How Python Executes Simple Code: A Step-by-Step Guide

Python dict: Fast Lookups and How Collisions Are Handled The "dict" is one of the most powerful and frequently used data structures in Python. It provides average O(1) time complexity for lookups, insertions, and deletions. But how does it stay so fast? Under the hood, a Python dictionary is implemented as a hash table. When you insert a key into a dictionary, Python first computes a hash value using the "hash()" function. This hash determines where the key–value pair should be placed in the internal table. However, different keys can sometimes produce the same hash index. This situation is called a hash collision. Python handles collisions using a technique called open addressing. Instead of storing multiple elements in the same bucket (like chained hash tables), Python searches for the next available slot in the table using a probing sequence. The simplified process looks like this: 1. Compute the hash of the key 2. Map the hash to an index in the table 3. If the slot is occupied, probe another position 4. Continue probing until an empty slot or the key itself is found Python dictionaries also maintain performance by resizing the table when it becomes too full. When the load factor increases, the dictionary allocates a larger table and rehashes the existing keys. This design allows Python dictionaries to remain extremely fast even with millions of entries. Understanding how collisions are handled helps explain why "dict" is both efficient and reliable for real-world applications. What’s the most interesting thing you’ve learned about Python dictionaries?

Whether you want to automate repetitive tasks, analyze data, build websites, or dive into machine learning — Python is the perfect starting point. And the best way to start is by getting solid on the fundamentals. In this guide you will learn every basic Python operation through concise explanations, real code examples, and the exact output you can expect to see when you run them. No fluff, no setup headaches — just Python. #Python #DataEngineering https://lnkd.in/g6bvfhHX

Today's python tip is: do not trust its GC. I had trouble with Python processes whose RSS was regularly growing, although object creation was well under control. I remembered then that, as Python objects do not move in memory, the memory very likely ended up being fragmented like crazy, since no compaction exists. The solution was to create a FreeListMeta metaclass for frequently allocated objects. That reduced the processes footprint dramatically! Edit: for those who want more details, https://lnkd.in/e-N6Qzy6 "The most frequent memory problem in Python is memory fragmentation, btw. Memory fragmentation occurs when the memory allocator cannot find a contiguous block of free memory that fits the requested size despite having enough total free memory. This is often due to the allocation and deallocation of objects of various sizes, leading to 'holes' in the memory. A lot of heterogeneity in the lifespans of objects (extremely common in real-world applications) can exacerbate the issue. The Python process grows over time, and people who haven't debugged it before are sure it's a memory leak. Once you are experiencing memory fragmentation, some of your techniques can help slow it down. The ultimate solution is generally to somehow create a separate memory pool for the problematic allocations - the easiest way is to allocate, aggregate, and deallocate them in a separate, short-lived process." My problem was due to having similarly sized objects with very different lifespans. The free list cured it.

Python may get async yield from. Nice syntax. Potentially ugly consequences. At first glance, it feels like a natural extension of yield from for async generators. It is convenient. It is visually consistent. And yes, sometimes it would make code cleaner. But async generators are already one of those Python features that look simpler than they really are. Go a bit deeper, and you run into things like explicit closing, cleanup that does not happen when you expect, wrong assumptions about generator behavior, dependence on an event loop just to finish safely, and bugs that appear the moment multiple tasks touch the same flow. That is the real discussion. Not whether async yield from looks elegant. But whether it makes a sharp edge even sharper. This is one of the things I find most interesting in Python: a feature can look like syntax sugar on the surface, while quietly expanding the state model underneath. And in production systems, that difference matters. Complexity in Python rarely arrives as one dramatic failure. It shows up as a chain of locally reasonable decisions that slowly become hard to reason about as a whole. That is why async-heavy systems need more than code that “works”. They need clear lifecycle rules, ownership boundaries, and predictable behavior under failure. Curious where others land on this: Would async yield from make async Python better, or just easier to misuse?

⛔ Python Tip: Don’t Use if Statements to Count Items. Use defaultdict You have a list of items, and you want to know how many times each item appears in the list; a common beginner pattern is to write something that looks like this: counts = {} for item in items: if item in counts: counts[item] += 1 else: counts[item] = 1 This works, but it's quite verbose and therefore not very Pythonic. The best way to do it is to use `defaultdict` from the `collections` module. Here is how the code will look using this method: from collections import defaultdict counts = defaultdict(int) for item in items:   counts[item] += 1 A defaultdict is a special type of dictionary from Python’s collections module. The key idea is that it automatically creates a default value for keys that do not yet exist. In this code, when we write: counts = defaultdict(int) We tell Python that if a key is missing, create it and assign it the default value returned by `int()`, which is 0. This means every new key starts at 0, and 1 is added to it if it occurs more than once. No if statement is required. This is much better: it is less verbose, requires no manual checks, and is therefore less prone to error. 🔑 You want more Python tips? Check out the Python Tips and Tricks: A Collection of 100 Basic & Intermediate Tips & Tricks Link: https://lnkd.in/eyq2Yg7F

