Personally, I feel like the biggest problems with Python for math is the lack of a native vector datatype and our subsequent reliance on Numpy which really disrupts the elegance/terseness of working with vectors/matrices.

First, there's the constant inelegance/clutter/inefficiency of having to cast into and out of arrays and lists, even when doing basic list comprehension. R, Julia, and Matlab are all vector-based languages (I think), so you avoid having this casting as much.

Secondly, having a native vector type means you don't have to worry about the performance penalty of operating directly on arrays if an existing prebuilt method exists. Since the efficiency of Numpy comes from calling it's underlying C library, you're forced to memorize and use prebuilt Numpy functions rather then just use the more obvious and elegant array manipulations. For example rather than calculating the the cumulative sum like this:

  cumsum = reduce(lambda a, b: a+b, [1,2,3,4]) 

 We have do this: 

  cumsum = np.cumsum([1,2,3,4])

(There are better examples, but this is all I can think of right now).

And once you add something like Pytorch tensors on top of this, we now have an additionally layer of casting/redunduncy/memorization of prebuilt functions!