Python Data Science & ML Journey with ParoCyber

Second PR merged, this time in skrub, a data-wrangling library in the scikit-learn ecosystem. PR: https://lnkd.in/eY6Sd4Qq The issue: gallery examples were accessing dataframes by directly unpacking Bunch objects instead of loading from file paths. A small inconsistency, but one that makes examples brittle as the API evolves. Updated 4 example files to use pd.read_csv() on the fetcher's path field instead. Straightforward change that established the pattern a follow-up PR would later extend across the rest of the codebase. Reviewed and merged by Gael Varoquaux . That alone made it memorable. #opensource #python #skrub #scikitlearn #documentation

🚀 Day 42/100 – LeetCode Challenge Solved 206. Reverse Linked List today! 🔍 Problem Summary: Given the head of a singly linked list, reverse the list and return the new head. 💡 Approach: Implemented an iterative solution using three pointers (prev, curr, next) to reverse the links efficiently in-place. Also explored the recursive approach to strengthen understanding of linked list manipulation. ⚡ Complexity: • Time Complexity: O(n) • Space Complexity: O(1) (Iterative) 📌 Key Takeaways: Importance of pointer manipulation Handling edge cases like empty list Difference between iterative vs recursive approaches Consistency is key — one step closer to mastering Data Structures & Algorithms! 💪 #LeetCode #100DaysOfCode #DataStructures #Algorithms #CodingJourney #Python #ProblemSolving #LinkedList

Today's session with Satish Dhawale sir was a deep dive into how Python handles text. I learned how to pinpoint any character in a string and pull out exactly what I need. Key takeaways: Zero-based Indexing: Starting from 0 to access specific characters. Negative Indexing: Counting from the end of a string (super useful for long texts!). Slicing [start:stop:step]: Extracting substrings with surgical precision. The coolest trick? Reversing a string in just one line: [::-1]! 🤯 #PythonMastery #SkillCourse #SatishDhawale #Day6 #CodingJourney #StringManipulation

🚀 Day 16 – DSA Daily Series Today’s Problem: Two Sum II – Input Array Is Sorted (LeetCode 167) Today’s problem was simple yet really interesting — especially because of how efficiently it can be solved using the two-pointer technique. 🧠 Problem Given a sorted array, find two numbers such that they add up to a target value and return their indices (1-based). Example: Input: [2,7,11,15], target = 9 Output: [1,2] 💡 Approach Instead of using extra space like a hashmap, I used: • Two pointers — one at the start and one at the end • If sum is too large → move right pointer left • If sum is too small → move left pointer right • Stop when target is found Clean and efficient ⚡ ⏱ Complexity Time Complexity: O(n) Space Complexity: O(1) 🔎 Key Learning When the array is already sorted, always think of two pointers before anything else — it saves both time and space. Solved it today and it felt really smooth to implement! 💯 Continuing to stay consistent and improve step by step 🚀 #DSA #LeetCode #Python #TwoPointers #CodingJourney #ProblemSolving

🚀 Day 57 of #100DaysOfCode Solved LeetCode 290 – Word Pattern today! 🧠 Problem Insight: We are given a pattern and a sentence. The goal is to check whether there exists a one-to-one mapping (bijection) between characters in the pattern and words in the sentence 💡 Key Learning: One character → one unique word One word → one unique character No duplicates allowed in mapping (both directions) ⚡ Approach I Used: Split sentence into words Use two hash maps: Character → Word Word → Character Validate mapping consistency while iterating 🎯 Why this problem matters: This is a classic example of: Hashing Data consistency checks Bidirectional mapping logic (very important in interviews) 📊 Performance: ✅ Runtime: 0 ms (Beats 100%) ✅ Memory: Optimized 🔥 Takeaway: Always think in two directions when mapping relationships — it avoids hidden edge cases. #DSA #LeetCode #CodingJourney #Python #ProblemSolving #TechGrowth #Consistency #100DaysChallenge

