NumPy adds a lot of extra math related functionality to Python. Sometimes this is a fairly straightforward addition. For example, the digitize function’s benefits in reinforcement learning are readily apparent. But the benefits of other elements and their relationship with native Python functionality can be more nebulous. A ‘numpy.float64’ object is not callable error is a perfect example of this often confusing relationship.
What is this error?
One of the larger issues with this error message is that it can stem from a wide variety of sources. What’s more, the fact that it’s associated with NumPy usually makes people assume some level of complexity which wouldn’t be there if it were a normal Python function. The second we type import numpy into cour code we often think that it’ll dominate every aspect of the new codebase. However, that’s not always the case. As we’ll see, NumPy is an important part of any Python project that makes use of it. But it’s still one piece within a larger whole.
On the surface, this error looks like something we’d often see with the standard Python libraries. If we received an object is not callable error within the standard Python system than we might assume we just made a call to something which doesn’t support it. For example, we can use a function to return items within an iterable sum. But we couldn’t do the same with a floating point number. The error essentially just means that we’re treating a 64 bit float value as if it were a function that can return data when called.
What causes it?
This simple explanation does lead into something more complex. The larger issue is why a float64 is being called. This can happen for a number of different reasons. However, the most likely scenario comes from an idiosyncrasy of Python. The programming language gives us the ability to give variables the same name as built-in functions. This obviously creates incompatibilities with the underlying data structure. Consider these code examples.
x = np.random.randn(500)
min = min(x)In this example, we’d call NumPy’s min function while also overwriting it with the returned element value in your dataframe column or multiple columns. Subsequent calls of min would instead call that number. This would return an object is not callable error. It’s generally a good idea to stick with Python’s recommended naming schemes from its style guide when choosing new column names for your dataset or module. This helps reduce the chances for this type of error. This is also important because it can easily creep into a method or function that we want to make public. Tracking down errors like this which come from a 3rd party library can sometimes prove tricky if we don’t know what to look for.
How do I fix it?
This might sound like an easy error to fix. However, it’s important to keep in mind that we should go beyond simply fixing the integer assignment that’s at the heart of things. We also need to track down any other conflicts that might arise from it elsewhere.
We should generally go through our code and look for anything which might overwrite a callable object with NumPy’s floating point value. Remember that this can come about in a number of ways. Missing values, functions with extra arguments, and elements within a numpy array could override a data type function with a returned floating point attribute. Even a returned nan value might cause this iterable problem. Slowly moving through our codebase to look for potentially overwritten functions can be a little tedious. But this highlights the importance of proper Python variable formatting.