’s one thing you’ve automated using Python?

Python: 06 🐍 Python Tip: Master the input() Function! Ever wondered how to make your Python programs interactive? It all starts with taking input from the user! ⌨️ 1) How to capture input? -To get data from a user, we have to use the input() function. To see it in action, you need to write in the terminal using: '$ python3 app.py' 2) The "Type" Trap 🔍 -By default, Python is a bit picky. If you want to know the type of our functions, You can verify this using the type() function: Python code: x = input("x: ") print(type(x)) Output: <class 'str'> — This means 'x' is a string! 3) Converting Types (Type Casting) 🛠️ If you want to do math, you have to convert that string into an integer. Let's take a look at this example- Python code: x = input("x: ") y = int(x) + 4 # Converting x to an integer so we can add 4! [Why do this? Without int(), here we called int() function to detect the input from the user, otherwise Python tries to do "x" + 4. Since you can't add text to a number, your code would crash! 💥] print(f"x is: {x}, y is {y}") The Result 🚀: If you input 4, the output will be: ✅ x is: 4, y is: 8 Happy coding! 💻✨ #Python #CodingTips #Programming101 #LearnPython #SoftwareDevelopment

In Python, multithreading and multiprocessing are two powerful ways to run tasks concurrently. Multithreading: Runs multiple threads within the same process, sharing memory. Ideal for I/O-bound tasks like file reading, web scraping, or network requests. Multiprocessing: Runs multiple processes in parallel, each with its own memory. Perfect for CPU-bound tasks like heavy computations, data processing, or machine learning training. 💡 Key Takeaways: GIL Awareness: Python’s Global Interpreter Lock (GIL) allows only one thread to execute Python bytecode at a time, limiting parallelism for CPU-heavy tasks. Memory Usage: Threads share memory, making them lightweight; processes use separate memory, which increases overhead but avoids GIL limitations. Task Suitability: Multithreading → I/O-bound tasks Multiprocessing → CPU-bound tasks Communication: Threads communicate easily via shared memory; processes communicate via queues, pipes, or other IPC mechanisms. Performance Boost: Using the right approach can drastically reduce execution time and make your applications scalable. Error Isolation: Errors in one process don’t crash others; threads are more sensitive to shared state issues. Python Libraries: threading for multithreading multiprocessing for multiprocessing Practical Example: Downloading multiple files → multithreading Image processing for large datasets → multiprocessing 🔥 Mastering concurrency in Python helps you write faster, smarter, and scalable programs! Manivardhan Jakka GALI VENKATA GOPI 10000 Coders Aravala Vishnu Vardhan

Stop passing the same variable through five different functions in Python. We have all been there. You need a user ID in a deeply nested database call. So you clutter up every single function signature just to pass it down. Thread local storage used to be the classic workaround for this problem. But modern asynchronous programming breaks thread locals entirely. If async task A pauses and task B takes over, they share the same underlying thread. That means they suddenly share the same thread local variables. This is a fast track to silent data corruption in concurrent applications. Try the Python 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘃𝗮𝗿𝘀 module. It solves this by storing state that is strictly bound to your current asynchronous task. You simply declare a 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝗩𝗮𝗿 object at the top of your module. When a new web request arrives, you use the set method to store the request data. Any function running inside that specific task can then use the get method to retrieve it. Even if a hundred other concurrent tasks are running, they will only see their own isolated values. You get the convenience of global access without the nightmare of shared global state. Your function signatures stay clean and focused on their actual business logic. --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

Episode 13 of What I Can Do with Python. Have you ever tried sorting a dataset that contains a mix of numbers and letters, only to realize Excel treats the values as text? Here is a relatable example: "12.4kg, 9kg, 45.4kg, 102kg" If you sort that column in ascending order in Excel, "9kg" will not come first. In fact, "102kg" may appear before smaller values because Excel is sorting the entries as text, not as numbers. That is not a bug. It is just how text-based sorting works internally. In this episode, I worked on solving that limitation. I built a Python-powered user-defined function for Excel that performs natural sorting. Instead of limiting it to a single column like Excel’s "SORT" function, I designed it more like a "SORTEDBY" function that supports multiple columns and custom sort directions. I named the function "natural_sort_by". It takes: one required positional argument: the data to be sorted additional arbitrary positional arguments that define the column index and sort direction The column index starts from 1 to make it more intuitive for Excel users. For sort direction, I used: "1" for ascending "-1" for descending By default, null values are pushed to the end. I also included proper error handling to help users quickly spot and correct mistakes. Behind the scenes, this project involved a lot of data manipulation with pandas, the "natsort" module for natural sorting logic, error handling, and xlwings as the bridge back to Excel. This is another example of how Python can help extend Excel beyond its default capabilities. Does this sound like a problem you have encountered when sorting data? A demo video showing how it works is attached to this post. See you in Episode 14

