NumPy is one of the best ways to augment Python’s mathematical capabilities. However, advanced math can come with advanced problems as well. We can see this especially clearly in a runtimewarning: invalid value encountered in doublescalars error message.

## What is this error?

The error message question might seem overly complex at first glance. But we can understand it a lot easier by considering how Python and NumPy interact with each other on the most basic level. The invalid value refers to an incorrect value found within an array composed of double data types. Any single location within a NumPy array needs to be consistent with the other items. In cases of conflict, or a nan value, the output will be consistent with this error.

## NumPy and Data Types

Python only provides us with one type of any given data class. For example, an integer in one Python program is going to operate in the same way as an integer in every other program. And the same goes for any other data class in Python. NumPy expands on Python by providing dozens of extra data classes for us to work with. One of these is the array scalar. A scalar is basically just an object inside of an array. The main restriction found with scalars comes from the data types they can represent.

The scalar will always be of a type consistent with the rest of the array. For example, if your code passes a parameter to the array then it needs to be consistent with the array’s scalar. These scalars are generally analogous to what we find with types in other programming languages.

This is also where we see some of the complications which can creep into our code when using NumPy. We don’t typically see the double data type used for a number in Python. However, NumPy provides us with many different variations of the double data type to use in any given iterations problem or calculation. This is normally a good thing. In fact, it’s one of the main reasons people use NumPy.

The NumPy library gives us far more mathematical power than the standard Python runtime. However, in this case sample this extra power can come with some hiccups. And we can sometimes inadvertently modify NumPy variables into an unsupported format. The doublescalars error typically comes about from an array value size changing from a supported to an unsupported format.

## How do I fix this error?

Finding the answer solution to this bug error involves finding the point where the array value is being incorrectly defined. Any given function or logistic regression computation can be responsible for the problem if it’s modifying the default value group inside a NumPy array. However, the error is most often associated with one specific issue, exception.

If a positive or negative number is divided by zero in the denominator it can produce an incorrect association or convergence. Normally this would result in a zero division output error that’s easy to track down. But to import NumPy’s larger amount of data types means that we also get the nan values as a result of a division by zero. And this can, in turn, conflict with the double values found inside an array. Fixing that simply means tracking down the incorrect division model by zero and rewriting it to produce the intended result.