I'm reading a file and using try catch to catch cases where the data might not be the type that I'm looking for:
for w in text.readlines():
try:
new = float(w.split()[3].replace(',', '.'))
if new < temp and w.split()[1][3:5] == '12':
temp = new
date = w
except ValueError:
''
except IndexError:
''
This is really a hacky solution, not something that I would use, just something that I wrote because I needed access to some data quick. However, if I wanted to parse a very large file or stream, would such a solution (modified so that calls are made more intelligently etc) be viable, or should one opt for other solutions. My main question is regarding the performance of a try catch, is it an expensive operation, and are there guidelines for good uses of try-except? Hope this question is not too broad. In case it is, focus on my example.
In the particular example you give, you can easily test for and avoid the IndexError case, so I would do that test. The ValueError is harder to check for so you should catch the exception.
for w in text:
words = w.split()
if len(words) >= 4:
try:
new = float(words[3].replace(',', '.'))
if new < temp and words[1][3:5] == '12':
temp = new
date = w
except ValueError:
pass
Don't use readlines() on a file, you should just iterate over the file instead.
A good rule of thumb when it is equally easy to check before or to handle an exception, is to consider how often the exception would be thrown. If in normal cases the exception triggers very rarely then just handle it, on the other hand if it will be a common case then it may be better to avoid throwing it. Since I know nothing about your data, I don't know what proportion of lines will have fewer than 4 fields, so in this case the choice is pretty arbitrary.
I can't say much about it as i am not expert but it is alwyas better to use try except in any codeing languages where you seems that code may produce some error, and in python glossary
EAFP rule clearly says it.
Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C.
for w in text.readlines():
try:
new = float(w.split()[3].replace(',', '.'))
if new < temp and w.split()[1][3:5] == '12':
temp = new
date = w
except ValueError:
Do Something
except IndexError:
Do Something
except Exception: #Exception is a Base class of all exceptions if any exception that isn't catch will go in it,
Do Something
TLDR summary
I wrote a function navigateDict that does a safe navigation on a dict, similar to dict.get() but nested. It replaces code like
if 1 in data and 'i' in data[1] and 'a' in data[1]['i']:
print data[1]['i']['a']
else:
print "Not found"
with the roughly equivalent
found = navigateDict(data, 1, 'i', 'a')
if found is not None:
print found
else:
print "Not found"
Is anything similar to this already part of the standard library?
Is there a more idiomatic way to do the same thing?
Any response that requires typing any path component key more than once is probably a non-answer.
Additional details
The implementation is as follows:
# Allow fallback value other than None
def navigateDictEx(d, keys, fallback=None):
for key in keys:
if key in d:
d = d[key]
else:
return fallback
return d
def navigateDict(d, *keys):
return navigateDictEx(d, keys)
See the summary for example usage.
Pythonic or not, this function reduces repetition in a place where redundancy is a bad idea. For example, changing one path component in the example requires up to three distinct values to be modified as one in the original example, but only one in the modified example. Given my regular tendency to err, this is a big win.
Ultimately I'm asking this: Is there something in the standard library that does this, or am I going to need to find a place for it in my project's library?
If hits are expected to dominate misses
brionius correctly points out that catching KeyError will work:
try:
print data[1]['i']['a']
except KeyError:
print "Not found"
This might be the way I go; it's pretty terse and cuts the repetition. However, it does reflect an assumption that there will be more hits than misses. If there's a better way of assuming the opposite I'd like to know that, also.
One way to do this is as follows:
try:
print data[1]['i']['a']
except KeyError:
print "Not found!"
It's in line with the spirit of duck-typing. It may or may not be as fast, as I believe handling exceptions carries a certain amount of overhead, but it's certainly "safe".
a solution like this is cool
https://twitter.com/raymondh/status/343823801278140417
>>> from collections import defaultdict
>>> infinite_defaultdict = lambda: defaultdict(infinite_defaultdict)
>>> d = infinite_defaultdict()
>>> d['x']['y']['z'] = 10
>>> if d['x']['y']['z']: print d['x']['y']['z'] #better reflects that misses are common
Years late to the game, but for anyone stumbling upon this, there still does not seem to be a native, fluent way to safely navigate a Python dict.
Enter RestResponse:
"RestResponse aims to be a fluent python object for interfacing with RESTful JSON APIs"
This library includes a NoneProp object that allows for safely navigating (and building) JSON data structures.
