NumPy - Numerical Python
Installation
Creating Arrays
import numpy as np
a = np.array([1, 2, 3]) # 1D array
b = np.array([[1, 2], [3, 4]]) # 2D array
np.zeros((2, 3)) # All zeros
np.ones((3, 3)) # All ones
np.eye(3) # Identity matrix
np.arange(0, 10, 2) # [0, 2, 4, 6, 8]
np.linspace(0, 1, 5) # [0. , 0.25, 0.5 , 0.75, 1. ]
Array Operations
a + b # Element-wise addition
a * 2 # Scalar multiplication
a @ b # Matrix multiplication (or np.dot(a, b))
a.T # Transpose
a.mean() # Mean of all elements
a.sum() # Sum of all elements
a.shape # Shape of array
a.reshape((2, 3)) # Reshape
np.concatenate
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
np.concatenate((a, b), axis=0) # Vertical stack
np.concatenate((a.T, b.T), axis=1) # Horizontal stack
np.cumsum (Cumulative Sum)
a = np.array([1, 2, 3, 4])
np.cumsum(a) # [ 1 3 6 10 ]
b = np.array([[1, 2], [3, 4]])
np.cumsum(b, axis=0)
# [[1 2]
# [4 6]]
Indexing & Slicing
a[0] # First element
a[1:3] # Slice
a[:, 1] # All rows, 2nd column
a[::2] # Every other element
a[a > 2] # Boolean masking
Random
np.random.seed(42) # For reproducibility
np.random.rand(3, 3) # Uniform [0, 1)
np.random.randn(3, 3) # Normal distribution
np.random.randint(0, 10, (2, 3)) # Random ints in [0, 10)
Useful Utilities
np.unique(a) # Unique values
np.clip(a, 0, 1) # Clamp values to [0, 1]
np.sort(a) # Sort
np.argmax(a) # Index of max
np.isnan(a) # Check NaNs