Let's say,
def sample():
if a==1:
print(a)
else:
continue
for i in language:
a=i
sample()
I want to use this function in a loop, but the continue command gives me an error because there is no loop. What can I do?
Return a boolean from the function and based on the return value make continue or not because continue must be within a loop
continue keyword in python is only available in for or while loops. Also block defined variables like a are not available on the global scope.
I don't know what you want to achieve but assuming your code, you want to extract a condition into a function, something like this:
def condition(a):
return a == 1
def sample(a):
print(a)
for i in language:
a=i
if condition(a):
sample(a)
else:
continue
There are several best-practice patterns of exactly how to do this, depending on your needs.
0. Factor your code better
Before doing any of the below, stop and ask yourself if you can just do this instead:
def sample(a):
print(a)
for i in language:
if i != 1:
continue
sample(i)
This is so much better:
it's clearer to the reader (everything you need to understand the loop's control flow is entirely local to the loop - it's right there in the loop, we don't have to look anywhere else farther away like a function definition to know when or why or how the loop will do the next thing),
it's cleaner (less boilerplate code than any of the solutions below),
it's more efficient, technically (not that this should matter until you measure a performance problem, but this might appeal to you; going into a function and coming back out of it, plus somehow telling the loop outside the function to continue - that's more work to achieve the same thing), and
it's simpler (objectively: there is less code complected together - the loop behavior is no longer tied to the body of the sample function, for example).
But, if you must:
1. Add boolean return
The simplest change that works with your example is to return a boolean:
def sample(a):
if a==1:
print(a)
else:
return True
return False
for i in language:
if sample(i):
continue
However, don't just mindlessly always use True for continue - for each function, use the one that fits with the function. In fact, in well-factored code, the boolean return value will make sense without even knowing that you are using it in some loop to continue or not.
For example, if you have a function called check_if_valid, then the boolean return value just makes sense without any loops - it tells you if the input is valid - and at the same time, either of these loops is sensible depending on context:
for thing in thing_list:
if check_if_valid(thing):
continue
... # do something to fix the invalid things
for thing in thing_list:
if not check_if_valid(thing):
continue
... # do something only with valid things
2. Reuse existing return
If your function already returns something, or you can rethink your code so that returns make sense, then you can ask yourself: is there a good way to decide to continue based on that return value?
For example, let's say inside your sample function you were actually trying to do something like this:
def sample(a):
record = select_from_database(a)
if record.status == 1:
print(record)
else:
continue
Well then you can rewrite it like this:
def sample(a):
record = select_from_database(a)
if record.status == 1:
print(record)
return record
for i in language:
record = sample(a)
if record.status != 1:
continue
Of course in this simple example, it's cleaner to just not have the sample function, but I am trusting that your sample function is justifiably more complex.
3. Special "continue" return
If no existing return value makes sense, or you don't want to couple the loop to the return value of your function, the next simplest pattern is to create and return a special unique "sentinel" object instance:
_continue = object()
def sample(a):
if a==1:
print(a)
else:
return _continue
for i in language:
result = sample(i):
if result = _continue:
continue
(If this is part of a module's API, which is something that you are saying if you name it like sample instead of like _sample, then I would name the sentinel value continue_ rather than _continue... But I also would not make something like this part of an API unless I absolutely had to.)
(If you're using a type checker and it complains about returning an object instance conflicting with your normal return value, you can make a Continue class and return an instance of that instead of an instance of object(). Then the type hinting for the function return value can be a type union between your normal return type and the Continue type. If you have multiple control flow constructs in your code that you want to smuggle across function call lines like this.)
4. Wrap return value (and "monads")
Sometimes, if the type union thing isn't good enough for some reason, you may want to create a wrapper object, and have it store either your original return value, or indicate control flow. I only mention this option for completeness, without examples, because I think the previous options are better most of the time in Python. But if you take the time to learn about "Option types" and "maybe monads", it's kinda like that.
(Also, notice that in all of my examples, I fixed your backdoor argument passing through a global variable to be an explicit clearly passed argument. This makes the code easier to understand, predict, and verify for correctness - you might not see that yet but keep an eye out for implicit state passing making code harder to follow and keep correct as you grow as a developer, read more code by others, and deal with bugs.)
