# How to Fix: invalid value encountered in true_divide

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 in x by the values in y
np.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 in x by the values in y
np.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.