Python’s expandability is one of the language’s biggest selling points. Python in its default state might not have the full level of functionality seen in other more specialized programming systems. However, it’s almost a given that 3rd party libraries can add any missing functionality. This is especially true for options related to advanced math and science.
Pandas and Numpy are particularly noteworthy Python libraries that can add a wealth of advanced mathematical functions and data types to the language’s toolkit. This additional functionality can range from multidimensional arrays to graphing. But advanced functionality tends to go in tandom with advancing difficulty. And people using Pandas may well notice that they’re suddenly seeing a “dataframe object is not callable” python error. However, it’s important to keep in mind that not every error with a complex library will be equally complex. And that’s the case for this particular error message. But to understand how to fix the error we first need to consider the larger context surrounding it.
What Does the Error Mean?
It’s best to begin by examining the keywords in an error message. In this case, we see that Python’s trying to call a function from an object. This isn’t a problem in and of itself. But success or failure rests on whether we’re looking at a callable object or not. And in this case, the error message is telling us that a Pandas DataFrame isn’t callable. Essentially, you can’t call a DataFrame as a function. This is a surprisingly easy error to make though, and callable error messages are relatively common.
How the Error Occurs
This error might still seem rather complex. Especially if you’re new to Pandas. The library’s new data types can be a little intimidating at first. And Python typerror messages are bound to spring up when learning about the new formats. But it’s important to remember that the new data types and containers still maintain Python’s standardized structure and syntax. And this is true for almost anything within Python. A series object, an int object, a float object, and more will all operate under the same syntactical rules.
The same goes for containers. A multidimensional array in Pandas, a list, and a dictionary all have a lot of unique differences. But they still operate using the same overall rule set. And, in particular, they all operate according to Python’s set syntax and lexicon. This can be most easily demonstrated with a small python code sample. Consider the following example where we work with a DataFrame object.
import pandas as pd
df = pd.DataFrame({‘Nut’: [‘Peanuts’, ‘Walnuts’, ‘Almonds’, ‘Chestnuts’], ‘Quantity’: [5, 10, 15, 20]})
print(df)
print(df(‘Quantity’).mean())
We begin by importing Pandas as pd. Next, we use that newly imported functionality to create a DataFrame called df. We’ll use a list of nuts and our quantity of each as an example to work with. This is simplistic by design, but it still illustrates a typical DataFrame structure. As you’ll see when the next line executes and prints the DataFrame to screen. At this point, everything looks fine in both the code and execution. But the script will fail at the second print statement and produce the “dataframe object is not callable” error.
The problem might not be apparent at first. We’re using print to display the results of running mean on the contents of df’s Quantity column. And we’re even using the same formatting for mean and Quantity. But that’s also the catch. Quantity is being used to indicate a position within the DataFrame rather than a function designed around the actual quantity of a container. This is in contrast to mean, which is a function that produces the mean values over a specific axis.
Again, this might seem a little complex at first glance. But it essentially just demonstrates that we’re dealing with a syntax error centered around the question of what is a callable object and what isn’t. We usually have a wide variety of functions associated with objects and DataTypes and it’s important to differentiate between them.
How To Fix the Error
So if syntax causes the problem, what changes can we make to fix it? Take a look at the following example.
import pandas as pd
df = pd.DataFrame({‘Nut’: [‘Peanuts’, ‘Walnuts’, ‘Almonds’, ‘Chestnuts’], ‘Quantity’: [5, 10, 15, 20]})
print(df)
print(df[‘Quantity’].mean())
The code is almost exactly the same as the earlier example which raised the Python TypeError. But look at the final print statement. We used square brackets instead of curly brackets for ‘Quantity’. This is the same syntax that we’d use with a Python dictionary collection to access a key-value pair. And it operates in a similar way here. With mean now having access to the contents of the Quantity column. With that change now in place, you can run the code error-free.
On a technical level, the fix simply comes down to changing the bracket style from curly to square. But the more important aspect comes from seeing why the need to do so is there in the first place. The Python language is heavily focused on formatting as a part of the larger lexicon. What appears to be a very minor formatting choice in Python can drastically change the underlying meaning of otherwise similar code. This is a widely known element of how tabs and spaces work in Python. But it’s also true for elements like brackets.