Writing to a File in Python: 'w' and 'a' Modes

Most Python code runs on one core — even if your machine has 8, 12, or 16. That’s fine… until your script starts taking forever 😪 ✨Multiprocessing✨ can change that! But here’s the catch: it's often misunderstood, misused, or missed entirely. This post isn’t just “how it works.” It’s about when it actually helps, what to avoid, and how it compares to the other options — threading and asyncio. You’ll leave knowing when not to reach for multiprocessing — which is just as important. 📎 Link in comments to get the full breakdown! (with code examples and real use cases) #Python #SoftwareEngineering #CodePerformance #DataEngineering #PythonTips #ScalableCode #BackendDevelopment #HighPerformanceComputing #AsyncProgramming #DeveloperInsights #StrataScratch

🚀 Using `hypothesis` for Property-Based Testing (Python) The `hypothesis` library in Python enables property-based testing. You define properties that your code must always satisfy, and `hypothesis` generates a wide range of inputs to test these properties. This helps in discovering edge cases and hidden bugs. Hypothesis can automatically shrink failing examples to find the minimal failing case, making debugging easier. It's a valuable tool for writing more robust and reliable code. Learn more on our app: https://lnkd.in/gefySfsc #Python #PythonDev #DataScience #WebDev #professional #career #development

Day 25 of 100 Days of Python — match–case (Python 3.10) Today I explored match–case (Python 3.10+), a powerful way to handle multiple conditions with clean, readable logic. Why it’s useful in real projects: ✅ Replaces messy if–elif chains ✅ Improves readability & maintainability ✅ Great for menus, commands & status handling ✅ Makes code feel more production-ready Key takeaway: match–case helps write clean, scalable, and professional Python code 🚀 💬 Would you use match–case in real projects, or stick with if–elif? Let’s discuss 👇 #Python #PythonDeveloper #BackendDevelopment #SoftwareEngineering #LearningInPublic #CodingJourney #TechCareers #100DaysOfPython

🚀 The `__name__` Variable (Python) Every Python module has a built-in variable named `__name__`. When a module is run directly, `__name__` is set to `"__main__"`. When a module is imported, `__name__` is set to the module's name. This allows you to write code that executes only when the module is run as a script, not when it's imported. Learn more on our website: https://techielearns.com #Python #PythonDev #DataScience #WebDev #professional #career #development

🐍 Did you know that in Python, NaN is always a float. This has some interesting consequences when working with Series in Pandas: x = pd.Series([10, 20], index=["a","b"]) y = pd.Series([10, 30, 5], index=["a","b","c"]) print((x + y).dtype) # float64 Pandas aligns Series by index, so missing values like "c" in x are treated as NaN, which can only be a float, causing all numbers in the result to be upcast to float64. #Python #Pandas #PythonTips #Coding

How Python Manages Memory: Mutable vs Immutable One concept that silently affects performance, bugs, and behavior in Python is mutability. Immutable objects 👉 int, float, str, tuple Their value cannot be changed in place Any “modification” creates a new object in memory Safer, predictable, hashable (used as dict keys) Mutable objects 👉 list, dict, set Can be modified without changing memory reference Faster updates, but risk of unexpected side effects Changes reflect across all references Why this matters in real projects - Unexpected bugs when modifying lists passed to functions - Memory inefficiency when repeatedly modifying strings - Confusing behavior in function arguments & shared data #Python #MemoryManagement #Mutable #Immutable #PythonInternals #SoftwareEngineering

What Really Happens When You Pass a Variable to a Function in Python? In Python, variables don’t hold values — they hold references to objects. When you pass a variable to a function: 👉 Python passes the reference, not a copy of the object. This model is often called “pass-by-object-reference” (or call by sharing). Why this confuses people? Immutable objects (int, str, tuple): Reassignment inside a function creates a new object → original stays unchanged. Mutable objects (list, dict, set): In-place modification changes the same object → caller sees the change. So it feels like: Immutable → pass by value Mutable → pass by reference But that’s just an illusion. The real rule Python always passes a reference to an object. What you do with that reference determines the outcome. #Python #ProgrammingConcepts #PythonInternals #Mutable #Immutable #CleanCode

In C, an integer takes 4 bytes. In Python, the number 1 takes 28 bytes. Why? As python is a "Dynamically Typed" language, it needs to store more than just the value 1. It needs to store "metadata" about that value so the interpreter knows what to do with it. How? In the CPython source code, every single thing is a PyObject. So when we create x = 1, following structure is created with it: 1. ob_refcnt (8 bytes): The Reference Counter. It tracks how many variables point to this object. 2. ob_type (8 bytes): A pointer to the "Type Object" (telling Python the datatype). 3. ob_size (8 bytes): For variable-sized objects (like lists) 4. The Actual Value (4-8 bytes): The actual number 1 Understanding this overhead explains why Python is memory-intensive being a dynamically typed language. I am trying to learn Python Internals in detail and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

🚀 The `super()` Function (Python) The `super()` function is used to call methods from a parent class within a subclass. It allows you to access and execute methods defined in the superclass, even if they have been overridden in the subclass. This is useful for extending the functionality of a superclass method without completely replacing it. `super()` promotes code reuse and avoids duplication. #Python #PythonDev #DataScience #WebDev #professional #career #development

Time Complexity in Python Operations In Python, performance issues rarely come from syntax. They come from misunderstanding how common operations scale as data grows. Key complexity considerations: - List access by index: constant time, but insertions and deletions in the middle are linear - Dictionary and set lookups: constant time on average, dependent on hashing - Membership checks: linear for lists, constant time for sets and dictionaries Sorting operations: typically O(n log n), regardless of data structure - Understanding these behaviors helps avoid hidden bottlenecks and supports writing code that scales predictably. Good performance starts with knowing how your code grows. 🚀 #Python #DataStructures #Algorithms #CodeOptimization #Performance