What is the difference between Python lists and NumPy arrays?

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Python lists and NumPy arrays are both used to store collections of elements, but they differ in structure, functionality, and performance.

1. Data Type Consistency:

  • Python Lists

    • Can store elements of different data types (e.g., integers, strings, floats in the same list).

    • Example: [1, "apple", 3.5]

  • NumPy Arrays

    • Store elements of the same data type, which makes them more efficient for numerical operations.

    • Example: np.array([1, 2, 3]) → All integers

2. Performance:

  • Python Lists

    • Slower for large-scale numerical computations.

    • Not optimized for vectorized operations.

  • NumPy Arrays

    • Much faster and more memory-efficient for numerical tasks.

    • Uses underlying C code for performance.

3. Functionality:

  • Python Lists

    • General-purpose, flexible, and built-in to Python.

    • Limited mathematical operations.

  • NumPy Arrays

    • Support element-wise operations, broadcasting, linear algebra, statistics, etc.

    • Example: a + b adds arrays element-wise (not possible with lists without loops).

4. Memory Efficiency:

  • NumPy arrays use less memory by storing data in a compact, homogeneous format.

5. Use Case:

  • Use Python lists for mixed data types or general-purpose programming.

  • Use NumPy arrays for scientific computing, data analysis, and numerical operations.

In summary, Python lists are versatile and easy to use, while NumPy arrays are powerful tools for fast, efficient numerical computation.

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