It is because the scope of the function doesn't know we are in a loop. You have to put the continue keyword inside the loop
continue keyword cannot be used inside a function. It must be inside the loop. There is a similar question here. Maybe you can do something like the following.
language = [1,1,1,2,3]
a = 1
def sample():
if a == 1:
print(a)
return False
else:
return True
for i in language:
if sample():
continue
else:
a = i
OR something like this:
language = [1,1,1,2,3]
a = 1
def gen(base):
for item in base:
if a == 1:
yield a
else:
continue
for i in gen(language):
a = i
print(a)
I'm not sure how to phrase this question exactly, but I'll give an example that explains what I am wondering about:
I have a function that is a permission check, let's call it A. And I call this function in another function, let's call B. If the permission check in A fails, I want function B to return. So what I would do is:
def permission_check_A(user):
# check if user has permission
return result_of_check
def another_function_B(user):
used_passed_permission_check = permission_check_A(user)
if not used_passed_permission_check:
return
# do other stuff if user passed
Now I'm wondering if it is possible to cause function B to return directly if the check in A fails. Something like:
def permission_check_A(user):
# check if user has permission
if not used_passed_permission_check:
# cause the calling function B to return
return
return True
def another_function_B(user):
permission_check_A(user)
# do other stuff if user passed
I guess in the example I am giving here, it would make sense to use a decorator for this kind of functionality. But if the security check happens somewhere in the middle of function B, this would not work.
My main motivation to do this, is that I don't want to repeat the "if, return" lines over and over again in every function that calls function A.
Also I'm wondering if would even be a good idea if this was possible because it could make the code less readable (a reader would have to check function A to realize that function B could be forced to return when A is called). What are your thoughts?
You could create an exception
class PermissionError(Exception):
pass
def permission_check_A(user):
# check if user has permission
if no_good():
raise PermissionError("No permission")
def another_function_B(user):
permission_check_A(user)
# do other stuff if user passed
Raising the exception stops execution of the current function. If the next higher function doesn't have a try/except block active, it goes to the next higher function until its caught or the whole program exits.
A function could call many other functions within a single try/except. Or a top level function could catch all of the errors from a large swath of code.
In cases where certain trivial conventions apply, you can achieve something like this with a decorator:
def check(p):
def make(f):
#functools.wraps(f)
def call(first,*a,**kw):
if p(first): return f(first,*a,**kw)
return call
return make
#check(permission_check_A)
def another_function_B(user): …
This of course will not work if someone calls another_function_B(user=…) or if another_function_B adds parameters before user.
I'm trying to repeat the tests N number of times (same tests that are collected).
Why: By doing do I want to see if the speed of the tests decreases or I can collect the "average time" of one parameter then pass the other parameter and get the "average time" again.
My understanding is to use def pytest_runtestloop() hook, however I have troubles with it.
Here is my code for the hook:
def pytest_runtestloop(session):
repeat = int(session.config.option.repeat)
assert isinstance(repeat, int), "Repeat must be an integer"
for i in range(repeat): ##UnusedVariable
session.config.pluginmanager.getplugin("main").pytest_runtestloop(session)
return True
The problem is that it the "setups" are run only the first time:
For example:
class TestSomething(object):
#classmethod
#pytest.fixture(scope = "class", autouse = True)
def setup(self):
//setup function
def test_something(self):
//test function
Here setup will be called during the first cycle only, and test_something would be called both times if I set session.config.option.repeat to 2
What am I doing wrong? Is there better approach?
It seems pytest-2.3.4 is keeping some state internally that keeps fixtures from running again. pytest_runtest_loop was not written with your use case in mind. You may file a "bug" issue about it and we can see what we can do to fix it. (I quickly looked but couldn't immediately see what was going wrong, requires more exploring).
