SOLID Principles in Python: Real-World Example

This is the correct way to annotate Python methods that return self. 👇 When a method returns the argument `self`, use `typing.Self` to annotate the return type of the method: ```py from typing import Self class P: def some_method(self) -> Self: ... return self ``` Why is this useful? This is especially useful if `C` inherits from `P`: ```py class C(P): pass ``` The return type of `C.some_method` is inferred to be the subclass itself: ```py from typing import reveal_type reveal_type(C().some_method()) # C ``` But only because of `Self`! If you didn't use `Self`, the return type would be the parent class: ```py from typing import reveal_type class P: def some_method(self) -> P: ... return self class C(P): pass reveal_type(C().some_method()) # P ?! ```

🐍 Master the Language: The Building Blocks of Python!📚 If you want to master Python, you have to speak its language. These 33 keywords are the reserved words that form the skeletal structure of every script, automation, and AI model you build. 💻🚀 🔍 Keyword Spotlight: The "in" Keyword The in keyword is one of Python's most versatile tools. It serves two main purposes: Membership Testing: It checks if a value exists within a sequence (like a list, string, or dictionary). Example: 'a' in 'apple' returns True. Iteration: It is used in for loops to iterate over an iterable object. Example: for item in list: 📂 Full Categorization: Logic & Truth: and, or, not, True, False, None Flow Control: if, elif, else, for, while, break, continue, pass Functions & Classes: def, return, lambda, class, yield Exception Handling: try, except, finally, raise, assert Structure & Scope: import, from, as, with, global, nonlocal, del, in, is 💡✨Pro-Tip for Beginners: You don't need to memorize these all at once! As you build projects, you’ll find yourself using if, for, and def 90% of the time. The others, like nonlocal or yield, are your "level-up" tools for more advanced logic. Which keyword gave you the most trouble when you first started learning? Let’s discuss in the comments! 👇 #PythonProgramming #CodingTips #DataScience #SoftwareEngineering #LearnToCode #PythonKeywords #TechEducation #Programming101

#Types of Errors: Errors are mistakes in a program that cause it to behave incorrectly or stop execution. The main types of errors are: 1. Syntax Errors: *Occur when the rules (syntax) programming language are violated. * Detected before program execution. Program will not run until they are fixed. #Example (Python): Copy code Python print("Hello") 2. Runtime Errors: *Occur while the program is running. *Cause the program to stop suddenly. #Examples: Division by zero Accessing an invalid index Copy code Python x = 10 / 0 3.Logical Errors: *Program runs without crashing, but the output is wrong. *Caused by incorrect logic or wrong formula. #Example: Copy code Python a = 5 b = 3 print(a - b) # Instead of addition 4.Semantic Errors: *Syntax is correct, but the meaning is incorrect. *Similar to logical errors. #Example: Using wrong variable or condition in a calculation. 5.ValueError: ValueError is a type of runtime error in Python.It occurs when a function receives an argument of the correct data type, but the value is invalid. #Example: Python x = int("abc") 6.Name error: NameError occurs when a variable or function name is used without being defined. #Example: Python print(x) 7.Type error: A Type Error occurs when an operation or function is applied to a value of an incorrect data type. #Example: Python a = "10" b = 5 print(a + b) 8.Indentation error: An Indentation Error occurs in Python when the spaces or tabs at the beginning of a line are not used correctly.Python uses indentation to define blocks of code, so incorrect indentation causes an error. #Example: if True: print("Hello") 9.Index error: IndexError occurs when a program tries to access an index that is outside the valid range. #Example: Copy code Python nums = [10, 20, 30] print(nums[5]).

If you’ve ever thought, “Why is Python behaving weird?” Here’s the honest answer 👇 You’re using the wrong data type. Python doesn’t guess what you mean. It only works with what you give. Here’s how to use data types practically: 1️⃣ Use int and float correctly If you’re doing math, convert inputs using int() or float() User input is always a string. 2️⃣ Use str only for display If it looks like a number but is inside quotes, Python treats it as text. 3️⃣ Use list when values grow or change Marks, tasks, names, never store these separately. 4️⃣ Use bool for decisions Conditions don’t work without True / False thinking. Most beginner bugs aren’t logic problems. They’re type problems. 👉 Save this before writing your next program.

Day 461: 8/1/2026 Why Python Strings Are Immutable? Python strings cannot be modified after creation. At first glance, this feels restrictive — but immutability is a deliberate design choice with important performance and correctness benefits. Let’s break down why Python does this. ⚙️ 1. Immutability Enables Safe Hashing Strings are commonly used as: --> dictionary keys --> set elements --> For this to work reliably, their hash value must never change. If strings were mutable: --> changing a string would change its hash --> dictionary lookups would break --> internal hash tables would become inconsistent By making strings immutable: --> the hash can be computed once --> cached inside the object --> reused safely for O(1) lookups This is a foundational guarantee for Python’s data structures. 🔐 2. Immutability Makes Strings Thread-Safe Immutable objects: --> cannot be modified --> can be shared freely across threads --> require no locks or synchronization This simplifies Python’s memory model and avoids subtle concurrency bugs. Even in multi-threaded environments, the same string object can be reused safely without defensive copying. 🚀 3. Enables Memory Reuse and Optimizations Because strings never change, Python can: --> reuse string objects internally --> safely share references --> avoid defensive copies Example: --> multiple variables can point to the same string --> no risk that one modification affects another This reduces: --> memory usage --> allocation overhead --> unnecessary copying 🧠 4. Predictable Performance Characteristics Immutability allows Python to store: --> string length --> hash value --> directly inside the object. As a result: --> len(s) is O(1) --> hashing is fast after the first computation --> slicing and iteration don’t need recomputation --> This predictability improves performance across many operations. Stay tuned for more AI insights! 😊 #Python #Programming #Performance #MemoryManagement

