Suppose you attempt to divide the NumPy arrays elements using the ** divide()** method with invalid values such as

*0/0*,

*NaN*

*Infinity*,

*zero*, etc. you will encounter a

**RuntimeWarning: invalid value encountered in true_divide.**

In this article, we will take a look at what exactly is R**untimeWarning: invalid value encountered in true_divide** and how to resolve this error with examples.

## What is RuntimeWarning: invalid value encountered in true_divide?

If you are working with NumPy arrays and attempting to perform division of one NumPy array values over another NumPy array values and if you have invalid values which lead to **NaN**, **Infinity **then Python interpreter will raise **invalid value encountered in true_divide **warning during Runtime.

Note:This is an absolute warning message and not an error. The code will still get executed successfully with the warning message.

Let us try to reproduce this error with a simple example.

```
import numpy as np
# define 2 NumPy arrays
a = np.array([8, 2, 9, 0])
b = np.array([4, 2, 3, 0])
# divide both the numpy arrays
print(np.divide(a, b))
```

**Output**

```
main.py:8: RuntimeWarning: invalid value encountered in true_divide
print(np.divide(a, b))
[ 2. 1. 3. nan]
```

If you look at the above code, we have two NumPy arrays, and we are performing the division of both the array values using the NumPy ** divide()** method.

The NumPy ** divide()** method will return the quotient value after the division. Hence in our case following division takes place.

- 8/4 =2 (This would be treated as a valid operation)
- 2/2 =1 (This would be treated as a valid operation)
- 9/3 =3 (This would be treated as a valid operation)
- 0/0 =infinity (This would be treated as an invalid operation as 0 divisible by 0 would lead to
; hence we get the warning).`nan`

## How to fix RuntimeWarning: invalid value encountered in true_divide?

As it’s just a warning, the NumPy has a ** seterr()** method where we can suppress this warning.

#### Syntax seterr()

`np.seterr(invalid='ignore')`

The above method tells NumPy to suppress all the warning messages with the term that has “**invalid**.” Ensue that the ** seterr()** method is called before the

**method so that if there are any warnings it will ignore.**

`divide()`

Let’s modify the code and suppress the “invalid” messages by using the NumPy ** seterr()** method.

```
import numpy as np
# define 2 NumPy arrays
a = np.array([8, 2, 9, 0])
b = np.array([4, 2, 3, 0])
# ignore the invalid warning message
np.seterr(invalid='ignore')
# divide both the numpy arrays
print(np.divide(a, b))
```

**Output**

```
[ 2. 1. 3. nan]
```

The code will run without showing any warning and still provide the same output.

## Conclusion

If we perform an invalid division operation between the elements of NumPy arrays For an example:- 0/0 we encounter **RuntimeWarning: invalid value encountered in true_divide**

Since this is not an error and only a warning we can resolve this **RuntimeWarning** by suppressing it. This can be done using ** np.seterr(invalid='ignore')** method and it will ignore all the warnings that have “

**invalid**” term in it.