Numpy Array get datatype by cell?

import numpy as np
arr = np.array([1, "sd", 3.6])

You'll notice that the values in this array are not numerics and strings, they're just strings.

>>> arr
array(['1', 'sd', '3.6'], dtype='<U32')

You'll also note that they're not python strings. There is a reason for this but it isn't important here.

>>> type(arr[1])
<class 'numpy.str_'>

>>> type(arr[1]) == type(str)
False

You should not try to mix data types like you are doing. Use a list instead. The difference in data types that you have in your input list is lost when you turn it into an array. I note that you're calling an array element a 'cell' - it isn't, arrays don't work like spreadsheets.

That said, if you absolutely must do this:

arr = np.array([1, "sd", 3.6], dtype=object)

>>> arr
array([1, 'sd', 3.6], dtype=object)

This will keep all the array elements as python objects instead of using numpy dtypes.

>>> np.array([type(x) == str for x in arr])
array([False,  True, False])

Then you can test the type of each element accordingly.