FUNDAMENTALS OF PYTHON It’s safe to say that Python fundamentals go far beyond just assigning variables or understanding iterations. ➠ Iteration, as the name implies, is the process of repeatedly executing a block of code using loops under specific conditions. From what I’ve learned so far: ➠ While loops handle indefinite iteration — they keep running as long as a condition remains true. ➠For loops handle definite iteratio — they run based on a sequence or a fixed range. Now here’s a real question: ➤ How many months do people spend learning functions, commands, operators, and loops… only to later call them “non-fundamentals”? Well… I’m that “someone.” And I moved past all of these about a week ago. Not because they don’t matter—but because this is my way of pushing myself forward and staying motivated for the journey ahead. To anyone on the same path as me: You’re doing well—but we can do even better. April 1st, 2026 marks the first draft on this journey. Hopefully, it won’t be the last. My Current Focus: Algorithm Building & Checking Right now, I’m diving into algorithm design and validation, which is honestly one of the most interesting parts of learning Python. It blends: ➼Basic algebra ➼ Binary thinking ➼Logical problem-solving The math itself is simple enough but improvisation using codes is where the fun begins ➤ First Concept: Guess-and-Check Algorithm (Also known as Exhaustive Enumeration) ➠ This algorithm works when: ➮ You can guess possible solutions, and ➮ You can **check if those guesses are correct** It keeps trying values until: ➮ A solution is found, or all possibilities are exhausted ➤ Simple Applications: ➠Finding square roots and cube roots of integers ➠Solving basic word problems ➠Building number guessing games and simple logic-based games This is just the beginning. More concepts, more challenges, more growth on the road to becoming a Python guru 🐍 Stay tuned. 👨💻👨💻

Python typing may be entering a very different phase. PEP 827 is interesting not because it adds “more types”. It introduces ways to operate on types themselves. Today Python typing works well for describing structure. But once you try to express transformations, it becomes much harder. For example, we often know what a function returns: get_field(user, "username") clearly returns `str`. But expressing that cleanly and generically in Python typing is still difficult. This proposal moves in a different direction: • conditional types • type member access • variadic generics • type-level operators • type introspection In practice, this could change how frameworks and libraries are designed. Instead of duplicating models: User UserPublic UserCreate You could define transformations once and let the type system carry them forward. That matters beyond tooling. Type systems are not only about static checks. They shape what abstractions are easy to express. I have not gone deep into every detail yet. But directionally, this looks like one of the more interesting discussions around Python typing in recent years. Curious how others see this evolving.

🚀 Why Should We Learn Python? Many people who are starting their tech journey often have one question:  “Why do we need to learn Python?” Let’s understand it in a simple way. 🐍 What is Python?  Python is an easy-to-learn, general-purpose, dynamically typed, object-oriented programming language. Because of its simple syntax, it is considered one of the best languages for beginners. 💡 What does Dynamically Typed mean?  In Python, we don’t need to define the data type while declaring a variable. The interpreter automatically detects the type at runtime. Syntax Example: print("Hello World") With just a single line of code, we can write our first Python program. This simplicity is one of the biggest reasons why Python is so popular. 📌 Where is Python used? Python is widely used in multiple domains such as: • Artificial Intelligence (AI)  • Machine Learning (ML)  • Web Development  • Game Development  • Data Analysis & Automation Because of its versatility and huge ecosystem of libraries, Python has become one of the most in-demand programming languages in the tech industry. If you are planning to enter fields like Data Engineering, Data Science, or AI, learning Python is definitely a great step. 💬 Are you currently learning Python or planning to start? Let’s discuss in the comments.

yield is one of those Python keywords that looks simple until someone asks you to explain it. Most developers can tell you a function with yield in it produces values and works in for loops. Fewer can explain why that same function doesn't actually run when you call it. Turns out, that's the whole point. Generators (functions with yield) are functions that pause mid-execution and resume exactly where they left off: local variables, loop counters, everything intact. In my Python Context Managers series, I'm covering generators as a dedicated article because they are not a standalone concept. They are the engine behind @contextmanager, a cleaner way to build context managers in Python. You can't fully understand one without understanding the other. This article is a deep dive into generator functions: https://lnkd.in/dSNegaWK A function that remembers where it left off changes everything. #Python #SoftwareEngineering #Backend #Programming #WebDevelopment #BuildBreakLearn

Day 21 – List Methods in Python Lists are one of the most commonly used data structures in Python. Python provides built-in methods to easily modify and manage lists. 1. append() Adds an element to the end of the list. numbers = [10, 20, 30] numbers.append(40) print(numbers) Output : [10, 20, 30, 40] 2. insert() Adds an element at a specific position. numbers = [10, 20, 30] numbers.insert(1, 15) print(numbers) Output : [10, 15, 20, 30] 3. remove() Removes a specific value from the list. numbers = [10, 20, 30, 40] numbers.remove(20) print(numbers) Output : [10, 30, 40] 4. pop() Removes an element by index and returns it. numbers = [10, 20, 30] numbers.pop() print(numbers) Output : [10, 20] 5. sort() Sorts the list in ascending order. numbers = [30, 10, 20] numbers.sort() print(numbers) Output : [10, 20, 30] 6. reverse() Reverses the order of elements. numbers = [1, 2, 3, 4] numbers.reverse() print(numbers) Output: [4, 3, 2, 1] 7. count() Counts how many times an element appears. numbers = [1, 2, 2, 3, 2] print(numbers.count(2)) Output:3 8. index() Finds the position of an element. numbers = [10, 20, 30] print(numbers.index(20)) Output:1 #Python #Programming #LearnPython #CodingJourney