**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 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.