NumPy Array Operations

Last Updated: 09 Nov 2025

NumPy performs them element-wise and super fast (no loops needed).

Hinglish Tip: “NumPy me loop likhne ki zarurat nahi, operations ek saath poore array par apply ho jaate hain — isse code fast aur clean hota hai.”

Basic Arithmetic Operations

These operations happen element-wise — NumPy matches each position in both arrays.

import numpy as np

a = np.array([10, 20, 30, 40])
b = np.array([1, 2, 3, 4])

print(a + b)   # Addition
print(a - b)   # Subtraction
print(a * b)   # Multiplication
print(a / b)   # Division
print(a % b)   # Modulus
print(a ** b)  # Power

Built-in Universal Functions (ufuncs)

NumPy provides special mathematical functions called ufuncs that work on entire arrays.

import numpy as np
a = np.array([1, 2, 3, 4])

print(np.add(a, 5))      # Add scalar to array
print(np.subtract(a, 2)) # Subtract scalar
print(np.multiply(a, 3)) # Multiply by scalar
print(np.divide(a, 2))   # Divide by scalar

Comparison Operations

We can compare arrays directly — the result is a boolean array.

x = np.array([10, 20, 30])
y = np.array([20, 20, 10])

print(x == y)
print(x > y)
print(x <= y)

Broadcasting Concept

  • Broadcasting lets NumPy operate on arrays of different shapes without copying data.
  • The smaller array is stretched along the missing dimension.

Example 1 – scalar + array

arr = np.array([1, 2, 3])
print(arr + 10)          # [11 12 13]

Example 2 – 2-D + 1-D

a = np.array([[1, 2, 3],
              [4, 5, 6]])
b = np.array([10, 20, 30])

print(a + b)
# [[11 22 33]
#  [14 25 36]]

Hinglish Tip: “Broadcasting ka matlab hai chhote array ko bada bana ke match kar dena — bina actual copy banaye!”

Filtering with Boolean Masks

Create a mask with a comparison, then use it to select elements.

data = np.array([12, 45, 67, 23, 89, 34, 91])

mask = data > 50
print("Mask:", mask)          # [False False  True False  True False  True]

print("Values > 50:", data[mask])
# [67 89 91]

# One-liner
print(data[data > 50])

Combine conditions

age = np.array([16, 22, 17, 65, 19])
mask = (age >= 18) & (age <= 60)
print("Eligible:", age[mask])

Warning: Use &, | (not and, or) inside masks.

Modify in-place

data[data > 80] = 80   # cap values
print(data)
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