Python Comparison Operators and Logic in 60 Seconds

🐍 Python in 60 Seconds — Day 13 if / elif / else — Turning Logic Into Decisions Comparisons and logic are great… but what do we do with them? Enter: if statements. ✅ Basic if age = 20 if age >= 18: print("You are an adult") Output: You are an adult Python executes the block only if the condition is True. 🔁 if / else age = 15 if age >= 18: print("You are an adult") else: print("You are a minor") Output: You are a minor 🔹 if / elif / else marks = 85 if marks >= 90: print("A+") elif marks >= 75: print("A") else: print("B or below") elif = else if Python checks top to bottom First True condition runs, rest are skipped ⚠️ Beginner trap Indentation matters Only the blocks under if, elif, or else run conditionally if True: print("Hi") # ❌ Indentation error 🧠 Key idea if statements turn True/False logic into actions. This is how programs make decisions. 💡 Insight Logic = thinking if = action Combine them, and your program starts “behaving” like you want. 🔮 Tomorrow Logical operators (and, or, not) inside conditions — making decisions smarter 🧠🐍 #Python #LearnPython #Programming #Coding #TechCareers #DataScience #100DaysOfCode

🔍 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝗶𝗽: 𝗪𝗵𝘆 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱𝗻’𝘁 𝗧𝗲𝘀𝘁 𝗙𝗹𝗼𝗮𝘁𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 𝗡𝘂𝗺𝗯𝗲𝗿𝘀 𝗳𝗼𝗿 𝗘𝗾𝘂𝗮𝗹𝗶𝘁𝘆 I recently revisited an important lesson every Python developer should know: 𝗙𝗹𝗼𝗮𝘁𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗻𝗼𝘁 𝗯𝗲 𝘁𝗲𝘀𝘁𝗲𝗱 𝗳𝗼𝗿 𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘂𝘀𝗶𝗻𝗴 ==. Due to the way numbers are represented in binary, certain decimal numbers can’t be stored with perfect accuracy. This means direct equality checks can lead to unexpected results. #python print(0.1 + 0.2 == 0.3)   # Output: False Even though mathematically 0.1 + 0.2 equals 0.3, Python returns False due to floating point precision errors. #python print(0.1 * 3 == 0.3)   # Output: False Multiplying 0.1 by 3 should give 0.3, but due to floating point representation, Python returns False. This issue can occur with division and more complex calculations as well. ✅ 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗨𝘀𝗲 𝗮 𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 The best practice is to check if numbers are “close enough,” not exactly equal. Python’s math.isclose() is perfect for this: #python import math print(math.isclose(0.1 + 0.2, 0.3))   # Output: True By using this approach, you can avoid subtle bugs and ensure your comparisons are robust. #Python #CodingTips #SoftwareEngineering #CleanCode #Programming #DeveloperTips 

Today’s Python focus was 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀. I worked on understanding why functions exist and how they make code reusable, readable, and easier to manage instead of repeating the same logic again and again. 𝗪𝗵𝗮𝘁 𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝗱 𝘁𝗼𝗱𝗮𝘆: • Writing a simple function to calculate the volume of a cylinder instead of doing direct calculations • Passing parameters to functions and returning values • Calling the same function multiple times with the same inputs • Understanding the difference between built in functions, library functions, and user defined functions • Using functions to calculate total expenses from a list • Comparing custom logic with built in functions like sum() • Using functions from the math module such as sqrt() and ceil() • Working with *args to accept a variable number of arguments • Working with **kwargs to pass key value pairs into a function • Writing and using lambda functions for simple operations • Creating placeholder functions using pass 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Functions help avoid repetition and keep code clean • Parameters and return values make functions flexible • Built in and library functions save time and reduce errors • *args and **kwargs make functions more dynamic • Lambda functions are useful for short, simple logic Functions made it clear that Python is not just about writing code that works once, but about writing code that can be reused and maintained. If you are learning Python too, which function related concept took you some time to fully understand? #Python #PythonLearning #FunctionsInPython #ProgrammingBasics #LearningInPublic #DataAnalytics #Upskilling

Day 8 — Decision Making in Python: Control Flow 🧭 Code without decisions is just text. Real programs think, compare, and choose — and that starts with control flow. Today you learned how Python makes decisions using: • `if` → when a condition is true • `elif` → when another condition fits • `else` → when nothing else matches • Logical operators → `and`, `or`, `not` • Indentation → the invisible rule that controls everything This is where Python turns from “running lines” into thinking logic. Every real application uses this: • Login systems • Feature access • Validations • Business rules If you master control flow, you master program behavior. --- Mini Challenge (Highly Recommended): Write a program that checks if a number is positive, negative, or zero. Post your solution in the comments 👇 --- I’m sharing Python fundamentals — one focused concept per day. Designed to build developer-level thinking, not just syntax memory. Next up: 👉 Loops — repeating tasks the smart way. --- 🛠️ Writing and debugging logic becomes much easier in PyCharm by JetBrains — especially when working with nested conditions and indentation. --- Follow for the full Python series Like • Save • Share with someone learning Python 🚀 #Python #LearnPython #PythonBeginners #ControlFlow #Programming #CodingJourney #Developer #Tech #JetBrains #PyCharm

