Implementing Data Structures in Python for Efficient Coding

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 9 of My Data Analytics Journey 🚀 Today I explored some important Python concepts that are essential for building strong programming fundamentals: 🔍 What I learned: • Iterating over dictionaries using ".keys()", ".values()", and ".items()" • Basics of Object-Oriented Programming (OOP) – classes, objects, and methods • How to import and use Python modules 🧠 Key Takeaways: • Iterating over dictionaries makes data handling more efficient • OOP helps in writing structured and reusable code • Modules allow us to use powerful built-in functionalities without rewriting code 💡 Slowly understanding how Python can be used to structure and manage real-world data. 📈 Building consistency and improving step by step. 🤝 If you're on a similar learning journey, let’s connect and grow together! #Python #DataAnalytics #OOP #LearningInPublic #Consistency #CareerGrowth

If you want to get better at Python, practice is everything. Reading tutorials is helpful, but real improvement comes when you start solving problems on your own. Some of the best ways to strengthen your Python fundamentals are by practicing programs like: ✔ Arithmetic operations ✔ Prime number checks ✔ Fibonacci sequence ✔ Factorial calculations ✔ Leap year logic ✔ Array and list operations ✔ Matrix calculations ✔ Recursion-based problems These kinds of exercises help you build: • Strong problem-solving skills • Clear programming logic • Confidence for technical interviews 💡 A small tip: Before looking at the solution, try writing the program yourself. Even if it takes time, the learning will stay with you much longer. Every great developer once started with simple programs. Consistency is what makes the difference. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐚𝐧𝐝 𝐠𝐫𝐨𝐰 𝐰𝐢𝐭𝐡 𝐦𝐲 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 👇  🔗 𝐖𝐡𝐚𝐭𝐬𝐚𝐩𝐩- https://lnkd.in/d_tQPMS7 🔗 𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦- https://t.me/LK_Data_world 💬 If you found this PDF useful, like, save, and repost it to help others in the community! 🔄 📢 Follow Lovee Kumar 🔔 for more content on Data Engineering, Analytics, and Big Data. #Python #Coding #Programming #PythonProgramming #Developer

Python is widely used in data analytics for data cleaning, analysis, and automation. While tools like Excel and SQL are great for starting out, Python allows analysts to handle larger datasets and perform more advanced analysis. I just published a beginner-friendly video explaining how Python is used in data analysis and where it fits in the analytics workflow. Watch here: https://lnkd.in/e54wm-68 #DataAnalytics #Python #DataAnalysis #DataAnalyst #TechSkills Coursera freeCodeCamp Udemy TechCrush Monierate

📌 Consistency Over Talent – My Data Analytics Journey Most people start learning Python… but very few stay consistent. I focused on doing small projects daily and uploading them on GitHub. 💻 What I’ve built so far: ✔ Python fundamentals (operators, functions, logic building) ✔ Data cleaning using Pandas ✔ Data visualization using Matplotlib ✔ Real dataset analysis (health & awareness data) 📊 What changed? I stopped just “learning” and started “building”. That’s when things started making sense. 🚀 Still learning. Still improving. Still building. 👉 GitHub Portfolio: https://lnkd.in/dqgHkRQm #DataAnalytics #Python #Consistency #LearningByDoing #GitHub

🚀 Day 12 & 13 – Consistency is the Key! Still going strong on my Python learning journey, and these two days were all about revision + real application 💻 🔁 Quick Revision: Revisited core concepts like loops, functions, and conditionals — because strong basics = strong foundation. 💡 Mini Project: Bill Generator Built a simple yet practical Python project using: ✔️ if-elif-else statements ✔️ Operators (arithmetic & logical) ✔️ User inputs for dynamic calculations 🔹 Features included: - Item selection & pricing - Quantity-based calculations - Discount logic - Final bill generation 🧠 What I Improved: - Better problem-solving approach - Writing cleaner, more readable code - Debugging with more confidence - Thinking in a more structured, logical way Every small project is making me more confident and bringing me one step closer to becoming a skilled data professional 📈 🙏 Special thanks to Anurag Srivastava and the Data Engineering Bootcamp for the constant guidance and support! #Python #LearningJourney #100DaysOfCode #DataEngineering #Coding #BeginnerToPro #Consistency

