Arrays in Python — array Module and NumPy

Arrays in Python

Python doesn't have a built-in array type like C or Java. Instead, you have three options depending on your needs:

list - general-purpose, mixed types, most common
array module - typed array, more memory-efficient than list
NumPy ndarray - powerful multi-dimensional arrays for math/science

Using Lists as Arrays

For most use cases, Python lists work perfectly as arrays.

Lists as Arrays

scores = [85, 92, 78, 95, 88]

# Access by index
print(scores[0])    # 85
print(scores[-1])   # 88

# Modify
scores[2] = 80
scores.append(91)

# Iterate
for score in scores:
    print(score)

# 2D array (list of lists)
matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]
print(matrix[1][2])  # 6

# Traverse 2D array
for row in matrix:
    for val in row:
        print(val, end=" ")
    print()

The array Module

The built-in array module provides typed arrays - all elements must be the same type. More memory-efficient than lists for large numeric data.

array Module

import array

# array(typecode, initializer)
# 'i' = signed int, 'f' = float, 'd' = double
nums = array.array('i', [1, 2, 3, 4, 5])
floats = array.array('f', [1.1, 2.2, 3.3])

print(nums[0])     # 1
print(nums[1:3])   # array('i', [2, 3])

nums.append(6)
nums.insert(0, 0)
nums.remove(3)
print(nums)        # array('i', [0, 1, 2, 4, 5, 6])

# Convert to list
as_list = nums.tolist()
print(as_list)     # [0, 1, 2, 4, 5, 6]

Type Code	C Type	Python Type	Size
`'b'`	signed char	int	1 byte
`'B'`	unsigned char	int	1 byte
`'i'`	signed int	int	2 bytes
`'I'`	unsigned int	int	2 bytes
`'l'`	signed long	int	4 bytes
`'f'`	float	float	4 bytes
`'d'`	double	float	8 bytes

NumPy Arrays

NumPy is the go-to library for numerical computing. Its ndarray supports multi-dimensional arrays and vectorized math operations.

NumPy Arrays

import numpy as np

# Create arrays
a = np.array([1, 2, 3, 4, 5])
b = np.zeros(5)           # [0. 0. 0. 0. 0.]
c = np.ones((2, 3))       # 2x3 matrix of ones
d = np.arange(0, 10, 2)   # [0 2 4 6 8]
e = np.linspace(0, 1, 5)  # [0.   0.25 0.5  0.75 1.  ]

# Shape and dimensions
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.shape)   # (2, 3)
print(matrix.ndim)    # 2
print(matrix.dtype)   # int64

# Vectorized operations (no loops needed!)
x = np.array([1, 2, 3, 4])
print(x * 2)          # [2 4 6 8]
print(x ** 2)         # [1 4 9 16]
print(x + x)          # [2 4 6 8]

# Slicing
print(matrix[0, :])   # [1 2 3] - first row
print(matrix[:, 1])   # [2 5]   - second column
print(matrix[1, 1:])  # [5 6]

# Aggregate functions
print(np.sum(x))      # 10
print(np.mean(x))     # 2.5
print(np.max(x))      # 4
print(np.std(x))      # standard deviation

Install NumPy with: pip install numpy