😳 Python says 6 - 5.7 = 0.2999999999999998 Is Python bad at maths? The first time I saw this, I thought I broke Python. 6 - 5.7 Expected: 0.3 Got: 0.2999999999999998 Turns out… Python is innocent. 🔍 The real reason: Computers don’t understand decimals the way humans do. They store numbers in binary (base-2), and decimals like 0.1, 0.2, 0.3, 5.7 cannot be stored exactly in binary. So Python stores the closest possible value. Just like: 1/3 = 0.3333... (never ends in decimal) 0.1 never ends in binary 💡 Fun fact: 0.1 + 0.2 == 0.3 # False This isn’t a Python bug. It’s a computer science reality (IEEE floating-point standard). 📌 Key takeaway: Integers → exact Floating-point numbers → approximations Once you know this, a LOT of “weird” bugs suddenly make sense. Learning Python is not just about syntax — it’s about understanding how computers think. #Python #LearningInPublic #ComputerScience #DataScience #Programming #FloatingPoint #TechInsights

Most Python programs fail not because the logic is wrong, but because the code is not prepared for unexpected situations. Users give wrong inputs. Files go missing. APIs fail. Systems behave unpredictably. If your code crashes in these scenarios, that is not bad luck. That is missing exception handling. Exception Handling is what separates code that works in notebooks from code that survives in real-world applications. It helps you write programs that are stable, readable, and production-ready, rather than fragile scripts that break at runtime. 🎯 That’s exactly what the new video covers. I have just launched a new video under my Python Programming playlist, where I explain Exception Handling in Python in depth. We go beyond syntax and focus on:  ✅Why exceptions exist and how Python handles them  ✅How try, except, else, and finally actually work  ✅Common mistakes learners make Practical, real-world examples you will face in projects and interviews If you are learning Python seriously or aiming to write professional-grade code, this is a topic you cannot skip. 📌 Watch the video here: https://lnkd.in/gxBUcce6 Be intentional about the fundamentals. Strong basics always compound faster. #PythonProgramming #ExceptionHandling #LearnPython #CodingFundamentals #SoftwareEngineering #YouTubeLaunch

🧠 Reverse a list | Python Problem Statement Given a list of n elements, reverse the list in place. 📌 Examples Input: n=5   myList = [20,1,10,5,59] Output: [59,5,10,1,20] 🔍 Approach Brute Force Approach 1. Traverse the list and store elements in a temporary list in reverse order 2. Copy the temporary list back to the original list ⏱️ Complexity Analysis Time Complexity: O(n) Space Complexity: O(n) ⚠️ This approach is easy to understand but uses extra space. Optimized Approach (Two-Pointer Technique) 1. Initialize two pointers: left = 0 and right = n − 1 2. Swap elements at left and right 3. Increment left and decrement right 4. Repeat until left < right ⏱️ Complexity Analysis Time Complexity: O(n) Space Complexity: O(1) 🧪 Python Code def reverse(self, arr: list, n: int) -> None: left = 0 right = n-1 while left < right: arr[left], arr[right] = arr[right], arr[left] left += 1 right -= 1     🧩 Edge Cases (Interview Tip) Empty list → no change Single-element list → already reversed List with duplicate elements → handled naturally #Python #DSA #CodingInterview #InterviewPreparation #CollegeStudents #CampusPlacements #LearningInPublic

Foundational knowledge is key to writing robust and efficient Python code. Understanding the nuances of different data types—like their mutability, ordering, and how they store values—is a critical skill for any developer. 💻 This infographic provides a high-level overview of Python's core data types: ✔️ Numeric Types (int, float, complex): Essential for arithmetic operations and mathematical modeling. ✔️ Sequence Types (str, list, tuple): The go-to for ordered collections. It's vital to remember that while lists are mutable, strings and tuples are immutable. ✔️ Mapping Type (dict): Invaluable for representing structured data with key-value pairs for fast lookups. ✔️ Set Types (set, frozenset): Highly efficient for membership testing and storing unique elements. Selecting the appropriate data structure is the first step toward writing clean, performant, and bug-free code. I hope this visual summary serves as a useful reference. What's your favorite use case for Python's dictionaries or sets? Share your insights below. #python #programming #softwareengineering #coding #developercommunity #technicalskills #datascience #backenddeveloper #codeayan

🐍 Python List Methods You Must Know to Write Better Code Lists are one of the most powerful data structures in Python. Mastering these core list methods helps you: ✔ Manage dynamic data ✔ Write clean, readable code ✔ Avoid unnecessary loops ✔ Improve performance • .append() --> Add a new item to a list dynamically • .clear() --> Reset a list without deleting the variable • .copy() --> Create a safe duplicate of a list • .count() --> Count occurrences of an element • .extend() --> Merge multiple lists into one • .index() --> Find the position of an item • .insert() --> Add an element at a specific position • .pop() --> Remove and return an item • .remove() --> Delete a specific value • .reverse() --> Reverse list order instantly • .sort() --> Arrange data in ascending/descending order 📌 Save this post if you’re learning Python 👇 Comment “List” if you want real-world examples next #Python #Coding #LearnToCode #Developer #PythonTips #ProgrammingTips

🧠 Python Concept That Saves You from Bugs: dict.get() Most beginners write this 👇 if "age" in data: age = data["age"] else: age = 0 Python says… simplify 😌 ✅ Pythonic Way age = data.get("age", 0) 🧒 Simple Explanation Imagine asking a friend: 👉 “Do you have a pencil?” ✏️ If yes → give pencil If no → give eraser (default) That’s dict.get(). 💡 Why This Is Important ✔ Avoids KeyError ✔ Cleaner code ✔ Perfect for APIs & JSON ✔ Frequently asked in interviews ⚡ More Examples user = {"name": "Asha"} print(user.get("name")) # Asha print(user.get("age")) # None print(user.get("age", 18)) # 18 💻 Clean code isn’t about writing less. 💻 It’s about writing safer code 🐍✨ 💻 dict.get() is one of those small habits that matter. #Python #PythonTips #CleanCode #LearnPython #DeveloperLife #Programming