I happen to come across the term:
np.array([[0. for _ in range(20)] for _ in range(50)])
It gives me all 0.0 for a matrix of 20 x 50.
However, I don't like this syntax. I am wondering if there alternative way of doing this? I want 0 to be 0.0 (as float).
Thanks
np.zeros((20, 50), dtype=np.float32) # or any other type; default: np.float64
Remark: no experienced numpy-user will use the approach in your example!
for _ in notation
_ is nothing special. Many people use that to mark a variable which will nevber be used. You can call it x too without changing anything! The rest is basic python-stuff (iteration syntax).
>>> a=np.zeros([5,8])
>>> a
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> type(a[1][1])
<type 'numpy.float64'>
>>>
You can see from the code that the default format is float64.
[[0.0 for i in range(3)] for j in range(5)]]. That's basic Python list comprehension. Don't be thrown by the use of_. Next try[[(i,j) for i in range(3)] for j in range(5)]]. The array part is just likenp.array([[1,2],[3,4]]).