the point is that what might take 1mil LOC in Java may take only 100k LOC in Python.
I love Python and haven't touched Java in years but my colleagues who use Java seem to write programs that are maybe around 3 times longer (this is a ballpark figure).
I get the feeling that programmers that produce gobs of Java code are probably going to do the same thing in Python. A good Fortran programmer can write Fortran in any language :).
After you read that, hopefully you'll start passing around functions as arguments inside another functions. It's tremendously fun and changes your way of thinking about code.
And this little answer in StackOverFlow summarizes what lambdas can do and how you can use it in python: http://stackoverflow.com/questions/890128/python-lambda-why/...
The simplest explanation is think of your program functions like mathematical functions that spit out values, with that in mind you can start sending in functions as arguments. A very brief e.g from the link:
def addition(n): return lambda x: x + n f = addition(3) f(4) # is 7
here, f is actually binded to a function - the addition function from above (because it returned a function called lambda).
I think basically what the parent comment is saying is that it's much easier to pass functions as arguments to other functions in Haskell than it is in python. They call them high order functions.
myList = [[1, 2], [3, 4], [5, 6]]
for elem in myList:
del elem[0]
map(lambda elem: del elem[0], myList)
map(lambda elem: print elem, myList)
function makeCounter() {
var value = 0;
return function() {
return value ++;
}
}
> c = makeCounter();
> c();
0
> c();
1
... and so forth
def makeCounter():
value = 0
def count():
nonlocal value
tmp, value = value, value + 1
return tmp
return count
Of course, Haskell has neither state nor statements, so the above examples don't literally show why Haskell's lambdas are necessarily superior, but the point isn't that Python disallows assignment in lambdas; it's that Python's lambdas can only express a subset of possible functions, while JavaScript and Haskell (and Scheme &al.) don't have that limitation.
my_list = [[1, 2], [3, 4], [5, 6]]
my_other_list = [ elem[1:] for elem in my_list ]
As for your second example, I would consider using "yield".
The issues with Python's lambdas really come up when you start trying to do things the Haskell-esque way—which is to say, the super-functional category-theory-on-the-brain way—which can be the right solution to certain problems (e.g. monadic parsers) but tends to be difficult to express in a way that's pleasant, non-hacky, and Pythonic. But I am of the personal opinion that Python's design choices make sense and I don't want to rail against it for not being Haskell or Ruby; I just wanted to show how anonymous functions in particular differ from language to language.
I side step the lack of closures by using named locals with the keyword arguments when defining inner functions. However, for numeric types, this doesn't really work:
def makeCounter():
value = 0
def count(value=value):
tmp, value = value, value + 1
return tmp
return count
inc = makeCounter()
print inc()
print inc()
def set_adder():
s = set()
def add(item, s=s):
s.add(item)
return s
return add
s = set_adder()
print s(1)
print s(2)
t = set_adder()
print t(3)
print t(4)
$ python mk.py
set([1])
set([1, 2])
set([3])
set([3, 4]) def set_adder():
s = set()
def add(item):
s.add(item)
return s
return add
def make_counter():
value = [0]
def count():
temp, = value
value[0] += 1
return temp
return count
Nonetheless, functional programming is not the encourage common style in Python: there are no data structures available, many methods have side effects, it's hard to isolate mutable values from mutable. Not saying it's right or wrong, it's just different.
I should also note that Common Lisp isn't all that functional compared to other languages: I'd rate it being just slightly more functional than Ruby. The reason it appeals to some Lispers is due to simple syntax, data structure literals and dynamic typing. These features are common now, are useful for experimental programming (of the sort that frequently happens in AI and ML work) but they originated with Lisp. Lack of syntax, presence of macros and resulting ease of metalinguistic abstraction are not present in Python, but not every Lisp(er) used them: some preferred functional style (e.g., never using loop macro and using higher order functions and closures as means of abstraction), some preferred macros and DSLs, some stayed away from both.
Java bashing is healthy: while I personally don't mind Java, many developers were forced into writing Java (having previously worked in Smalltalk, Lisp, C++, Perl, and others) as the language was heavily marketed to managers. Java hatred and bashing is thus a well understood reaction (although the culprit for verbosity are awfully horrid libraries such as Spring and J2EE: alternatives like Guice, for example, are much less verbose). However, JVM is now home to Scala, a much stronger candidate for an OO/functional hybrid language than Python (that's not to say Python isn't an elegant and powerful language with many advantages, especially when it comes to tools/automation development).
I'm definitely going to use this definition of 'verboten' from now on. I like your style. Fuck the dictionary!
Also, to save you the work:
ver·bo·ten
forbidden, as by law; prohibited.
Inexperienced programmers write sub-optimal (or plain weird) code. Once they learn their tools, they will write better code. That's not surprising at all.
In my opinion there are 3 things that help with maintenance: * automated tests (to see what breaks if you change something) * refactoring tools (helps in keeping code clean) * domain driven design (helps in mapping a business concept to a piece of code)
These are all things you could do in Python as well.