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refactoring a python data validation interactive shell

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refactoring is the process of restructuring existing computer code without changing its external behavior. it’s a critical practice for maintaining code quality and ensuring that your code is clean, efficient, and easy to understand. in this tutorial, we’ll look at how to refactor a simple python data validation interactive shell.
goals of refactoring
1. **improve readability**: make the code easier to read and understand.
2. **enhance maintainability**: make it easier to modify the code in the future.
3. **reduce duplication**: eliminate repetitive code.
4. **encapsulate functionality**: group related functionality.
example: initial version of a data validation interactive shell
let’s start with a simple interactive shell that validates a user's input. the initial version might look like this:
```python
def validate_age(input_age):
age = int(input_age)
if 0 = age = 120:
return true
else:
return false
return false
def validate_email(input_email):
if "@" in input_email and "." in input_email:
return true
return false
def main():
while true:
user_input = input("enter 'age' to validate age or 'email' to validate email (or 'quit' to exit): ")
if user_input == 'quit':
break
if user_input == 'age':
age_input = input("enter your age: ")
if validate_age(age_input):
print("valid age.")
else:
print("invalid age.")
elif user_input == 'email':
email_input = input("enter your email: ")
if validate_email(email_input):
print("valid email.")
else:
print("invalid email.")
else:
print("unknown command. please try again.")
if __name__ == "__main__":
main()
```
refactoring steps
1. **encapsulate validation logic**: create a generic validation fun ...
#Python #DataValidation #windows
refactoring
Python
data validation
interactive shell
code optimization
software development
programming best practices
user input validation
error handling
modular design
testing
code readability
maintainability
debugging
automation
goals of refactoring
1. **improve readability**: make the code easier to read and understand.
2. **enhance maintainability**: make it easier to modify the code in the future.
3. **reduce duplication**: eliminate repetitive code.
4. **encapsulate functionality**: group related functionality.
example: initial version of a data validation interactive shell
let’s start with a simple interactive shell that validates a user's input. the initial version might look like this:
```python
def validate_age(input_age):
age = int(input_age)
if 0 = age = 120:
return true
else:
return false
return false
def validate_email(input_email):
if "@" in input_email and "." in input_email:
return true
return false
def main():
while true:
user_input = input("enter 'age' to validate age or 'email' to validate email (or 'quit' to exit): ")
if user_input == 'quit':
break
if user_input == 'age':
age_input = input("enter your age: ")
if validate_age(age_input):
print("valid age.")
else:
print("invalid age.")
elif user_input == 'email':
email_input = input("enter your email: ")
if validate_email(email_input):
print("valid email.")
else:
print("invalid email.")
else:
print("unknown command. please try again.")
if __name__ == "__main__":
main()
```
refactoring steps
1. **encapsulate validation logic**: create a generic validation fun ...
#Python #DataValidation #windows
refactoring
Python
data validation
interactive shell
code optimization
software development
programming best practices
user input validation
error handling
modular design
testing
code readability
maintainability
debugging
automation