Profiling and debugging NumPy applications involve identifying performance bottlenecks, memory usage, and errors in code execution. Here are methods and tools for profiling and debugging NumPy code:

1. Profiling:

a. Time Profiling:

• Use Python’s built-in timeit module or time library to measure execution times of specific code segments.

b. Profiling Tools:

cProfile and profile modules help profile code to identify performance bottlenecks and function call statistics.

Debugging NumPy applications involves identifying and resolving errors or unexpected behavior in code. Here are some debugging techniques with examples:

1. Printing and Assertions:

Example: Printing Variable Values

 2. Error Handling:

Example: Using Try-Except Blocks