NUMPY Tutorial

🐍 NumPy vs Python Lists

Python Lists and NumPy Arrays are both used to store sequences of data, but they serve different purposes and perform differently. Here's a comparison to help you understand when and why to use NumPy instead of plain Python lists.

🧠 Basic Syntax

# Python list
py_list = [1, 2, 3, 4]

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

📊 Performance Comparison

• Speed: NumPy arrays are much faster for large data operations due to vectorization.
• Memory: NumPy uses less memory than Python lists.
• Functionality: NumPy provides many advanced operations out-of-the-box (e.g., mean, std, reshape).

🚀 Example: Adding Two Lists vs NumPy Arrays

# Python List
list1 = [1, 2, 3]
list2 = [4, 5, 6]
result = [a + b for a, b in zip(list1, list2)]
print(result)  # [5, 7, 9]

# NumPy Array
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1 + arr2)  # [5 7 9]

🔍 Memory Size Comparison

import sys

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

print("Python list size:", sys.getsizeof(py_list))      # Larger
print("NumPy array size:", np_array.nbytes)             # Smaller

✅ Conclusion

While Python lists are versatile and great for general use, NumPy arrays are essential for numerical computing. They are faster, more memory efficient, and offer many built-in mathematical functions.