TypeError: 'DataFrame' object is not callable error [Solved]
Last updated: Apr 10, 2024
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# TypeError: 'DataFrame' object is not callable error [Solved]
The Python "TypeError: 'DataFrame' object is not callable" error occurs when
we use parentheses () instead of square brackets [] to access a column in a
DataFrame.
To solve the error, make sure to use square brackets when accessing the column.
Assume you have the following DataFrame object.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) print(df)
The code sample produces the following output.
id name salary 0 1 Alice 100 1 2 Bobby 200 2 3 Carl 300
If you try to access a specific column using parentheses (), you'd get an
error.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) # ⛔️ TypeError: 'DataFrame' object is not callable df('name') # 👈️ Using parentheses ()
We used parentheses () instead of square brackets [] when accessing the
column which caused the error.
# Use square when getting a subset of a DataFrame's rows and columns
Instead, use square brackets [] to select a subset of a DataFrame.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) print(df['name']) # ✅ Using square brackets []
Notice that we used square brackets [] when accessing the name column.
The code sample produces the following output.
0 Alice 1 Bobby 2 Carl Name: name, dtype: object
# Calling a method with the values in a column
If you need to call a method with the values in a column, use square brackets
[] when accessing the column and parentheses () to invoke the method.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) print(df['salary'].mean()) # 👉️ 200.0
We used square brackets [] to access the salary column and then called the
mean() method using parentheses.
DataFrame are accessed using square brackets [].Functions and methods are invoked using parentheses ().
# Slicing a DataFrame object
If you need to get a subset of a DataFrame object, use square brackets.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) print(df[0:2])
The code sample returns the first 2 rows of the DataFrame object.
id name salary 0 1 Alice 100 1 2 Bobby 200
# Accessing a specific column after slicing
You can also access a specific column after the slicing operation.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) print(df[0:2]['name'])
The code sample produces the following output.
0 Alice 1 Bobby Name: name, dtype: object
We only selected the first and second rows of the name column in the
DataFrame.
# Calling a method after selecting a subset of rows
If you need to call a method with a subset of the rows of a column, use parentheses.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } df = pd.DataFrame(data) print(df[0:2]['salary'].mean()) # 👉️ 150.0
We selected the first 2 rows of the salary column and then called the mean()
method on the values.
# Calculating the variance over an axis
If you need to calculate the variance over an axis, use the pandas.DataFrame.var() method.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'height': [1.61, 1.87, 1.49] } df = pd.DataFrame(data) print(df.var())
The code sample produces the following output.
id 1.000000 height 0.037733 dtype: float64
By default, the ddof (Delta Degrees of Freedom) argument is set to N - ddof
where N is the number of elements.
You can set ddof to 0 to normalize by N instead of N - 1.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'height': [1.61, 1.87, 1.49] } df = pd.DataFrame(data) # id 0.666667 # height 0.025156 # dtype: float64 print(df.var(ddof=0))
The code sample produces the following output.
id 0.666667 height 0.025156 dtype: float64
# Clashing names of a function and a DataFrame object
Another common cause of the error is having a function with the same name as the
variable that stores your DataFrame object.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } def df(): return 'bobbyhadz.com' df = pd.DataFrame(data) # ⛔️ TypeError: 'DataFrame' object is not callable print(df())
We initially named a function df and then declared a df variable, setting it
to a DataFrame object.
When we try to call the df variable, we actually end up trying to call the
DataFrame object which causes the error.
To solve the error, rename the function or the DataFrame object so that the
names don't clash.
import pandas as pd data = { 'id': [1, 2, 3], 'name': ['Alice', 'Bobby', 'Carl'], 'salary': [100, 200, 300], } def df(): return 'bobbyhadz.com' # 👇️ Renamed variable my_df = pd.DataFrame(data) print(df()) # 👉️ bobbyhadz.com
I renamed the variable, so it doesn't clash with the function anymore.
The df() line now calls the function as expected.
# Additional Resources
You can learn more about the related topics by checking out the following tutorials:
