How to Fix TypeError: ‘numpy.ndarray’ object is not callable

Diagram

To fix the TypeError: ‘numpy.ndarray’ object is not callable error, you can use the “square brackets([ ])” to access array elements in the numpy array and avoid using the round(( )) brackets.

Python raises the TypeError: ‘numpy.ndarray’ object is not callable error when you “try to call a NumPy array as if it were a function”. This occurs if you use round brackets ( ) instead of square brackets [ ] to retrieve items from a list.

import numpy as np

arr = np.array([11, 21, 19, 46])

print(arr())

Output

TypeError: 'numpy.ndarray' object is not callable

Using the np.array() method, we created an array and used that array as a function, and called it using the double parenthesis.

The ndarrays are not functions, so you can’t call it the way you call a normal function.

How to fix TypeError: ‘numpy.ndarray’ object is not callable

import numpy as np

arr = np.array([11, 21, 19, 46])

print(arr[2])

Output

19

In this code, we access the third element of the array, which is 19. Array indexing starts with 0, so the third element will have a “2” index. And that’s how you resolve this kind of TypeError.

Numpy ndarrays can store and manipulate large arrays of homogeneous data in Python.

Python does not have arrays, but using a third-party library like “Numpy“ can use this type of data structure in our application.

Alternate solution

Use the appropriate method or attribute of the ndarray object to access or manipulate array data.

For example, ndim, size, and shape are methods you can apply on a ndarray object as it is.

import numpy as np

arr = np.array([11, 21, 19, 46])

print(arr.size)

Output

4

The array has four elements, and its size is 4. That’s why the size attribute returns 4 as output.

That’s it.

Further reading

TypeError: ‘numpy.float64’ object is not iterable

TypeError: string indices must be integers

TypeError: unhashable type: ‘numpy.ndarray’

Krunal

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.