One warning you may encounter when using NumPy is:

RuntimeWarning: invalid value encountered in true_divide

This warning occurs when you attempt to divide by some invalid value (such as NaN, Inf, etc.) in a NumPy array.

It’s worth noting that this is only a **warning** and NumPy will simply return a nan value when you attempt to divide by an invalid value.

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

**How to Reproduce the Error**

Suppose we attempt to divide the values in one NumPy array by the values in another NumPy array:

import numpy as np #define NumPy arrays x = np.array([4, 5, 5, 7, 0]) y = np.array([2, 4, 6, 7, 0]) #divide the values inxby the values inynp.divide(x, y) array([2. , 1.25 , 0.8333, 1. , nan]) RuntimeWarning: invalid value encountered in true_divide

Notice that NumPy divides each value in x by the corresponding value in y, but a **RuntimeWarning** is produced.

This is because the last division operation performed was zero divided by zero, which resulted in a **nan** value.

**How to Address this Warning**

As mentioned earlier, this RuntimeWarning is only a **warning** and it didn’t prevent the code from being run.

However, if you’d like to suppress this type of warning then you can use the following syntax:

np.seterr(invalid='ignore')

This tells NumPy to hide any warning with some “invalid” message in it.

So, if we run the code again then we won’t receive any warning:

import numpy as np #define NumPy arrays x = np.array([4, 5, 5, 7, 0]) y = np.array([2, 4, 6, 7, 0]) #divide the values inxby the values inynp.divide(x, y) array([2. , 1.25 , 0.8333, 1. , nan])

A **nan** value is still returned for the last value in the output, but no warning message is displayed this time.

**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