Numpy Tutorial

Numpy is a powerful library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Installation

To install Numpy, you can use pip:

pip install numpy

Basic Usage

Here's a simple example of how to create a Numpy array and perform some operations on it:

import numpy as np

# Create a 1D array
arr = np.array([1, 2, 3, 4, 5])

# Print the array
print(arr)

# Create a 2D array
arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# Print the array
print(arr2)

# Perform some operations
sum_arr = np.sum(arr)
print("Sum of array:", sum_arr)

mean_arr = np.mean(arr2)
print("Mean of array:", mean_arr)

Advanced Features

Numpy offers a wide range of advanced features, including:

Broadcasting: Allows you to perform operations on arrays of different shapes.
Slicing: Extracting parts of arrays.
Indexing: Accessing elements of arrays.
Ufuncs: Universal functions that operate element-wise on arrays.

For more information, you can visit the Numpy documentation.

Example: Image Processing

Numpy is commonly used in image processing. Here's a simple example of loading an image using Numpy:

import numpy as np
from PIL import Image

# Load an image
img = Image.open('example.jpg')

# Convert the image to a Numpy array
img_array = np.array(img)

# Print the shape of the array
print("Image shape:", img_array.shape)

For more information on image processing with Numpy, you can check out our image processing tutorial.