Data Cleaning Leads to Clear Insights

When it comes to data visualization in Python, Matplotlib and Seaborn are two of the most widely used libraries. Matplotlib provides full control and flexibility, while Seaborn simplifies the process with better default visuals and statistical features. The best approach is to understand both and use them together depending on your needs. If you're working with data, mastering these tools is essential. Read the full post here: https://lnkd.in/eXWfxWyH #Python #DataVisualization #DataScience #Analytics

The Outlier You Shouldn't Have Removed 🐍 The outlier looked wrong. So, you removed it. Clean data. Clean chart. Clean analysis. Except that outlier was the most important row in the dataset. The problem isn't the outlier. It's removing it without asking why it's there. 👇 See the visual below — when to remove, when to keep and real-world scenarios where blind removal backfires. #DataAnalytics #Python #AnalyticsThinking #LearningInPublic

Dove into working with files in Python — from accessing and importing text files to actually parsing and making sense of the data inside them. It’s one of those things that seems simple at first… until you realize how powerful it actually is. There’s something satisfying about going from “just reading a file” to actually extracting useful information from it.

Day 33/100 – #100DaysOfCode 🚀 Solved LeetCode #1480 – Running Sum of 1d Array (Python). Today I practiced prefix sum logic to compute the running sum of an array. Approach: 1) Initialize an empty list to store the running sum. 2) Maintain a variable sum = 0. 3) Traverse the array and keep adding each element to sum. 4) Append the updated sum to the result list. 5) Return the final running sum array. Time Complexity: O(n) Space Complexity: O(n) Understanding prefix sums helps solve many array problems efficiently 💪 #LeetCode #Python #DSA #Arrays #PrefixSum #ProblemSolving #100DaysOfCode

Excited to share my latest blog on Getting Started with Matplotlib in Python! 📊 In this article, I’ve covered the basics of data visualization using Matplotlib and how we can turn raw data into meaningful insights. This project helped me strengthen my understanding of Python and data visualization concepts. 🔗 Read here: https://lnkd.in/g5HZZ4jt I’d love to hear your feedback! #Python #Matplotlib #DataVisualization #MediumBlog #LearningJourney

Day 17 | Problem-Solving Practice Today I worked on: • Counting the number of digits in a number Implemented a basic approach and then handled edge cases like negative numbers and zero. Focusing on writing correct logic for all scenarios, not just the common ones. GitHub: https://lnkd.in/g35tV9Gj #ProblemSolving #Python #LearningInPublic #Consistency

#3 — Named Constants 9, 5, and 32 finally have names. The formula now explains itself. Part of the series: One Problem – Different Approaches Start here: https://lnkd.in/dxPDnRXZ #OneProblemDifferentApproaches #CelsiusToFahrenheit #Python #sedatçapar

Lately, I have been working with list comprehension to improve the code's performance. List Comprehension is a very powerful tool that creates a new list based on another list, in a single, readable line. While working with nested lists, with varying levels of depth, the traditional approach of code is better than using a list comprehension. I have explained them in the Python notebook below with examples. #Python #DataAnalytics #DataScience #Analytics #MachineLearning

Back to consistency 💻🚀 Recently, I worked on implementing Pascal’s Triangle in Python — and it turned out to be a great exercise in logic building. While solving this, I learned: 🔹 How each row depends on the previous one 🔹 Better understanding of nested loops 🔹 Using mathematical logic instead of brute force It’s interesting how such a simple-looking pattern involves deeper thinking behind the scenes. Here’s my implementation 👇 Small steps like these are helping me build a strong foundation in Data Structures & Algorithms. #Python #DSA #CodingJourney #LearningInPublic #100DaysOfCode