How to get type of multidimensional Numpy array elements in Python

Use the dtype attribute:

>>> import numpy>>> ar = numpy.array(range(10))>>> ar.dtypedtype('int32')

Explanation

Python lists are like arrays:

>>> [[1, 2], [3, 4]][[1, 2], [3, 4]]

But for analysis and scientific computing, we typically use the numpy package's arrays for high performance calculations:

>>> import numpy as np>>> np.array([[1, 2], [3, 4]])array([[1, 2],       [3, 4]])

If you're asking about inspecting the type of the data in the arrays, we can do that by using the index of the item of interest in the array (here I go sequentially deeper until I get to the deepest element):

>>> ar = np.array([[1, 2], [3, 4]])>>> type(ar)<type 'numpy.ndarray'>>>> type(ar[0])<type 'numpy.ndarray'>>>> type(ar[0][0])<type 'numpy.int32'>

We can also directly inspect the datatype by accessing the dtype attribute

>>> ar.dtypedtype('int32')

If the array is a string, for example, we learn how long the longest string is:

>>> ar = numpy.array([['apple', 'b'],['c', 'd']])>>> ararray([['apple', 'b'],       ['c', 'd']],       dtype='|S5')>>> ar = numpy.array([['apple', 'banana'],['c', 'd']])>>> ararray([['apple', 'banana'],       ['c', 'd']],       dtype='|S6')>>> ar.dtypedtype('S6')

I tend not to alias my imports so I have the consistency as seen here, (I usually do import numpy).

>>> ar.dtype.type<type 'numpy.string_'>>>> ar.dtype.type == numpy.string_True

But it is common to import numpy as np (that is, alias it):

>>> import numpy as np>>> ar.dtype.type == np.string_True

fruits = [['banana'], [1],  [11.12]]for first_array in range(len(fruits)):    for second_array in range(len(fruits[first_array])):        print('Type :', type(fruits[first_array][second_array]), 'data:', fruits[first_array][second_array])

That show the data type of each values.