Pydantic Data Validation in Python with BaseModel and Field

Day 15 of #WhatILearnedToday 🐍 Today I learned about Python Dictionaries—one of the most powerful and commonly used data structures in Python. Dictionaries store data in key–value pairs, making data retrieval fast, flexible, and highly efficient. Understanding how to create, update, loop through, and use built-in dictionary methods really helped me see how Python handles real-world data structures. From APIs to configuration files and backend logic—dictionaries are everywhere. 💬 What’s your most-used Python data structure: list, dictionary, or set? #WhatILearnedToday #Python #PythonDictionary #Programming #PythonLearning #Upskilling #DeveloperJourney #ContinuousLearning

Day 17 of #WhatILearnedToday 🐍 Today I learned about Sets in Python—an unordered collection that stores unique elements only. Sets are extremely useful for removing duplicates, performing union, intersection, and difference operations, and handling membership testing efficiently. They play a big role in data processing and logic-based problems. Learning sets made me realize how Python provides the right data structure for every use case. 💬 Where have you used Python sets in real-world projects? #WhatILearnedToday #Python #PythonSets #Programming #PythonLearning #DataStructures #Upskilling #DeveloperJourney

🎥 Python Sets and Methods Today I shared a video explaining Python sets, a powerful data structure used to store unique elements. Sets are especially useful for removing duplicates, performing mathematical operations like union and intersection, and working with unordered data. In this video, I also covered commonly used set methods with examples and problem-solving scenarios to show how they work in real coding situations. Understanding sets helps write cleaner, faster, and more efficient Python code 💻✨ #Python #Sets #Programming #Coding #ProblemSolving #LearningJourney

Practiced Python pattern programs using numbers and alphabets This helped me improve my understanding of: • Loops • Nested loops • Logic building Small steps, consistent learning 💻✨ #Python #CodingPractice #BCA #LearningJourney https://lnkd.in/grJA3Ran

🚀 Master Python Input & Output – The Right Way Every Python program starts with taking input and producing output — yet this is where most beginners get confused. In my latest PyWired tutorial, I explain: ✅ How input() actually works ✅ Why user input is always a string ✅ Using print() effectively ✅ Common beginner mistakes (and how to avoid them) ✅ Clean, real-world examples If you’re serious about learning Python from fundamentals to professional-level coding, this is a must-watch. 🎥 Watch here: 👉 https://lnkd.in/g39VkyeQ 📌 Subscribe to PyWired for Python basics, DSA, and projects. #Python #Programming #LearnPython #Coding #SoftwareEngineering #PyWired #PythonBeginners

Exploring how dictionaries can be used to manage product data in Python. Each product is stored with attributes like quantity, price, and release year using key-value pairs. This structure makes it easy to update, retrieve, and even delete specific details #Python #DataStructures #InventoryManagement #CodingJourney #TechLearning #JupyterNotebook #ProgrammingBasics

🚀 Python Task | Finding All position of a Word in a String 🔹 Problem: Create a dynamic Python function that finds all index positions of a given word inside a sentence. 🔹 Approach: First, check if the word exists in the sentence Use string.find() to locate the first occurrence Use a while loop with a condition (index != -1) Move the search forward by using + 1 to find the next occurrence 🔹 Why this matters: This kind of logic is helpful in: Text processing Log analysis Data cleaning Interview problem-solving 📌 The function works for: ✔ Any sentence ✔ Any word ✔ Any number of occurrences #Python #Programming #DataAnalytics #ProblemSolving #CodingPractice #PythonBasics #DataScience

🚀 Feature Engineering Made Smarter with Python! 🧠🐍 Just read an excellent article on KDnuggets that every machine learning practitioner should check out! It shares 5 highly practical Python scripts that automate core feature engineering tasks and help you build better models faster. ✨ 🔹 What’s covered: ✔️ Smart encoding of categorical features ✔ Automatic transformations for numerical data ✔ Systematic generation of feature interactions ✔ Rich datetime feature extraction ✔ Automated feature selection to reduce noise and improve performance Whether you’re battling high-cardinality categories or trying to uncover powerful feature interactions — these scripts help streamline the process and save tons of manual effort. 💡 📈 If you’re working on machine learning models in Python, this is a must-read! Check it out and let me know which script you’ll try first! 👇 Source: KDnuggets https://search.app/wChSs #MachineLearning #Python #FeatureEngineering #DataScience #KDnuggets

Practicing Python basics using the Python REPL 🐍 Generated a multiplication table to understand how expressions work in real time. Learning fundamentals step by step 💻 #Python #REPL #PythonBasics #LearningPython #CodingPractice #BeginnerJourney

Input and output operations are fundamental to building interactive Python programs. In this post, I explain: • How input() works and why it always returns a string • Typecasting user input using int(), float(), and str() • Displaying output with print() • Formatting output using f-strings and .format() • Common beginner mistakes and best practices This content is part of my ongoing effort to share clear, practical Python concepts from basics to advanced topics. #Python #PythonBasics #LearnPython #Programming #Coding #PythonForBeginners