Python offers a variety of built-in functions that can enhance your coding efficiency. Here are 10 useful ones: 1. len() → counts items in an iterable. 2. zip() → combines multiple iterables into a single iterable of tuples. 3. map() → applies a specified function to each item in an iterable. 4. filter() → filters values based on a specified condition. 5. any() → checks if any element in an iterable is True. 6. all() → checks if all elements in an iterable are True. 7. sum() → adds up all elements in an iterable. 8. sorted() → sorts data in ascending order. 9. enumerate() → provides both the index and value of items in an iterable. 10. range() → generates a sequence of numbers. These functions can significantly streamline your coding process and improve readability.

Most Python learners follow this path ⬇️ Tutorial → Notes → Another tutorial → Stuck Here’s the path I’d take instead in 2026 🐍 ❌ Skip this • Watching hours of videos • Collecting resources • Chasing “advanced” topics ✅ Do this instead • Pick one real problem • Write ugly code • Break it • Fix it • Repeat What I’d focus on daily: → Reading errors → Refactoring old code → Writing small scripts that save time → Explaining my code in plain English Projects I’d build first: • automation scripts • API connectors • data cleaners • simple tools people actually use Career rule: Python alone is common. Python + impact is rare. Final truth: Busy ≠ skilled. Builders win. 🔖 Save this. Build, don’t binge.

PEP-800: typing.disjoint_base PEP: https://lnkd.in/eHzws5n6 Discussion: https://lnkd.in/ewrrUSKY Implementation in typing_extensions: https://lnkd.in/e-pZED3M In python, you can inherit from several classes (and generally with the __mro_entries__ method, yes). Which is sometimes quite convenient if used without fanaticism. But the problem is that not all sets of classes are suitable for multiple inheritance. For example: >>> class What(str, int): ... Traceback (most recent call last): TypeError: multiple bases have instance lay-out conflict Why this happens, you can read here (https://lnkd.in/eNd2Mxhc). But, it is important for us to know that CPython has the concept of "solid base", which is considered for all created classes here. (https://lnkd.in/e3vZ_s_9). In short, CPython should be able to build a proper memory layout for all descendant classes. For example, all int descendants should be stored in memory like this: Otherwise, the basic int logic will stop working. But strs are arranged differently. Details (https://lnkd.in/eMUeHaGT ) from the ashes about "solid bases". Previously, this code was used in some typecheckers. After all, they thought that a type subclass of int and str could exist. And now they will know that this is impossible at the definition level and throw out the right mistake. Excellent PEP, the type system has become a little better. Discussion: Did you know about solid and disjoint bases in python? Have you shot yourself in the foot like that?

Opening the Python prompt does not feel the same anymore. In the new Python version, the default interactive shell now highlights syntax out of the box so your code is easier to read and you spot mistakes faster. Why this matters: clarity reduces cognitive load. When the structure stands out and common typos are obvious, you move quicker and debug less. Here is what changed in the REPL: - Built-in syntax highlighting using standard ANSI 4-bit VGA colors, designed to work across virtually any terminal. - Import auto-completion. Type "import co" then press Tab to see modules starting with co. Or try "from concurrent import i" to reveal related submodules. - An experimental theme API: call _colorize.set_theme() in interactive mode or via PYTHONSTARTUP to customize colors. This is experimental and may change. Worried it could clash with your terminal theme? You can turn colors off via an environment variable. Hoping for attribute auto-complete on modules? That is not available yet, but this is already a big leap in day-to-day productivity. If you write Python in a terminal, teach it, or are just starting out, this upgrade is for you. It is a clear signal that Python is investing in developer experience from the very first line you type. At borntoDev, we help you turn updates like these into simple habits that boost your workflow, not just your toolset. Give it a try today, then tell us how it changes your flow. Follow borntoDev for practical, no-fluff dev upgrades. 🚀 #borntoDev #Python #DeveloperExperience #REPL #Productivity #SoftwareEngineering

🧠 Reverse a list | Python Problem Statement Given a list of n elements, reverse the list in place. 📌 Examples Input: n=5   myList = [20,1,10,5,59] Output: [59,5,10,1,20] 🔍 Approach Brute Force Approach 1. Traverse the list and store elements in a temporary list in reverse order 2. Copy the temporary list back to the original list ⏱️ Complexity Analysis Time Complexity: O(n) Space Complexity: O(n) ⚠️ This approach is easy to understand but uses extra space. Optimized Approach (Two-Pointer Technique) 1. Initialize two pointers: left = 0 and right = n − 1 2. Swap elements at left and right 3. Increment left and decrement right 4. Repeat until left < right ⏱️ Complexity Analysis Time Complexity: O(n) Space Complexity: O(1) 🧪 Python Code def reverse(self, arr: list, n: int) -> None: left = 0 right = n-1 while left < right: arr[left], arr[right] = arr[right], arr[left] left += 1 right -= 1     🧩 Edge Cases (Interview Tip) Empty list → no change Single-element list → already reversed List with duplicate elements → handled naturally #Python #DSA #CodingInterview #InterviewPreparation #CollegeStudents #CampusPlacements #LearningInPublic