🐍📰 In this tutorial, you'll learn about the Python modulo operator (%). You'll look at the mathematical concepts behind the modulo operation and how the modulo operator is used with Python's numeric types. #python
Python Modulo Operator Tutorial
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🚀 Advanced Python Tips #2: Binary Parity Checking Tricks and tips you may not know, and that are rarely taught in Python courses. In Python, you can use &, |, and ^ for bitwise operations: AND, OR, and XOR. How does it work? If you write 17 & 1, Python performs a bitwise AND operation between the binary representations of 17 (10001) and 1 (00001). The AND operator returns 1 only if both bits are 1, and 0 if either of them is 0. So 10001 AND 00001 = 00001 For this reason, x & 1 == 0 checks whether a number is even, and this is faster than using x % 2 == 0. There are many other situations where binary operations can be useful, especially when using the bitwise shift operators '<<' and '>>', but that’s a topic for another Python tip. Using binary operators shows maturity and a solid understanding of computational logic. This can be very valuable in job interviews and LeetCode challenges. So tell me, have you ever used bitwise operators in a LeetCode problem?
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Python is an object-oriented language. You’ve probably heard this sentence many times. But what does it actually mean in simple terms? It means that all data items in Python are objects. In Python, similar data items are grouped under a type, also called a class. The terms type and class mean the same thing, so you can use them interchangeably. So it means that everything in Python is an object. Numbers, text, lists, dictionaries all of them are objects For example: 5 is an object of type int 3.14 is an object of type float "hello" is an object of type str [1, 2, 3] is an object of type list {"a": 1} is an object of type dict You can also get help for any type by typing help(typename) in the Python shell, where typename is a type or class in Python.
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Joining Sets in Python: Understanding Union and Update Combining sets in Python is an essential skill for managing collections of unique items. This process is crucial when you need to eliminate duplicates while merging data. The code above demonstrates two methods of achieving this: using the `union` method and the `update` method. Both serve to combine sets but have distinct effects on the sets involved. The `union` method creates a new set containing all unique elements from both sets. It's a non-destructive operation, meaning that the original sets remain unchanged. By using `set1.union(set2)` or the shorthand `set1 | set2`, you get a combined set that includes every unique item from both sets. This is particularly useful when you want to retain the original data for further operations. On the other hand, the `update` method modifies the original set in place. When you call `set1.update(set2)`, you're adding the unique elements from `set2` directly into `set1`. This can save memory and potentially improve performance for very large sets since it avoids creating a new set entirely. However, it's essential to remember that `set1` is permanently altered, which may or may not be desirable depending on your context. Understanding when to use each method becomes critical as you work with more complex datasets. You may encounter scenarios where you might prefer to keep original sets intact while merging them or when you'd like to simplify your data structure in place. Quick challenge: What would the output be if you apply `set1.update(set2)` first, followed by `print(set2)`? #WhatImReadingToday #Python #PythonProgramming #DataStructures #SetOperations #Programming
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🟢 DAY 8: Lists in Python 📋🐍 Ever tried storing multiple values in different variables? Python gives us a smarter way — Lists 💡 👀 Simple example in the image below 👇 🧠 What’s happening here? 👉 fruits is a list that stores multiple values together 👉 Lists use square brackets [ ] 👉 Each item is separated by a comma 👉 print(fruits) displays all items at once 📦 Output: ['apple', 'banana', 'mango'] ✨ Why Lists matter ✔ Manage data easily ✔ Used in real-world programs ✔ Foundation for advanced Python topics 🌱 Small basics → Powerful programs 🚀 💬 Comment LISTS if you’re learning Python 📌 Save for revision 👉 Follow for Day 9: List Methods
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Python Tip: sort() vs sorted() Sorting in Python is simple, but choosing the right method can save you headaches. sort() → Modifies the original list in-place → Only works on lists sorted() → Returns a new sorted list (original stays intact) → Works with any iterable Key Takeaways: → Use sort() when you want to reorder a list in-place. → Use sorted() when you need a new sorted iterable without changing the original. Which one do you reach for more often in your Python projects?
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Understanding the def function in Python The def function in Python is used to define a function (a structured block of code) that can be reused to perform specific tasks, improving code efficiency and readability. Syntax The basic syntax for defining a function involves the def keyword, a function name, parentheses for optional parameters, and a colon. The function body must be indented. def function_name(parameter1, parameter2): # Function body (indented code block) # Perform operations return result # Optional: returns a value The def function is essential for breaking down complex tasks into small, manageable pieces. Example: Calculate doughnut volume #Import math library import math # Define the function def doughnut_volume(r, R): vol_result = (2*math.pi*((R+r)/2))*(math.pi*(((R-r)/2)**2)) return vol_result # Call the function and store the result volume = doughnut_volume(10, 15) # Print the result print(f"The doughnut volume is: {volume}")
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