When you are doing physics simulations you should definitely use floats for everything. 0 is an integer constant in Python, and thus np.tile creates integer arrays; use 0.0 as the argument to np.tile to do floating point arrays; or preferably use the np.zeros(N) instead:

You can check the datatype of any array variable from its dtype member:

>>> np.tile(0, 10).dtype
dtype('int64')
>>> np.tile(0.0, 10).dtype
dtype('float64')
>>> np.zeros(10)
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])
>>> np.zeros(10).dtype
dtype('float64')

To get a zeroed array of float32 you'd need to give a float32 as the argument:

>>> np.tile(np.float32(0), 10)
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)

or, preferably, use zeros with a defined dtype:

>>> np.zeros(10, dtype='float32')
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)

Try this:

import numpy as np
a = np.empty([2, 2], dtype = float)
print(a)

Output:.

[[0. 0.]
 [0. 0.]]

Well, if you're reading the data in as a list, just do np.array(map(float, list_of_strings)) (or equivalently, use a list comprehension). (In Python 3, you'll need to call list on the map return value if you use map, since map returns an iterator now.)

However, if it's already a numpy array of strings, there's a better way. Use astype().

import numpy as np
x = np.array(['1.1', '2.2', '3.3'])
y = x.astype(np.float)

Using struct

Base on the struct module documentation the struct.unpack() function must have defined exact format for unpacking multiple values at once. So if you need to use struct module, then you either have to define the format using format as '<ff' or iterate over the array using struct.iter_unpack(format, buffer).

Using array

Another option is to use array module, which allows you to decode the binary data at once. You can change endianness using array.byteswap() method. The only restriction is that the whole array must have same type.

import array

arr = array.array('f', b'\x00\x00\x80?\x00\x00\x00@')

arr.tolist()
# [1.0, 2.0]

# change endianness
arr.byteswap()

arr.tolist()
# [4.600602988224807e-41, 8.96831017167883e-44]