📅 Weekly Learning Log — Phase 1, Week 1 Committing to learning Python in public. Here's what I covered this week: 🗓 Day 2 — Variables, built-in functions & type casting (int, float, str, list, tuple) 🗓 Day 3 — Booleans, arithmetic, comparison & logical operators. Applied maths: slope, Euclidean distance, area 🗓 Day 4 — Strings: slicing, f-strings, escape sequences, 20+ string methods 🗓 Day 5 — Lists: indexing, unpacking with *rest, append/pop/del, sort, extend 💻 Also grinding the HackerRank Python track — currently at 115/220 points with Hello World & If-Else solved, Loops next! Key insight: the [start:end:step] slicing pattern works the same way on strings AND lists — that clicked everything into place. Onwards to tuples, sets, and dictionaries next week. 🐍 Following the 30 Days of Python challenge by Asabeneh Yetayeh. #Python #30DaysOfPython #LearningInPublic #HackerRank #CodingJourney #PythonProgramming #UMPSA #Phase1 #WeeklyLog #CodeNewbie

Wrapping up my NumPy learning journey After exploring different concepts, I realized how powerful NumPy is for handling data efficiently. Here’s a quick recap of what I learned:- 🔹 Arrays vs Python Lists 🔹 Vectorization (faster computations) 🔹 Broadcasting 🔹 Indexing & Slicing 🔹 Performance optimization 💡 My biggest takeaway: NumPy helps write less code while performing faster operations — which is crucial in real-world data analysis. This marks my NumPy learning phase ✅ Moving forward to data visualization next… Excited to keep learning and sharing 🚀 #Python #NumPy #DataAnalytics #LearningJourney #Consistency

Solved a nice Binary Tree Problem today: Print all root to leaf paths. The approach is really simple: we'll traverse the tree and store all paths that start at the root and end at a leaf node. So here’s what I did for this problem: • Used recursion with DFS to traverse the tree • Used a path list to keep track of all paths • Whenever I encountered a leaf node, I pushed it into the answer list • Once I visited a node, I removed it from my path list to backtrack and explore all paths An interesting thing I observed while solving this question: Since lists in Python are mutable references, if we push the path into the answer list, it will change all paths in the answer list. Hence, we push a copy of the path into the answer list. This is another nice example of how we use DFS and backtracking for trees. These kinds of small questions really help us solidify our recursion intuitions. #DSA #BinaryTree #Recursion #CodingPractice

📊 Data Science Foundations Series – Part 1: NumPy Basics I’ve started strengthening my fundamentals in data science, beginning with NumPy. Here are some key takeaways: ✅ NumPy is faster than Python lists due to contiguous memory storage ✅ Supports vectorized operations (no need for loops) ✅ Efficient for handling large numerical datasets Some concepts I explored: 🔹 Array creation using np.array() and np.arange() 🔹 Reshaping data with .reshape() 🔹 Indexing and slicing (including negative indexing) 🤯 One interesting learning: m1[-5:-1:-1] returns an empty array. Reason: When stepping backwards, the start index must be greater than the stop index. ✔️ Correct approaches: m1[-1:-5:-1] m1[-5::-1] This small detail helped me better understand how slicing actually works under the hood. 📌 Next: Vectorization & Broadcasting #DataScience #Python #NumPy #LearningInPublic #CareerGrowth

Day 21/30 – Advanced Visualization with Seaborn Today I moved beyond basic charts and explored how to make data actually speak. Seaborn makes it easier to understand patterns, relationships, and distributions without writing complex code. Instead of just plotting data, I focused on: Understanding correlations using heatmaps Visualizing distributions with histograms and KDE plots Comparing categories using boxplots and violin plots What I realized: A good visualization is not about making charts look fancy. It’s about making insights obvious. Two simple examples: A heatmap can quickly show which variables are strongly related instead of checking numbers manually A boxplot can instantly reveal outliers that you might miss in raw data Still learning, but getting better at choosing the right chart instead of just any chart. #Day21 #DataAnalytics #Seaborn #Python #LearningJourney