#20135: merge with 3.4.

This commit is contained in:
Ezio Melotti 2014-07-06 20:59:19 +03:00
commit 1f6b103440
2 changed files with 56 additions and 56 deletions

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@ -664,62 +664,6 @@ before you write any of the actual code. Of course Python allows you to be
sloppy and not write test cases at all.
Why are default values shared between objects?
----------------------------------------------
This type of bug commonly bites neophyte programmers. Consider this function::
def foo(mydict={}): # Danger: shared reference to one dict for all calls
... compute something ...
mydict[key] = value
return mydict
The first time you call this function, ``mydict`` contains a single item. The
second time, ``mydict`` contains two items because when ``foo()`` begins
executing, ``mydict`` starts out with an item already in it.
It is often expected that a function call creates new objects for default
values. This is not what happens. Default values are created exactly once, when
the function is defined. If that object is changed, like the dictionary in this
example, subsequent calls to the function will refer to this changed object.
By definition, immutable objects such as numbers, strings, tuples, and ``None``,
are safe from change. Changes to mutable objects such as dictionaries, lists,
and class instances can lead to confusion.
Because of this feature, it is good programming practice to not use mutable
objects as default values. Instead, use ``None`` as the default value and
inside the function, check if the parameter is ``None`` and create a new
list/dictionary/whatever if it is. For example, don't write::
def foo(mydict={}):
...
but::
def foo(mydict=None):
if mydict is None:
mydict = {} # create a new dict for local namespace
This feature can be useful. When you have a function that's time-consuming to
compute, a common technique is to cache the parameters and the resulting value
of each call to the function, and return the cached value if the same value is
requested again. This is called "memoizing", and can be implemented like this::
# Callers will never provide a third parameter for this function.
def expensive(arg1, arg2, _cache={}):
if (arg1, arg2) in _cache:
return _cache[(arg1, arg2)]
# Calculate the value
result = ... expensive computation ...
_cache[(arg1, arg2)] = result # Store result in the cache
return result
You could use a global variable containing a dictionary instead of the default
value; it's a matter of taste.
Why is there no goto?
---------------------

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@ -352,6 +352,62 @@ the import inside the class but outside of any method still causes the import to
occur when the module is initialized.
Why are default values shared between objects?
----------------------------------------------
This type of bug commonly bites neophyte programmers. Consider this function::
def foo(mydict={}): # Danger: shared reference to one dict for all calls
... compute something ...
mydict[key] = value
return mydict
The first time you call this function, ``mydict`` contains a single item. The
second time, ``mydict`` contains two items because when ``foo()`` begins
executing, ``mydict`` starts out with an item already in it.
It is often expected that a function call creates new objects for default
values. This is not what happens. Default values are created exactly once, when
the function is defined. If that object is changed, like the dictionary in this
example, subsequent calls to the function will refer to this changed object.
By definition, immutable objects such as numbers, strings, tuples, and ``None``,
are safe from change. Changes to mutable objects such as dictionaries, lists,
and class instances can lead to confusion.
Because of this feature, it is good programming practice to not use mutable
objects as default values. Instead, use ``None`` as the default value and
inside the function, check if the parameter is ``None`` and create a new
list/dictionary/whatever if it is. For example, don't write::
def foo(mydict={}):
...
but::
def foo(mydict=None):
if mydict is None:
mydict = {} # create a new dict for local namespace
This feature can be useful. When you have a function that's time-consuming to
compute, a common technique is to cache the parameters and the resulting value
of each call to the function, and return the cached value if the same value is
requested again. This is called "memoizing", and can be implemented like this::
# Callers will never provide a third parameter for this function.
def expensive(arg1, arg2, _cache={}):
if (arg1, arg2) in _cache:
return _cache[(arg1, arg2)]
# Calculate the value
result = ... expensive computation ...
_cache[(arg1, arg2)] = result # Store result in the cache
return result
You could use a global variable containing a dictionary instead of the default
value; it's a matter of taste.
How can I pass optional or keyword parameters from one function to another?
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