Priyanka SG’s Post

The coolest move in Python syntax: The "Moonwalk" loop. 🕺 Beginners often overcomplicate counting backwards in Python. I’ve seen clunky while loops, manually decrementing counters, or creating lists just to reverse them [::-1]. Stop working so hard. The "Pro" move is to unlock the often-ignored third parameter of the built-in range() function: the step. The syntax is: range(start, stop, step) By default, the step is positive (+1), moving you forward. But if you set the step to -1, you tell Python to slide backwards. In the visual below: Start at 10. Stop before 0 (Remember, the stop index is exclusive!). Step back by -1 each time. It’s clean, readable, and honestly, it just looks way cooler than a messy while loop. 😎 Want to learn a new Python trick every morning? We turn boring documentation into fun, bite-sized lessons. Get them delivered straight to your inbox daily. 👉 Join the PyDaily community here: https://lnkd.in/ducXvs-y #Python #CodingTips #LearnPython #SoftwareEngineering #Developer #PyDaily

Day 25 | The Python Feature That Made My Code Cleaner 🐍 One small Python concept that felt confusing at first — but now I love: List Comprehensions. Earlier, I used to write: squares = [] for i in range(10): squares.append(i * i) Then I learned this: squares = [i * i for i in range(10)] Same result. Cleaner. Shorter. More readable. At first it looked complicated. Now it feels natural. That’s the thing about Python — Many concepts look hard until you understand the pattern. List comprehensions taught me: • Think in expressions • Write concise logic • Read code more efficiently Still practicing. Still improving. What Python concept took time to “click” for you? #Day25 #PythonLearning #ListComprehension #CodingJourney #AIJourney #DataScienceStudent #LearningInPublic #TechGrowth

🚀 Day 23/100 | #100DaysOfCode with Python 🐍 Today I learned three super useful concepts that make Python code shorter, cleaner, and more powerful 👇 ✨ Lambda Functions Small, anonymous functions written in a single line. Perfect when the logic is simple and you don’t need a full function. 🔁 map() Function Used to apply the same operation to every element in a list or iterable. Great for transforming data quickly and efficiently. 🎯 filter() Function Helps extract only those values that match a condition. Super helpful when working with real-world data. What I loved today: Less code ✅ Better readability ✅ More confidence with Python ✅ Taking one step forward every day, no matter how small 💪 Consistency > Perfection 🚀 If you’re learning Python too, what did you practice today? Let’s share and grow 👇 #Day23 #PythonLearning #Lambda #MapFunction #FilterFunction #100DaysOfCode #CodingJourney #LearnToCode #DeveloperInMaking #DailyLearning

🚀 Day 29/100 | #100DaysOfCode — Python Learning Journey 🐍 Today I explored two very important file handling methods in Python: 👉 tell() and seek() — and they completely changed how I think about reading files 📄➡️🧠 Here’s what I learned today 👇 🔹 tell() — Where am I in the file? tell() helps to find the current position of the cursor inside the file. It tells us exactly where Python is reading or writing from. 🔹 seek() — Let’s move the cursor With seek(), we can move the file pointer to any position we want. This means we can re-read data, skip data, or jump to a specific part of the file. 🔹 Why this matters Now I understand how Python controls from where to read and where to write in large files — which is super useful in real projects. Small concepts, but very powerful when building real applications 💡🔥 Still learning. Still showing up. One step closer every day 💪 👉 Trust the process. Keep coding. #Python #FileHandling #tell #seek #100DaysOfCode #LearningInPublic #CodingJourney #Consistency

How FINALLY keyword in Python can silently change your function’s behaviour? How does the try except flow works? 1. When Python enters a try block, it pushes a "cleanup" instruction onto the stack. 2. When you hit a return statement inside try, Python doesn't actually exit the function immediately, it just "saves" the return value. 3. If you return inside the finally block itself, the original return value is discarded. More worse - This same thing happens with exceptions too. If your try block raises a error, but your finally block has a return or a break, the error vanishes! Takeaway - 1. Never use return, break, or continue inside a finally block. It can lead to "silent failures" and unexpected bugs. 2. Finally is meant only for cleanup (closing files, releasing locks) and not logic. I’m deep-diving into Python internals. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

I once thought that having an index meant fast access and lacking one meant slow performance. This belief seemed logical until I learned about Python. Here’s what I discovered: - If something has an index, it should be O(1). - If it doesn’t, it must be slow. However, Python defies this expectation. For example: - Accessing an element with an index: `arr[5]` is O(1). - Accessing a value in a dictionary: `data["id"]` is also O(1) even without an index. So, how does a dictionary maintain its speed without indexes? Python achieves this by: - Hashing the key - Jumping directly to memory - Retrieving the value This process avoids looping or scanning. On the other hand, lists are efficient due to contiguous memory management, not because of any special feature in Python. The key takeaway is that performance stems from internal design rather than the presence of an index. This realization has influenced my approach to selecting data structures in real projects. Has Python surprised you in similar ways? #Python #DataStructures #TimeComplexity #BackendDevelopment #LearningInPublic #DeveloperJourney #ComputerScience

Day 7 of Python 🐍 | Understanding Lists & Memory Today I dove deep into one of Python's most powerful data structures - Lists! Here's what I explored today :✅ 📌 Indexing - Accessing elements is easier than I thought. Python's zero-based indexing means the first element is at index [0], and negative indexing lets you work backwards from the end . 📌 List Operations - Lists are incredibly flexible. Unlike some languages, Python lists can hold different data types in one container, making them super versatile for real-world applications. 📌 Memory Allocation - This was eye-opening! Python allocates memory dynamically for lists. When a list grows, Python doesn't just add one slot - it over-allocates to optimize performance. Understanding this helps write more efficient code. 📌 The len() Function - Simple but essential. len() returns the number of elements, and it's O(1) time complexity because Python stores the list size internally. 🎯Key Takeaway: Lists aren't just arrays - they're dynamic, flexible, and optimized for Python's philosophy of making code readable and efficient. What's your favorite Python data structure? Drop it in the comments! 👇 #Python #100DaysOfCode #DataStructures #PythonProgramming #LearnInPublic #CodingJourney #TechLearning #DeveloperCommunity

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

Day 26 | Python Tricks Beginners Don’t Know 🐍 When I started Python, I thought writing longer code meant better code. Turns out… smarter Python is often shorter. Here are a few simple tricks that changed how I write code: 1️⃣ Multiple Assignment Instead of: a = 5 b = 10 You can write: a, b = 5, 10 2️⃣ Swapping Variables (Without Temp Variable) Instead of: temp = a a = b b = temp Just write: a, b = b, a 3️⃣ Using enumerate() Instead of Manual Indexing Instead of: for i in range(len(items)): print(i, items[i]) Use: for index, value in enumerate(items): print(index, value) Cleaner. More readable. More Pythonic. Python isn’t about writing more code. It’s about writing clear, efficient code. Which Python trick surprised you when you learned it? #Day26 #PythonLearning #PythonTips #CodingJourney #AIJourney #DataScienceStudent #LearningInPublic #TechGrowth