How do I do this? Can I do this?
def aFunction(argument):
def testSomething():
if thisValue == 'whatItShouldBe':
return True
else:
return False
if argument == 'theRightValue': # this is actually a switch using elif's in my code
testSomething()
else:
return False
def aModuleEntryPoint():
if aFunction(theRightValue) == True:
doMoreStuff()
else:
complain()
aModuleEntryPoint()
aModuleEntryPoint() needs to first make sure that a condition is true before it starts doing things. Because of encapsulation, aModuleEntryPoint doesn't know how to check the condition, but aFunction() has a sub-function called testSomething() that does know how to check the condition. aModuleEntryPoint() calls aFunction(theRightValue).
Because theRightValue was passed to aFunction() as an argument, aFunction() calls testSomething(). testSomething() performs the logic test, and either returns True or False.
I need for aModuleEntryPoint() to know what testSomething() decided. I do not want aModuleEntryPoint() to know anything about how testSomething() came to its conclusion.
It would actually be an accomplishment to post my actual source while removing other functions and what-not, so I had to setup the general gist like this.
The only thing I see wrong right now is you need a return before testSomething() on line 9.
Perhaps a sub-function is not the right encapsulation tool for you here. You want to expose internal functionality to an external entity. Python classes provide a better mechanism for expressing this than sub-function. Having a class, you can expose whatever parts of internal functionality you want in a very controlled manner.
My first thought upon looking at your code is that it's a little too complicated. Why have aFunction at all? You could just write
def aModuleEntryPoint():
argument = ...
if argument in (theRightValue, theOtherRightValue, theOtherOtherRightValue)\
and testSomething():
doMoreStuff()
else:
complain()
This if clause will first check whether argument is one of the possible right values, and if it is, then it will proceed to call testSomething() and check the return value of that. Only if that return value is true will it call doMoreStuff(). If either of the tests fails (that's why I used and), it will complain().
At the moment, I'm doing stuff like the following, which is getting tedious:
run_once = 0
while 1:
if run_once == 0:
myFunction()
run_once = 1:
I'm guessing there is some more accepted way of handling this stuff?
What I'm looking for is having a function execute once, on demand. For example, at the press of a certain button. It is an interactive app which has a lot of user controlled switches. Having a junk variable for every switch, just for keeping track of whether it has been run or not, seemed kind of inefficient.
I would use a decorator on the function to handle keeping track of how many times it runs.
def run_once(f):
def wrapper(*args, **kwargs):
if not wrapper.has_run:
wrapper.has_run = True
return f(*args, **kwargs)
wrapper.has_run = False
return wrapper
#run_once
def my_function(foo, bar):
return foo+bar
Now my_function will only run once. Other calls to it will return None. Just add an else clause to the if if you want it to return something else. From your example, it doesn't need to return anything ever.
If you don't control the creation of the function, or the function needs to be used normally in other contexts, you can just apply the decorator manually as well.
action = run_once(my_function)
while 1:
if predicate:
action()
This will leave my_function available for other uses.
Finally, if you need to only run it once twice, then you can just do
action = run_once(my_function)
action() # run once the first time
action.has_run = False
action() # run once the second time
Another option is to set the func_code code object for your function to be a code object for a function that does nothing. This should be done at the end of your function body.
For example:
def run_once():
# Code for something you only want to execute once
run_once.func_code = (lambda:None).func_code
Here run_once.func_code = (lambda:None).func_code replaces your function's executable code with the code for lambda:None, so all subsequent calls to run_once() will do nothing.
This technique is less flexible than the decorator approach suggested in the accepted answer, but may be more concise if you only have one function you want to run once.
Run the function before the loop. Example:
myFunction()
while True:
# all the other code being executed in your loop
This is the obvious solution. If there's more than meets the eye, the solution may be a bit more complicated.
I'm assuming this is an action that you want to be performed at most one time, if some conditions are met. Since you won't always perform the action, you can't do it unconditionally outside the loop. Something like lazily retrieving some data (and caching it) if you get a request, but not retrieving it otherwise.
def do_something():
[x() for x in expensive_operations]
global action
action = lambda : None
action = do_something
while True:
# some sort of complex logic...
if foo:
action()
There are many ways to do what you want; however, do note that it is quite possible that —as described in the question— you don't have to call the function inside the loop.
