Choosing the Right Python Data Structure

🚀 Day 13/30 of My LeetCode Journey (Python + SQL) Showing up every day and pushing my limits a little more! 💻🔥 🔹 SQL Problem of the Day 👉 Customer with Most Orders Given an Orders table, write a query to find the customer_number who has placed the highest number of orders. 💡 Key Concept: GROUP BY + COUNT() with ordering/aggregation to find maximum. 🔹 Python Problem of the Day 👉 Subarray Sum Equals K Given an array and an integer k, return the total number of subarrays whose sum equals k. 💡 Key Concept: Prefix Sum + HashMap for optimizing from O(n²) → O(n). Learning how to optimize brute force solutions into efficient ones is a big win ⚡ Day 13 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #PrefixSum #Learning

Everyone asks whether they should learn Python or Excel. Wrong question. Both tools solve the same problems. The difference is scale, speed, and how repeatable you need the work to be. If you know Excel, you already understand the logic behind Python's most used functions. You just do not know the syntax yet. A pivot table is groupby().  XLOOKUP is merge().  Remove Duplicates is drop_duplicates().  Go To Special to find blanks is dropna(). The logic is identical. The syntax is new. That is all. The cheatsheet below maps 10 tasks every analyst does regularly across both tools, with real examples. 𝐏.𝐒. I share your thoughts and follow for more 👉Sagar Sharma #DataAnalytics #DataScience #Excel #Python #MySQL #PowerBI

Starting my journey into databases with Python 🐍 One of the first things I’m learning is how to connect Python to a database and begin interacting with data using SQL. To make this easier, I’m using SQLite a simple and lightweight database alongside SQLAlchemy, which helps Python communicate with different types of databases. Here’s what I’ve learned so far: Import create engine from SQLAlchemy Create a database engine by specifying the database type and name. Use the engine to connect and interact with the database. Explore the database by retrieving table names using engine.table_names() It’s a small step, but an important foundation for querying and analyzing data. Small steps, big growth 🚀 #Python #SQL #DataEngineering #LearningJourney #TechGrowth

DAY 14. 📊 Visualizing Data with Pie Charts in Python Today I practiced creating a pie chart using Python to represent how different technologies are distributed. In this visualization: • Each slice represents a category (Python, C++, Java, Pandas) • The size of each slice shows its proportion in the dataset • It gives a quick overview of how values are divided What I found interesting is how easily a pie chart shows the relative importance or share of each category without needing to read numbers. 💡 What I learned from this practice: • How pie charts help in understanding proportions • How to visually compare different categories • When to use pie charts for simple and clear insights I’m continuing to explore different types of visualizations using Python to better understand how to present data effectively. Step by step, building my skills in data visualization and analytics. import matplotlib.pyplot as plt languages=['python','c++','java','pandas'] values=[40,25,20,15] plt.pie(values,labels=languages) plt.show() Tajwar Khan Ethical Learner Invertis University Dr. Rajeev Singh Bhandari. Dr. Nitesh Saxena ITSector #Python #DataVisualization #DataAnalytics #PieChart #LearningJourney

PowerOracle is a Python CLI tool that analyzes historical *Power &all* drawing data to generate statistically optimized number picks. It combines frequency analysis (hot/cold/due numbers), pattern rec… I went from $0.00 to $7.00; not a millionaire, but hey, it paid for itself. Built with Python, scikit-learn, pandas, numpy, rich, and BeautifulSoup, anthropic CCode. https://lnkd.in/eTksia36

Day 2 of Learning Python Most people don’t fail in Python… They fail because they ignore the basics. Here are 4 things you MUST know 👇 1. Data Types Everything in Python has a type: int, float, str, bool 🎥 👉 https://lnkd.in/gDNAyz6E 2. Data Structures Store multiple values efficiently: ✔ List → ordered, changeable ✔ Tuple → ordered, fixed ✔ Set → unique values ✔ Dictionary → key-value pairs 🎥 👉 https://lnkd.in/gqWWihBJ 3. Indexing & Slicing Access data like a pro: list[0] → first element list[-1] → last element list[0:3] → slice 🎥 👉 https://lnkd.in/g7QVQFzK 4. Operators Perform actions: ➕ Addition ➖ Subtraction ✖ Multiplication ➗ Division 🤔 Logical , Comparison 🎥 👉https://lnkd.in/g_7gZcUZ 💡 Reality Check: You can’t become a Data Scientist just by watching tutorials… Just like you can’t become a cricketer 🏏 by watching IPL. 👉 You need practice. #Python #Coding #DataScience #MachineLearning #LearnToCode