>>> import RestResponse
>>> data = RestResponse.parse({})
>>> data.property.is_none
None
>>> bool(data.property.is_none)
False
>>> isinstance(data.property.is_none, RestResponse.NoneProp)
True
>>> data.property.is_none = None
>>> isinstance(data.property.is_none, RestResponse.NoneProp)
False
>>> print data.pretty_print()
{
"property": {
"is_none": null
}
}
From time to time in Python, I see the block:
try:
try_this(whatever)
except SomeException as exception:
#Handle exception
else:
return something
What is the reason for the try-except-else to exist?
I do not like that kind of programming, as it is using exceptions to perform flow control. However, if it is included in the language, there must be a good reason for it, isn't it?
It is my understanding that exceptions are not errors, and that they should only be used for exceptional conditions (e.g. I try to write a file into disk and there is no more space, or maybe I do not have permission), and not for flow control.
Normally I handle exceptions as:
something = some_default_value
try:
something = try_this(whatever)
except SomeException as exception:
#Handle exception
finally:
return something
Or if I really do not want to return anything if an exception happens, then:
try:
something = try_this(whatever)
return something
except SomeException as exception:
#Handle exception
"I do not know if it is out of ignorance, but I do not like that
kind of programming, as it is using exceptions to perform flow control."
In the Python world, using exceptions for flow control is common and normal.
Even the Python core developers use exceptions for flow-control and that style is heavily baked into the language (i.e. the iterator protocol uses StopIteration to signal loop termination).
In addition, the try-except-style is used to prevent the race-conditions inherent in some of the "look-before-you-leap" constructs. For example, testing os.path.exists results in information that may be out-of-date by the time you use it. Likewise, Queue.full returns information that may be stale. The try-except-else style will produce more reliable code in these cases.
"It my understanding that exceptions are not errors, they should only
be used for exceptional conditions"
In some other languages, that rule reflects their cultural norms as reflected in their libraries. The "rule" is also based in-part on performance considerations for those languages.
The Python cultural norm is somewhat different. In many cases, you must use exceptions for control-flow. Also, the use of exceptions in Python does not slow the surrounding code and calling code as it does in some compiled languages (i.e. CPython already implements code for exception checking at every step, regardless of whether you actually use exceptions or not).
In other words, your understanding that "exceptions are for the exceptional" is a rule that makes sense in some other languages, but not for Python.
"However, if it is included in the language itself, there must be a
good reason for it, isn't it?"
Besides helping to avoid race-conditions, exceptions are also very useful for pulling error-handling outside loops. This is a necessary optimization in interpreted languages which do not tend to have automatic loop invariant code motion.
Also, exceptions can simplify code quite a bit in common situations where the ability to handle an issue is far removed from where the issue arose. For example, it is common to have top level user-interface code calling code for business logic which in turn calls low-level routines. Situations arising in the low-level routines (such as duplicate records for unique keys in database accesses) can only be handled in top-level code (such as asking the user for a new key that doesn't conflict with existing keys). The use of exceptions for this kind of control-flow allows the mid-level routines to completely ignore the issue and be nicely decoupled from that aspect of flow-control.
There is a nice blog post on the indispensibility of exceptions here.
Also, see this Stack Overflow answer: Are exceptions really for exceptional errors?
"What is the reason for the try-except-else to exist?"
The else-clause itself is interesting. It runs when there is no exception but before the finally-clause. That is its primary purpose.
Without the else-clause, the only option to run additional code before finalization would be the clumsy practice of adding the code to the try-clause. That is clumsy because it risks
raising exceptions in code that wasn't intended to be protected by the try-block.
The use-case of running additional unprotected code prior to finalization doesn't arise very often. So, don't expect to see many examples in published code. It is somewhat rare.
Another use-case for the else-clause is to perform actions that must occur when no exception occurs and that do not occur when exceptions are handled. For example:
recip = float('Inf')
try:
recip = 1 / f(x)
except ZeroDivisionError:
logging.info('Infinite result')
else:
logging.info('Finite result')
Another example occurs in unittest runners:
try:
tests_run += 1
run_testcase(case)
except Exception:
tests_failed += 1
logging.exception('Failing test case: %r', case)
print('F', end='')
else:
logging.info('Successful test case: %r', case)
print('.', end='')
Lastly, the most common use of an else-clause in a try-block is for a bit of beautification (aligning the exceptional outcomes and non-exceptional outcomes at the same level of indentation). This use is always optional and isn't strictly necessary.
