Python Function Structure: def keyword, arguments, return

Have you ever written a perfectly “correct” Python function… that still feels slow and clumsy? Not because the logic is bad, but because it keeps doing expensive work over and over again: • re-reading files • re-parsing data • recomputing values that never change In today’s video, I walk through 10 Python features hiding in the standard library that make your code faster, clearer, and easier to reason about. Things like caching expensive operations, expressing intent more clearly, managing resources safely, and writing logic that scales without turning into spaghetti. 👉 Watch the video here: https://lnkd.in/ePuhn3VB #Python #PythonTips #ArjanCodes #CleanCode #SoftwareDesign #Pythonic

Have you ever written a perfectly “correct” Python function… that still feels slow and clumsy? Not because the logic is bad, but because it keeps doing expensive work over and over again: • re-reading files • re-parsing data • recomputing values that never change In today’s video, I walk through 10 Python features hiding in the standard library that make your code faster, clearer, and easier to reason about. Things like caching expensive operations, expressing intent more clearly, managing resources safely, and writing logic that scales without turning into spaghetti. 👉 Watch the video here: https://lnkd.in/eXcxPAQe #Python #PythonTips #ArjanCodes #CleanCode #SoftwareDesign #Pythonic

📘 Day 13 of my #90DaysPythonChallenge Today, I practiced Dictionaries in Python and learned how to work with key–value pairs. 🔹 Created and updated dictionaries 🔹 Accessed, added, and removed dictionary data 🔹 Iterated through keys and values Dictionaries are extremely useful for organizing and managing real-world data efficiently. 📂 Practice code available on GitHub: https://lnkd.in/dnNfeh_f #Python #90DaysPythonChallenge #LearningInPublic #CodingJourney #DataStructures

Extracting a text's composition in terms of characters and the number of occurrences is quite straightforward using Python's collections module. Using SQL and without any fancy functions is another story. Swipe to see how I've done it before. Have you needed to do something similar? If so, how did you go about it? #SQL #DataEngineering

Module: Python Fundamentals Class: 09 Topic: Python Fundamentals- Variables, Data Types, Methods, Dataframe, Google Colab ▪️Knowing about Python and Google Colab ▪️Google Colab UI Tour ▪️Variable; Variable Assignment and Naming Convention ▪️Python Data Types: Numeric, String, Boolean, List, Tuple, Set, None ▪️Dictionary and Pandas Dataframe ▪️Knowing different Methods ▪️List vs. Tuples   ▪️Conditionals and Order of Execution; Indentation #python #machinelearning #ML #DataScience

Day 2 of my #100DaysOfCode challenge 🚀 Today I implemented a Python program to check whether two strings are anagrams. An anagram means two words contain the same characters in a different order (for example: listen and silent). What the program does: • Takes two strings as input from the user • Removes spaces and converts both strings to lowercase to ensure fair comparison • Sorts the characters of both strings • Compares the sorted results to determine if they are anagrams If both sorted strings match, the program returns True; otherwise, it returns False. Example: Input: listen, silent → True Input: cricket, ticket → False Key learnings from Day 2: – Writing reusable functions in Python – String preprocessing using replace() and lower() – Understanding how sorting helps in comparison problems – Building logical thinking for problem solving #100DaysOfCode #Day1 #Python #PythonProgramming #Anagram #ProblemSolving #CodingJourney #LearningPython #LogicBuilding #ProgrammingBasics #CodeDaily #PythonLearner

Dates and times shouldn’t break your code, but they often do. These five DIY Python functions help turn real-world dates and times into clean, usable data. https://lnkd.in/ezhm5Ahy

📘 Day 15 of my #90DaysPythonChallenge Topic: Recursion Today, I explored recursion in Python and learned how functions can call themselves to solve problems step by step. 🔹 Practiced recursive functions 🔹 Worked on factorial, sum of numbers, and Fibonacci 🔹 Understood the importance of base cases Recursion helped me think differently about problem-solving and breaking problems into smaller parts. 📂 Practice code available on GitHub: https://lnkd.in/dnNfeh_f #Python #90DaysPythonChallenge #LearningInPublic #CodingJourney #Recursion

Know #Python #Pandas series methods? You already know data frame methods! s.mean() → returns a single value df.mean() → returns a series (one per column) s.fillna(0) → returns a series df.fillna(0) → returns a data frame Same methods, applied column-wise!

Python Tip of the Day 🐍 range() and slicing may look similar — both use start : stop : step — but they serve different purposes. 🔹 range() generates numbers for iteration. 🔹 Slicing extracts elements from existing data. One creates. One selects. Understanding the difference makes your logic clearer and your code more intentional. Day 14 of building Python basics #PythonDaily #Python #LearningPython #DataAnalytics