It's likely because there a built-in functions with the same name, abs. The same is true for np.amax, np.amin and np.round_.
The aliases for the NumPy functions abs, min, max and round are only defined in the top-level package.
So np.abs and np.absolute are completely identical. It doesn't matter which one you use.
There are several advantages to the short names: They are shorter and they are known to Python programmers because the names are identical to the built-in Python functions. So end-users have it easier (less to type, less to remember).
But there are reasons to have different names too: NumPy (or more generally 3rd party packages) sometimes need the Python functions abs, min, etc. So inside the package they define functions with a different name so you can still access the Python functions - and just in the top-level of the package you expose the "shortcuts". Note: Different names are not the only available option in that case: One could work around that with the Python module builtins to access the built-in functions if one shadowed a built-in name.
It might also be the case (but that's pure speculation on my part) that they originally only included the long-named functions absolute (and so on) and only added the short aliases later. Being a large and well-used library the NumPy developers don't remove or deprecate stuff lightly. So they may just keep the long names around because it could break old code/scripts if they would remove them.
Videos
It's likely because there a built-in functions with the same name, abs. The same is true for np.amax, np.amin and np.round_.
The aliases for the NumPy functions abs, min, max and round are only defined in the top-level package.
So np.abs and np.absolute are completely identical. It doesn't matter which one you use.
There are several advantages to the short names: They are shorter and they are known to Python programmers because the names are identical to the built-in Python functions. So end-users have it easier (less to type, less to remember).
But there are reasons to have different names too: NumPy (or more generally 3rd party packages) sometimes need the Python functions abs, min, etc. So inside the package they define functions with a different name so you can still access the Python functions - and just in the top-level of the package you expose the "shortcuts". Note: Different names are not the only available option in that case: One could work around that with the Python module builtins to access the built-in functions if one shadowed a built-in name.
It might also be the case (but that's pure speculation on my part) that they originally only included the long-named functions absolute (and so on) and only added the short aliases later. Being a large and well-used library the NumPy developers don't remove or deprecate stuff lightly. So they may just keep the long names around because it could break old code/scripts if they would remove them.
There also is Python's built-in abs(), but really all those functions are doing the same thing. They're even exactly equally fast! (This is not the case for other functions, like max().)
Code to reproduce the plot:
import numpy as np
import perfplot
def np_absolute(x):
return np.absolute(x)
def np_abs(x):
return np.abs(x)
def builtin_abs(x):
return abs(x)
b = perfplot.bench(
setup=np.random.rand,
kernels=[np_abs, np_absolute, builtin_abs],
n_range=[2 ** k for k in range(25)],
xlabel="len(data)",
)
b.save("out.png")
b.show()