What is the reason for the try-except-else to exist?
A try block allows you to handle an expected error. The except block should only catch exceptions you are prepared to handle. If you handle an unexpected error, your code may do the wrong thing and hide bugs.
An else clause will execute if there were no errors, and by not executing that code in the try block, you avoid catching an unexpected error. Again, catching an unexpected error can hide bugs.
Example
For example:
try:
try_this(whatever)
except SomeException as the_exception:
handle(the_exception)
else:
return something
The "try, except" suite has two optional clauses, else and finally. So it's actually try-except-else-finally.
else will evaluate only if there is no exception from the try block. It allows us to simplify the more complicated code below:
no_error = None
try:
try_this(whatever)
no_error = True
except SomeException as the_exception:
handle(the_exception)
if no_error:
return something
so if we compare an else to the alternative (which might create bugs) we see that it reduces the lines of code and we can have a more readable, maintainable, and less buggy code-base.
finally
finally will execute no matter what, even if another line is being evaluated with a return statement.
Broken down with pseudo-code
It might help to break this down, in the smallest possible form that demonstrates all features, with comments. Assume this syntactically correct (but not runnable unless the names are defined) pseudo-code is in a function.
For example:
try:
try_this(whatever)
except SomeException as the_exception:
handle_SomeException(the_exception)
# Handle a instance of SomeException or a subclass of it.
except Exception as the_exception:
generic_handle(the_exception)
# Handle any other exception that inherits from Exception
# - doesn't include GeneratorExit, KeyboardInterrupt, SystemExit
# Avoid bare `except:`
else: # there was no exception whatsoever
return something()
# if no exception, the "something()" gets evaluated,
# but the return will not be executed due to the return in the
# finally block below.
finally:
# this block will execute no matter what, even if no exception,
# after "something" is eval'd but before that value is returned
# but even if there is an exception.
# a return here will hijack the return functionality. e.g.:
return True # hijacks the return in the else clause above
It is true that we could include the code in the else block in the try block instead, where it would run if there were no exceptions, but what if that code itself raises an exception of the kind we're catching? Leaving it in the try block would hide that bug.
We want to minimize lines of code in the try block to avoid catching exceptions we did not expect, under the principle that if our code fails, we want it to fail loudly. This is a best practice.
It is my understanding that exceptions are not errors
In Python, most exceptions are errors.
We can view the exception hierarchy by using pydoc. For example, in Python 2:
$ python -m pydoc exceptions
or Python 3:
$ python -m pydoc builtins
Will give us the hierarchy. We can see that most kinds of Exception are errors, although Python uses some of them for things like ending for loops (StopIteration). This is Python 3's hierarchy:
BaseException
Exception
ArithmeticError
FloatingPointError
OverflowError
ZeroDivisionError
AssertionError
AttributeError
BufferError
EOFError
ImportError
ModuleNotFoundError
LookupError
IndexError
KeyError
MemoryError
NameError
UnboundLocalError
OSError
BlockingIOError
ChildProcessError
ConnectionError
BrokenPipeError
ConnectionAbortedError
ConnectionRefusedError
ConnectionResetError
FileExistsError
FileNotFoundError
InterruptedError
IsADirectoryError
NotADirectoryError
PermissionError
ProcessLookupError
TimeoutError
ReferenceError
RuntimeError
NotImplementedError
RecursionError
StopAsyncIteration
StopIteration
SyntaxError
IndentationError
TabError
SystemError
TypeError
ValueError
UnicodeError
UnicodeDecodeError
UnicodeEncodeError
UnicodeTranslateError
Warning
BytesWarning
DeprecationWarning
FutureWarning
ImportWarning
PendingDeprecationWarning
ResourceWarning
RuntimeWarning
SyntaxWarning
UnicodeWarning
UserWarning
GeneratorExit
KeyboardInterrupt
SystemExit
A commenter asked:
Say you have a method which pings an external API and you want to handle the exception at a class outside the API wrapper, do you simply return e from the method under the except clause where e is the exception object?
No, you don't return the exception, just reraise it with a bare raise to preserve the stacktrace.
try:
try_this(whatever)
except SomeException as the_exception:
handle(the_exception)
raise
Or, in Python 3, you can raise a new exception and preserve the backtrace with exception chaining:
try:
try_this(whatever)
except SomeException as the_exception:
handle(the_exception)
raise DifferentException from the_exception
I elaborate in my answer here.
