Python dict.get() vs Direct Access for Safe Dictionary Values

Python 3.15 might be shipping one of the most exciting performance tools Python has added in a while, that too right after the GIL-free release. It is called Tachyon. Python is adding a new statistical sampling profiler under `profiling.sampling` called Tachyon, and the positioning is what caught my attention: - attach to a running Python process - no code changes - no restart - designed for low-overhead profiling - supports outputs like flamegraphs, speedscope-compatible stacks, Firefox Profiler, heatmaps, live TUI, async-aware views, and even opcode-level profiling The docs go even further and describe it as capable of sampling at up to 1,000,000 Hz and suitable for production performance debugging. With ML pipeline domination and RESTful servers now written increasingly in Python, this profiler will be a great addition. No longer will the notion of “run a profiler locally and hope the issue reproduces” carry on, and profiling becomes a first-class citizen, as in languages like Java. This is from the Python 3.15 alpha docs, so details may change before final release. But this is absolutely worth watching. A big thanks to the Python Software Foundation and Hugo van Kemenade for the updates and articles. Read more in the link below: https://lnkd.in/eUJ3C4N7 #Python #Python315 #Performance #Profiling #SystemsEngineering #Observability #Backend #SoftwareEngineering

🚀 Day 5/60 – If-Else Conditions (Make Decisions in Python) Your code shouldn’t just run. It should think and decide. That’s where if-else comes in 👇 🧠 What is If-Else? It helps your program make decisions based on conditions. ✅ Basic Example age = 20 if age >= 18: print("You can vote") else: print("You cannot vote") 👉 If condition is True → first block runs 👉 Else → second block runs 🔁 Multiple Conditions (elif) marks = 75 if marks >= 90: print("Grade A") elif marks >= 60: print("Grade B") else: print("Fail") ⚡ Real-World Example temperature = 35 if temperature > 30: print("It's hot") elif temperature > 20: print("Nice weather") else: print("It's cold") ❌ Common Mistake if age > 18 print("Adult") # ❌ Missing colon Correct: if age > 18: print("Adult") # ✅ 🔥 Pro Tip Indentation matters in Python 👇 if True: print("Hello") # ❌ Error if True: print("Hello") # ✅ 🔥 Challenge for today Write a program: 👉 Take a number 👉 Check if it is: • Positive • Negative • Zero Comment “DONE” when you solve it ✅ Follow Adeel Sajjad to stay consistent for 60 days 🚀 #Python #LearnPython #PythonProgramming #Coding #Programming

🧠 Python Concept: is vs == ✨ Both compare values, but they check different things. ✨ == → Checks value equality a = [1, 2, 3] b = [1, 2, 3] print(a == b) Output True Because both lists have the same values. ✨ is → Checks object identity a = [1, 2, 3] b = [1, 2, 3] print(a is b) Output False Because they are different objects in memory. 🧪 Example with None value = None if value is None: print("Value is None") Using is is the recommended way to check None. 🧒 Simple Explanation 📚 Imagine two identical books 📚 == → checks if the content is the same 📚 is → checks if it is the exact same book 💡 Why This Matters ✔ Avoid logic bugs ✔ Important for None checks ✔ Helps understand Python memory ✔ Common interview question 🐍 In Python, == compares values, while is compares identities 🐍 Two objects may look the same but still be different in memory. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

You don't need a 40-hour Python course. Master these 7 concepts and you're already ahead of 90% of beginners. I did a deep-dive into Python fundamentals today. The thing that hit hardest: input() always returns a STRING. Always. If a user types "25" — Python doesn't see a number. It sees text. So input() + 5 gives you... TypeError. Fix? One line: int(input("Enter number: ")) Beginners spend 3 hours debugging this. I did too. Here's what actually matters for real-world Python: → // vs / — floor division vs float division (this shows up in interviews) → String immutability — .upper() doesn't change the original, it returns a new string → is vs == — identity vs equality (confuse these and you've got a silent bug factory) → f-strings over concatenation — cleaner, faster, looks professional → divmod() — returns quotient AND remainder in one call (barely anyone knows this) → Escape characters \n \t \\ — miss \\ in file paths and you get bugs with zero error messages Fundamentals feel boring. But this is exactly where production bugs are born. Which concept caught you off guard when you first started? Drop it below 👇 #Python #PythonProgramming #LearnPython #100DaysOfCode #WebDevelopment #MachineLearning #VikrantUniversity #StudentDeveloper #CodingLife #PythonTips #TechIndia #BuildInPublic

