Checking the Data Type of an Array

• The NumPy array object has a property called dtype that returns the data type of the array:

• Example

• Get the data type of an array object:

import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr.dtype)

• Get the data type of an array containing strings:

import numpy as np
arr = np.array([‘apple’, ‘banana’, ‘cherry’])
print(arr.dtype)

Check Number of Dimensions?

• NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have.

• Example

• Check how many dimensions the arrays have:

• Program:

import numpy as np
a = np.array(42)
b = np.array([1, 2, 3, 4, 5])
c = np.array([[1, 2, 3], [4, 5, 6]])
d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

print(a.ndim)
print(b.ndim)
print(c.ndim)
print(d.ndim)

Difference Between List and Numpy

             A common beginner question is what is the real difference here. The answer is performance. Numpy data structures perform better in:

Size:- Numpy data structures take up less space

Performance:- they have a need for speed and are faster than lists

Functionality:- SciPy and Numpy have optimized functions such as linear algebra operations built in.