Why NumPy?

1Two ways to store numbers

A Python list is wonderfully flexible — it can hold different data types at once (an integer, a string, a float, a boolean). But that flexibility has a cost: doing maths on every element needs a loop, which gets slow on large datasets.

# A list can mix types...
mixed_list = [10, "hello", 3.14, True]

print(type(mixed_list[0]))   # <class 'int'>
print(type(mixed_list[1]))   # <class 'str'>

# ...but maths needs an explicit loop
numbers = [1, 2, 3, 4, 5]
squared = []
for n in numbers:
    squared.append(n ** 2)
print(squared)               # [1, 4, 9, 16, 25]

A NumPy array is homogeneous — every element is the same type. That uniformity lets NumPy store data compactly and run vectorized operations: maths on the whole array at once, with no Python loop. When you print one, it still sits inside square brackets [ ] just like a list — but with a tell-tale difference: the values are separated by spaces, not commas (e.g. [1 2 3 4] instead of [1, 2, 3, 4]).

import numpy as np

numbers_array = np.array([10, 20, 30, 40, 50])

# One operation on the WHOLE array at once
doubled = numbers_array * 2

print(doubled)               # vectorized!
print(numbers_array.dtype)

2See the difference

Press each button and watch how the work happens — one at a time for the list, all at once for the array.

3Recap