NumPy Array Operations

How to work with arrays in NumPy.

NumPy provides powerful operations to manipulate arrays efficiently. Here are the key operations:

Arithmetic Operations

NumPy allows element-wise arithmetic operations.

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

# Addition
print(arr1 + arr2)  # [5 7 9]

# Subtraction
print(arr1 - arr2)  # [-3 -3 -3]

# Multiplication
print(arr1 * arr2)  # [4 10 18]

# Division
print(arr1 / arr2)  # [0.25 0.4  0.5 ]

# Exponentiation
print(arr1 ** 2)  # [1 4 9]

Broadcasting

Element-wise Operations on Different Shapes. Broadcasting allows NumPy to perform operations on arrays of different shapes.

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

print(arr * scalar)

Output:

 [[ 2  4  6]
  [ 8 10 12]]

Aggregation Functions

Aggregation functions operate on arrays and return a single value. Ex. Sum, Mean, Min, Max, etc.

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

print(arr.sum())     # 15
print(arr.mean())    # 3.0
print(arr.min())     # 1
print(arr.max())     # 5
print(arr.std())     # Standard Deviation
print(arr.prod())    # Product of all elements

Matrix Operations

NumPy supports linear algebra operations like dot products and transposition.

  • Matrix multiplication (Dot Product)
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.array([[5, 6], [7, 8]])

print(np.dot(mat1, mat2))

Output:

 [[19 22]
  [43 50]]
  • Transpose of a matrix
print(mat1.T)

Output:

 [[1 3]
  [2 4]]

Logical Operations

You can compare elements using logical operators.

arr = np.array([10, 20, 30, 40])

print(arr > 20)  # [False False  True  True]
print(arr == 10) # [ True False False False]

# Filtering elements using conditions
print(arr[arr > 20])  # [30 40]

Stacking & Concatenation

You can join arrays vertically or horizontally.

  • Vertical Stack
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])


print(np.vstack((arr1, arr2)))

Output:

 [[1 2 3]
  [4 5 6]]
  • Horizontal Stack
print(np.hstack((arr1, arr2)))

Output:

 [1 2 3 4 5 6]
No questions available.