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“The UDF That Shouldn’t Exist” Python UDFs were everywhere. Beautiful logic, terrible performance. Every row serialized, processed, deserialized — painfully slow. Replaced all with native Spark functions: # Before udf_clean = F.udf(lambda x: x.strip().lower()) df = df.withColumn("clean_name", udf_clean("name")) # After df = df.withColumn("clean_name", F.lower(F.trim("name"))) ✅ No Python overhead ✅ Vectorized execution ✅ 10× faster The best UDFs are the ones you never had to write. #RealTimeDataEngineering

🚀 Set in Python - A Set in Python is a collection data type that is unordered, unindexed, and contains unique elements. It is mainly used when you want to store non-duplicate items and perform mathematical set operations like union, intersection, and difference. 🧩 Key Features: ▪️ Unordered: Elements have no defined order. ▪️ Mutable: You can add or remove items after creation. ▪️ No duplicates: Automatically removes repeated elements. ▪️ Supports set operations like union(), intersection(), difference(), etc. 💡 When to Use: 🔸 You need unique values. 🔸 You want to perform fast membership testing. 🔸 You need set-based operations (like finding common elements). #Python #PythonLearning #PythonBasics #DataStructures #Coding #LearnPython #SetInPython

Skill Seekers: It is a Python tool that scrapes documentation sites, GitHub repos and PDFs to automatically generate skills for the Claude AI platform. https://lnkd.in/eJtSae4Z

Today I learned something that genuinely changed how I look at data processing in Python — Generators. Instead of creating and storing an entire list in memory, generators produce items one at a time, only when needed. And that simple idea makes them incredibly efficient. Here’s a simple example that explains the difference: What surprised me is this: yield doesn’t return all results at once. It pauses the function, remembers its state, and continues from where it left off the next time it’s called. This makes generators perfect for: 🔹 Large datasets 🔹 Streaming data 🔹 Memory-efficient pipelines 🔹 Infinite sequences Instead of thinking in terms of “lists”, generators helped me start thinking in terms of flows — generating data only when the program actually needs it. Learning Python is slowly shifting from “how to write code” to “how to write efficient code.” 👉 This code prints the square of every number from 0 to 4, but it does NOT create any list in memory. It only generates the next value when needed. #Python #LearningInPublic #Generators #DeveloperJourney #ProgrammingConcepts #Efficiency #100DaysOfCode

🚀 Python Tip of the Day Ever wondered how to handle multiple conditions cleanly in one line? Check out this elegant one-liner that decides the discount type based on the customer’s tier 👇 # Decide discount type based on customer type customer = {"type": "Gold"} discount_type = (   "Platinum Discount" if customer["type"] == "Platinum"   else "Gold Discount" if customer["type"] == "Gold"   else "Silver Discount" if customer["type"] == "Silver"   else "Regular Discount" ) print(discount_type) 💡 Output: Gold Discount What’s Happening: Each condition is checked in order — Python picks the first one that’s true! It’s a clean way to replace multiple if-elif-else blocks when your logic is short and simple. #Python #CodingTips #SoftwareDevelopment #100DaysOfCode #PythonDeveloper #LearningPython

Python Tip – Day 7: Using zip() to Combine Lists The zip() function is a handy built-in tool in Python that lets you combine two or more iterables (like lists or tuples) element-wise. Example: names = ["Alice", "Bob", "Charlie"] scores = [85, 90, 95] for name, score in zip(names, scores): print(f"{name} scored {score}") Output: Alice scored 85 Bob scored 90 Charlie scored 95 Why it’s useful: 1) Helps pair related data easily. 2) Makes your loops clean and readable. 3) Can be converted to a dictionary using dict(zip(keys, values)). 🔥Day 7 of 30 Days of Python code The zip() function — because combining lists should be as smooth as Python itself! Clean, readable, and efficient. #Python #Coding #30DaysOfPythoncode #LearnCoding #PythonTips

💡 Python Tip of the Day: Lambda Functions 1️⃣ Lambda functions are small, anonymous functions in Python. 2️⃣ They let you write quick, one-line functions without using def. 3️⃣ Useful for short tasks where defining a full function feels heavy. 4️⃣ Syntax: lambda arguments: expression. 5️⃣ Example — lambda a, b: a + b does the same as a regular add() function. 6️⃣ Ideal for use with map(), filter(), and sorted() functions. 7️⃣ Improves code readability when used wisely. 8️⃣ Avoid overusing — too many lambdas can reduce clarity. 9️⃣ Great for clean, concise, and functional-style Python code. 🔟 Keep learning one Python trick a day to write better, smarter code! 🚀 #Python #CodingTips #CleanCode #SoftwareEngineering #LearningInPublic #AbhishekPR

🐍 Python Trick Question: Can You Guess the Output? 🐍 Here’s a classic loop puzzle that often surprises even experienced Python devs 👇 nums = [1, 2, 3, 4] squares = [lambda: n**2 for n in nums] print([f() for f in squares]) 🤔 What do you think this prints? Most people expect: [1, 4, 9, 16] But the actual output is: [16, 16, 16, 16] 😲 Why? Because the lambda inside the list comprehension captures the variable n, not its value at each iteration. By the time the lambdas run, n equals the last value (4) — so each lambda returns 4**2. ✅ Fix it: Bind the variable in the lambda’s default argument: squares = [lambda n=n: n**2 for n in nums] print([f() for f in squares]) # Output: [1, 4, 9, 16] 💡 Lesson: In Python, closures capture references, not values! #Python #CodingInterview #ProgrammingTips #LearnPython #CodeTricks #Developers