🚀 From Beginner to Builder (My Coding Progress) When I started coding, even simple logic was confusing. Now I can: ✔ Write structured Python code ✔ Analyze datasets ✔ Build visualizations ✔ Share projects publicly on GitHub 💡 Biggest shift: I stopped being afraid of errors. Errors are not problems… they are guides. 📌 Next Goal: To work on industry-level data analytics projects If you’re also learning, don’t quit. Keep building 💪 🔗 GitHub: https://lnkd.in/dqgHkRQm #Python #DataAnalytics #CodingJourney #NeverGiveUp

🚀 Mini Project Showcase: Python File Organizer As part of my Data Analyst learning journey, I worked on a small Python project while revising my SQL concepts. 📂 Project: File Organizer using Python : This script automatically organizes files into folders like Images, Documents, Videos, etc., based on their file types. 🔧 What I used : Python (os, shutil modules) Logical structuring of file types Automation concepts 📊 Why this matters for Data Analytics : While learning SQL helps in querying data, Python helps in automating repetitive tasks and handling real-world data files. 💡 Key Learnings: File handling in Python Automation basics Writing cleaner and reusable code 🔗 GitHub Repository : https://lnkd.in/dGcnCmXT This is a small step, but I’m consistently building my skills in both Python and SQL to become job-ready as a Data Analyst. #Python #SQL #DataAnalytics #BeginnerProjects #LearningJourney

Machine Learning Time Series Data using pmdarima #machinelearning #datascience #timeseriesdata #pmdarima pmdarima brings R’s beloved auto.arima to Python, making an even stronger case for why you don’t need R for data science. pmdarima is 100% Python + Cython and does not leverage any R code, but is implemented in a powerful, yet easy-to-use set of functions & classes that will be familiar to scikit-learn users. pmdarima is essentially a Python & Cython wrapper of several different statistical and machine learning libraries (statsmodels and scikit-learn), and operates by generalizing all ARIMA models into a single class (unlike statsmodels). It does this by wrapping the respective statsmodels interfaces (ARMA, ARIMA and SARIMAX) inside the pmdarima.ARIMA class, and as a result there is a bit of monkey patching that happens beneath the hood. The auto_arima function itself operates a bit like a grid search, in that it tries various sets of p and q (also P and Q for seasonal models) parameters, selecting the model that minimizes the AIC (or BIC, or whatever information criterion you select). To select the differencing terms, auto_arima uses a test of stationarity (such as an augmented Dickey-Fuller test) and seasonality (such as the Canova-Hansen test) for seasonal models. https://lnkd.in/gjnJVA5T

Understanding Python Class Properties for Better Data Management Properties in Python classes provide a way to manage access to an attribute while encapsulating behavior around getting and setting that attribute. This can make your code cleaner and prevent the direct manipulation of potentially sensitive data. In the example above, we have a `Circle` class with a private attribute `_radius`. The `area` property allows users to retrieve the calculated area based on the `_radius` without needing direct access to the radius itself. This encapsulation helps to maintain control over how the radius is modified. We also defined a setter for the `area` property, allowing the user to set the area value. This means when the area is set, the class automatically recalculates the radius using the formula for the area of a circle, thus keeping all attributes consistent. When using properties, it's important to think about what should happen if someone tries to set an unexpected value. For instance, if a user accidentally sets a negative area, you'd typically want to raise an error or handle it gracefully to prevent inconsistent states. Quick challenge: How would you modify the `Circle` class to prevent setting a negative radius? #WhatImReadingToday #Python #PythonProgramming #OOP #CleanCode #Programming