Upgrade to pathlib for Smarter Directory Management

🚀Day 2/60 – 60 Days Python challenge 🕐 🦾 Today I get to know that *How Python works*. I learned there are two different ways of transforming a program from a high-level programming language into machine language:   ➡️  Compiler   ➡️   Interpreter ✔️ Execution Speed: Compiler generally faster because the code is pre-translated into native machine instructions.             Interpreter generally slower due to the overhead of real-time translation during every execution. ✔️ Translation Process: Compiler translates the entire source code in one go. Interpreter translates and executes the source code line-by-line during runtime. Python is a high-level, dynamically typed language that emphasizes readability and productivity. It is primarily interpreted, with code executed by an interpreter at runtime rather than pre-compiled to machine code. Learning Python has been a rewarding experience that aligns well with my professional goals. Its clear syntax and rich ecosystem empower me to prototype ideas quickly, automate repetitive tasks, and collaborate more effectively with cross-functional teams. I’m appreciating the balance between readability and power, which makes it easier to grow my skills while delivering tangible results. 🔥

Day 7 of 10: Environment Management & Functional Python 🐍⚙️ We are on Day 7 of my 10-day Python sprint! Today’s module from the CodeWithHarry handbook focused on "Advanced Python 2," covering how to manage project dependencies and utilize functional programming patterns. Coming from an ecosystem that relies heavily on NPM and package.json, seeing how Python handles isolated environments is incredibly refreshing. Here are my top takeaways: 📌 Virtual Environments (virtualenv): Creating an environment isolated from the main system interpreter is crucial for avoiding dependency conflicts across different projects. 📌 Dependency Tracking: Running pip freeze > requirements.txt is the perfect way to snapshot installed packages and their exact versions. Distributing this file allows other developers to perfectly recreate the environment using pip install -r requirements.txt. 📌 Lambda Functions: Python’s version of anonymous or "arrow" functions are created using the lambda keyword. They evaluate a single expression and are perfect for passing quick, throwaway logic into other methods. 📌 Map, Filter, & Reduce: Python brings strong functional programming concepts to the table. map applies a function to all items in an input list, filter creates a list of items that return true for a given condition, and reduce applies a rolling computation to sequential pairs. As I push forward with backend and AI development, mastering how to isolate project dependencies is non-negotiable before deploying to production. Python devs: When manipulating data, do you prefer using map and filter, or do you strictly stick to List Comprehensions for readability? Let’s debate below! 👇 #Python #SoftwareEngineering #BackendDevelopment #10DayChallenge #CodeWithHarry

How Python Reads Your Code". It explains exactly what happens behind the scenes when Python encounters a simple line of code like r = 1 + 1: Step 1: Chopping It Up First, Python takes your line of code and breaks it down into individual, bite-sized pieces. For the equation r = 1 + 1, it specifically identifies r as the variable name, = as the assignment operator, the first 1 as the first operand, + as the addition operator, and the final 1 as the second operand. Step 2: Structuring & Trimming Once the pieces are separated, Python builds a blueprint by creating a structure that shows exactly how all of these parts connect together. To make sure it works as efficiently as possible, Python then "trims the fat" by removing any unnecessary complexity from this blueprint. Step 3: Analyzing the Ingredients Next, Python looks closely at each piece to identify its specific type—for example, recognizing that the number 1 is an "Integer". After figuring out exactly what kind of data it's holding, Python selects the correct operation (or "tool") needed to handle it. Step 4: The Final Cook Finally, Python executes the operation to process the code and produce your final result. The Process at a Glance The entire workflow boils down to four simple stages: 01 Chopping: Breaking the code into pieces. 02 Structuring: Building a logical blueprint. 03 Analyzing: Identifying data types and the right tools. 04 Executing: Running the code and producing the result.

