Python's dataclasses.replace for Immutable Objects

Efficient Ways to Loop Through Dictionaries Looping through dictionaries in Python can be intuitive, but knowing the available methods can enhance your understanding of this data structure. When you iterate through a dictionary directly, you're looping over its keys. This is useful when you only need the keys without the values. To access values, use the `.values()` method, focusing solely on the data without the overhead of key references. If both keys and values are necessary, the `.items()` method provides a straightforward way to access them simultaneously. This becomes particularly beneficial in use cases like price lists or attribute mappings, where values depend significantly on the keys. Interestingly, these methods can affect performance as well. For large dictionaries, using `.items()` is more efficient than fetching keys first and then accessing their corresponding values individually. Also, dictionaries are unordered in Python versions prior to 3.7, meaning the order of iteration wasn’t guaranteed. However, in later versions, the order is preserved, making your loops more predictable. Quick challenge: Given the dictionary structure provided, how would you modify the code to print only keys that start with the letter 'b'? #WhatImReadingToday #Python #PythonProgramming #Dictionaries #DataStructures #LearnPython #Programming

🚀 Day 8/30 – Python OOPs Challenge 💡 Encapsulation in Python In real life, we don’t allow direct access to everything. For example: - You can use an ATM - But you cannot access the bank’s internal system That’s called Encapsulation. 🔹 What is Encapsulation? Encapsulation means: 👉 Wrapping data (variables) and methods together 👉 Restricting direct access to some data It helps in: - Data protection - Better control - Secure code 🔹 How to achieve Encapsulation in Python? We use private variables by adding double underscore __ 🔹 Example: ``` class BankAccount: def __init__(self, balance): self.__balance = balance # Private variable def deposit(self, amount): self.__balance += amount def show_balance(self): print("Balance:", self.__balance) acc = BankAccount(1000) acc.deposit(500) acc.show_balance() ``` 🔹 What happened here? - __balance cannot be accessed directly - We use methods to modify it This protects the data 🔒 📌 Key takeaway: Encapsulation = Data hiding + Controlled access 👉 Day 9: Public vs Private Variables (coming tomorrow) 👍 Like | 💬 Comment | 🔁 Share 📍 Follow me to learn Python OOP step by step #Python #OOP #LearningInPublic #30DaysOfPython #CodingJourney

Today I tried something new in Python. I implemented an external module called pyttsx3, which is a text-to-speech library. Since it is not built into Python by default, I installed it using the terminal with the pip install pyttsx3 command. After installing it, I wrote a simple program to test how it works. What I really liked is how simple and powerful it is. I initialize the engine, type any sentence I want, and the engine converts my text into speech. For example, when I wrote, “Hello world, I am the next data engineer,” the program actually spoke the same sentence back to me. The interesting part is that I can change the text to anything. Whatever I write, the engine reads it out loud. Through this small project, I understood how external modules work, how to install them, and how to use their functions properly in code. It may look like a small step, but for me, it is part of building strong fundamentals and exploring how Python interacts with real-world applications. Thank you.🤲❣️ GitHub: https://lnkd.in/dr5x6JSC

📘 Python Data Types – Strengthening the Basics Today, I revised Python Data Types, which are the foundation for writing clean, efficient, and error-free code. 🔹 What are Data Types? Data types define the kind of data a variable can store and the operations that can be performed on it. Python is dynamically typed, meaning the data type is determined at runtime. 📌 Key Data Types Covered Numeric: int, float, complex Boolean: bool Sequence: str, list, tuple Set: set Mapping: dict NoneType: None 📌 Important Concepts Mutable vs Immutable data types Type checking using type() and isinstance() Type conversion (int, float, str) Real-time usage of lists, dictionaries, and sets 💡 Understanding data types helps in: Writing optimized code Avoiding runtime errors Handling real-world data efficiently Building strong fundamentals, one concept at a time 🚀 #Python #DataTypes #PythonLearning #ProgrammingBasics #DataAnalytics #CodingJourney #TechSkills

🐍 Basic #Python Variables – The Foundation of Every Python Program If you’re starting your journey with Python, understanding variables and data types is your first major milestone. Variables are containers for storing data. In Python, they are simple to declare but incredibly powerful in how they shape your programs. Here’s a quick breakdown of the core data types every beginner should know: 🔢 Integer Whole numbers without decimals. Example: 10, -5 🔹 Float Numbers with decimal points. Example: 4.5, -0.4 ✅ Boolean Represents logical values: True or False Essential for decision-making in programs. 📦 List An ordered collection that can store multiple data types. Example: [22, "Hello world", 3.14, True] 🔁 Tuple Similar to a list but immutable (cannot be changed after creation). Example: (7, 5, 8) 🎯 Set An unordered collection of unique elements. Example: {7, 5, 8} 🗂 Dictionary Stores data in key–value pairs. Example: {"name": "Alice", "age": 25} 🚫 None Represents the absence of a value. Mastering these fundamental data types builds the groundwork for writing efficient Python code. Every advanced concept — from data structures to machine learning — relies on these basics. Strong foundations create strong developers. #Python #Programming #Coding #SoftwareDevelopment #LearnPython #TechSkills

