Java Introduction Java is one of the most popular and widely used programming languages in the world. It is used for developing desktop applications, web applications, mobile apps (Android), games, cloud systems, enterprise software,...
🖥️ NumPy (Python) Editors To write and run NumPy code, you need a Python editor or IDE. You can choose from simple text editors, full IDEs, or online notebooks depending on your workflow. ✅...
🔢 NumPy Set Operations NumPy provides a set of functions to perform set-like operations on arrays. These operations are element-wise, unique, and sorted by default, similar to Python sets but optimized for arrays. ✅...
🌊 NumPy Hyperbolic Functions NumPy provides hyperbolic functions that operate element-wise on arrays. Hyperbolic functions are analogues of trigonometric functions but for a hyperbola instead of a circle. ✅ 1. Basic Hyperbolic Functions Function...
🌐 NumPy Trigonometric Functions NumPy provides vectorized trigonometric functions that operate element-wise on arrays. These functions are faster than Python’s built-in math module and support radians by default. ✅ 1. Basic Trigonometric Functions Function...
🔢 NumPy GCD (Greatest Common Divisor) NumPy provides a vectorized function np.gcd() to compute the greatest common divisor of integers element-wise. ✅ 1. Basic GCD of Two Numbers import numpy as np a =...
🔢 NumPy LCM (Lowest Common Multiple) NumPy provides a fast and vectorized function np.lcm() to compute the lowest common multiple of integers element-wise. ✅ 1. Basic LCM of Two Numbers import numpy as np...
➖ NumPy Differences (np.diff) The NumPy diff() function calculates the difference between consecutive elements in an array. It is useful for numerical differentiation, trend analysis, and signal processing. ✅ 1. Basic np.diff() on 1D...
✖️ NumPy Products NumPy provides functions to calculate the product of array elements, similar to summation but using multiplication. These operations are fast, vectorized, and can operate along specific axes in multi-dimensional arrays. ✅...
➕ NumPy Summations NumPy provides fast and efficient functions to calculate summations over arrays. These are vectorized operations, much faster than Python loops, and can operate along specific axes in multi-dimensional arrays. ✅ 1....
