Suman Saha’s Post

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 🐍 | 𝗕𝘂𝗶𝗹𝘁-𝗶𝗻𝘀 – 𝗔𝗻𝘆() & 𝗔𝗹𝗹() ⚡ | 📅 𝗗𝗮𝘆 𝟱𝟴 🚀 Today’s task: ✅ 𝗧𝗮𝗸𝗲 𝗮 𝗹𝗶𝘀𝘁 𝗼𝗳 𝗶𝗻𝘁𝗲𝗴𝗲𝗿𝘀. ✅ 𝗖𝗵𝗲𝗰𝗸 𝗶𝗳 𝗮𝗹𝗹 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗮𝗿𝗲 𝗽𝗼𝘀𝗶𝘁𝗶𝘃𝗲. ✅ 𝗧𝗵𝗲𝗻 𝗰𝗵𝗲𝗰𝗸 𝗶𝗳 𝗮𝗻𝘆 𝗻𝘂𝗺𝗯𝗲𝗿 𝗶𝘀 𝗮 𝗽𝗮𝗹𝗶𝗻𝗱𝗿𝗼𝗺𝗲. Only if you understand Python’s logical helpers: all() and any() Core idea from the code: 𝙖𝙡𝙡(𝙞𝙣𝙩(𝙞) > 0 𝙛𝙤𝙧 𝙞 𝙞𝙣 𝙣_𝙡𝙞𝙨𝙩) Checks whether every number is positive. 𝙖𝙣𝙮(𝙞 == 𝙞[::-1] 𝙛𝙤𝙧 𝙞 𝙞𝙣 𝙣_𝙡𝙞𝙨𝙩) Checks whether any number is a palindrome. Final condition: Both must be True. 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: all() → True if all conditions pass any() → True if at least one condition passes Strong candidates understand: • Generator expressions • Logical evaluation of iterables • Writing compact Pythonic conditions Because Python isn’t about long loops. It’s about expressing logic clearly. Cleaner logic. Smarter code. #Python #PythonBuiltins #InterviewPrep #HackerRank #ProblemSolving #DailyCoding #Consistency

𝗧𝗵𝗲 𝗗𝗕 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗸𝗲𝗱 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. 𝗧𝗵𝗲 𝗳𝗶𝗹𝗲 𝗵𝗮𝗻𝗱𝗹𝗲 𝘀𝘁𝗮𝘆𝗲𝗱 𝗼𝗽𝗲𝗻. 𝗧𝗵𝗲 𝗿𝗼𝘄 𝗹𝗼𝗰𝗸 𝗵𝘂𝗻𝗴 𝗶𝗻𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗲𝗹𝘆. These aren't edge cases—they are the inevitable result of making resource cleanup the caller's responsibility. In Python, 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 move that responsibility from the developer to the type itself. The resource becomes self-healing. 🔹 __exit__ is called even if an exception is raised—that is the safety guarantee. 🔹 @contextmanager lets you write the same protocol with 'yield'—no class needed. 🔹 Any resource with an acquire/release lifecycle belongs in a context manager. The 𝘸𝘪𝘵𝘩 statement isn't just syntactic sugar—it’s a contract. The caller writes business logic; the object handles the cleanup. #Python #SoftwareEngineering #BackendDevelopment #SoftwareArchitecture #CleanCode

