Working with APIs in Python 2026

Today I explored some advanced concepts in Python functions and variable scope that are super important for writing clean and scalable code 💻✨ 🔹 What I learned today: ✅ Default Arguments → Functions can have predefined values if no argument is passed ✅ Variable Length Arguments → *args → Non-keyword arguments (tuple) → **kwargs → Keyword arguments (dictionary) ✅ Functions, Modules & Libraries → Functions = reusable blocks → Modules = file of functions → Libraries = collection of modules ✅ Types of Variables in Python 🔸 Local Variables → Defined inside a function → Accessible only within that function 🔸 Global Variables → Defined outside functions → Accessible throughout the program 💡 Understanding these concepts helps in writing modular, reusable, and efficient code Consistency is key 🔥 Learning step by step, growing every day 💪 ✨ Write once, reuse everywhere with Python functions! Global Quest Technologies #Python #PythonLearning #Functions #VariableScope #CodingJourney #LearnToCode #Developers #TechSkills #Programming #GlobalQuestTechnologies

Advanced Python 2026 (Part 6) is Live: Modules & Packages As your Python projects grow, structure becomes everything. In Part 6 of the Advanced Python 2026 series, we explore how to organize code like a professional using: • Modules → Breaking code into reusable files • Packages → Structuring projects into logical folders This is how developers move from messy scripts to clean, scalable applications. If you want your code to be easier to maintain, debug, and expand — this is a must-learn. Read Part 6 here: https://lnkd.in/dk9Mmaeq #Python #Programming #JMSM #KNKA #SoftwareDevelopment #Coding #Developers #TechEducation #CleanCode #Python2026

Advanced Python 2026 (Part 7) is Live: Virtual Environments As your Python projects grow, managing dependencies becomes critical. In Part 7 of the Advanced Python 2026 series, we explore virtual environments—a must-have skill for every serious developer. Key highlights: • Isolating project dependencies • Avoiding version conflicts • Creating clean, reproducible setups • Managing libraries professionally This is how developers ensure their code runs consistently across different systems. If you want to build reliable, production-ready applications, this is a step you can’t skip. Read Part 7 here: https://lnkd.in/ewjUtv-7 #Python #Programming #JMSM #KNKA #SoftwareDevelopment #Developers #TechEducation #CleanCode #DevOps #Python2026

🚀 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

🚀 Day 20 of my Python learning journey Today I learned about Operator Overloading in Python. I understood how we can define the behavior of operators like +, -, * and == for custom objects by overriding specific magic methods in our class. I practiced building a Vector class from scratch and tested different operations on Vector objects to see how Python internally calls these magic methods. I always used w3schools.com to understand the concepts more clearly. Today's learning helped me understand how powerful and flexible OOP really is. You can make your custom objects behave just like built-in types. My work is here https://lnkd.in/gGsjWtee #Python #100DaysOfCode #LearningInPublic #OOP #Programming #Developers #DSA #W3Schools

Advanced Python 2026 (Part 5) is Live: Inheritance & Encapsulation Writing code that works is one thing. Writing code that is reusable, scalable, and secure is another. In Part 5 of the Advanced Python 2026 series, we dive deeper into Object-Oriented Programming by exploring: • Inheritance → Reuse code and reduce duplication • Encapsulation → Protect and control your data These concepts are essential for building real-world systems—from backend applications to enterprise software. If you're serious about becoming a better developer, this is a key step forward. Read Part 5 here: https://lnkd.in/eAbjRmks #Python #Programming #JMSM #KNKA #SoftwareDevelopment #OOP #Coding #Developers #TechEducation #CleanCode #Python2026

Deep Dive into Python Dictionaries yesterday, I explored key concepts of Python Dictionaries and strengthened my understanding of how they work in real-world scenarios 💡 🔹 Adding data to an empty dictionary 🔹 Heterogeneous data for both keys and values 🔹 Accessing dictionary data using keys 🔹 Keys can be any immutable type 🔹 Duplicate keys are not allowed but values can be duplicated 🔹 Dictionary is mutable 🔹 Another representation of dictionary using dict() 🔹 Deleting elements from dictionary 📌 Key Insight: Dictionaries are one of the most flexible and powerful data structures in Python, making data handling efficient and dynamic. Consistency is the key — learning something new every day and moving one step closer to becoming a better developer 💻🔥 #Globalquesttechnologies #GR Narendra Reddy #Python #LearningJourney #100DaysOfCode #Programming #DataStructures #Coding #DeveloperJourney #PythonBasics

🔁 Python Revision – Dictionaries Continuing my Python fundamentals revision 🐍 In this session, I focused on: ✔️ Dictionaries (key-value pairs) ✔️ Accessing and updating values ✔️ Dictionary methods (keys(), values(), items()) ✔️ Iterating through dictionaries Practiced storing and managing structured data using dictionaries, which is essential for handling real-world datasets. Documented my practice in a Colab Notebook and shared it as a PDF to track my progress. Learning how to organize and manipulate data more effectively 📊 #Python #Revision #Dictionaries #Programming #DataAnalytics #LearningJourney #Coding

Same Data, Different Memory — Python Data Types Comparison We often choose Python data structures based on ease of use… But rarely think about memory. That can quietly impact performance — especially with large datasets. So I tested how different Python data types behave when storing the same data. Here’s what stood out: • sys.getsizeof() helps measure object memory. • Tuples use less memory than Lists. • Sets consume more memory due to hashing. • String size varies based on content. One important note: - sys.getsizeof() shows memory used by the object itself (in bytes), not the full picture. - Small choices in data structures can lead to big differences in performance. - Something I’ve started paying more attention to while writing code. Do you consider memory usage when choosing data structures, or focus mostly on readability? #Python #Programming #Developers #Coding #SoftwareEngineering #PythonTips #BackendDevelopment #LearningToCode