Python doesn't subscribe to the idea that exceptions should only be used for exceptional cases, in fact the idiom is 'ask for forgiveness, not permission'. This means that using exceptions as a routine part of your flow control is perfectly acceptable, and in fact, encouraged.
This is generally a good thing, as working this way helps avoid some issues (as an obvious example, race conditions are often avoided), and it tends to make code a little more readable.
Imagine you have a situation where you take some user input which needs to be processed, but have a default which is already processed. The try: ... except: ... else: ... structure makes for very readable code:
try:
raw_value = int(input())
except ValueError:
value = some_processed_value
else: # no error occured
value = process_value(raw_value)
Compare to how it might work in other languages:
raw_value = input()
if valid_number(raw_value):
value = process_value(int(raw_value))
else:
value = some_processed_value
Note the advantages. There is no need to check the value is valid and parse it separately, they are done once. The code also follows a more logical progression, the main code path is first, followed by 'if it doesn't work, do this'.
The example is naturally a little contrived, but it shows there are cases for this structure.
See the following example which illustrate everything about try-except-else-finally:
for i in range(3):
try:
y = 1 / i
except ZeroDivisionError:
print(f"\ti = {i}")
print("\tError report: ZeroDivisionError")
else:
print(f"\ti = {i}")
print(f"\tNo error report and y equals {y}")
finally:
print("Try block is run.")
Implement it and come by:
i = 0
Error report: ZeroDivisionError
Try block is run.
i = 1
No error report and y equals 1.0
Try block is run.
i = 2
No error report and y equals 0.5
Try block is run.
Is it a good practice to use try-except-else in python?
The answer to this is that it is context dependent. If you do this:
d = dict()
try:
item = d['item']
except KeyError:
item = 'default'
It demonstrates that you don't know Python very well. This functionality is encapsulated in the dict.get method:
item = d.get('item', 'default')
The try/except block is a much more visually cluttered and verbose way of writing what can be efficiently executing in a single line with an atomic method. There are other cases where this is true.
However, that does not mean that we should avoid all exception handling. In some cases it is preferred to avoid race conditions. Don't check if a file exists, just attempt to open it, and catch the appropriate IOError. For the sake of simplicity and readability, try to encapsulate this or factor it out as apropos.
Read the Zen of Python, understanding that there are principles that are in tension, and be wary of dogma that relies too heavily on any one of the statements in it.
You should be careful about using the finally block, as it is not the same thing as using an else block in the try, except. The finally block will be run regardless of the outcome of the try except.
In [10]: dict_ = {"a": 1}
In [11]: try:
....: dict_["b"]
....: except KeyError:
....: pass
....: finally:
....: print "something"
....:
something
As everyone has noted using the else block causes your code to be more readable, and only runs when an exception is not thrown
In [14]: try:
dict_["b"]
except KeyError:
pass
else:
print "something"
....:
Just because no-one else has posted this opinion, I would say
avoid else clauses in try/excepts because they're unfamiliar to most people
Unlike the keywords try, except, and finally, the meaning of the else clause isn't self-evident; it's less readable. Because it's not used very often, it'll cause people that read your code to want to double-check the docs to be sure they understand what's going on.
(I'm writing this answer precisely because I found a try/except/else in my codebase and it caused a wtf moment and forced me to do some googling).
So, wherever I see code like the OP example:
try:
try_this(whatever)
except SomeException as the_exception:
handle(the_exception)
else:
# do some more processing in non-exception case
return something
I would prefer to refactor to
try:
try_this(whatever)
except SomeException as the_exception:
handle(the_exception)
return # <1>
# do some more processing in non-exception case <2>
return something
<1> explicit return, clearly shows that, in the exception case, we are finished working
<2> as a nice minor side-effect, the code that used to be in the else block is dedented by one level.
Whenever you see this:
try:
y = 1 / x
except ZeroDivisionError:
pass
else:
return y
Or even this:
try:
return 1 / x
except ZeroDivisionError:
return None
Consider this instead:
import contextlib
with contextlib.suppress(ZeroDivisionError):
return 1 / x
This is my simple snippet on howto understand try-except-else-finally block in Python:
def div(a, b):
try:
a/b
except ZeroDivisionError:
print("Zero Division Error detected")
else:
print("No Zero Division Error")
finally:
print("Finally the division of %d/%d is done" % (a, b))
Let's try div 1/1:
div(1, 1)
No Zero Division Error
Finally the division of 1/1 is done
Let's try div 1/0
div(1, 0)
Zero Division Error detected
Finally the division of 1/0 is done
I'm attempting to answer this question in a slightly different angle.
