Numpy Examples

2018-12-19

Numpy is a powerful Python library for numerical computing, providing efficient array operations, linear algebra, and random number generation. Below are practical examples for common tasks.

Basic Array Creation

import numpy as np

# Create a 4x3 array of zeros
np.zeros([4, 3])
# Create a 3x4 array of ones
np.ones([3, 4])
# Create a 3x2 array of random numbers (uniform [0, 1))
np.random.random([3, 2])

Matrix Properties and Operations

x = np.array([3, 5])
x.shape  # (2,)

y = np.array([[3, 5, 2], [2, 4, 2]])
y.shape  # (2, 3)

# Transpose
y.transpose()

# Add a number to all elements
z = y + 3

# Element-wise multiplication
z * y

# Matrix multiplication
np.matmul(x, z)

Basic Math Functions

x = np.array([3, 5])

np.exp(x)      # Exponential
np.sin(x)      # Sine
np.cos(x)      # Cosine
np.tanh(x)     # Hyperbolic tangent

Max, Min, Mean, and Norm

x = np.array([-10, 3, 5, 9, 21, 8])

np.max(x)      # Maximum value
np.min(x)      # Minimum value
x.max()        # Maximum value (method)
x.min()        # Minimum value (method)
x.mean()       # Mean value

# Euclidean norm (L2 norm)
np.linalg.norm(x)

Random Arrays and Numbers (Uniform Distribution)

np.random.random([2, 1])   # 2x1 array
np.random.random([5, 6])   # 5x6 array
np.random.random()         # Single random number

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