Python Coding Tip Why You Should Use isinstance() for Safer Type Checking One of the safest ways for validating types in Python is with the use of isinstance (). Python is dynamically typed. That flexibility is powerful, but it also means your variables can hold unexpected values especially when working with user input, API responses, JSON data, AI agent states, or external libraries. This is where isinstance() becomes essential. isinstance() allows you to safely check whether an object belongs to a specific type and more importantly, it respects inheritance. Unlike type(), isinstance() correctly recognizes subclasses, making your code more robust and future-proof. Why isinstance() matters in real-world applications: • Prevents runtime errors before operations are performed • Supports inheritance-aware type checking • Enables validation of multiple types at once • Encourages defensive programming • Makes systems more stable and production-ready In dynamic systems such as AI agents, state-driven workflows, backend services, or data pipelines, assumptions about data types can easily break your application. A simple type check using isinstance() can prevent unexpected crashes and improve reliability. Clean Python code is not just about writing logic that works. It’s about writing code that anticipates failure and handles it gracefully. If you’re serious about writing professional, production-level Python Code, isinstance() should be part of your toolkit. Subscribe to my YouTube Channel where I teach AI Engineering and Coding in general. https://lnkd.in/ePQf7EPh

Python Coding Tip String Formatting in Python: f-strings vs .format() One important skill for writing clean and readable Python code is string formatting, the ability to dynamically insert variables or values into a string. Python has evolved over the years, and today there are two common approaches developers use: formatted string literals (f-strings) and the .format() method. Using f-strings Formatted string literals, commonly known as f-strings, were introduced in Python 3.6 (PEP 498). They allow you to embed variables or expressions directly inside a string by prefixing the string with f. The expressions inside curly braces {} are evaluated at runtime and inserted into the string. F-strings are widely preferred in modern Python because they are: • More readable • More concise • Generally faster • Easier to maintain in complex expressions For most everyday formatting tasks, f-strings are the recommended approach. Using .format() The .format() method was introduced earlier in Python 2.6 and Python 3.0 (PEP 3101) as an improvement over the older % string formatting style. With .format(), you define placeholders inside a string and then pass values that will be inserted into those placeholders. Although f-strings are now the most common approach, .format() is still extremely useful in scenarios such as: • Building dynamic string templates • Formatting strings programmatically • Working with long reusable text templates I still use .format() frequently in my AI projects while constructing prompt templates. In many cases, the prompt is a long string defined separately with placeholders, and .format() is used to inject values dynamically. The same approach is also useful when generating structured emails, reports, or long formatted messages. Understanding when to use f-strings for quick inline formatting and when to use .format() for reusable templates helps you write cleaner, more flexible Python code. Subscribe to my YouTube Channel where I teach AI Engineering and Coding in general. https://lnkd.in/ePQf7EPh Which of the two methods do you use more often and why? lets discus in the comment session

🚀 Understanding Polymorphism in Python – OOPS Practice As part of strengthening my Python Object-Oriented Programming (OOPS) concepts, I practiced Polymorphism and its different implementations in Python. Polymorphism means “one interface, multiple behaviors.” It allows the same function, method, or operator to behave differently depending on the object. For example: 5 + 3 = 8 "Hello " + "World" = "Hello World" The same + operator works for numbers and strings but produces different results. This is a simple example of polymorphism. To understand this concept better, I implemented several examples using operator overloading and comparisons in Python. 📚 Concepts Practiced ✔ Operator Overloading (__add__, __sub__) ✔ Comparison Operators (__eq__, __lt__, __gt__) ✔ Object-to-object operations ✔ Implementing custom behavior for operators ✔ Writing clean class-based logic 🧑🎓 Student Class Examples • Adding marks of multiple students using + operator • Comparing student marks using ==, <, and > 🛒 Cart / Product Examples • Subtracting product prices using - operator • Comparing item prices inside a cart system 📖 Polymorphism Types Covered 🔹 Method Overloading Using the same method name with different arguments. 🔹 Method Overriding Child class redefining a method already defined in the parent class. 🔹 Constructor Overloading Initializing objects in multiple ways using default parameters. 🔹 Constructor Overriding Child class redefining the parent constructor. 🔹 Operator Overloading Allowing operators like +, -, ==, < to work with objects. 💡 Why Polymorphism is Important ✔ Improves code readability ✔ Encourages reusable code ✔ Makes applications flexible and scalable ✔ Reduces complexity in large systems Strong understanding of OOPS concepts is essential for: Backend Development System Design Writing scalable applications Learning step by step. Improving every day 💪 📄 Code and explanation attached in the PDF. 🙏 Thanks to 10000 Coders venubabu vajja Kotesh bommu and Akshaya Koyedi for continuously motivating me to learn and grow. #Python #OOPS #Polymorphism #OperatorOverloading #BackendDeveloper #CodingJourney #Learning #SoftwareDevelopment

