Mastering Python Basics for Control and Power

Honest confession: When I first learned Python for data analysis, I wasted 3 weeks on things that don't matter. Week 1: Spent 5 hours on decorators. (Used them 0 times in data analysis.) Week 2: Built a calculator app. (Never needed a calculator in analytics.) Week 3: Learned classes and OOP. (Pandas doesn't need you to write classes.) What I actually needed from Day 1: → Pandas (80% of the job) → Reading CSVs and Excel files → Filtering, grouping, merging data → Basic Matplotlib charts → Cleaning messy data So I built a Python course that skips the fluff. Lesson 1: You're already working with a DataFrame. No "print hello world." No theory-first approach. Data-first. Always. First module free. Full course ₹50. https://lnkd.in/davEZifS #Python #Pandas #DataAnalyst #DataScience #LearnPython #OnlineCourse #FreeResources #DataAnalytics

Day 1 of learning Pandas… and I got humbled real quick 😭 I spent almost 30 minutes trying to read a simple CSV file and kept getting: FileNotFoundError: [Errno 2] No such file or directory: 'order.csv' At first, I thought it was a location issue. Maybe my Python file and CSV weren’t in the same folder. So I moved them into the same folder… Still the same error. Then I started doing the most I changed the file name multiple times… Even renamed it order.csv again (which probably turned it into order.csv.csv behind the scenes) At that point, I knew… yeah, this logic is not it. Finally, I tried using the full file path: df = pd.read_csv('C:\\Users\\HP\\OneDrive\\Dokumente\\Pandas Practice\\order.csv') …and boom. It worked. The relief I felt 😮💨 Lesson: Python doesn’t care about your assumptions. It works with your working directory. File paths will humble you before data does. Day 1 done. Many more lessons to go! What’s one error that stressed you out when you were starting out? #DataAnalytics #Python #Pandas #LearningInPublic #TechJourney #VeekayBuilds

3MTT Week 6 Reflection Week 6 hit different. When my Python code kept throwing errors, my first thought was — it must be the phone.I genuinely believed the phone was giving me a different experience from a computer and that was the problem.That assumption had me looking in the completely wrong direction. The real issue? I didn't understand that every data type in Python plays by its own rules.Strings need quotation marks. Integers and floats don't.Items in a list or dictionary need to be separated by commas. Simple rules but until you truly get them,nothing works and you don't even know why. Once that clarity hit,everything shifted. Now before I write anything, I ask myself:what data type am I working with, and what does it expect from me? That one question has saved me from so many errors. I also made mistakes with Pandas; writing the import but forgetting to call the variable properly under it. My tutor corrected me in class and it stuck so hard that I've since corrected two of my classmates making the same mistake. That felt good.Really good. The assumption I had to kill this week: that my tools were the problem. The real work was always in understanding the rules.#My3MTT #3MTTWeeklyReflection

Day 65 of the #three90challenge 📊 Today I learned about File Handling in Python — working with external data files. This is a big step because real-world data doesn’t live inside code — it comes from files like .txt, .csv, etc. What I practiced today: • Opening files using open() • Reading data (read(), readline()) • Writing data to files • Understanding file modes (r, w, a) • Closing files properly Example thinking: Instead of hardcoding data, I can now read data from files, process it, and even write results back. Example: with open("data.txt", "r") as file: content = file.read() print(content) This makes Python much more powerful for handling real datasets. From working with code → to working with real data 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

Days 60–63 of the #three90challenge 📊 Started April 2026 by transitioning into Python — an essential tool for data analysis. This week was all about building the foundation. 📅 01-04-2026: Set up Python environment and tools 📅 02-04-2026: Learned variables & data types — the building blocks of any program 📅 03-04-2026: Worked with lists & dictionaries to store and manage data 📅 04-04-2026: Practiced loops to automate repetitive tasks Key Takeaways: • Python makes handling data more flexible compared to spreadsheets • Lists & dictionaries are powerful for structuring data • Loops help automate what would otherwise be manual work • Strong basics make advanced concepts easier later After SQL, stepping into Python feels like expanding from querying data → programming with data. Excited for what’s next 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

Day 3 — Data Structures in Python Today I learned: • Lists • Tuples • Sets • Dictionaries Practiced these concepts with real-world examples to understand how data is stored and managed Key takeaway: Data structures make it easier to organize, access, and manage data efficiently. Example: {"name": "Rahul", "marks": 85} Small step, but feels powerful already. GitHub: https://lnkd.in/gNxJa4TR #Python #DataStructures #CodingJourney #LearningInPublic #Consistency

