Vectorize lorenz_curve and gini

[Smit-create]

• Replaced the python loops with vectorized code for enhancing the performance in lorenz_curve and gini_coefficient.

[coveralls]

Coverage: 92.988% (+0.01%) from 92.975% when pulling c448868 on Smit-create:opt into eeb0865 on QuantEcon:main.

[oyamad]

Maybe add tests?

[Smit-create]

Hmm, The tests are already present in https://github.com/QuantEcon/QuantEcon.py/blob/main/quantecon/tests/test_inequality.py so I thought to skip adding them.

[oyamad]

Is this adding new features or refactoring the code?

[Smit-create]

This is just refactoring the code and removing some for loops for performance optimizations.

[oyamad]

@Smit-create It is usually advised to use for loops (and avoid creation of intermediate arrays) in njited functions. Do you have a reason to go against that in this case?

[Smit-create]

It is usually advised to use for loops (and avoid creation of intermediate arrays) in njited functions.

I found this better in terms of time performance as this is vectorized version while the extra space complexity (creating new arrays) is still the same for both the functions -- O(n)

[mmcky]

@Smit-create thanks for opening this PR.

Would you mind to update the top level comment box with a description of the change and document the performance improvements you mention in this thread so it's clear what this change is about.

[Smit-create]

Thanks @mmcky. Done

[mmcky]

@Smit-create I haven't merged this as it isn't clear what level of performance improvement is being made. Is it 2% or 20% for example, it would be helpful to have timings between the different versions to make an assessment here. @oyamad makes a good point around njit so we need a clear reason to change to vectorized code.