Solving Numpy "TypeError: 'numpy.float64' Object Is Not Callable"


Sometimes, programming can be a bit tricky. Numpy is no exception, and when working with the numpy.float64 object, you may occasionally run across a difficult error: "TypeError: 'numpy.float64' object is not callable". This can be a confusing problem to work through, but this guide can help you out.

What Causes “TypeError: 'numpy.float64' Object Is Not Callable”

This issue occurs when a user tries to call a numpy.float64 object as if it were a function. This happens when the user treats the numpy.float64 object as if it were callable, meaning that it should return a value or execute code.

However, this isn’t possible as the numpy.float64 object is not callable — it’s simply a data type and not a function. As such, trying to call it as a function results in an error.

How To Solve “TypeError: 'numpy.float64' Object Is Not Callable”

This error can be easily solved. The solution is to simply remove the call to the numpy.float64 object. Because it is not a function, it doesn’t need to be called like one. The code should work without this call.

For example, here is a line of code that performs a call to the numpy.float64 object:

x = numpy.float64()

The call should be removed altogether, like this:

x = numpy.float64

By removing the parentheses, the code should no longer attempt to call the numpy.float64 object and the error should be solved.


Q: What is a numpy.float64 object?

A: A numpy.float64 object is a data type that is used to store floating-point data. It is a 64-bit data type and is typically used for scientific calculations.

Q: What does it mean when an object is not callable?

A: This means that it cannot be used as a function — it cannot be called to return a value or run a specific set of instructions. The numpy.float64 object is not callable and attempting to call it will result in an error.


The “TypeError: 'numpy.float64' object is not callable” error is an annoying but easily fixed issue that can occur when programming with Numpy. By taking the time to understand why the error occurs and how to solve it, you can ensure that your code runs without any problems.

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