Why is True printed with leading whitespace in numpy array

Question

I noticed that in NumPy 1.13.1 , when a dtype=np.bool_ array is printed the True values always have a leading whitespace.

>>> import numpy as np
>>> np.asarray([1, 0, 1, 1, 0], dtype=np.bool_)
array([ True, False,  True,  True, False], dtype=bool)
>>> #  ^             ^      ^
... # I would have expected: array([True, False, True, True, False], dtype=bool)
...
>>> str(np.asarray([0, 1, 1, 0], dtype=np.bool_))
'[False  True  True False]'
>>> #   ^     ^
... # Again I would have expected: '[False True True False]'
...
>>> repr(np.asarray([1, 1, 1, 0, 0], dtype=np.bool_))
'array([ True,  True,  True, False, False], dtype=bool)'
>>> #   ^      ^      ^
... # repr() does it too.

Is there any particular reason for formatting it like this?

Answer 1

This happens for float and bool arrays.

• For float arrays with negative values the extra space ensures alignment (which is nice for multidimensional arrays) leaving space for the negative sign.

   >>> np.array([-4, 1, 2, -3], dtype=np.float64).reshape(2, 2) array([[-4., 1.], [ 2., -3.]]) 

• For bool arrays with multiple values it ensures alignment between True and False , which are different lengths.

   >>> np.array([0, 1, 1, 0], dtype=np.bool_).reshape(2, 2) array([[False, True], [ True, False]], dtype=bool) 

There is no particular reason when it is not needed (ie all positive float values, single element bool array), and in this case it could be done without.

>>> np.array([4, 1, 2, 3], dtype=np.float64).reshape(2, 2)
array([[ 4.,  1.],
       [ 2.,  3.]])

There's an open PR #9130 on GitHub to make this print spacing more consistent when the additional spacing is not needed with some support, and so it may change in an upcoming version.