As alternative to changing type after floor division, you can provide an output array of the desired data type to np.floor (and to any other numpy ufunc). For example, imagine you want to convert the output to np.int32, then do the following:

import numpy as np
arr = np.random.rand(1, 10) * 10
out = np.empty_like(arr, dtype=np.int32)
np.floor(arr, out=out, casting='unsafe')

As the casting argument already indicates, you should know what you are doing when casting outputs into different types. However, in your case it is not really unsafe.

Although, I would not call np.floor in your case, because all values are greater than zero. Therefore, the simplest and probably fastest solution to your problem would be a direct casting to integer.

import numpy as np
arr = (np.random.rand(1, 10) * 10).astype(int)

np.ceil is one of the ufuncs. The general documentation for this category is:

op(X, out=None)
Apply op to X elementwise

Parameters
----------
X : array_like
    Input array.
out : array_like
    An array to store the output. Must be the same shape as `X`.

Returns
-------
r : array_like
    `r` will have the same shape as `X`; if out is provided, `r`
    will be equal to out.

out and r are different ways of getting the function output. The simplest is to just let the function return the value. But sometimes you may want give it the array out which it will fill. Controlling the dtype is one reason to use out. Another is to conserve memory by 'reusing' an array that already exists.

The array returned by np.ceil can also be cast to your desired type, e.g. np.ceil(x).astype('int').