Hii Everyone, Today we gonna see some more new Topics. 1.Comments : How Do You Write Comments? In Python, the hash mark (#) indicates a comment. Anything following a hash mark in your code is ignored by the Python interpreter. 2.What Kind of Comments Should You Write? The main reason to write comments is to explain what your code is sup posed to do and how you are making it work. If you want to become a professional programmer or collaborate with other programmers, you should write meaningful comments. 3.what Is a list? In Python, a list is a collection of items stored in a single variable. It allows you to keep multiple values together in order. Think of a list like a container or a box that holds many items. Accessing Elements in a List : Accessing elements in a list means getting a specific item from the list using its position (index). In Python, list items are accessed using index numbers inside square brackets [ ]. Index Positions Start at 0, Not 1 : Most programming languages start indexing at 0 because it represents the starting position in memory. So counting begins from 0 instead of 1. Using Individual Values from a List : Using individual values from a list means taking one specific item from the list and using it in your program (for printing, calculations, messages, etc.). Modifying Elements in a List : Modifying elements in a list means changing the value of an item that already exists in the list. In Python, you modify a list element by using its index position and assigning a new value. Adding Elements to a List : Adding elements to a list means putting new items into an existing list. Python provides several ways to add items.

Writing Python tests can be incredibly tedious when you have to manually hardcode complex expected values. 𝗶𝗻𝗹𝗶𝗻𝗲-𝘀𝗻𝗮𝗽𝘀𝗵𝗼𝘁 changes how we handle test assertions. Instead of typing out massive dictionaries or strings, you let the tool write the code for you. You simply replace your expected test output with an empty snapshot function call. When you run your test suite, it automatically records the correct values directly into your source file. This workflow drastically reduces boilerplate and keeps your focus entirely on application logic. Here is what makes this library a game changer for Python developers. ✅ It integrates seamlessly with pytest to automate value recording. ✅ It supports normal assertions without requiring complex new syntax. ✅ It strictly preserves Black formatting so your codebase stays completely uniform. ✅ It can instantly outsource large string payloads to external files. ✅ It easily handles nested structures and creates sub-snapshots on demand. 🔗 Link to repo: github(.)com/15r10nk/inline-snapshot --- ♻️ Found this useful? Share it with another builder. ➕ For daily practical AI and Python posts, follow Banias Baabe.

🚀 Why Should We Learn Python? Many people who are starting their tech journey often have one question:  “Why do we need to learn Python?” Let’s understand it in a simple way. 🐍 What is Python?  Python is an easy-to-learn, general-purpose, dynamically typed, object-oriented programming language. Because of its simple syntax, it is considered one of the best languages for beginners. 💡 What does Dynamically Typed mean?  In Python, we don’t need to define the data type while declaring a variable. The interpreter automatically detects the type at runtime. Syntax Example: print("Hello World") With just a single line of code, we can write our first Python program. This simplicity is one of the biggest reasons why Python is so popular. 📌 Where is Python used? Python is widely used in multiple domains such as: • Artificial Intelligence (AI)  • Machine Learning (ML)  • Web Development  • Game Development  • Data Analysis & Automation Because of its versatility and huge ecosystem of libraries, Python has become one of the most in-demand programming languages in the tech industry. If you are planning to enter fields like Data Engineering, Data Science, or AI, learning Python is definitely a great step. 💬 Are you currently learning Python or planning to start? Let’s discuss in the comments.

My latest Python project's test suite passed with flying colors. But when I ran it in a live Python session, the package wouldn't build. Why? I'd leaned on Codex 5.4 for much of the development work. I orchestrated, it handled implementation, and it built incredible infrastructure with thorough tests. But it only built support up to Python 3.12, where versions 3.13 and 3.14 (which I was using in my live session) are now available! I see this constantly when developing with LLMs. They produce "cutting-edge work" that only goes up to the limits of their training data. "The latest ACS data from tidycensus" pulled for 2022, where 2024 is now available. Or web maps built with MapLibre 4.0, where the current release is 5.20. This can be resolved by giving LLMs explicit instructions to search the web for the latest versions and data. But that requires a human who knows what to check setting it up. I knew the 2024 ACS is available because I maintain tidycensus. I knew MapLibre was on 5.x because I maintain mapgl. Someone running a fully automated workflow without that domain knowledge wouldn't have caught either gap. LLMs as amplifiers for human expertise yield incredible results. But those "fully automated" AI workflows? They might be producing last year's insights with this year's confidence.