Day 48 of my #100DaysOfCode challenge 🚀 Today I implemented the Majority Element problem using the Boyer-Moore Voting Algorithm in Python. A majority element is the element that appears more than n/2 times in an array. What the program does: • Takes an array as input • Finds a potential majority candidate • Verifies if it actually appears more than n/2 times • Returns the majority element or None How the logic works: • Initialize count = 0 and candidate = None • Traverse the array: – If count == 0, set current number as candidate – If number equals candidate → increment count – Else → decrement count • This step finds a potential majority candidate • Traverse again to count its actual occurrences • If it appears more than n // 2 → return candidate • Otherwise return None Example: Input: [3, 2, 3] Output: 3 Another example: Input: [2, 2, 1, 1, 1, 2, 2] Output: 2 Another example: Input: [1, 2, 3, 4, 5] Output: None (No majority element) Why this algorithm is powerful: – Time Complexity: O(n) – Space Complexity: O(1) – Very efficient compared to brute force Key learnings from Day 48: – Understanding Boyer-Moore Voting Algorithm – Optimizing space complexity – Working with candidate selection logic – Solving real interview-level problems #100DaysOfCode #Day48 #Python #PythonProgramming #BoyerMoore #Algorithms #DataStructures #Arrays #ProblemSolving #CodingPractice #InterviewPrep #LearnByDoing #ProgrammingJourney #DeveloperGrowth #BTech #CSE #AIandML #VITBhopal #TechJourney

Didn't know you could extract tables from a Word doc using Python until today. python-docx lets you loop through tables, pull cell data, and load it straight into a DataFrame. Spent some time cleaning it up — splitting on ':', transposing, fixing headers — but it worked. Also practiced groupby() and lambda functions inline. Small things but they make the code so much cleaner. Notebook here 👉 https://lnkd.in/dfTwrvqT #Python #Pandas #DataAnalysis #LearningInPublic

Topic 7/100 🚀 🧠 Topic 7 — Lambda Functions Want to write a quick function in just one line? ⚡ 👉 What is it? Lambda functions are small anonymous functions defined using the lambda keyword. 👉 Use Case: Used in real-world applications for: Quick operations inside map(), filter() Sorting with custom logic Short, throwaway functions 👉 Why it’s Helpful: Reduces boilerplate code Makes code concise Useful for functional programming 💻 Example: # Normal function def square(x): return x * x # Lambda version square = lambda x: x * x print(square(5)) 🧠 What’s happening here? We replaced a full function definition with a single-line lambda expression. ⚡ Pro Tip: Use lambdas for small logic only — avoid them for complex functions. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

INSTEAD OF WASTING TIME AND TRYING TO GET FIGURES. WHY NOT USING CODE?? Sometimes, lecturers or organizations need to generate different sets of questions for multiple candidates, especially when working with matrices. However, this often requires a lot of manual effort and can be time-consuming. Why not simplify the process using NumPy in Python? With just a few lines of code, you can easily generate multiple variations of matrix-based questions efficiently and save valuable time. #randint is an inbuilt function of the random module of numpy #Syntax: np.random.randint(start, stop (rows, columns)) a=np.random.randint(2,30, (3,3)) b=np.random.randint(2,30, (3,3)) c=np.random.randint(2,30, (3,3)) d=np.random.randint(2,30, (3,3)) e=np.random.randint(2,30, (3,3)) #DataScience #Python #NumPy #Education #Automation

𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 🐍 | 𝗦𝗲𝘁𝘀 – 𝗦𝗲𝘁 𝗠𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀 🔄 | 📅 𝗗𝗮𝘆 𝟱𝟮 🚀 Today’s task: ✅ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗲𝘁 A. ✅ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘀𝗲𝘁 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀. ✅ 𝗨𝗽𝗱𝗮𝘁𝗲 𝘁𝗵𝗲 𝘀𝗲𝘁 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆. ✅ 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗽𝗿𝗶𝗻𝘁 𝘁𝗵𝗲 𝘀𝘂𝗺 𝗼𝗳 𝗲𝗹𝗲𝗺𝗲𝗻𝘁𝘀. Operations used: • update() • intersection_update() • difference_update() • symmetric_difference_update() Simple? Only if you understand set mutation vs set operation. Core idea from the code: Instead of creating new sets, these operations modify the original set directly. Example: A.update(B) → adds elements of B into A A.intersection_update(B) → keeps only common elements A.difference_update(B) → removes elements present in B A.symmetric_difference_update(B) → keeps elements not common in both 💡 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Mutation operations are important when: • You want memory-efficient updates • You want to modify the original dataset • You want faster in-place operations Because strong Python developers don’t just know operations. They understand when data is modified vs copied. Cleaner logic. Better performance. #Python #Sets #InterviewPrep #HackerRank #DataStructures #ProblemSolving #DailyCoding #Consistency