If you insist in having the function call inside the loop, you can also do:
needs_to_run= expensive_function
while 1:
…
if needs_to_run: needs_to_run(); needs_to_run= None
…
I've thought of another—slightly unusual, but very effective—way to do this that doesn't require decorator functions or classes. Instead it just uses a mutable keyword argument, which ought to work in most versions of Python. Most of the time these are something to be avoided since normally you wouldn't want a default argument value to change from call-to-call—but that ability can be leveraged in this case and used as a cheap storage mechanism. Here's how that would work:
def my_function1(_has_run=[]):
if _has_run: return
print("my_function1 doing stuff")
_has_run.append(1)
def my_function2(_has_run=[]):
if _has_run: return
print("my_function2 doing some other stuff")
_has_run.append(1)
for i in range(10):
my_function1()
my_function2()
print('----')
my_function1(_has_run=[]) # Force it to run.
Output:
my_function1 doing stuff
my_function2 doing some other stuff
----
my_function1 doing stuff
This could be simplified a little further by doing what #gnibbler suggested in his answer and using an iterator (which were introduced in Python 2.2):
from itertools import count
def my_function3(_count=count()):
if next(_count): return
print("my_function3 doing something")
for i in range(10):
my_function3()
print('----')
my_function3(_count=count()) # Force it to run.
Output:
my_function3 doing something
----
my_function3 doing something
Here's an answer that doesn't involve reassignment of functions, yet still prevents the need for that ugly "is first" check.
__missing__ is supported by Python 2.5 and above.
def do_once_varname1():
print 'performing varname1'
return 'only done once for varname1'
def do_once_varname2():
print 'performing varname2'
return 'only done once for varname2'
class cdict(dict):
def __missing__(self,key):
val=self['do_once_'+key]()
self[key]=val
return val
cache_dict=cdict(do_once_varname1=do_once_varname1,do_once_varname2=do_once_varname2)
if __name__=='__main__':
print cache_dict['varname1'] # causes 2 prints
print cache_dict['varname2'] # causes 2 prints
print cache_dict['varname1'] # just 1 print
print cache_dict['varname2'] # just 1 print
Output:
performing varname1
only done once for varname1
performing varname2
only done once for varname2
only done once for varname1
only done once for varname2
One object-oriented approach and make your function a class, aka as a "functor", whose instances automatically keep track of whether they've been run or not when each instance is created.
Since your updated question indicates you may need many of them, I've updated my answer to deal with that by using a class factory pattern. This is a bit unusual, and it may have been down-voted for that reason (although we'll never know for sure because they never left a comment). It could also be done with a metaclass, but it's not much simpler.
def RunOnceFactory():
class RunOnceBase(object): # abstract base class
_shared_state = {} # shared state of all instances (borg pattern)
has_run = False
def __init__(self, *args, **kwargs):
self.__dict__ = self._shared_state
if not self.has_run:
self.stuff_done_once(*args, **kwargs)
self.has_run = True
return RunOnceBase
if __name__ == '__main__':
class MyFunction1(RunOnceFactory()):
def stuff_done_once(self, *args, **kwargs):
print("MyFunction1.stuff_done_once() called")
class MyFunction2(RunOnceFactory()):
def stuff_done_once(self, *args, **kwargs):
print("MyFunction2.stuff_done_once() called")
for _ in range(10):
MyFunction1() # will only call its stuff_done_once() method once
MyFunction2() # ditto
Output:
MyFunction1.stuff_done_once() called
MyFunction2.stuff_done_once() called
Note: You could make a function/class able to do stuff again by adding a reset() method to its subclass that reset the shared has_run attribute. It's also possible to pass regular and keyword arguments to the stuff_done_once() method when the functor is created and the method is called, if desired.
And, yes, it would be applicable given the information you added to your question.
Assuming there is some reason why myFunction() can't be called before the loop
from itertools import count
for i in count():
if i==0:
myFunction()
Here's an explicit way to code this up, where the state of which functions have been called is kept locally (so global state is avoided). I don't much like the non-explicit forms suggested in other answers: it's too surprising to see f() and for this not to mean that f() gets called.
