Responsible Public Data Collection with Python

Many people believe starting with Python is the best route to becoming a data analyst because of its powerful features. However, I believe building from the basics to the advanced level is a better path. Understanding the fundamentals—such as data concepts, spreadsheets, and logical thinking—creates a stronger foundation before moving to tools like Python. In learning, it’s not about how far you go, but how well you understand each step. #DataAnalytics #LearningJourney #ContinuousLearning

🔁 Python Revision – Sets Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Sets (creation and properties) ✔️ Unique elements and unordered nature ✔️ Set methods (add, remove, discard, etc.) ✔️ Set operations (union, intersection, difference) Practiced using sets to handle unique data and perform efficient operations like finding common or different elements between datasets. Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Understanding sets is helping me work better with data and avoid duplicates 📊 Next: dictionaries and real-world data handling 🚀 #Python #Revision #Sets #Programming #DataAnalytics #LearningJourney #Coding

Today I explored one of the most powerful data structures in Python – Dictionaries 🐍 📌 Key Takeaways: 🔹 Dictionaries store data in key-value pairs 🔹 Keys are unique, but values can be duplicated 🔹 Easy data access using keys 🔹 Efficient for storing structured data 💡 Important Operations Covered: ✔️ Creating dictionaries using {} and dict() ✔️ Accessing values using keys and .get() ✔️ Removing elements using del, .pop(), .clear() ✔️ Understanding dictionary length using len() ✔️ Using .popitem() to remove the last inserted item 📊 Dictionaries are widely used in real-world applications like: ➡️ JSON data handling ➡️ APIs ➡️ Database-like structures Learning dictionaries strengthens the foundation for real-world Python development 💻 🔥 Consistency is the key — one step closer to mastering Python! Global Quest Technologies ✨ #GlobalQuestTechnologies #GQT #Python #PythonProgramming #100DaysOfCode #CodingJourney #LearnPython #DataStructures #Programming #Developer #CodingLife #TechLearning #SoftwareDevelopment #PythonBasics #CareerGrowth

🚀 Day 68 | Python Revision (Up to Recursion) Today I focused on revising all Python concepts up to recursion 📘 🔹 What I Revised: • Basics → variables, data types, input/output • Control statements → if-else, loops • Functions → user-defined functions, arguments • Built-in functions → len(), sum(), min(), max(), etc. • String methods → strip(), split(), replace(), join() • List & Dictionary operations • Lambda functions and functional programming basics • Recursion → factorial, list flattening 💡 Key Learning: • Revision helps in connecting all concepts together • Improved clarity on when to use loops vs recursion • Strengthened understanding of problem-solving approaches 🔥 Takeaway: 👉 Strong fundamentals come from consistent revision Consistency + Revision = Confidence 🚀 #Day68 #Python #Revision #Recursion #ProblemSolving #CodingJourney #10000Coders #PythonDeveloper #SravanKumarSir

🚀 Exploring Python Data Structures: The Building Blocks of Efficient Code In Python, choosing the right data structure is key to writing clean, efficient, and optimized programs. Here’s a quick overview of the four fundamental data structures every developer should master: 🔹 List Ordered, mutable, and allows duplicate elements. Ideal for storing collections that may change over time. 🔹 Tuple Ordered but immutable. Useful when data integrity is important and values should not be modified. 🔹 Set Unordered collection with no duplicate elements. Perfect for operations like union, intersection, and removing duplicates. 🔹 Dictionary (Dict) Stores data in key-value pairs. Highly efficient for fast lookups and structured data representation. 💡 Understanding when and where to use each of these structures can significantly improve both performance and readability of your code. 📌 Keep learning, keep building! Python offers endless possibilities when you master its core concepts. #Python #Programming #DataStructures #Coding #SoftwareDevelopment #LearningJourney

Let's now talk about Variables in Python. What is a variable? Think of it like a box, you give it a name and store a value inside it. Example: a = 10 name = 'Alice' price = 19.99 Here, a, name, price are all variables in which we have stored some value or data. Simple right? But here's where most beginners make mistakes- naming their variables wrong. There are 3 conventions you need to follow: 1️⃣ First letter should be lowercase (best practice as per PEP8) 2️⃣ Never start a variable name with a number 3️⃣ Never use spaces, use an underscore instead Break any of these and Python will throw an error before your code even runs. Get these right from day one and you'll save yourself a lot of frustration later. #Python #DataAnalytics #data #python #learnpython #dataanalyst #pythonforbeginners

If you work with Python, have you ever wondered:   • What are magic methods?   • Are they the same as special methods?   • What about “dunder methods”? First thing is, magic methods are not really magic.They are just special methods defined by the Python Data Model. And, guess what, magic methods, special methods and dunder methods are all the same thing. Amazing, right? Let’s look at a simple example: 𝗰𝗹𝗮𝘀𝘀 𝗠𝘆𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻:   𝗱𝗲𝗳 __𝗹𝗲𝗻__(𝘀𝗲𝗹𝗳):     𝗿𝗲𝘁𝘂𝗿𝗻 𝟰𝟮 Now, when you call: 𝗹𝗲𝗻(𝗼𝗯𝗷) You’re NOT calling your method directly, Python is. This is the key insight and it is very important as we are going to see on another post. Takeaway: “Magic methods” are not magic. They are contracts with the Python interpreter. Implementing such methods are going to open a lot of new doors and it is a very pythonic whay of implementing your code. #python #magicmethods #dundermethods #specialmethods

Real world data is messy. So I built my own 100-record JSON dataset to practice cleaning it with Python. The dataset included: • Duplicate entries • Missing values • Ratings in mixed formats like "five", "4", and "3.5" • Different types of customer feedback • Inconsistent formatting Using Python, I cleaned the data, removed duplicates, standardised ratings, and generated basic insights. Big takeaway: data cleaning is just as important as analysis. GitHub Repo: https://lnkd.in/gYr-4kkm #Python #DataScience #DataAnalytics #Projects #Coding

🕸️ Built a Web Scraper using Python! 🚀 I recently created a simple project where I extracted quotes and author names from a website using Python. 💡What this project does: • Scrapes data (quotes & authors) from a website • Uses BeautifulSoup and requests • Stores the extracted data in CSV format This project gave me a better understanding of how websites work behind the scenes. 🔗 GitHub Project:https://lnkd.in/dzYvPjCi Synent Technologies #Python #WebScraping #BeginnerProject #Coding #Learning #GitHub