There were 2 parts of the OP's question, and I add the 3rd one, too.
What is the reason for the try-except-else to exist?
Does the try-except-else pattern, or the Python in general, encourage using exceptions for flow control?
When to use exceptions, anyway?
Question 1: What is the reason for the try-except-else to exist?
It can be answered from a tactical standpoint. There is of course reason for try...except... to exist. The only new addition here is the else... clause, whose usefulness boils down to its uniqueness:
It runs an extra code block ONLY WHEN there was no exception happened in the try... block.
It runs that extra code block, OUTSIDE of the try... block (meaning any potential exceptions happen inside the else... block would NOT be caught).
It runs that extra code block BEFORE the final... finalization.
db = open(...)
try:
db.insert(something)
except Exception:
db.rollback()
logging.exception('Failing: %s, db is ROLLED BACK', something)
else:
db.commit()
logging.info(
'Successful: %d', # <-- For the sake of demonstration,
# there is a typo %d here to trigger an exception.
# If you move this section into the try... block,
# the flow would unnecessarily go to the rollback path.
something)
finally:
db.close()
In the example above, you can't move that successful log line into behind the finally... block. You can't quite move it into inside the try... block, either, due to the potential exception inside the else... block.
Question 2: does Python encourage using exceptions for flow control?
I found no official written documentation to support that claim. (To readers who would disagree: please leave comments with links to evidences you found.) The only vaguely-relevant paragraph that I found, is this EAFP term:
EAFP
Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C.
Such paragraph merely described that, rather than doing this:
def make_some_noise(speaker):
if hasattr(speaker, "quack"):
speaker.quack()
we would prefer this:
def make_some_noise(speaker):
try:
speaker.quack()
except AttributeError:
logger.warning("This speaker is not a duck")
make_some_noise(DonaldDuck()) # This would work
make_some_noise(DonaldTrump()) # This would trigger exception
or potentially even omitting the try...except:
def make_some_noise(duck):
duck.quack()
So, the EAFP encourages duck-typing. But it does not encourage using exceptions for flow control.
Question 3: In what situation you should design your program to emit exceptions?
It is a moot conversation on whether it is anti-pattern to use exception as control flow. Because, once a design decision is made for a given function, its usage pattern would also be determined, and then the caller would have no choice but to use it that way.
So, let's go back to the fundamentals to see when a function would better produce its outcome via returning a value or via emitting exception(s).
What is the difference between the return value and the exception?
Their "blast radius" are different. Return value is only available to the immediate caller; exception can be automatically relayed for unlimited distance until it is caught.
Their distribution patterns are different. Return value is by definition one piece of data (even though you could return a compound data type such as a dictionary or a container object, it is still technically one value).
The exception mechanism, on the contrary, allows multiple values (one at a time) to be returned via their respective dedicate channel. Here, each except FooError: ... and except BarError: ... block is considered as its own dedicate channel.
Therefore, it is up to each different scenario to use one mechanism that fits well.
All normal cases should better be returned via return value, because the callers would most likely need to use that return value immediately. The return-value approach also allows nesting layers of callers in a functional programming style. The exception mechanism's long blast radius and multiple channels do not help here.
For example, it would be unintuitive if any function named get_something(...) produces its happy path result as an exception. (This is not really a contrived example. There is one practice to implement BinaryTree.Search(value) to use exception to ship the value back in the middle of a deep recursion.)
If the caller would likely forget to handle the error sentinel from the return value, it is probably a good idea to use exception's characterist #2 to save caller from its hidden bug. A typical non-example would be the position = find_string(haystack, needle), unfortunately its return value of -1 or null would tend to cause a bug in the caller.
If the error sentinel would collide with a normal value in the result namespace, it is almost certain to use an exception, because you'd have to use a different channel to convey that error.
If the normal channel i.e. the return value is already used in the happy-path, AND the happy-path does NOT have sophisicated flow control, you have no choice but to use exception for flow control. People keep talking about how Python uses StopIteration exception for iteration termination, and use it to kind of justify "using exception for flow control". But IMHO this is only a practical choice in a particular situation, it does not generalize and glorify "using exception for flow control".
