Strengthening Python with NumPy Fundamentals

Day 19/30 – Numerical Computing with NumPy Today was all about getting comfortable with NumPy and actually understanding why people use it instead of plain Python. I focused on: • Working with arrays instead of lists • Performing fast calculations without writing long loops • Using built-in functions to simplify complex operations What I realized: NumPy isn’t just about speed — it reduces unnecessary code and forces you to think in a cleaner, more structured way. Two quick takeaways: 1. Instead of looping through values one by one, NumPy lets you operate on entire datasets at once. 2. Small problems feel simple, but NumPy really shows its value when data size increases. Still a lot to explore, but this feels like a solid step toward data handling and analysis. #Day19 #Python #NumPy #LearningJourney #DataSkills

𝗪𝗲𝗲𝗸 𝟯 of my 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠L program with ParoCyber wrapped up with two solid labs on Python operators and string methods. We covered a lot of ground on arithmetic, comparison, logical, assignment, bitwise, membership, and identity operators. Each one serves a specific purpose in how Python reads and evaluates data. 💡The operators '𝙞𝙨' vs '==' were a good reminder that two values can be equal without pointing to the same object in memory. The second lab focused on string methods like split(), join(), replace(), and more.  💡One thing that stuck: strings in Python are 𝗶𝗺𝗺𝘂𝘁𝗮𝗯𝗹𝗲. Any operation returns a new string, not a modified original. Small detail, but it matters. To see the full breakdown, it's all documented on my GitHub. Link below. 🔗 https://lnkd.in/dVnTd3jS #DataScience #Python #MachineLearning #ParoCyber #LearningInPublic #CareerGrowth #WomenInTech

Day 18 of my #100DaysOfCode challenge 🚀 Today I worked on a Python program to find the longest word in a given sentence. The goal was to practice string manipulation and comparison logic. What the program does: • Takes a sentence as input • Splits the sentence into individual words • Compares the length of each word • Stores the longest word found • Prints the final result How the logic works: A sentence is defined as a string The sentence is split into a list of words using .split() A variable longest_word is initialized as an empty string The program loops through each word in the list If the length of the current word is greater than or equal to the stored word, it updates longest_word After the loop ends, the longest word is printed Example: Input sentence: The quick orange fox jumps over the lazy dog Output: Longest word: orange Key learnings from Day 18: – Using .split() for string processing – Comparing string lengths – Iterating through lists efficiently – Strengthening logical thinking skills #100DaysOfCode #Day18 #Python #PythonProgramming #StringManipulation #ProblemSolving #CodingPractice #LogicBuilding #LearnByDoing #ComputerScience #BTech #CSE #AIandML #VITBhopal #TechJourney

🐍 Day 3 of brushing up my Python skills! Today was a big one — covered 6 lessons back to back: ✅ Super Keyword — letting the parent run first before the child ✅ Polymorphism — same name, many forms ✅ Practice Problems — Shape, Student, Vehicle, Manager classes ✅ Encapsulation — locking data with __ and using getters & setters ✅ Exception Handling — try, except, else, finally ✅ File Handling — reading, writing and appending files Honestly, encapsulation clicked for me today 💡 Double underscore (__) = lock on a safe 🔒 You need the right key (getter/setter) to open it! Dropping my full notes PDF below for anyone on the same journey 👇 #Python #LearningInPublic #PythonProgramming #OOP #CodingJourney #100DaysOfCode

Machine Learning Data Visualization using nptsne #machinelearning #datascience #datavisualization #nptsne The nptsne package is designed to export a number of python classes that wrap GPGPU linear complexity tSNE or the hierarchical SNE (hSNE) method. nptsne is a numpy compatible python binary package that offers a number of APIs for fast tSNE calculation and HSNE modelling. https://lnkd.in/gd57cGwj

🚀 Day 11/30 – Defining & Calling Functions in Python Today, I learned how to write cleaner and smarter code using functions. Instead of repeating the same logic multiple times, we can define it once and reuse it whenever needed. That’s powerful. 📌 What I practiced: • Defining a function using def • Calling the function • Passing values (arguments) • Returning results Here’s a simple example I built today: . 💡 Key Takeaway: Functions make programs organized, efficient, and professional. Small concept. Big impact. Day 11 complete ✅ #Python #30DaysChallenge #LearningInPublic #ProgrammingJourney #Consistency Aditya ChaturvediJECRC UniversityYash Raj ChoudharyRaj Gehlot

Day 3 of my Build in Public journey 🚀 Today’s focus: Advanced Python concepts and aptitude practice. Here’s what I worked on today 👇 💻 Python • Explored lambda functions, map(), filter(), and reduce() • Practiced recursion (tail and non-tail) • Learned about decorators and how functions act as first-class objects 🧠 Aptitude • Solved questions to improve logical thinking ⚠️ Note: Didn’t work on development today — focusing on strengthening fundamentals. 💡 Lesson: Understanding concepts deeply takes time, but it makes everything easier later. Back again tomorrow to keep building. #BuildInPublic #100DaysOfCode #LearningInPublic #TechJourney

“Kids With the Greatest Number of Candies” from the LeetCode 75 study plan. Problem Idea We are given: candies[i] → number of candies each kid has extraCandies → extra candies we can give to one kid The task is to determine whether a kid can have the greatest number of candies after receiving the extra candies. Approach Find the maximum number of candies any kid currently has. For each kid, add extraCandies. Check if the result is greater than or equal to the maximum. Store True or False in the result list. Complexity Time Complexity: O(n) Space Complexity: O(n) Practicing problems from LeetCode 75 to strengthen my problem-solving and data structures skills. #LeetCode #LeetCode75 #Python #CodingPractice #ProblemSolving #DataStructures

🔹 Topic: Returning Multiple Values from a Function 📘 Day 1 – #30DaysOfPython Today, I explored how Python functions can return multiple values efficiently using tuples. I implemented practical examples such as calculating mean, median, and mode within a single function, and practiced using conditional return statements to produce dynamic outputs based on user input. Strengthening core fundamentals step by step as I continue building my foundation in Python and Data Science. #Python #DataScience #ProfessionalDevelopment #ContinuousLearning #Programming #SoftwareDevelopment #TechSkills #CareerGrowth #CodingJourney

📌 Creating Arrays with NumPy NumPy provides a powerful object called ndarray (N-dimensional array) used for storing and working with data efficiently. An array is a collection of elements stored in a single variable, and in NumPy arrays are homogeneous (all elements are usually of the same data type). Example steps: 🔹 Install NumPy pip install numpy 🔹 Import NumPy import numpy as np 🔹 Create an array arr = np.array([1,2,3,4,5]) This creates a NumPy array, and its type will be numpy.ndarray. NumPy arrays are faster and more efficient for numerical operations compared to regular Python lists. #Python #NumPy #DataAnalytics #Programming #LearningPython