Python is also an embarrassingly bad language for numerics. It comes without support for different floating point types does not have an n-D Array data type and is extremely slow.

At the same time it is an absolute necessity to know if you are doing numerics. What this shows, at least to me, is that it is "good enough" and that the million integrations, examples and pieces of documentation matter more than whether the peculiarities of the language work in favor of its given use case, as long as the shortcomings can be mostly addressed.

Native python is hopeless for numerics, which is why just about everyone just uses numpy, which solves all of these issues. Of course, a separate package. But the strength of python is that it can fairly seamlessly incorporate these kinds of packages that add core capabilities. Another important example: pytorch.