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 For example, testing os.path.exists results in information that may be out-of-date by the time you use it.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.
I would like to handle a NameError exception by injecting the desired missing variable into the frame and then continue the execution from last attempted instruction.
The following pseudo-code should illustrate my needs.
def function():
return missing_var
try:
print function()
except NameError:
frame = inspect.trace()[-1][0]
# inject missing variable
frame.f_globals["missing_var"] = ...
# continue frame execution from last attempted instruction
exec frame.f_code from frame.f_lasti
Read the whole unittest on repl.it
Notes
As pointed out by ivan_pozdeev in his answer, this is known as resumption.
After more research, I found Veedrac's answer to the question Resuming program at line number in the context before an exception using a custom sys.excepthook posted by lc2817 very interesting. It relies on Richie Hindle's work.
Background
The code runs in a slave process, which is controlled by a parent. Tasks (functions really) are written in the parent and latter passed to the slave using dill. I expect some tasks (running in the slave process) to try to access variables from outer scopes in the parent and I'd like the slave to request those variables to the parent on the fly.
p.s.: I don't expect this magic to run in a production environment.
On the contrary to what various commenters are saying, "resume-on-error" exception handling is possible in Python. The library fuckit.py implements said strategy. It steamrollers errors by rewriting the source code of your module at import time, inserting try...except blocks around every statement and swallowing all exceptions. So perhaps you could try a similar sort of tactic?
It goes without saying: that library is intended as a joke. Don't ever use it in production code.
You mentioned that your use case is to trap references to missing names. Have you thought about using metaprogramming to run your code in the context of a "smart" namespace such as a defaultdict? (This is perhaps only marginally less of a bad idea than fuckit.py.)
from collections import defaultdict
class NoMissingNamesMeta(type):
#classmethod
def __prepare__(meta, name, bases):
return defaultdict(lambda: "foo")
class MyClass(metaclass=NoMissingNamesMeta):
x = y + "bar" # y doesn't exist
>>> MyClass.x
'foobar'
NoMissingNamesMeta is a metaclass - a language construct for customising the behaviour of the class statement. Here we're using the __prepare__ method to customise the dictionary which will be used as the class's namespace during creation of the class. Thus, because we're using a defaultdict instead of a regular dictionary, a class whose metaclass is NoMissingNamesMeta will never get a NameError. Any names referred to during the creation of the class will be auto-initialised to "foo".
This approach is similar to #AndréFratelli's idea of manually requesting the lazily-initialised data from a Scope object. In production I'd do that, not this. The metaclass version requires less typing to write the client code, but at the expense of a lot more magic. (Imagine yourself debugging this code in two years, trying to understand why non-existent variables are dynamically being brought into scope!)
The "resumption" exception handling technique has proven to be problematic, that's why it's missing from C++ and later languages.
Your best bet is to use a while loop to not resume where the exception was thrown but rather repeat from a predetermined place:
while True:
try:
do_something()
except NameError as e:
handle_error()
else:
break
You really can't unwind the stack after an exception is thrown, so you'd have to deal with the issue before hand. If your requirement is to generate these variables on the fly (which wouldn't be recommended, but you seem to understand that), then you'd have to actually request them. You can implement a mechanism for that (such as having a global custom Scope class instance and overriding __getitem__, or using something like the __dir__ function), but not as you are asking for it.
I'm curious if it is considered safe or good practice to depend on python's with...as statement. For example when opening a file:
with open("myfile","w") as myFile:
#do something
So in this example I neglected to explicitly call myFile.close() however I can assume it was called when python exited the with...as statement by calling the objects __exit__() method. Is it good practice/safe to depend upon this or would it be better to always explicitly call file.close()
This is what context managers are for, to rely on them to close the file for you. Context managers are called even if there was an exception.
The alternative is to use an finally block instead:
myFile = open("myfile","w")
try:
# do something with myFile
finally:
myFile.close()
but because the block inside of the try: might be long, by the time you get to the finally statement you have forgotten what you were setting this up for.
Context managers are more powerful still. Because the __exit__ method is informed of any exceptions, they can act as exception handlers as well (ignore the exception, raise another, etc.).
Yes, the with statement is better way. Since Python 2.5, the file object has been equipped with __enter__() and __exit__() methods. The __exit__() method closes the file object.
Python guarantees that it will call the __exit__() method, but there is not guarantee that the __exit__() method will close the resource, especially with 3rd party code. You need to manually verify that.
I have to open a file-like object in python (it's a serial connection through /dev/) and then close it. This is done several times in several methods of my class. How I WAS doing it was opening the file in the constructor, and then closing it in the destructor. I'm getting weird errors though and I think it has to do with the garbage collector and such, I'm still not used to not knowing exactly when my objects are being deleted =\
The reason I was doing this is because I have to use tcsetattr with a bunch of parameters each time I open it and it gets annoying doing all that all over the place. So I want to implement an inner class to handle all that so I can use it doing
with Meter('/dev/ttyS2') as m:
I was looking online and I couldn't find a really good answer on how the with syntax is implemented. I saw that it uses the __enter__(self) and __exit(self)__ methods. But is all I have to do implement those methods and I can use the with syntax? Or is there more to it?