🚀 𝗗𝗮𝘆 𝟮𝟭/𝟯𝟬 – 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Continuing my journey of building one Python project every day to improve consistency and real-world problem-solving. Today’s focus: **Core Python Concepts** 🧠 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗧𝘆𝗽𝗶𝗻𝗴 𝗦𝗽𝗲𝗲𝗱 𝗧𝗲𝘀𝘁 Build a Python-based typing speed test that measures how fast and accurately a user can type a given sentence. A simple yet powerful project to understand timing, user input handling, and performance metrics. ✨ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Random sentences loaded from external file 📄 • Real-time typing speed calculation (WPM) ⚡ • Error detection (word-based comparison) ❌ • Accuracy calculation (%) 🎯 • Continuous testing loop for practice 🔁 💡 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗨𝘀𝗲𝗱: • File handling in Python (`.txt` file) • Time module for performance tracking • Random module for sentence selection • String manipulation & comparison • Looping and control flow 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/d3MUYR2T A simple idea, but very useful and practical — and a great way to improve both coding and typing skills together. Building discipline through code — one project at a time. Follow along as I complete 30 Python projects in 30 days 🚀 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #PythonProjects #Coding #Automation #Learning

Just practiced Pandas and data cleaning hits different when you're working with real messy data. Covered data types, type conversion, handling missing values, replacing inconsistent entries, and using category dtype to save memory — FuelType column went from 11488 bytes to 1460 bytes just by changing the dtype. Notebook here 👉 https://lnkd.in/d3djYPvp #Python #Pandas #DataAnalysis #LearningInPublic

𝗧𝘄𝗼 𝗪𝗮𝘆𝘀 𝘁𝗼 𝗪𝗿𝗶𝘁𝗲 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — 𝗪𝗵𝗶𝗰𝗵 𝗙𝗲𝗲𝗹𝘀 𝗠𝗼𝗿𝗲 𝗡𝗮𝘁𝘂𝗿𝗮𝗹? Most Python developers have used context managers. Fewer have written their own.   And when you do write one for the first time, you'll likely discover there are two ways to do it and they both work perfectly well.   Look at the two approaches in the image.   ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟭 uses a class with __enter__ and __exit__ methods. ➝ 𝗢𝗽𝘁𝗶𝗼𝗻 𝟮 uses the @𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝗺𝗮𝗻𝗮𝗴𝗲𝗿 decorator with a yield statement.   Neither is wrong. But they feel very different to write.   The class-based approach gives you more control, useful when you need to store state or handle complex cleanup logic. It's also more explicit about what's happening.   The decorator approach is lighter and faster to write, great for simpler cases where you just need setup, yield, and teardown.   𝗜 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗹𝘆 𝗿𝗲𝗮𝗰𝗵 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗱𝗲𝗰𝗼𝗿𝗮𝘁𝗼𝗿 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗳𝗶𝗿𝘀𝘁. It reads almost like plain English once you understand what yield is doing in this context.   But the class approach has saved me more than once when I needed to pass state between __enter__ and __exit__.   𝗪𝗵𝗶𝗰𝗵 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗱𝗶𝗱 𝘆𝗼𝘂 𝗹𝗲𝗮𝗿𝗻 𝗳𝗶𝗿𝘀𝘁 𝗮𝗻𝗱 𝗱𝗼 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 𝗮 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘁𝗼𝗱𝗮𝘆? #Python #PythonTips #BackendDevelopment #CleanCode #SoftwareEngineering