At this point, if you already make a sound decision on whether your function get_stock_price() would produce only return-value or also raise exceptions, or if that function is provided by an existing library so that its behavior has long be decided, you do not have much choice in writing its caller calculate_market_trend(). Whether to use get_stock_price()'s exception to control the flow in your calculate_market_trend() is merely a matter of whether your business logic requires you to do so. If yes, do it; otherwise, let the exception bubble up to a higher level (this utilizes the characteristic #1 "long blast radius" of exception).
In particular, if you are implementing a middle-layer library Foo and you happen to be making a dependency on lower-level library Bar, you would probably want to hide your implementation detail, by catching all Bar.ThisError, Bar.ThatError, ..., and map them into Foo.GenericError. In this case, the long blast radius is actually working against us, so you might hope "only if library Bar were returning its errors via return values". But then again, that decision has long been made in Bar, so you can just live with it.
All in all, I think whether to use exception as control flow is a moot point.
OP, YOU ARE CORRECT. The else after try/except in Python is ugly. it leads to another flow-control object where none is needed:
try:
x = blah()
except:
print "failed at blah()"
else:
print "just succeeded with blah"
A totally clear equivalent is:
try:
x = blah()
print "just succeeded with blah"
except:
print "failed at blah()"
This is far clearer than an else clause. The else after try/except is not frequently written, so it takes a moment to figure what the implications are.
Just because you CAN do a thing, doesn't mean you SHOULD do a thing.
Lots of features have been added to languages because someone thought it might come in handy. Trouble is, the more features, the less clear and obvious things are because people don't usually use those bells and whistles.
Just my 5 cents here. I have to come along behind and clean up a lot of code written by 1st-year out of college developers who think they're smart and want to write code in some uber-tight, uber-efficient way when that just makes it a mess to try and read / modify later. I vote for readability every day and twice on Sundays.
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
Python FAQ: “How fast are exceptions?”
I remember reading that Python implements a "Better to seek forgiveness than to ask permission" philosophy with regards to exceptions. According to the author, this meant Python code should use a lot of try - except clauses, rather than trying to determine ahead of time if you were about to do something that would cause an exception.
I just wrote some try - except clauses on my web app in which an exception will be raised most of the time the code is run. So, in this case, raising and catching an exception will be the norm. Is this bad from an efficiency point of view? I also remember someone telling me that catching a raised exception has a large performance overhead.
Is it unnecessarily inefficient to use try - except clauses in which you expect an exception to be raised and caught almost all of the time?
Here's the code -- its using the Django ORM to check for objects that associate users with various third party social providers.
try:
fb_social_auth = UserSocialAuth.objects.get(user=self, provider='facebook')
user_dict['facebook_id'] = fb_social_auth.uid
except ObjectDoesNotExist:
user_dict['facebook_id'] = None
try:
fs_social_auth = UserSocialAuth.objects.get(user=self, provider='foursquare')
user_dict['foursquare_id'] = fs_social_auth.uid
except ObjectDoesNotExist:
user_dict['foursquare_id'] = None
try:
tw_social_auth = UserSocialAuth.objects.get(user=self, provider='twitter')
user_dict['twitter_id'] = tw_social_auth.uid
except ObjectDoesNotExist:
user_dict['twitter_id'] = None
The first one will rarely take the exception, since right now we are enforcing "Sign In With Facebook" as the primary method for new users to join the site. But, Twitter and Foursquare are optional, in case they want to import friends or followers, and I expect most people will not.
I'm open to better ways to code this logic.
Whenever you code there is a balancing of concerns: performance, readability, correctness, extendability, maintainability, etc.
Unfortunately, it is often not possible to improve code in each of these directions at the same time. What is fast may not be as readable for instance.
One of the reasons why try..except is encouraged in Python is because you often can not anticipate all the ways your code may be used, so rather than checking if a specific condition exists, it is more general to just catch any of a certain class of error that might arise. Thus try..except may make your code more reusable.
However, it is also true that try..except is slow if the except clause is often being reached.
Is there a way to code that block so that an exception is not being raised and use try..except to catch the less frequent condition?
Or if not, for the sake of efficiency, you may choose not to use try..except. There are few hard and fast rules in programming. You have to choose your way based on your balance of concerns.
