Learn Python with Anaconda in 10 minutes

I’ve been practicing Python pandas regularly, solving data problems, writing cleaner transformations, and building visualizations. Here’s today’s exercise 👇 Question and solution are in the image. Kept the solution simple and readable. All datasets and exercises are available on my GitHub if you want to practice along. Link is in the comments. If you have a different approach or idea, share it. I’m always open to learning and discovering new ways to solve problems. #Python #Pandas #DataAnalytics #PracticeDaily #LearningInPublic #DataScience

I’ve been practicing Python pandas regularly, solving data problems, writing cleaner transformations, and building visualizations. Here’s today’s exercise 👇 Question and solution are in the image. Kept the solution simple and readable. All datasets and exercises are available on my GitHub if you want to practice along. Link is in the comments. If you have a different approach or idea, share it. I’m always open to learning and discovering new ways to solve problems. #Python #Pandas #DataAnalytics #PracticeDaily #LearningInPublic #DataScience

I’ve been practicing Python pandas regularly, solving data problems, writing cleaner transformations, and building visualizations. Here’s today’s exercise 👇 Question and solution are in the image. Kept the solution simple and readable. All datasets and exercises are available on my GitHub if you want to practice along. Link is in the comments. If you have a different approach or idea, share it. I’m always open to learning and discovering new ways to solve problems. #Python #Pandas #DataAnalytics #PracticeDaily #LearningInPublic #DataScience

Weekly Challenge 4: Binary Search (Iterative). Why search harder when you can search smarter? Use Binary Search. Imagine looking for a word in a dictionary. Do you read every page from the beginning? No. You open it in the middle and decide: "Left or Right?". That is the essence of Binary Search, and it's the focus of my coding challenge for Week 4. The Implementation: I wrote a Python script (using NumPy) that generates a random environment to test the algorithm. Key Takeaway: While a simple loop (Linear Search) checks elements one by one ($O(n)$), Binary Search cuts the problem in half with every step ($O(\log n)$). Check out the trace in the console output below to see how few attempts it takes to find the target! 👇 📂 Full code on GitHub: https://lnkd.in/ezPaaiDM #Python #BinarySearch #Algorithms #CodingChallenge #DataStructures #Engineering

Headline: Stop writing loops to clean your data. 🛑 One of the most common tasks in Python is handling duplicate entries. While you could write a for-loop with a conditional check, there’s a much faster, more "Pythonic" way to do it: Sets. Sets are unordered collections of unique elements. By casting your list to a set, Python handles the heavy lifting of deduplication instantly. Why use this? ✅ Cleaner, more readable code. ✅ Better performance for large datasets. ✅ Built-in membership testing (O(1) complexity). How are you using Sets in your current workflow? Let’s discuss below! 👇 #PythonProgramming #Pyspiders #CodingTips #SoftwareDevelopment

𝗗𝗮𝘆 𝟭𝟰: 𝗦𝗲𝘁𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 🐍 𝗦𝗲𝘁𝘀: 🔹 Sets are unordered collections of items 🔹 Elements are unique (no duplicate values) 🔹 Items are separated by commas and enclosed in curly brackets { } 🔹 Sets do not maintain order 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗦𝗲𝘁: 🔹 s = {1, 2, 3} 🔹 Use set() to create an empty set : s = set() ⚠️ {} creates an empty dictionary, not a set 𝗦𝗲𝘁 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:  1️⃣𝗨𝗻𝗶𝗼𝗻 (|) → combines elements  2️⃣𝗜𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 (&) → common elements  3️⃣𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 (-) → elements in one set but not the other  4️⃣𝗦𝘆𝗺𝗺𝗲𝘁𝗿𝗶𝗰 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 (^) → elements not common 📝Important Notes: 🔹 Sets do not support indexing or slicing 🔹 Elements must be immutable (int, string, tuple) Building strong Python fundamentals, one concept at a time 🚀 #Python #Tuples #LearningPython #LearningInPublic #AspiringDataScientist #Consistency

Today, I worked on understanding and implementing Linear Regression from scratch using Python and scikit-learn. I focused not just on writing code, but on understanding why the model works. One key insight I learned today is the importance of model evaluation. Metrics like MAE, MSE, and RMSE help quantify how wrong a model’s predictions are, rather than relying on visual plots or intuition alone. This matters because in real-world data science, a model that looks good can still fail if it doesn’t generalize well. Learning to evaluate models properly is just as important as building them. I documented the full process and code here on GitHub: https://lnkd.in/gdM6rcRx Looking forward to building stronger foundations step by step. #MachineLearning #DataScience #LearningInPublic #Python #StudentDeveloper

🚀 Day 2 – Setting Up & Understanding Core Concepts Today I officially installed Python and continued strengthening my fundamentals, referring to the official Python tutorial: https://lnkd.in/eHp7FHbb . Focused on: • Numbers (integers & floats) • Text (strings) • Lists (basic data collections) Getting comfortable with how Python handles data is the first step toward working with real datasets in data engineering and AI. Strong basics build strong systems. One step at a time. #DataEngineering #Python #AI #NewCareer #SelfLearning

Day 12 of #30DaysOfPython: Unlocking Power with the Standard Library 🏗️ Today’s lesson was about Modules and the value of using existing tools instead of reinventing everything from scratch. A big part of software engineering is knowing how to extend your capabilities efficiently. I explored Python’s import system and built a small synthetic data generator to simulate real-world AI inputs. Along the way, I worked with: 🎲 The random module to generate varied test data 📐 The math module to apply mathematical transformations and simple loss calculations 📦 A more modular coding style by importing utilities instead of rewriting logic (hello, DRY principle) And yes — starting to feel more comfortable working directly in the terminal too. Getting familiar with the Python Standard Library feels like an important bridge toward industry tools like NumPy, Pandas, and Scikit-learn. 📂 Explore today’s implementation here: https://lnkd.in/g_Q25442 #Python #SoftwareEngineering #DataScience #MachineLearning #AI #BuildInPublic #30DaysOfPython #CleanCode

Day - 4 ✅ Today was intense. Almost 12 hours of focused learning + hands-on practice. Here’s what I covered and actually implemented while learning 👇 📌 Topics Studied: ✔ Short-hand if-else statements ✔ Enumerate function ✔ Virtual Environment in Python ✔ How import works internally ✔ __name__ == "__main__" concept ✔ OS module ✔ File Handling (File I/O) ✔ read(), readlines() and file methods ✔ Local vs Global variables ✔ Exercise implementation & solutions #Python #Django #WebDevelopment #20DaysOfCoding