NumPy: Python’s Numerical Powerhouse
Introduction NumPy (Numerical Python) is a fast and powerful library used for numerical computing in Python. It provides support for multi-dimensional arrays, mathematical functions, and efficient data handling, making it a must-have for data science, machine learning, and scientific computing. Installing Numpy To install Numpy, simply run: pip install numpy Creating a NumPy Array import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr) Basic Operations with NumPy a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print(a + b) print(a * b) print(np.dot(a, b)) Reshaping and Slicing Arrays matrix = np.array([[1, 2, 3], [4, 5, 6]]) print(matrix.shape) print(matrix[0, :]) print(matrix[:, 1]) Why Use NumPy? ✅ Faster than Python lists ✅ Built-in mathematical functions ✅ Efficient memory management NumPy is essential for anyone working with data in Python! Try it out and unlock the full potential of numerical computing.
Introduction
NumPy (Numerical Python) is a fast and powerful library used for numerical computing in Python. It provides support for multi-dimensional arrays, mathematical functions, and efficient data handling, making it a must-have for data science, machine learning, and scientific computing.
Installing Numpy
To install Numpy, simply run:
pip install numpy
Creating a NumPy Array
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
Basic Operations with NumPy
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)
print(a * b)
print(np.dot(a, b))
Reshaping and Slicing Arrays
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.shape)
print(matrix[0, :])
print(matrix[:, 1])
Why Use NumPy?
✅ Faster than Python lists
✅ Built-in mathematical functions
✅ Efficient memory management
NumPy is essential for anyone working with data in Python! Try it out and unlock the full potential of numerical computing.