If you are attempting to optimize this function for speed, you should focus on what is likely to be the actual bottleneck. Your three database queries, each of which will cause the operating system to context switch, almost certainly take an order of magnitude longer than catching an exception. If you want to make the code as fast as possible, begin by combining all three database queries into one:
auth_objects = UserSocialAuth.objects.filter(user=self, provider__in=('facebook', 'foursquare', 'twitter'))
and then loop through the objects. The provider__in filter may be unnecessary if those three providers are the only ones in the database.
It's true that catching an exception is moderately expensive (see below for some timings) and you wouldn't want to this it in the bottleneck of your program, but in the examples you give, catching the exception is going to be a very small part of the runtime in comparison with the call to Model.objects.get which has to build a SQL query, transmit it to the database server, and wait for the database to report that there's no such object.
Some example timings. Function f2 throws and catches an exception, while f1 implements the same functionality without using exceptions.
d = dict()
def f1():
if 0 in d: return d[0]
else: return None
def f2():
try: return d[0]
except KeyError: return None
>>> timeit(f1)
0.25134801864624023
>>> timeit(f2)
2.4589600563049316
And f3 tries to get a non-existent object from the database (which is running on the same machine) via Django's ORM:
def f3():
try:
MyModel.objects.get(id=999999)
except MyModel.DoesNotExist:
pass
This takes about 400 times longer than f2 (so long that I didn't want to wait for the default number=1000000 iterations to complete):
>>> timeit(f3, number=1000)
1.0703678131103516
I have a basic "best practices" Python question. I see that there are already StackOverflow answers tangentially related to this question but they're mired in complicated examples or involve multiple factors.
Given this code:
#!/usr/bin/python
def test_function():
try:
a = str(5)
raise
b = str(6)
except:
print b
test_function()
what is the best way to avoid the inevitable "UnboundLocalError: local variable 'b' referenced before assignment" that I'm going to get in the exception handler?
Does python have an elegant way to handle this? If not, what about an inelegant way? In a complicated function I'd prefer to avoid testing the existence of every local variable before I, for example, printed debug information about them.
Does python have an elegant way to
handle this?
To avoid exceptions from printing unbound names, the most elegant way is not to print them; the second most elegant is to ensure the names do get bound, e.g. by binding them at the start of the function (the placeholder None is popular for this purpose).
If not, what about an inelegant way?
try: print 'b is', b
except NameError: print 'b is not bound'
In a complicated function I'd prefer
to avoid testing the existence of
every local variable before I, for
example, printed debug information
about them
Keeping your functions simple (i.e., not complicated) is highly recommended, too. As Hoare wrote 30 years ago (in his Turing acceptance lecture "The Emperor's old clothes", reprinted e.g. in this PDF):
There are two ways of constructing a
software design: One way is to make it
so simple that there are obviously no
deficiencies, and the other way is to
make it so complicated that there are
no obvious deficiencies. The first
method is far more difficult.
Achieving and maintaining simplicity is indeed difficult: given that you have to implement a certain total functionality X, it's the most natural temptation in the world to do so via complicated accretion into a few complicated classes and functions of sundry bits and pieces, "clever" hacks, copy-and-paste-and-edit-a-bit episodes of "drive-by coding", etc, etc.
However, it's a worthwhile effort to strive instead to keep your functions "so simple that there are obviously no deficiencies". If a function's hard to completely unit-test, it's too complicated: break it up (i.e., refactor it) into its natural components, even though it will take work to unearth them. (That's actually one of the way in which a strong focus on unit testing helps code quality: by spurring you relentlessly to keep all the code perfectly testable, it's at the same time spurring you to make it simple in its structure).
You can initialize your variables outside of the try block
a = None
b = None
try:
a = str(5)
raise
b = str(6)
except:
print b
You could check to see if the variable is defined in local scope using the built-in method locals()
http://docs.python.org/library/functions.html#locals
#!/usr/bin/python
def test_function():
try:
a = str(5)
raise
b = str(6)
except:
if 'b' in locals(): print b
test_function()
def test_function():
try:
a = str(5)
raise
b = str(6)
except:
print b
b = str(6) is never run; the program exits try block just after raise. If you want to print some variable in the except block, evaluate it before raising an exception and put them into the exception you throw.
class MyException(Exception):
def __init__(self, var):
self.var = var
def test_function():
try:
a = str(5)
b = str(6)
raise MyException(b)
except MyException,e:
print e.var