Logic in Python – Understanding and, or, and not! 🔗🐍 Programs aren’t just about calculations—they need to make decisions. That’s where logical operators come in. They let us combine multiple conditions and control the flow of our code. This session was all about mastering the three core logical operators in Python: and, or, and not. Here’s what I learned: --- 🔹 The and Operator · Returns True only if both operands are True. · Otherwise, it returns False. A and B True + True = True True + False = False False + True = False False + False = False ```python print((2 < 3) and (1 < 2)) # True and True → True print((5 > 3) and (2 > 5)) # True and False → False ``` --- 🔹 The or Operator · Returns True if at least one operand is True. · Only False if both are False. A B A or B True + True = True True + False = True False + True = True False + False = False ```python print(True or False) # True print(False or False) # False ``` --- 🔹 The not Operator · Reverses the boolean value. · not True → False · not False → True ```python print(not True) # False print(not False) # True ``` --- ✅ Key Takeaways: · Logical operators work with boolean values and return booleans. · and → all must be true · or → at least one true · not → flips the truth value · You can combine them with relational operators to build complex conditions. --- 💬 Let’s Discuss: Have you ever used and/or in a real project? What’s the most creative condition you’ve built with them? Drop your examples below! 👇 --- 🔖 #Python #LogicalOperators #CodingBasics #LearnToCode #ProgrammingLogic #NXTWave #TechJourney

Practice Day of Python into Real Working Programs No new topic today — just focused practice. The goal was to take everything learned over the past 7 days and apply it by building real programs that combine multiple concepts together. Because understanding concepts is one thing — applying them is what truly builds skill. 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐭 𝐓𝐨𝐝𝐚𝐲: Patterns – Down triangle using reverse range logic – Number triangle with index-based values – Diamond pattern combining upper and lower structures – Multiplication table grid using tab spacing Combined Programs – Vowel counter using loops, conditionals, and string methods – Multiplication table using user input and formatted output – String analyzer using indexing, conditions, and built-in methods 𝐖𝐡𝐚𝐭 𝐓𝐨𝐝𝐚𝐲 𝐏𝐫𝐨𝐯𝐞𝐝: Every program combined multiple concepts — input handling, string methods, conditionals, and loops — all working together in real scenarios. That’s how programming actually works. You don’t use concepts in isolation — you connect them. Learning teaches you the building blocks. Practice teaches you how to use them. Read all programs with full code and explanations on Medium 👇 https://lnkd.in/dyRQb_9W #DataScienceJourney #Python #Practice #Programming #Learning #Developers

Python Mini Project : Bill Splitter As part of my Python learning journey, I built a simple Bill Splitter project to strengthen my understanding of numbers and mathematical operations. Concepts Applied: Numeric Data Types – Worked with both int and float values Mathematical Operations – Performed addition, multiplication, and division Real-world Logic – Calculated total bill, added tip, and split among friends Key Takeaway: This project helped me understand how Python handles numeric operations using both integers and floating-point numbers, which is essential for real-world applications like financial calculations. Why this matters: Useful in budgeting and financial tools Core logic used in many real-world applications Strengthens problem-solving with numbers Step by step, I’m building a strong foundation in Python by creating practical mini projects. Next step: Enhancing this with user input and better formatting. #Python #MiniProject #LearningPython #DataAnalytics #CodingJourney #100DaysOfCode #TechSkills #Freecodecamp

This Python concept can make your code 100x faster. Most beginners completely ignore it. It’s called time complexity. Let me explain it in the simplest way possible. Imagine you’re in a library trying to find a book. Option 1: You check every book one by one until you find it. This is how Python lists work. numbers = [1,2,3,4,5,6,7,8,9,10] print(10 in numbers) Output: True But internally, Python scans each element one by one. This is O(n) → slower as data grows. Option 2: You use a system that tells you exactly where the book is. This is how sets work. numbers = {1,2,3,4,5,6,7,8,9,10} print(10 in numbers) Output: True This uses a hash table. So Python finds the value almost instantly. This is O(1) → constant time. Same result. Completely different performance. Now imagine this with 1,000,000 items. The real lesson: Lists are like searching blindly. Sets are like having a map. If you’re working with large data: • use lists for storing • use sets for fast lookup This is the difference between code that works and code that scales. What’s one Python concept that completely changed how you think? #Python #Programming #DataScience #CodingTips

𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗹𝗲𝘀𝘀𝗼𝗻𝘀, 𝘂𝗻𝗳𝗶𝗹𝘁𝗲𝗿𝗲𝗱 I added logging to my Python app and thought I was done. Two lessons later — I wasn't even started. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟭 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴 𝗳𝗶𝗹𝗲 𝗶𝘀 𝗮 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝗯𝗼𝗺𝗯 No size limit. No rotation. No cleanup. It just grows. Forever. Dev - fine. Production - it silently eats your disk until your app is down at 2AM. One swap in Python's standard library fixes this. Max size. Backup count. Auto-rotates. Auto-deletes oldest. That's it. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟮 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴𝘀 𝗮𝗿𝗲 𝘂𝗻𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 🔍 Human-readable text is perfect for dev. In production with thousands of requests ⁉️ Useless. Switch to JSON logs. Every line becomes a structured event : timestamp, level, request_id, user_id. Want all errors from one specific request? One query. Done. No regex. No grepping through walls of text. The difference between logging and logging for production is bigger than I thought. That gap - that's what this series is about #Python #Backend #LearnInPublic #SoftwareEngineering