🚀 Day 1/30 of again starting My LeetCode Journey (Python + SQL) Starting a 30-day challenge to sharpen my problem-solving skills with a mix of Python and SQL! 💻📊 🔹 **SQL Problem of the Day** 👉 *Question:* Write a query to report the first name, last name, city, and state of each person. If a person doesn’t have an address, still include them in the result. 💡 *Key Concept:* LEFT JOIN to ensure all records from the PERSON table are included. 🔹 **Python Problem of the Day** 👉 *Question:* Write a function to find the **longest common prefix** string among an array of strings. If there is no common prefix, return an empty string. 💡 *Key Concept:* Iterative prefix reduction & string comparison. Done and dusted both the question in a reasonable time Consistency > Motivation. Excited to grow one problem at a time! 🔥 #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #ProblemSolving #DataStructures #LearningInPublic

🚀 Day 3/30 of My LeetCode Journey (Python + SQL) Showing up daily and building consistency, one problem at a time! 💻🔥 🔹 **Python Problems of the Day** 👉 *1. Move Zeroes* Given an integer array, move all 0’s to the end while maintaining the relative order of non-zero elements. Do it in-place without making a copy. 💡 *Key Concept:* Two-pointer technique for efficient in-place rearrangement. 👉 *2. Remove Element* Given an array and a value, remove all occurrences of that value in-place and return the number of remaining elements. 💡 *Key Concept:* In-place filtering using pointer overwrite approach. 🔹 **SQL Problem of the Day** 👉 *Find Duplicate Emails* Given a `Person` table with an email column, write a query to report all duplicate emails. 💡 *Key Concept:* GROUP BY with HAVING COUNT > 1. Small steps daily = Big progress over time 📈 Staying consistent and enjoying the process! #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #LearnInPublic

🚀 Day 12/30 of My LeetCode Journey (Python + SQL) Consistency continues… and the concepts are getting sharper! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Invalid Tweets* Given a `Tweets` table, write a query to find tweet IDs where the content length is strictly greater than 15 characters. 💡 *Key Concept:* String functions like `LENGTH()` for validation. 🔹 **Python Problem of the Day** 👉 *Single Number* Given an array where every element appears twice except one, find that single element. 💡 *Key Concept:* Bit manipulation using XOR for optimal O(n) time and O(1) space. Loving how problem-solving is becoming more intuitive day by day ⚡ Day 12 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning #BitManipulation

𝗗𝗔𝗬 𝟮: 𝗟𝗲𝘃𝗲𝗹𝗶𝗻𝗴 𝗨𝗽 𝗳𝗿𝗼𝗺 𝗖++ 𝘁𝗼 𝗣𝘆𝘁𝗵𝗼𝗻 Today was all about getting comfortable with Python’s powerful built-in data structures and control flow. 𝗠𝗮𝘀𝘁𝗲𝗿𝗲𝗱: 1) 𝑫𝒊𝒄𝒕𝒊𝒐𝒏𝒂𝒓𝒊𝒆𝒔 – Working with key-value pairs efficiently 2) 𝑺𝒆𝒕𝒔 – Handling unique elements with ease 3) 𝑰𝒇-𝑬𝒍𝒔𝒆 – Clean conditional logic 4) 𝑳𝒐𝒐𝒑𝒔 (𝒇𝒐𝒓 & 𝒘𝒉𝒊𝒍𝒆) – Iterating through data smoothly Common methods and operations for all of them 𝑪𝒐𝒎𝒊𝒏𝒈 𝒇𝒓𝒐𝒎 𝒂 𝒔𝒕𝒓𝒐𝒏𝒈 𝑪++ 𝒃𝒂𝒄𝒌𝒈𝒓𝒐𝒖𝒏𝒅, 𝑷𝒚𝒕𝒉𝒐𝒏 𝒇𝒆𝒆𝒍𝒔 𝒊𝒏𝒄𝒓𝒆𝒅𝒊𝒃𝒍𝒚 𝒊𝒏𝒕𝒖𝒊𝒕𝒊𝒗𝒆 𝒂𝒏𝒅 𝒇𝒂𝒔𝒕 𝒕𝒐 𝒘𝒓𝒊𝒕𝒆. The syntax is much cleaner, and solving problems has become more enjoyable. Now putting in serious practice time today to truly master these concepts and make them second nature 💪 C++ gave me the strong foundation Thanks to CoderArmy and Rohit Negi. Python is making me faster and more productive. Excited to keep building! What’s your experience moving between languages? Any tips for mastering Python data structures quickly? Majid Shafi #Python #CtoPython #CodingJourney #Programming #DataStructures #Day2