Numpy Basics

Introduction

NumPy is the fundamental package for scientific computing in Python, providing efficient array operations.

Creating Arrays

import numpy as np

# From list
arr = np.array([1, 2, 3, 4, 5])

# Range of values
arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]
arr = np.linspace(0, 1, 5)  # [0, 0.25, 0.5, 0.75, 1]

# Zeros and ones
zeros = np.zeros(5)
ones = np.ones((3, 3))

# Random arrays
rand = np.random.rand(3, 4)       # Uniform [0, 1)
randn = np.random.randn(3, 4)    # Normal distribution
randint = np.random.randint(1, 10, (3, 4))  # Integers

Array Attributes

arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr.shape)    # (2, 3)
print(arr.ndim)     # 2
print(arr.size)     # 6
print(arr.dtype)    # int64

Basic Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)      # [5, 7, 9]
print(a * 2)      # [2, 4, 6]
print(a ** 2)     # [1, 4, 9]
print(np.sum(a))  # 6
print(np.mean(a)) # 2.0

Practice Problems

1. Create array of first 20 even numbers
2. Reshape array to different dimensions
3. Calculate row-wise and column-wise sums
4. Find index of maximum value
5. Create identity matrix and array of random values