Python KeyError is raised when we try to access a key from dict, which doesn’t exist. It’s one of the built-in exception classes and raised by many modules that work with dict or objects having key-value pairs.
Let’s look at a simple example where KeyError is raised by the program.
emp_dict = {'Name': 'Pankaj', 'ID': 1}
emp_id = emp_dict['ID']
print(emp_id)
emp_role = emp_dict['Role']
print(emp_role)
Output:
1
Traceback (most recent call last):
File "/Users/pankaj/Documents/PycharmProjects/hello-world/journaldev/errors/keyerror_examples.py", line 6, in <module>
emp_role = emp_dict['Role']
KeyError: 'Role'
We can handle the KeyError exception using the try-except block. Let’s handle the above KeyError exception.
emp_dict = {'Name': 'Pankaj', 'ID': 1}
try:
emp_id = emp_dict['ID']
print(emp_id)
emp_role = emp_dict['Role']
print(emp_role)
except KeyError as ke:
print('Key Not Found in Employee Dictionary:', ke)
Output:
1
Key Not Found in Employee Dictionary: 'Role'
We can avoid KeyError by using get() function to access the key value. If the key is missing, None is returned. We can also specify a default value to return when the key is missing.
emp_dict = {'Name': 'Pankaj', 'ID': 1}
emp_id = emp_dict.get('ID')
emp_role = emp_dict.get('Role')
emp_salary = emp_dict.get('Salary', 0)
print(f'Employee[ID:{emp_id}, Role:{emp_role}, Salary:{emp_salary}]')
Output: Employee[ID:1, Role:None, Salary:0]
There are a few functions in Pandas DataFrame that raises KeyError exception.
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