Python Function Essentials Cheat Sheet

Built a Simple CLI Calculator with Python Today I practiced core Python fundamentals by building a small command-line calculator that performs basic operations like addition, subtraction, and multiplication based on user input. ♦️Concepts I reinforced: • User input handling • Type conversion (int) • Conditional logic (if statements) • Clean output formatting Even simple projects like this help strengthen problem-solving skills and build confidence in writing structured, readable code. Next step: expanding this into a more robust version with error handling and division support. #Python #LearningByDoing #DataAnalytics #CodingJourney #Azure #dataengineering #data

Day 24/100 — Assignment Operators in Python 🐍 Today I explored one of the most underrated fundamentals — assignment operators! They're not just shortcuts. They make your code cleaner, faster to read, and more Pythonic. Here's what I covered: → = — Basic assignment → += — Add and assign → -= — Subtract and assign → *= — Multiply and assign → //= — Floor divide and assign → **= — Power and assign → %= — Modulus and assign The real insight? Instead of writing score = score + 10, just say score += 10. Your future self (and your teammates) will thank you. ✅ Small habits like using shorthand operators are what separate messy code from readable code. 24 days in. Still going strong. 💪 #Python #100DaysOfCode #FullStack #LearnPython #CodingJourney #PythonBeginners #DevLife

𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗕𝗘𝗚𝗜𝗡𝗡𝗘𝗥𝗦 𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀: 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀, 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀, 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻𝘀 & 𝗟𝗮𝗺𝗯𝗱𝗮 Good Python code is rarely about knowing more features — it’s about choosing the right tools. In this notebook, I dive into concepts that directly impact performance, clarity, and design: • How dictionaries power fast lookups • Why selecting the right data structure matters • Writing cleaner loops with list comprehensions • Simplifying logic with lambda functions #Python #PythonProgramming #LearnPython #Coding #DataStructures #SoftwareDevelopment #ProgrammingLife

Python Clarity Series – Episode 9 Topic: Dictionary get() vs Direct Access 📌 Why does this code crash sometimes? d = {"a": 10} print(d["b"]) Error ❌ KeyError Better way: print(d.get("b")) Output: None 👉 d["key"] → Throws error if key missing 👉 d.get("key") → Returns None (safe access) 💡 Smart Tip: Use .get() when key might not exist. In real-world coding, this prevents crashes. This is beyond exam — this is practical Python. Have you used .get() before? #PythonTips #CodingClarity #FutureProgrammers

I used to think Python was just “write and run.” But when I started using it for automation, I noticed its technical nature really matters. Here’s what defines Python technically: 🔹 Sequential execution – runs top to bottom 🔹 Dynamic typing – types decided at runtime 🔹 Interpreted runtime – instant execution & debugging 🔹 Rich standard libraries – built-in support for OS & files 🔹 Strong system integration – works with files, APIs, and environment variables 🔹 Flexible structure – task-focused, not architecture-heavy 💡 Takeaway: Python is optimized for fast development and task automation, not complexity. Understanding how Python behaves internally makes your scripts smarter — not longer. #Python #Scripting #TechnicalLearning #Automation #DeveloperJourney

Solved the "Valid Parentheses" problem today using Python. Instead of checking every combination, I used a Stack data structure  to track opening brackets and ensure they close in the correct order. Key Learnings: * Stack is very useful for matching problems * Clean logic improves efficiency * Consistent practice strengthens fundamentals GitHub: https://lnkd.in/gWGSyu9V Time Complexity: O(n) Sharing my implementation on GitHub. #Python #DSA #ProblemSolving #Coding #MachineLearning

📘 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

Day 11 – List Comprehension in Python Today I learned one of the cleanest and most powerful Python features: List Comprehension. List comprehension allows us to create new lists in a single line of code.... making logic more readable and efficient. What I learned today: • Basic list comprehension syntax • Applying conditions inside comprehension • Transforming datasets in one line • Replacing loops with cleaner logic Why This Matters in Data Analytics: List comprehension helps in: •Transforming datasets •Filtering values quickly •Cleaning data efficiently •Writing compact and readable code •Improving performance For example: Instead of writing a loop to filter profitable transactions, list comprehension does it in one clean line. Clean code improves clarity and speed. GitHub Repository: https://lnkd.in/gdD4yAvR #Python #DataAnalytics #LearningInPublic #DataAnalystJourney #ProgrammingBasics #CareerGrowth

#PythonCoding 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁: 𝗥𝗲𝗺𝗼𝘃𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗟𝗶𝘀𝘁 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗨𝘀𝗶𝗻𝗴 𝗣𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Write a Python function that takes a list as input and returns a new list with duplicate elements removed, while maintaining the original order of elements. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: The function should work for both numbers and strings. The function should not use built-in functions like set(), dict.fromkeys(), or collections.OrderedDict. The function should preserve the original order of elements. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 1.Create an empty list called unique_list to store only unique elements. 2.Iterate through the original list using a loop. 3.Check if the element is already in unique_list: -->If not, add it to unique_list. -->If it already exists, skip it. 4.Return the unique_list, which now contains elements without duplicates while preserving the original order. #Python #PythonProgramming #LearnPython #PythonDeveloper #CodingLife #PySpark #MachineLearning #Automation

📘 Day 17 of my #90DaysPythonChallenge Topic: Lambda, Map, Filter & Reduce Today, I learned how to write cleaner and more efficient Python code using functional programming concepts. 🔹 Practiced lambda functions 🔹 Used map() for transforming data 🔹 Used filter() for selecting elements 🔹 Applied reduce() for aggregation These tools are very powerful for data processing and writing concise Python code. 📂 Practice code available on GitHub: https://lnkd.in/dnNfeh_f #Python #90DaysPythonChallenge #LearningInPublic #CodingJourney #