Alexey Vyskubov’s Post

Good Evening! Let’s talk about Object Serialization in Python — a concept that sounds technical but is incredibly useful. Ahmed: "Bro, I spent two hours training a model yesterday... and I just lost it. Again. My laptop crashed." Zayd: "You didn’t save the model object?" Ahmed: "I saved the results, not the model itself. Is there a way to save entire objects in Python?" Zayd: [smiling] "Welcome to Serialization." Ahmed: "Serialization?" Zayd: "Yeah. Think of it like freezing your object in time — and unfreezing it whenever you want. Python’s `pickle` module lets you do that." Ahmed: "So I can save a whole model... and load it later like nothing happened?" Zayd: "Exactly. One line to dump, one to load." ```python import pickle Save it with open('model.pkl', 'wb') as f: pickle.dump(my_model, f) Load it with open('model.pkl', 'rb') as f: my_model = pickle.load(f) ``` Ahmed: "That’s a game-changer. What else can I serialize?" Zayd: "Almost anything — models, configs, custom objects. Just be careful: don’t unpickle files from untrusted sources." Ahmed: "Bro, you just saved me hours. Literally."

🚀 What I Revised today in Python: 💡 Variable in python: a=12 12 will get stored into RAM and a will hold the address of 12. print(id(12)) hashtag#output: 4404716384 a=12 print(id(a)) hashtag#output: 4404716384 🤔 proof using id()-----> id tells the memory address a=12 b=a print(id(a)) print(id(b)) hashtag#output: 4404716384 4404716384 a and b have the same memory address. a and b refer to same memory address. # this clears that 12 is stored into RAM, a and b stores the address of RAM. 👍 Clean variable names in python? use clear, descriptive and readable variable names------your future self will thank you! ✅ Good practice: clear and readable user_age=23 total_price=43.33 is_logged_in=True ❌ Avoid: unclear x=25 tp=49.99 p=True 💡 Tip: 1. Use snake_case_name. e.g., user_name, item_count 2. Don't reuse variable names for different things.

🐍 A Python mistake that looks innocent… but bites HARD 👇 Common misconception: «“This creates a 3×3 grid.”» grid = [[0] * 3] * 3 Looks fine, right? Most beginners (and many intermediates) think this makes 3 separate rows. ❌ It doesn’t. What actually happens (easy explanation): Python doesn’t copy the inner list. It copies the reference. So all rows point to the same list. Watch this 👀 grid[0][0] = 1 print(grid) Output: [[1, 0, 0], [1, 0, 0], [1, 0, 0]] 😱 Why did all rows change? Because Python didn’t create 3 rows — it created 1 row, reused 3 times. ✅ The correct way: grid = [[0] * 3 for _ in range(3)] Now each row is truly independent. This single concept explains a ton of “Python is acting weird” moments. If you like clear, bite-sized Python lessons that explain why things behave this way — 👉 Get one daily in your inbox: https://lnkd.in/dVNDF-xw One idea. One email. Zero overwhelm 🐍

How does len() function knows how to handle a List, a String, and a Dictionary all the same way in Python? TL;DR: Python Protocols A Protocol is just the set of rules (special methods) an object follows. "Duck Typing" Contract In Python does not care about what the object is, it cares only about what it does. These are represented as dunders (__method__) in Python. 3 Protocols we use everyday in Python: -> Sized (__len__): Tells Python that object has a size. (Powers len()) -> Container (__contains__): Tells Python how to check if something is "inside" the object. (Powers the in keyword) -> Iterable (__iter__): Tells Python that object can be looped over. (Powers for x in obj) Why is this useful? As a developer, we want our code to be intuitive. By following these protocols, we make our custom objects compatible with all of Python’s built-in tools. Protocols are the API of the Python language itself. Using them make custom objects start feeling like native Python parts. I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

A Python f-string (formatted string literal) is a way to embed expressions directly inside string constants, introduced in Python 3.6. It makes it easy to insert variable values into strings without needing concatenation or the .format() method. Syntax: name = "Sam" age = 30 print(f"My name is {name} and I am {age} years old.") Output: My name is Sam and I am 30 years old. How it works: The f before the opening quote tells Python to interpret the string as an f-string. Expressions inside {} are evaluated and replaced with their values. Benefits: Clean and readable More concise than using + or .format() You can use any valid Python expression inside the {} (e.g. {age + 5}, {name.upper()}) #Python #f-strings

