Even in python you can have control of what types of numbers are used under the hood with numpy arrays (and chances are if you are using floats in any quantity you want to be using numpy). I would be very surprised if array([1,2,3], dtype=uint8) == array([1,2,3], dtype=int16) gave [False, False, False]. In general I think == for numbers should give mathematical equivalence, with the understanding that comparing floats is highly likely to give false negatives unless you are extremely careful with what you are comparing.
More Fortran than C, but its the same for any language doing those sorts of array mathematics, they will be calling compiled versions of blas and lapack. Numpy builds up low level highly optimised compiled functions into a coherant python ecosystem. A numpy array is a C array with some metadata sure, but a python list is also just a C array of pointers to pyobjects.
Even in python you can have control of what types of numbers are used under the hood with numpy arrays (and chances are if you are using floats in any quantity you want to be using numpy). I would be very surprised if
array([1,2,3], dtype=uint8) == array([1,2,3], dtype=int16)gave[False, False, False]. In general I think==for numbers should give mathematical equivalence, with the understanding that comparing floats is highly likely to give false negatives unless you are extremely careful with what you are comparing.Numpys more or less a math wrapper for c isnt it?
More Fortran than C, but its the same for any language doing those sorts of array mathematics, they will be calling compiled versions of blas and lapack. Numpy builds up low level highly optimised compiled functions into a coherant python ecosystem. A numpy array is a C array with some metadata sure, but a python list is also just a C array of pointers to pyobjects.