Python Practice with Real-Time Product Data

Day 15 📊 Today I explored Python Basics 🐍 At first, it felt like just variables and data types… But then I understood how it connects to real-world problems 💡 👉 Real-world example: Imagine an e-commerce company tracking daily sales 🛒 They have raw data like: Product, Quantity, Price 👉 Using Python, we can: Store data in variables Calculate total revenue Find average order value Identify top-selling product 💡 In seconds, Python turns raw data into insights 📊 👉 Example output: Total Revenue: ₹1,07,500 Average Order Value: ₹5,375 Top Product: Laptop This made me realize: 👉 Python is not just coding 👉 It’s a tool to solve real business problems Slowly building strong foundations 💻 Still learning step by step 🚀 ❓What was the first thing you built using Python? #Python #DataAnalytics #LearningJourney #Beginner #Coding

I used to think Python was just about writing code. That changed when I started working with libraries. Once I got into NumPy, Pandas, and the rest, I realized it’s less about coding and more about solving problems with the right tools. Each library started to click in its own way: • Pandas → messy, real-world data that needs cleaning and shaping • NumPy → handling performance-heavy numerical operations • Matplotlib & Seaborn → actually understanding what the data is saying • Scikit-learn → taking it a step further with predictions But the biggest shift? Not just learning the libraries… 👉 Learning when to use which one That’s what made everything start to make sense. I’m still learning, but now I approach problems differently: Not “how do I code this?” But “what’s the right tool for this?” Curious - what’s the one Python library you use the most, and why? #Python #DataAnalytics #MachineLearning #Libraries

Let's Become ML Engineers Together — Phase 1: Foundation (Part-1) Python for Machine Learning There are many apps and environments where you can write and run Python code, But I would suggest Google Colab 1️⃣ Basics Variables & types— No type declarations needed. Python figures it out. Math & comparisons if / elif / else for loops 2️⃣ Data Structures List — ordered, changeable Dict — key → value pairs Tuple — immutable list Set — unique values List comprehension — compact loops 3️⃣ Functions & OOP 🌟 Defining functions Default arguments Lambda — one-line function Defining a class Inheritance 💭 You can learn from https://lnkd.in/gsYx6j8J #ML #Python #Basic

🚀 Day 49 Today I explored Python’s HTMLParser and learned how to extract meaningful information from HTML snippets. 🔍 Key takeaways: • How to handle single-line and multi-line comments using handle_comment() • How to process text data inside HTML tags using handle_data() • The importance of ignoring unnecessary data like empty lines ('\n') • Understanding how parsers read content sequentially from top to bottom 💡 What I built: A Python program that reads HTML input and prints: ✔️ Single-line comments ✔️ Multi-line comments ✔️ Data content This task improved my understanding of how web data is structured and how parsers interpret it — a small step toward mastering web scraping and data processing! Consistency > Perfection. See you on Day 50 💻🔥 #Python #CodingJourney #LearningEveryday #HTMLParser #DeveloperLife

Mastering Data Ingestion: Why NumPy is the Standard For anyone working with numerical data in Python, the transition from built-in functions to NumPy is a game-changer. While Python’s open() function handles basics, NumPy arrays offer a level of efficiency and speed that standard lists simply cannot match. Why use NumPy for flat files? The Industry Standard: NumPy arrays are the backbone of the Python data ecosystem. Essential for ML: If you plan to use libraries like scikit-learn, your data needs to be in a NumPy format. Built-in Efficiency: Functions like loadtxt() and genfromtxt() make importing arrays seamless. Pro-Tips for np.loadtxt() When importing data, the real power lies in the customization arguments: delimiter: Remember that the default is whitespace. For CSVs, always specify delimiter=','. skiprows: Perfect for bypassing headers (e.g., skiprows=1) so string labels don't break your numerical array. usecols: Optimization starts at ingestion. Only grab what you need by passing a list of indices, like usecols=[0, 2]. dtype: Control your data types from the start (e.g., dtype='str'). The Catch While loadtxt() is excellent for clean, uniform datasets, it hits a wall with mixed data types (like the Titanic dataset). When your columns vary between strings and floats, it’s time to level up to genfromtxt() or move into the world of Pandas. #DataEngineering #python #Numpy #Learninginpublic

