The python programming language can sometimes produce errors that will get you cracking your brain, all in attempts to get answers.

**A little recap:**

In our previous post, we explained the “invalid value encountered in true_divide” error, which we highlighted that it’s an error associated with NumPy when a division operation between two zeros is carried out within multiple arrays.

Now that we have gone back down memory lane, we can talk about the reason why the “invalid value encountered in double_scalars” error comes up and how to get rid of it.

Let’s take a look at how the error looks in the first place:

It always comes in four lines, all stating the same thing. The question now is, is this is a NumPy warning? and what is a double scalar?

The answer to these questions is, YES, it is a NumPy warning and A value of type double is referred to as a double scalar. To distinguish it from double arrays, it is referred to as a scalar in NumPy.

**What causes invalid value encountered in double_scalars?**

- This warning is raised when a zero-size array is supplied to numpy.mean.
- This warning might also be caused by division by zero.

*In some other situations,*

- On an empty array, both min and argmin throw ValueError.
- randn accepts *arg and returns a single random number
- std,var return nan on an empty array.

This error is sometimes generated by NaNs or null values in data when using Numpy. If you are collecting data from a CSV file or maybe something similar and then working on the data utilizing numpy arrays, the fault might be with your data intake. You may test your code by feeding it a tiny batch of data with constants and seeing if you obtain the same response.

**There are a few things you can take to avoid this challenge:**

- In place of or in addition to a sophisticated mathematical function, employ a built-in function to avoid human mistake.
- The simplest method of preventing this error is to use a function that can handle that large number so that the execution does not fail.
- You can make a mathematical adjustment in the implementation so that the result does not exceed the value, preventing an error from occurring.

**Real-life example ***invalid value encountered in double_scalars* **error and how to fix it**

*invalid value encountered in double_scalars*

Assume we’re running np.mean([]) with Python 3.7 and the most recent version. The NumPy will generate the following results:

Numpy displays a warning that you are attempting to compute the mean of an empty slice while there’s an erroneous value, but then continues processing and returns nan. These warnings are normally useful in diagnosing problems with the data you’re supplying, but in certain circumstances, you’ll want to turn them off since the data is correct.

The np.errstate context manager, that only modifies the warning status while the content is active, is the safest approach to silence Numpy warnings. However, there are several issues

The problem with this is that it will quiet the RuntimeWarning: invalid value encountered in double scalars warning, but not the RuntimeWarning: Mean of empty slice warning. A better method is to utilize Python’s warnings module can be seen below; unfortunately, this comes at the risk of silencing a bigger range of warnings (what if a RuntimeWarning was generated here for a different reason?).

You may use the following command to turn all warnings to exceptions. This is especially harmful since it applies to every code that follows this call.

The code line above is causing a Divide by Zero Error. When the value or result is greater than the designated operation or data type within that program, an overflow error is thrown, indicating that the value has exceeded the given or stated limit value.

In a nutshell, solving this warning can either be done by using scipy.special.logsumexp like this

Or by completely disabling run time warnings, which is highly not recommended. Below is a sample implementation: