Python beginners usually use lists first, but Python also has an array module for compact typed numeric data.
An array stores values of one basic type, such as integers or floats, and can be useful when memory shape matters.
For scientific computing, NumPy arrays are usually the professional choice; for normal beginner programs, lists are often enough.
Python doesn't have a built-in array type like C or Java. Instead, you have three options depending on your needs:
For most use cases, Python lists work perfectly 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 built-in array module provides typed arrays - all elements must be the same type. More memory-efficient than lists for large numeric data.
| 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 |
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]NumPy is the go-to library for numerical computing. Its ndarray supports multi-dimensional arrays and vectorized math operations.
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 deviationTry this next
0 of 3 completed
Start with lists. Use array when you specifically need compact storage for same-type numeric values.
No. The built-in array module is simpler. NumPy arrays are much more powerful for numerical computing.
No. The array module stores one basic type at a time. Use a list for mixed values.
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