I used to think Data Structures were just a hard exam topic. Then StemLink lectures showed me how they actually work in Python — and everything changed. 🐍 Here's what I learned, broken down simply 👇 --- 🔷 The 4 main data structures in Python: 📋 Lists → ordered, mutable, most used   mylist = []   mylist.append("item")  # add to end   mylist.sort()      # sort in place 📖 Dictionaries → key-value pairs, O(1) lookup   user = {"name": "Abiya", "age": 20} 🔸 Tuples → like lists but NOT mutable   coords = (6.9, 79.8)  # can't change this 🔹 Sets → unique values only, no duplicates   tags = {"python", "dsa", "python"} # stores only once --- 🔷 How data lives inside structures: Everything stored inside these is called an Element. Elements are ordered in groups → accessed using Indexes. mylist[0]    # first item mylist[-1]    # last item mylist[0:3]   # slice from 0 to 2 --- 🔷 How we move through data: Loops + Indexes work together to iterate through elements. for elem in mylist:   # loop through every item   print(elem) mylist[int] = x     # modify by assignment --- 🔷 The big insight: Lists are the most popular data structure in Python. They connect everything — elements, loops, indexes, methods, and assignment — all in one place. Once you understand Lists deeply, the rest makes sense. These fundamentals go directly into the projects I build. Not just theory. Real code. Which Python data structure do you use the most? 👇 #Python #DSA #DataStructures #StemLink #IITColombo #LearnToCode #CS #StudentDeveloper #BuildInPublic #Programming

🚀 Python Essentials: Range, For Loop, Enumerate & List Comprehension   💡"Write cleaner, smarter, and more Pythonic code."   🔢 range Definition: Generates a sequence of numbers. Syntax: range(start, stop, step) Example: for i in range(1, 6):    print(i)  # 1 to 5 🔄 for loop Definition:  Iterates over a sequence. Syntax: for variable in sequence: Example: fruits = ["apple", "banana", "cherry"] for fruit in fruits:    print(fruit) 🏷️ enumerate Definition: Adds a counter to an iterable. Syntax:  enumerate(iterable, start=0) Example: for index, fruit in enumerate(fruits, start=1):    print(index, fruit) ⚡ List Comprehension Definition: Concise way to build lists. Syntax: [expression for item in iterable if condition] Example: squares = [x**2 for x in range(1, 6)] print(squares)  # [1, 4, 9, 16, 25] ✨ These four tools are the backbone of writing efficient loops and data transformations in Python. Master them, and your code will be cleaner, faster, and more elegant.  "Python isn’t just about writing code—it’s about writing it beautifully.” 🔖#PythonProgramming #LearningJourney #CodingInPublic #EntriLearning #CodeNewbie #Python #ProgrammingBasics #DataAnalytics #CareerGrowth #LinkedInLearning #LearnWithMe #BeginnerFriendly #AnalyticsInAction #CodeSmart

Why Everyone Says Python is Slow (and Why They Are Wrong) The Performance Comes from Smart Profiling. Code structure is the reason for slow execution and profiling reveals the bottleneck. Stop guessing which function slows the program. Do these 5 things to speed up your code: 1) Run cProfile to find functions with high cumulative time. 2) Replace append loops with list comprehensions to move work to the C level. 3) Use generators for large datasets to keep memory usage constant. 4) Move global variables into local scope inside loops to avoid dictionary lookups. 5) Add slots to your classes to reduce memory usage by 80%. Built-in functions like sum() or map() run in C. Converting their results back to lists adds computation time. 📌Here is the detailed article: https://lnkd.in/d4QpkT9E P.S. I share data analytics tips and my experience in a free newsletter. Join here: https://lnkd.in/d79Zv532

Python: sort() vs sorted() Have you ever had to pause for a second and think: “Do I need sort() or sorted() here?” 😅 This is the common Python confusions. Let’s clear it up. 🔹 list.sort() ◾ A method (belongs to list objects) ◾ Works only on lists ◾ Sorts the list in-place ◾ Changes the original list ◾ Returns None Example: numbers = [3, 1, 4, 2] numbers.sort() print(numbers) # [1, 2, 3, 4] 🔹 sorted() ◾ A function (built-in Python function) ◾ Returns a new sorted list ◾ Does NOT change the original ◾ Works on any iterable Example: numbers = [3, 1, 4, 2] new_numbers = sorted(numbers) print(new_numbers) # [1, 2, 3, 4] print(numbers) # [3, 1, 4, 2] The key difference: sort() → changes your original data sorted() → keeps your original data safe 💡 Quick way to remember: 👉 If you want to keep the original, use sorted() 👉 If you want to modify the list directly, use sort() #Python #Programming #LearnPython #DataScience #LearningJourney #WomenInTech

Amazon, YouTube, Google — all have bugs in production right now. 🐛 And that's completely fine. The difference between a server that crashes and one that keeps running isn't perfect code — it's knowing how to catch failures before they become disasters. I just published a deep-dive on the Python concept that powers it all: 🔥 try / except / else / finally — the full anatomy 🔥 Every built-in exception you'll hit in production 🔥 Logging that actually alerts you in real-time 🔥 Custom exceptions & real-world patterns used at scale Small concept. Massive impact. If you're building anything in Python — this one's for you. 👇 https://lnkd.in/dxDqRyNv #Python #SoftwareEngineering #BackendDevelopment #Programming