This works by using dict.pop which looks up a key in a dict, removes the key from the dict, and takes a default value to use in case the key isn't found.
def do_nothing(*args, *kwargs):
pass
# A list of all the functions you want to run just once.
actions = [
my_function,
other_function
]
actions = dict((action, action) for action in actions)
while True:
if some_condition:
actions.pop(my_function, do_nothing)()
if some_other_condition:
actions.pop(other_function, do_nothing)()
I use cached_property decorator from functools to run just once and save the value. Example from the official documentation https://docs.python.org/3/library/functools.html
class DataSet:
def __init__(self, sequence_of_numbers):
self._data = tuple(sequence_of_numbers)
#cached_property
def stdev(self):
return statistics.stdev(self._data)
You can also use one of the standard library functools.lru_cache or functools.cache decorators in front of the function:
from functools import lru_cache
#lru_cache
def expensive_function():
return None
https://docs.python.org/3/library/functools.html
If I understand the updated question correctly, something like this should work
def function1():
print "function1 called"
def function2():
print "function2 called"
def function3():
print "function3 called"
called_functions = set()
while True:
n = raw_input("choose a function: 1,2 or 3 ")
func = {"1": function1,
"2": function2,
"3": function3}.get(n)
if func in called_functions:
print "That function has already been called"
else:
called_functions.add(func)
func()
You have all those 'junk variables' outside of your mainline while True loop. To make the code easier to read those variables can be brought inside the loop, right next to where they are used. You can also set up a variable naming convention for these program control switches. So for example:
# # _already_done checkpoint logic
try:
ran_this_user_request_already_done
except:
this_user_request()
ran_this_user_request_already_done = 1
Note that on the first execution of this code the variable ran_this_user_request_already_done is not defined until after this_user_request() is called.
A simple function you can reuse in many places in your code (based on the other answers here):
def firstrun(keyword, _keys=[]):
"""Returns True only the first time it's called with each keyword."""
if keyword in _keys:
return False
else:
_keys.append(keyword)
return True
or equivalently (if you like to rely on other libraries):
from collections import defaultdict
from itertools import count
def firstrun(keyword, _keys=defaultdict(count)):
"""Returns True only the first time it's called with each keyword."""
return not _keys[keyword].next()
Sample usage:
for i in range(20):
if firstrun('house'):
build_house() # runs only once
if firstrun(42): # True
print 'This will print.'
if firstrun(42): # False
print 'This will never print.'
I've taken a more flexible approach inspired by functools.partial function:
DO_ONCE_MEMORY = []
def do_once(id, func, *args, **kwargs):
if id not in DO_ONCE_MEMORY:
DO_ONCE_MEMORY.append(id)
return func(*args, **kwargs)
else:
return None
With this approach you are able to have more complex and explicit interactions:
do_once('foobar', print, "first try")
do_once('foo', print, "first try")
do_once('bar', print, "second try")
# first try
# second try
The exciting part about this approach it can be used anywhere and does not require factories - it's just a small memory tracker.
Depending on the situation, an alternative to the decorator could be the following:
from itertools import chain, repeat
func_iter = chain((myFunction,), repeat(lambda *args, **kwds: None))
while True:
next(func_iter)()
The idea is based on iterators, which yield the function once (or using repeat(muFunction, n) n-times), and then endlessly the lambda doing nothing.
The main advantage is that you don't need a decorator which sometimes complicates things, here everything happens in a single (to my mind) readable line. The disadvantage is that you have an ugly next in your code.
Performance wise there seems to be not much of a difference, on my machine both approaches have an overhead of around 130 ns.
If the condition check needs to happen only once you are in the loop, having a flag signaling that you have already run the function helps. In this case you used a counter, a boolean variable would work just as fine.
signal = False
count = 0
def callme():
print "I am being called"
while count < 2:
if signal == False :
callme()
signal = True
count +=1
I'm not sure that I understood your problem, but I think you can divide loop. On the part of the function and the part without it and save the two loops.