Python offers two popular data structures for storing collections: built-in lists and NumPy arrays. Understanding their differences is crucial for efficient programming.

Key Differences:

Data Types

• Lists: Can mix types

  mixed_list = [1, "hello", 3.14, True]

• NumPy: Homogeneous

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

Performance

• Lists: Slower for numerical operations

  list_data = list(range(1000000))
  %time squared_list = [x**2 for x in list_data]
  # Output: CPU times: user 26.4 ms, sys: 7.96 ms, total: 34.3 ms

• NumPy: Optimized for numerical computations

  np_data = np.arange(1000000)
  %time squared_np = np_data**2
  # Output: CPU times: user 9 ms, sys: 830 μs, total: 9.83 ms

Functionality

• Lists: Basic operations

lst = [1, 2, 3]
lst.append(4)
lst.insert(0, 0)
print(lst)  # Output: [0, 1, 2, 3, 4]

• NumPy: Advanced mathematical operations and broadcasting

  arr = np.array([1, 2, 3])
  print(np.sin(arr))  # Output: [0.84147098 0.90929743 0.14112001]
  print(arr + np.array([10, 20, 30]))  # Output: [11 22 33]

Dimensionality

• Lists: Nesting for multi-dimensions

  nested_list = [[1, 2], [3, 4], [5, 6]]
  print(nested_list[1][0])  # Output: 3

• NumPy: Native support for multi-dimensional arrays

  matrix = np.array([[1, 2], [3, 4], [5, 6]])
  print(matrix.shape)  # Output: (3, 2)
  print(matrix[:, 1])  # Output: [2 4 6]

When to Use Python Lists:

• Storing mixed data types
• Frequently changing collection size
• Working with small to medium-sized data
• General-purpose programming

Example:

user_data = [
    {"name": "Alice", "age": 30, "active": True},
    {"name": "Bob", "age": 25, "active": False}
]

When to Use NumPy Arrays:

• Large numerical datasets
• Scientific computing and data analysis
• Need for advanced mathematical operations
• Working with multi-dimensional data

Example:

import numpy as np

data = np.array([[1, 2, 3], [4, 5, 6]])
mean = np.mean(data)
std_dev = np.std(data)
print(f"Mean: {mean}, Standard Deviation: {std_dev}")