⚠️ Most Python bugs don’t crash your program… they crash your logic. That’s where exception handling comes in. If you’re not using it properly, your code isn’t production-ready. Here’s what you actually need to know: 🔹 try Run code safely → Wrap risky operations 🔹 except Catch errors → Handle specific exceptions 🔹 else Runs if no error occurs → Keep success logic clean 🔹 finally Always runs → Perfect for cleanup (closing files, connections) 🔹 raise Throw errors manually → Enforce rules in your code 💡 Pro tips that most beginners miss: 👉 Catch specific errors (not just except:) 👉 Avoid silent failures 👉 Use finally for cleanup 👉 Use raise to control logic Because… 🚀 Good developers write code that works. Great developers write code that fails safely. 🎯 Want to build real Python skills? Start here: 💻 Python Automation 🔗 https://lnkd.in/dyJ4mYs9 📊 Data + Python 🔗 https://lnkd.in/dTdWqpf5 🧠 AI with Python 🔗 https://lnkd.in/duHcQ8sT 👉 What’s the most common error you run into in Python?

Some python list tutorials stop at my_list.append(x). That is the surface. Underneath, a list is a C struct called PyListObject holding an array of pointers to PyObject instances. The list does not store your data. It stores references to wherever your data lives on the heap. That single fact is the root cause of the aliasing bugs that catch developers off guard. A few things that land differently once you understand the memory model: Why append() is O(1) amortized. CPython over-allocates on resize using the growth sequence 0, 4, 8, 16, 24, 32, 40, 52, 64, 76... so the O(n) copy cost spreads across many appends. Why b = a and then mutating b also mutates a. They are two names pointing at the same PyListObject. Why list.sort() runs in O(n) on nearly-sorted data. Timsort, written by Tim Peters in 2002, finds already-sorted runs and merges them. Stability has been a documented guarantee since Python 2.2. Why list.pop() from the end is O(1) but list.pop(0) is O(n). Elements after the index have to shift. I put together an 11-tutorial learning path on PythonCodeCrack that walks through lists from first principles through the copy semantics and aliasing patterns that cause hard-to-trace bugs. Fundamentals first (creation, slicing, append vs extend, sorting, comprehensions), then the advanced group (flattening, shallow vs deep copy, why your list keeps changing unexpectedly). https://lnkd.in/g5uUXj6d #Python #SoftwareEngineering #CPython #Programming

A common mistake in async Python is using async and await without actually designing for concurrency. The code looks asynchronous, but if execution is still mostly sequential, latency will remain poor. To get real value from async Python, you need to understand when to use direct await, when to schedule work with create_task(), and when to use TaskGroup for structured concurrency. The core distinction is simple: → direct await is sequential → create_task() lets independent work start earlier → TaskGroup gives you structured concurrency with better failure handling A common issue in real code is hidden serialization: multiple I/O operations are written as separate awaits, so each one waits for the previous one to finish. In the first version, latency accumulates across each network call. In the second, those operations can overlap, so total latency is closer to the slowest individual call rather than the sum of all of them. That is the difference between writing asynchronous code and designing a concurrent system. One more important distinction: → concurrency is not the same as parallelism → asyncio is mainly useful for I/O-bound workloads → CPU-bound work still runs into the GIL → for CPU-heavy workloads, you usually need multiprocessing or another form of offloading Async Python does not improve performance automatically. It gives you tools to avoid wasting time on idle waiting. If concurrency is not structured intentionally, the code may be asynchronous in syntax while still behaving too much like synchronous code.

Habemus smooth in python! For those not familiar with it, smooth is an implementation of Single Source of Error (SSOE) time series forecasting models (ETS, ARIMA and many more) by Ivan Svetunkov. I first came across the package while I was checking the benchmarks for the M5 competition (https://lnkd.in/eKYD7hMG) and then also at work where it was the preferred forecasting package for an old yet durable project. Contrary to most forecasting libraries that prioritise ease of use, smooth prioritises flexibility and control, making it possible to take full advantage of the models capabilities. Needless to say, I like it! Smooth was developed in R, with a lot of C++ code doing the heavy lifting. Within the forecasting community, "smooth in python" became the equivalent of "play Freebird!" (looking at you Nicolas Vandeput). So at some point three to four years ago I texted Ivan and told him that I'll help him port it to python. With the typical confidence that comes with ignorance, I thought it would be a piece of cake! After sorting out all the plumbing between C++ and python, we hit a big wall, tens of thousands of complex R code that had to be translated into python. Luckily we had an ace up our sleeve, Filotas Theodosiou. While most programmers, including me, were arguing about the effectiveness of coding with AI, Filotas was already ahead of the curve and was using his great powers to demolish that wall. Turns out you need all the AI help you can get to produce a fraction of the output of a young Ivan, up in Lancaster doing his PhD. Long story short, a few years later, we have the first python release of smooth on pypi (https://lnkd.in/eKmztdih). We still have a lot of work to do to get to full feature parity with the R version, but we're getting there. For more details and some benchmarks, check the post on Ivan's blog -> https://lnkd.in/ePDy9G8F PS: Special thanks to Ralph Urlus for developing and maintaining CARMA (https://lnkd.in/eMx3d7ns) which was a drop-in replacement for RcppArmadillo and made the whole thing possible!