Is there either an example on how to do this or some documentation on how it's implemented on file objects already that I can look at?
Those methods are pretty much all you need for making the object work with with statement.
In __enter__ you have to return the file object after opening it and setting it up.
In __exit__ you have to close the file object. The code for writing to it will be in the with statement body.
class Meter():
def __init__(self, dev):
self.dev = dev
def __enter__(self):
#ttysetattr etc goes here before opening and returning the file object
self.fd = open(self.dev, MODE)
return self
def __exit__(self, type, value, traceback):
#Exception handling here
close(self.fd)
meter = Meter('dev/tty0')
with meter as m:
#here you work with the file object.
m.fd.read()
Easiest may be to use standard Python library module contextlib:
import contextlib
#contextlib.contextmanager
def themeter(name):
theobj = Meter(name)
try:
yield theobj
finally:
theobj.close() # or whatever you need to do at exit
# usage
with themeter('/dev/ttyS2') as m:
# do what you need with m
m.read()
This doesn't make Meter itself a context manager (and therefore is non-invasive to that class), but rather "decorates" it (not in the sense of Python's "decorator syntax", but rather almost, but not quite, in the sense of the decorator design pattern;-) with a factory function themeter which is a context manager (which the contextlib.contextmanager decorator builds from the "single-yield" generator function you write) -- this makes it so much easier to separate the entering and exiting condition, avoids nesting, &c.
The first Google hit (for me) explains it simply enough:
http://effbot.org/zone/python-with-statement.htm
and the PEP explains it more precisely (but also more verbosely):
http://www.python.org/dev/peps/pep-0343/
In the question How do I "cd" in python, the accepted answer recommended wrapping the os.chdir call in a class to make the return to your original dir exception safe. Here was the recommended code:
class Chdir:
def __init__( self, newPath ):
self.savedPath = os.getcwd()
os.chdir(newPath)
def __del__( self ):
os.chdir( self.savedPath )
Could someone elaborate on how this works to make an unsafe call exception safe?
Thread safety and exception safety are not really the same thing at all. Wrapping the os.chdir call in a class like this is an attempt to make it exception safe not thread safe.
Exception safety is something you'll frequently hear C++ developers talk about. It isn't talked about nearly as much in the Python community. From Boost's Exception-Safety in Generic Components document:
Informally, exception-safety in a
component means that it exhibits
reasonable behavior when an exception
is thrown during its execution. For
most people, the term “reasonable”
includes all the usual expectations
for error-handling: that resources
should not be leaked, and that the
program should remain in a
well-defined state so that execution
can continue.
So the idea in the code snippet you supplied is to ensure that in the case of the exception, the program will return to a well-defined state. In this case, the process will be returned in the directory it started from, whether os.chdir itself fails, or something causes an exception to be thrown and the "Chdir" instance to be deleted.
This pattern of using an object that exists merely for cleaning up is a form of "Resource Acquisition Is Initialization", or "RAII". This technique is very popular in C++, but is not so popular in Python for a few reasons:
Python has try...finally, which serves pretty much the same purpose and is the more common idiom in Python.
Destructors (__del__) in Python are unreliable/unpredicatble in some implementations, so using them in this way is somewhat discouraged. In cpython they happen to be very reliable and predictable as long as cycles aren't involved (ie: when deletion is handled by reference counting) but in other implementations (Jython and I believe also IronPython) deletion happens when the garbage collector gets around to it, which could be much later. (Interestingly, this doesn't stop most Python programmers from relying on __del__ to close their opened files.)
Python has garbage collection, so you don't need to be quite as careful about cleanup as you do in C++. (I'm not saying you don't have to be careful at all, just that in the common situations you can rely on the gc to do the right thing for you.)
A more "pythonic" way of writing the above code would be:
saved_path = os.getcwd()
os.chdir(new_path)
try:
# code that does stuff in new_path goes here
finally:
os.chdir(saved_path)
The direct answer to the question is: It doesn't, the posted code is horrible.
Something like the following could be reasonable to make it "exception safe" (but much better is to avoid chdir and use full paths instead):
saved_path = os.getcwd()
try:
os.chdir(newPath)
do_work()
finally:
os.chdir(saved_path)
And this precise behavior can also be written into a context manager.
__del__ is called when the instance is about to be destroyed. So when you instantiate this class, the current working directory is saved to an instance attribute and then, well, os.chdir is called. When the instance is destroyed (for whatever reason) the current directory is changed to its old value.
This looks a bit incorrect to me. As far as I know, you must call parent's __del__ in your overriden __del__, so it should be more like this:
class Chdir(object):
def __init__(self, new_path):
self.saved_path = os.getcwd()
os.chdir(new_path)
def __del__(self):
os.chdir(self.saved_path)
super(Chdir, self).__del__()
That is, unless I am missing something, of course.
(By the way, can't you do the same using contextmanager?)
This code alone is neither thread-safe nor exception-safe. Actually I'm not really sure what you mean by exception-safe. Following code comes to mind:
try:
# something thrilling
except:
pass
And this is a terrible idea. Exceptions are not for guarding against. Well written code should catch exceptions and do something useful with them.