One error you may encounter when using NumPy is:

AttributeError: 'numpy.ndarray' object has no attribute 'index'

This error occurs when you attempt to use the **index()** function on a NumPy array, which does not have an index attribute available to use.

The following example shows how to address this error in practice.

**How to Reproduce the Error**

Suppose we have the following NumPy array:

import numpy as np #create NumPy array x = np.array([4, 7, 3, 1, 5, 9, 9, 15, 9, 18])

We can use the following syntax to find the minimum and maximum values in the array:

#find minimum and maximum values of array min_val = np.min(x) max_val = np.max(x) #print minimum and maximum values print(min_val, max_val) 1 18

Now suppose we attempt to find the index position of the minimum and maximum values in the array:

#attempt to print index position of minimum value x.index(min_val) AttributeError: 'numpy.ndarray' object has no attribute 'index'

We receive an error because we can’t apply an **index()** function to a NumPy array.

**How to Address the Error**

To find the index position of the minimum and maximum values in the NumPy array, we can use the NumPy **where()** function:

#find index position of minimum value np.where(x == min_val) (array([3]),) #find index position of maximum value np.where(x == max_val) (array([9]),)

From the output we can see:

- The minimum value in the array is located in index position
**3**. - The maximum value in the array is located in index position
**9**.

We can use this same general syntax to find the index position of any value in a NumPy array.

For example, we can use the following syntax to find which index positions are equal to the value 9 in the NumPy array:

#find index positions that are equal to the value 9 np.where(x == 9) (array([5, 6, 8]),)

From the output we can see that the values in index positions 5, 6, and 8 are all equal to **9**.

**Additional Resources**

The following tutorials explain how to fix other common errors in Python:

How to Fix KeyError in Pandas

How to Fix: ValueError: cannot convert float NaN to integer

How to Fix: ValueError: operands could not be broadcast together with shapes