What are the differences between lambda functions and vectorized operations?

What are the differences between lambda functions and vectorized operations?

Lambda functions are small anonymous functions defined with the keyword 'lambda', while vectorized operations apply a function to an entire array or series at once.

Can you give me an example of each?

Sure! A lambda function in Python could be lambda x: x + 1, which adds 1 to a number. A vectorized operation using NumPy might look like numpy_array + 1, which adds 1 to every element in the array.

When should I use one over the other?

Use lambda functions for simple, one-off operations, and vectorized operations for performance and efficiency when working with large datasets.

Are there any performance differences?

Yes, vectorized operations are generally faster than lambda functions because they leverage optimized C libraries and avoid Python's loop overhead.