Why Use NumPy Over Python Lists
NumPy vs List Explained

When working with numbers and large datasets in Python, you'll often need tools that are faster and more efficient than built-in data types like lists. That's where NumPy comes in.

NumPy (Numerical Python) is a powerful library for numerical and scientific computing. It offers a multidimensional array object, tools for performing mathematical operations on arrays. Let's see why it’s better than Python lists.

Key Differences Between Python Lists and NumPy Arrays

Feature	Python List	NumPy Array
Performance	Slower for large computations	Much faster due to C-implementation and vectorization
Memory Usage	Consumes more memory	Efficient memory storage
Functionality	Limited mathematical functions	Rich set of numerical operations
Data Type	Can store mixed data types	All elements must be of the same type
Multidimensional Support	Manual nesting required	Built-in support for multi-dimensional arrays

Example 1: Performance Comparison

import time
import numpy as np

# Using Python list
py_list = list(range(1_000_000))
start = time.time()
py_list = [x * 2 for x in py_list]
end = time.time()
print("List time:", end - start)

# Using NumPy array
np_array = np.arange(1_000_000)
start = time.time()
np_array = np_array * 2
end = time.time()
print("NumPy time:", end - start)

Output: NumPy is typically 10x to 100x faster!

List time: 0.05619931221008301
NumPy time: 0.0019538402557373047

Example 2: Memory Usage

import sys
import numpy as np

py_list = list(range(1000))
np_array = np.arange(1000)

print("List size in bytes:", sys.getsizeof(py_list))
print("NumPy array size in bytes:", np_array.nbytes)

NumPy arrays consume significantly less memory for large datasets.

Summary

• NumPy is faster, more memory-efficient, and better suited for numerical computations.
• Python lists are flexible but not optimized for heavy math or scientific use cases.
• For data science, machine learning, or scientific computing, always prefer NumPy arrays.