Most Python beginners confuse variables, data types, and casting. Here's the clearest breakdown I know: A variable is just a label on a box: age = 25 name = "Tanvir" active = True Python has 4 core data types you'll use every day: 1. int → whole numbers (25, 100) 2. float → decimals (9.99, 3.14) 3. str → text ("hello", "42") 4. bool → True or False Type casting = converting one type to another. Python does it automatically sometimes (implicit): x = 5 + 2.0  # result is 7.0 (int + float = float) You do it manually when needed (explicit): int("42")   # → 42 str(99)    # → "99" float("3.14") # → 3.14 ⚠️ The gotcha: int("hello")  # → ValueError! Only cast when the value is actually compatible. Understanding this saves hours of debugging type errors in real projects. What Python concept confused you the most as a beginner? Drop it below 👇 #Python #ProgrammingForBeginners #LearnPython #PythonTips #CodingBangladesh

Most Python beginners confuse variables, data types, and casting. Here's the clearest breakdown I know: A variable is just a label on a box: age = 25 name = "Tanvir" active = True Python has 4 core data types you'll use every day: 1. int → whole numbers (25, 100) 2. float → decimals (9.99, 3.14) 3. str → text ("hello", "42") 4. bool → True or False Type casting = converting one type to another. Python does it automatically sometimes (implicit): x = 5 + 2.0  # result is 7.0 (int + float = float) You do it manually when needed (explicit): int("42")   # → 42 str(99)    # → "99" float("3.14") # → 3.14 ⚠️ The gotcha: int("hello")  # → ValueError! Only cast when the value is actually compatible. Understanding this saves hours of debugging type errors in real projects. What Python concept confused you the most as a beginner? Drop it below 👇 #Python #ProgrammingForBeginners #LearnPython #PythonTips #CodingBangladesh

I used to throw everything into a Python list. 🐍 Need to store data? List. Track config values? List. Remove duplicates? List + awkward manual looping. It worked — but it was the programming equivalent of using a Swiss Army knife to cut a steak. So I wrote about it. My latest blog breaks down all 4 core Python data structures — List, Tuple, Set, and Dictionary — and more importantly, teaches you *when* to reach for each one. 📌 Key takeaways: → Lists are your ordered, flexible workhorse — but mutability can bite you → Tuples signal immutability and are faster + hashable (great for dict keys) → Sets handle deduplication and membership checks in O(1) time — huge at scale → Dictionaries are the backbone of almost every real-world Python application The moment you stop defaulting to lists for everything, your code gets faster, cleaner, and easier to reason about. If you're learning Python — or brushing up before interviews — this one's for you. 👇 🔗 [https://lnkd.in/gm2NBypi] #Python #DataScience #MachineLearning #PythonProgramming #100DaysOfCode #DataStructures #Innomatics #InnomaticsResearchLabs #InnomaticsResearchLabs

Day 10/20: Data Visualisation Python One of the interesting lessons when you're actually seeing the data in action,running the libraries and experimenting on stuff .Day 10 was mainly about learning data visualisation libs in Python ,Matplotlib, Seaborn, Plotly. With each having its strengths Matplotlib for basic plots like line, bar, scatter and great for simple visualization. Seaborn's got statistical viz like heatmaps, pairplots levels up your data story. Plotly's all about interactive plots make it pop! Apply them line plots for trends over time, bar charts for categorical comparisons, heatmaps for correlations. Those charts are really interesting,the boxcharts 😂it reminds of forex trading charts ..well I should dig deeper into those .I realised visualization is key to making data speak. What's your go-to viz tool in Python? 💡 #DataVisualisation #Python #LearningIn20