NumPy Operations

Introduction

NumPy provides efficient vectorized operations for mathematical computations.

Mathematical Functions

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

# Trigonometric
np.sin(arr)
np.cos(arr)
np.tan(arr)

# Exponential and log
np.exp(arr)       # e^1, e^2, ...
np.log(arr)       # Natural log
np.log10(arr)     # Base 10 log
np.log2(arr)      # Base 2 log

# Power and roots
np.sqrt(arr)      # Square root
np.power(arr, 3)  # Cubed

Array Manipulation

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

# Transpose
arr.T

# Reshape
arr.reshape(4, 1)

# Flatten
arr.flatten()

# Concatenate
np.concatenate([arr, arr], axis=0)  # Vertical
np.concatenate([arr, arr], axis=1)  # Horizontal

Statistical Functions

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

np.min(arr)
np.max(arr)
np.mean(arr)
np.median(arr)
np.std(arr)      # Standard deviation
np.var(arr)      # Variance
np.percentile(arr, 75)  # 75th percentile

Practice Problems

1. Normalize array to 0-1 range
2. Calculate moving average
3. Find correlation between arrays
4. Implement matrix multiplication
5. Compute covariance matrix