If you’re using NumPy then it’s probably just a matter of time until you see a ‘numpy.ndarray’ object is not callable error typeerror. It’s a fairly common error that’s related to the basic syntax of python, when using a dataframe or other data type. Thankfully it’s also quite easy to fix this error within your code.
What is this error?
One of python’s most laudable elements comes from the fact that it rethinks a lot of the common assumptions found in other programming languages. This is a huge benefit once people are used to python’s syntax, dtype, value indexing, and other parameter elements of the data structure. However, it can also create some stumbling blocks when people treat python like other languages. In this instance, python’s typing system can cause some confusion, as some new users may question the way each argument and number element is input into columns, a ‘list’ object, and other callable object formats. The answer is learning the syntax, though we can also help users who are unfamiliar with the language fix these kind of error typeerror problems.
How does this happen?
The error message comes from mistyping an array built through the NumPy module. Basically, it’s telling us that we’re making a call to a NumPy array as if it were a function. But we can’t call a sequence of numbers in the same way we would a standard python function.
This error typically comes about in two different ways. The first explanation is the simplest. It’s quite common for the error to just arise from a simple typo. We’ve all had moments where we jot down a quick idea in a text file which eventually evolves into a full-blown python script. This can end up with many of the variables using quick off-the-cuff naming conventions that can become confusing later on.
We might name an integer, object and NumPy ndarray with similar naming schemes. These might be named test1, test2 and test3 because we’d intended the script to do little more than perform a simple calculation. Over time we might wind up improving on it until it becomes quite complex. But the heart of the code will still have variables that might be called incorrectly by simply typing one wrong character in a variable name.
Typos can appear in another context as well. We might have simply used round brackets instead of square. When indexing we need to use square brackets. And when we’re performing a function call we use round brackets.
The other explanation comes from the fact that python is both strongly and dynamically typed. We can create an integer in one line of code and then change it to a string in another. Whether or not a variable is callable or not can change fairly easily. A data structure can change to a string with a single statement. And this is often the underlying cause of the error message. At some point within the code we might have simply redefined a function with a NumPy array.
How do I fix it?
If the problem is due to inconsistent variable names then it’s a good idea to go through your code and rename everything for clarity. A method, object, function, variable and everything in between should have a name that reflects their functionality within our code. Comments and documentation are important. But on some level, our code should always speak for itself. We can also go through the code and look for any instances where we’re calling a NumPy array as a function. For example, simply change the “()” brackets to “”.