When multiple conditional judgments are encountered, the if-elif-else chain can be simplified through dictionary mapping, match-case syntax, policy mode, early return, etc. 1. Use dictionaries to map conditions to corresponding operations to improve scalability; 2. Python 3.10 can use match-case structure to enhance readability; 3. Complex logic can be abstracted into policy patterns or function mappings, separating the main logic and branch processing; 4. Reducing nesting levels by returning in advance, making the code more concise and clear. These methods effectively improve code maintenance and flexibility.
When writing code, when encountering multiple conditional judgments, it is easy to write a long list of if-elif-else chains. Although this structure is intuitive, once there are too many branches, the code will become lengthy, difficult to read and difficult to maintain. There are actually many ways to simplify these "long conditional chains".
Use dictionary instead of if-else to judge
If each of your conditions is just used to return a fixed value or call a function, you can consider using a dictionary instead.
For example, you have a code like this:
if cmd == 'start':
start()
elif cmd == 'stop':
stop()
elif cmd == 'pause':
pause()
else:
print("Unknown command")It can be rewritten like this:
commands = {
'start': start,
'stop': stop,
'pause': pause,
}
cmd_func = commands.get(cmd)
if cmd_func:
cmd_func()
else:
print("Unknown command")This method is simpler and convenient to expand. If you want to add a new command, just add an item to the dictionary.
Using match-case (Python 3.10)
If you are using Python 3.10 or above, you can use match-case to replace multi-layer if-else , so that the syntax is clearer and the logic is easier to see.
For example, it turns out to be like this:
if status == "success":
handle_success()
elif status == "fail":
handle_fail()
elif status == "retry":
handle_retry()
else:
handle_unknown()Can be changed to:
match status:
case "success":
handle_success()
case "fail":
handle_fail()
case "retry":
handle_retry()
case _:
handle_unknown()Although the functions are the same, the structure is more regular and readable.
Abstract the policy pattern or function map
When each condition judgment corresponds to relatively complex logic, you can encapsulate each branch into an independent function or class, and then call it through a mapping relationship.
For example, suppose you are dealing with different types of user behavior:
if user_type == 'admin':
do_admin_tasks()
elif user_type == 'editor':
do_editor_tasks()
elif user_type == 'viewer':
do_viewer_tasks()You can abstract it into:
def handle_user(user_type):
handlers = {
'admin': do_admin_tasks,
'editor': do_editor_tasks,
'viewer': do_viewer_tasks,
}
handler = handlers.get(user_type, default_handler)
return handler()This not only makes the main logic refreshing, but also facilitates unit testing and reuse.
Return in advance or use guard clause
Sometimes we don’t have to use a lot of elifs, but we can reduce the nesting level by returning in advance.
for example:
def check_value(x):
if x < 0:
return "negative"
elif x == 0:
return "zero"
else:
return "positive"It can be rewritten as:
def check_value(x):
if x < 0:
return "negative"
if x == 0:
return "zero"
return "positive" Remove else and elif , which makes it clearer and easier to expand.
Basically these are the methods. The key to avoiding long if-else chains is not "can't write if", but selecting more appropriate expressions based on the scene, making the logic clearer and the structure more flexible.
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