The *args
and **kwargs
is a common idiom to allow arbitrary number of arguments to functions as described in the section more on defining functions in the Python documentation.
The *args
will give you all function parameters as a tuple:
def foo(*args):
for a in args:
print(a)
foo(1)
# 1
foo(1,2,3)
# 1
# 2
# 3
The **kwargs
will give you all
keyword arguments except for those corresponding to a formal parameter as a dictionary.
def bar(**kwargs):
for a in kwargs:
print(a, kwargs[a])
bar(name='one', age=27)
# name one
# age 27
Both idioms can be mixed with normal arguments to allow a set of fixed and some variable arguments:
def foo(kind, *args, **kwargs):
pass
It is also possible to use this the other way around:
def foo(a, b, c):
print(a, b, c)
obj = {'b':10, 'c':'lee'}
foo(100,**obj)
# 100 10 lee
Another usage of the *l
idiom is to unpack argument lists when calling a function.
def foo(bar, lee):
print(bar, lee)
l = [1,2]
foo(*l)
# 1 2
In Python 3 it is possible to use *l
on the left side of an assignment (Extended Iterable Unpacking), though it gives a list instead of a tuple in this context:
first, *rest = [1,2,3,4]
first, *l, last = [1,2,3,4]
Also Python 3 adds new semantic (refer PEP 3102):
def func(arg1, arg2, arg3, *, kwarg1, kwarg2):
pass
Such function accepts only 3 positional arguments, and everything after *
can only be passed as keyword arguments.
Note:
- A Python
dict
, semantically used for keyword argument passing, are arbitrarily ordered. However, in Python 3.6, keyword arguments are guaranteed to remember insertion order.
- "The order of elements in
**kwargs
now corresponds to the order in which keyword arguments were passed to the function." - What’s New In Python 3.6
- In fact, all dicts in CPython 3.6 will remember insertion order as an implementation detail, this becomes standard in Python 3.7.
Maybe a bit of example code will help: Notice the difference in the call signatures of foo
, class_foo
and static_foo
:
class A(object):
def foo(self, x):
print(f"executing foo({self}, {x})")
@classmethod
def class_foo(cls, x):
print(f"executing class_foo({cls}, {x})")
@staticmethod
def static_foo(x):
print(f"executing static_foo({x})")
a = A()
Below is the usual way an object instance calls a method. The object instance, a
, is implicitly passed as the first argument.
a.foo(1)
# executing foo(<__main__.A object at 0xb7dbef0c>, 1)
With classmethods, the class of the object instance is implicitly passed as the first argument instead of self
.
a.class_foo(1)
# executing class_foo(<class '__main__.A'>, 1)
You can also call class_foo
using the class. In fact, if you define something to be
a classmethod, it is probably because you intend to call it from the class rather than from a class instance. A.foo(1)
would have raised a TypeError, but A.class_foo(1)
works just fine:
A.class_foo(1)
# executing class_foo(<class '__main__.A'>, 1)
One use people have found for class methods is to create inheritable alternative constructors.
With staticmethods, neither self
(the object instance) nor cls
(the class) is implicitly passed as the first argument. They behave like plain functions except that you can call them from an instance or the class:
a.static_foo(1)
# executing static_foo(1)
A.static_foo('hi')
# executing static_foo(hi)
Staticmethods are used to group functions which have some logical connection with a class to the class.
foo
is just a function, but when you call a.foo
you don't just get the function,
you get a "partially applied" version of the function with the object instance a
bound as the first argument to the function. foo
expects 2 arguments, while a.foo
only expects 1 argument.
a
is bound to foo
. That is what is meant by the term "bound" below:
print(a.foo)
# <bound method A.foo of <__main__.A object at 0xb7d52f0c>>
With a.class_foo
, a
is not bound to class_foo
, rather the class A
is bound to class_foo
.
print(a.class_foo)
# <bound method type.class_foo of <class '__main__.A'>>
Here, with a staticmethod, even though it is a method, a.static_foo
just returns
a good 'ole function with no arguments bound. static_foo
expects 1 argument, and
a.static_foo
expects 1 argument too.
print(a.static_foo)
# <function static_foo at 0xb7d479cc>
And of course the same thing happens when you call static_foo
with the class A
instead.
print(A.static_foo)
# <function static_foo at 0xb7d479cc>
Best Answer
My company, Resolver Systems, develops what is probably the biggest application written in IronPython yet. (It's called Resolver One, and it's a Pythonic spreadsheet). We are also hosting the Ironclad project (to run CPython extensions under IronPython) and that is going well (we plan to release a beta of Resolver One & numpy soon).
The reason we chose IronPython was the .NET integration - our clients want 100% integration on Windows and the easiest way to do that right now is .NET.
We design our GUI (without behaviour) in Visual Studio, compile it into a DLL and subclass it from IronPython to add behaviour.
We have found that IronPython is faster at some cases and slower at some others. However, the IronPython team is very responsive, whenever we report a regression they fix it and usually backport it to the bugfix release. If you worry about performance, you can always implement a critical part in C# (we haven't had to do that yet).
If you have experience with C#, then IronPython will be natural for you, and easier than C#, especially for prototypes.
Regarding IronPython studio, we don't use it. Each of us has his editor of choice (TextPad, Emacs, Vim & Wing), and everything works fine.