Some fun with Python iterators. Let's take some list >>> lst = [1, 2, 3, 4] and build an iterator out of it >>> lst_i = iter(lst) Now I have an iterator over lst, so it should produce elements 1, 2, 3, 4. And indeed, the first element is 1. >>> list(zip(lst_i, ['a'])) [(1, 'a')] But let's see what is left... >>> list(zip(lst_i, ['b', 'c', 'd'])) [(3, 'b'), (4, 'c')] Where did 2 go? Well, the explanation is as following. The first zip gets 1 from lst_i and 'a' from ['a']. Then it gets 2 from lst_i and nothing from ['a'], so 2 is **discarded**. What is left for the second zip is 3 and 4. #Python #gotcha

Tuples in Python: Immutable (cannot be changed after creation), ordered collection that allows duplicate elements. Syntax:() Two methods: Index(),count() Python 3.14.0 (tags/v3.14.0:ebf955d, Oct 7 2025, 10:15:03) [MSC v.1944 64 bit (AMD64)] on win32 Enter "help" below or click "Help" above for more information. >>> #tuples() >>> a=(4,5.7,"python",4+9j,True,False) >>> print(a) (4, 5.7, 'python', (4+9j), True, False) >>> type(a) <class 'tuple'> >>> len(a) 6 >>> a.count("python") 1 >>> a.index(True) 4 Pooja Chinthakayala Mam,Saketh Kallepu Sir,Uppugundla Sairam Sir

#Learning hashtag #Python through Chunks. Lets start journey together (Beginner to Master). Lets code together !! #ABCC - Any Body Can CODE Chunk 2: How Python Actually Runs Code (Interpreter Explained Simply) NOTE: Python is an interpreted language, meaning it runs your code line-by-line. Python sits in the middle and converts readable code into machine-executable steps. 🧩 Python Interpreter in Real Life Terms Think of Python as: 💬 A translator who reads your instructions one line at a time 🎧 Tells the computer what to do 🛠️ Stops if something is unclear and shows you an error message Code: x = 5 print(x * 2) The interpreter does this: 1️⃣ Reads x = 5 → remembers it 2️⃣ Reads print(x * 2) → calculates → prints: Output 10 💡 Key takeaways from this chunk Python runs line-by-line (interpreted). It stops immediately if something doesn’t make sense. It shows error messages to help you fix problems.

Why Python prefers 𝘵𝘳𝘺 / 𝘦𝘹𝘤𝘦𝘱𝘵 over 𝘪𝘧 𝘦𝘭𝘴𝘦 checks? TL;DR: In Python, exceptions are part of normal flow, not rare events! There are two ways to walk through a uncertain code block - 1. Look Before You Leap (LBYL) - - Check conditions before doing the action - The 𝘪𝘧 𝘦𝘭𝘴𝘦 statements. - But here, Python looks at the dictionary twice. Once to check if the key exists, and once to actually fetch it. 2. Easier to Ask Forgiveness than Permission (EAFP) - Assume things will work and wrap the action in a try block. - If the key exists (which is usually the case), execution stays on the fast path. EAFP is the "Pythonic" approach. It is the way Python is built internally too. Python uses exceptions internally for everything. When using a for loop, Python doesn't check the length of the list; it just keeps asking for items until the list raises a StopIteration exception. Takeaway - -> Using try excepts will not add to any extra processing time, if not reduce it in most of the practical cases. -> Use LBYL if the failure is expected to happen often. -> Use EAFP if the failure is unexpected and rare. I’m deep-diving into Python internals and performance. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

Why range(1,000,000) is cheap, but list(range(1,000,000)) is costly in Python? TL;DR: Iteration Protocol in Python needs to know only next item and not full list. The "Next Page" Rule Iteration in Python isn't about having a collection of items; it’s about knowing how to get the next item. Two special methods make this possible: 1. __iter__() → tells Python “I can be looped over” 2. __next__() → returns the next value, one at a time When there’s nothing left, StopIteration tells Python to stop the loop. Why this matters? When we use a list, we pay for all the memory upfront. When we use the Iteration Protocol, we only pay for one item at a time. This is called Lazy Evaluation. Takeaway - If the object represents a collection or a stream of data, implement __iter__ and __next__. It makes the code more memory-efficient and much more "Pythonic." I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment