are not used when they would be appropriate, just because the
keyword "lambda" makes code look complex. Omitting lambda in some
special cases is possible, with small and backwards compatible
changes to the grammar, and provides a cheap cure against such
Sometimes people do not use lambdas because they fear to introduce
a term with a theory behind it. This proposal makes introducing
argumentless lambdas easier, by omitting the "lambda" keyword.
...lambda x: x, lambda x: x**2 if f(1) == 1]
(I.e. a list comprehension where the sequence following 'in' is an
unparenthesized series of lambdas -- or just one lambda, even.)
In Python 3.0, the series of lambdas will have to be
[f for f in (lambda x: x, lambda x: x**2) if f(1) == 1]
This is because lambda binds less tight than the if-else
expression, but in this context, the lambda could already be
followed by an 'if' keyword that binds less tightly still (for
...lambda x: x + 1).
In general, languages like that are strongly typed, so the compiler
always knows the number of arguments expected and can do the right
thing when presented with a functor and less arguments than expected.
Python does not implement multi-argument functions by currying, so if
you want a function with partially-applied arguments you would
probably use a lambda as above, or define a named function for each
However, lambda syntax is not to everyone's taste, so say the leas...
In Python 2.4, the operator module gained two new functions,
itemgetter() and attrgetter() that can replace common uses of
the lambda keyword. The new functions run faster and
are considered by some to improve readability.
lambda r: r --> itemgetter(2)
lambda r: r.myattr --> attrgetter('myattr')
sort(studentrecords, key=attrgetter('gpa')) # set a sort field
map(attrgetter('lastname'), studentrecords) # extract a field
Locating: grep lam...
Python-style instead of Lua-style
x = xpcall(lambda: expression(), lambda e: default(e))
This actually returns (true, expression()) or (false, default(e)), but I think we can ignore that part.
Haskell is actually similar to Lua here (except that it's all done
with monads, of course):
x = do catch(lambda: expression(), lambda e: default(e))
You can write a pattern matching expression within the function to decide
what to do with it; catching and re-raising exceptions you don't want is...
...lambda: cls_target += 1 # Error due to class scope
incr_x = lambda: x += 1 # Error due to missing target "x"
incr_x = lambda: x += 1 # Error due to "x" being declared after use
x = 1
lambda arg: arg += 1 # Error due to attempt to target formal parameter
[x += 1 for x in data] # Error due to attempt to target iteration variable
As augmented assignments currently can't appear inside scoped expressions, the
above compile time name resolution...
...lambda expression yields an unnamed function that evaluates a
single expression. It is often used for callback functions. In
the example below (written using the Python 2.0 rules), any name
used in the body of the lambda must be explicitly passed as a
default argument to the lambda.
from Tkinter import *
root = Tk()
Button(root, text="Click here",
command=lambda root=root: root.test.configure(text="..."))
This approach is cumbersome, particularly when there are several
names used in t...
lambda's syntax does not support annotations. The syntax of
lambda could be changed to support annotations, by requiring
parentheses around the parameter list. However it was decided
 not to make this change because:
It would be an incompatible change.
Lambdas are neutered anyway.
The lambda can always be changed to a function.
Accessing Function Annotations
Once compiled, a function's annotations are available via the
function's __annotations__ attribute. This attribute is
...lambda will not be renamed.
At one point lambda was slated for removal in Python 3000.
Unfortunately no one was able to come up with a better way of
providing anonymous functions. And so lambda is here to stay.
But it is here to stay as-is. Adding support for statements is a
non-starter. It would require allowing multi-line lambda
expressions which would mean a multi-line expression could suddenly
exist. That would allow for multi-line arguments to function
calls, for instance. That is jus...
...lambda n: n*n < 50, range(100))
The takewhile code achieves the same result as the proposed syntax,
albeit in a longer (some would say "less-elegant") fashion. Also,
the takewhile version requires an extra function call (the lambda
in the example above) with the associated performance penalty.
A simple test shows that:
for n in (n for n in range(100) if 1): pass
performs about 10% better than:
for n in takewhile(lambda n: 1, range(100)): pass
though they achieve similar results. (The f...
<function f.<locals>.<lambda> at 0x7f6f46faeae0>
Anonymous functions (or functions that share a name) within the same scope
will still share representations (aside from the object ID), but this is
still a major improvement over the historical situation where everything
except the object ID was identical.
Possible Implementation Strategy
This proposal has at least one titanic advantage over PEP 3150:
implementation should be relatively straightf...
(lambda: x := 1) # INVALID
lambda: (x := 1) # Valid, but unlikely to be useful
(x := lambda: 1) # Valid
lambda line: (m := re.match(pattern, line)) and m.group(1) # Valid
This allows lambda to always bind less tightly than :=; having a
name binding at the top level inside a lambda function is unlikely to be of
value, as there is no way to make use of it. In cases where the name will be
used more than once, the expression is likely to need parenthesizing anyway,
reduce(lambda s, a: s + a.myattr, data, 0)
reduce(lambda s, a: s + a, data, 0)
These simplify to:
sum(a.myattr for a in data)
sum(a for a in data)
List comprehensions greatly reduced the need for filter() and map().
Likewise, generator expressions are expected to minimize the need
for itertools.ifilter() and itertools.imap(). In contrast, the
utility of other itertools will be enhanced by generator expressions:
dotproduct = sum(x*y for x,y in itertools.izip(x_vector, y_vector...
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...lambda string, item: string + chr(item), list, "")
This version performs exactly the same set of string operations as the
first one, but gets rid of the for loop overhead in favor of the faster,
implied loop of the reduce() function.
Sure, I replied, but it does so at the cost of a function call (the lambda
function) per list item. I betcha it's slower, since function call
overhead in Python is bigger than for loop overhead.
(OK, so I had already done the comparisons. f2() t...
...lambda expressions, then they might
choose to write it like this:
sorted_list = sorted(original, key=(lambda v: v.attr1, v.attr2))
That gets the job done, but it hardly reaches the standard of executable
pseudocode that fits Python's reputation.
If they don't like lambda specifically, the operator module offers an
alternative that still allows the key function to be defined inline:
sorted_list = sorted(original,
key=operator.attrgetter(v. 'attr1', 'attr2'))
Again, it get...
...lambda x: x % 2 == 0
>>> forall([2, 4, 6, 8], even)
>>> forall('this is a test'.split(), lambda x: len(x) == 4)
def exists(a, fn = bool):
"""Returns True if there is at least one true value in a.
>>> exists('this is a test'.split(), lambda x: len(x) == 4)
"""Sort a list.
pre: isinstance(a, type(list))
# array size is unchanged
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