Setting up a Python environment used to take forever. pip installs…   dependency conflicts…   broken virtual environments… Now there’s a new tool developers are switching to: uv It’s a modern Python package manager built in Rust and designed to replace multiple tools. Here’s why it’s getting popular: ⚡ Extremely fast Traditional install: pip install pandas With uv: uv pip install pandas Same command style — but much faster. --- 🧠 Creates virtual environments automatically Instead of: python -m venv venv   source venv/bin/activate You can simply run: uv venv And your environment is ready. --- 📦 Installs dependencies from requirements instantly uv pip install -r requirements.txt For large Data Science projects (NumPy, Pandas, PyTorch), this can save a lot of time. --- Why this matters for Data Scientists: Setting up environments is one of the most frustrating parts of Python workflows. Tools like uv make the process faster and simpler. The Python ecosystem keeps evolving. Learning these tools early gives you an edge. Have you tried uv yet? #DataScience #MachineLearning #Python

In Python, these are all the same number: 10 0b1010 0o12 0xA Same value. Different bases: decimal, binary, octal, hex. Most devs only use decimal. But when you need colors (#FF0000), file permissions (0o755), or low-level work, the other bases matter. I wrote a full guide that covers: → What number systems are and why they exist → How to write integers with 0b, 0o, 0x → Rules (valid digits, integers only, no floats) → Using them with complex numbers and input() → Common mistakes and practice exercises If you’ve ever wondered what 0xFF or 0b1010 really mean, this is for you. Full guide (free): https://lnkd.in/dgusMje5 #Python #Programming #Coding #NumberSystems #LearnPython #SoftwareDevelopment

9 Modern Python Libraries You Must Know in 2026 Python is evolving fast. If you're still using only the “classic stack,” you're missing out on some powerful modern tools. If you master these, you’ll be ahead of 90% of Python developers in 2026. 1. Polars – lightning-fast DataFrame library (faster than pandas) 2. Pydantic v2 – powerful data validation & settings management 3. FastAPI – high-performance APIs with automatic docs 4. Chain – build AI & LLM applications 5. PyTorch 2 – next-gen deep learning framework 6. DuckDB – Analytics database that runs directly in Python 7. Typer – build beautiful CLI apps effortlessly 8. Rich – stunning terminal formatting & dashboards 9. Pandera – schema validation for DataFrames Which one are you already using?

🚀 Revisiting Python Fundamentals Day 5: Operators in Python Operators are the actions in Python. 🔴 Arithmetic Operators Used for basic mathematical operations. + Addition - Subtraction * Multiplication / Division Example: result = 10 + 5 🔵 Relational Operators (Compare Values) Used to compare two values. == Equal to != Not equal to > Greater than < Less than Example: x > 10 🟢 Logical Operators Used to work with True/False values. and or not Example: x > 5 and y < 10 🟡 Assignment Operators Used to assign or update variables. = += -= *= Example: x += 5 🔵 Bitwise Operators Used at the bit level. & AND | OR ^ XOR ~ NOT << Left shift >> Right shift Example: x = 5 & 3 🟣 Membership Operators Used to check if a value exists in a collection. in not in Example: "Python" in ["Python", "SQL"] 🟢 Identity Operators (Check Same Object) Used to check memory identity, not value. is is not Example: a is b #Python #Operators #PythonBasics #LearnPython #CodingJourney

Day 9– Important Python Functions & Operators Today, I revised some powerful Python functions and operator concepts that are extremely useful in problem solving and logic building. 🔹 Conversion Functions bin() → Convert number to binary ord() → Character to ASCII value chr() → ASCII value to character 🔹 Number Thumb Rules Divisibility check → num % divisor == 0 Get last digit → num % 10 Remove last digit → num // 10 Increase number → + or * 🔹 Logical Operators and, or, not → Used to combine conditions 🔹 Assignment Operators +=, -=, *=, /=, //=, %=, **= → Short and efficient updates 🔹 Membership Operators in, not in → Check presence in sequences 🔹 Identity Operators is, is not → Compare memory locations Understanding these small but powerful concepts makes coding cleaner and more efficient 💡 Step by step, strengthening my Python fundamentals 🚀 #PythonLearning #Day10 #PythonBasics #ProgrammingFundamentals #AIMLStudent #LearningJourney #Consistency #KeepLearning