Develop Reference

What is the syntax behind the sorted argument? [duplicate]

This question already has answers here:
What is `lambda` in Python code? How does it work with `key` arguments to `sorted`, `sum` etc.?
(4 answers)
Closed 6 months ago.
I want to know what exactly is lambda in python? and where and why it is used.
thanks

Lambda is more of a concept or programming technique then anything else.
Basically it's the idea that you get a function (a first-class object in python) returned as a result of another function instead of an object or primitive type. I know, it's confusing.
See this example from the python documentation:
def make_incrementor(n):
return lambda x: x + n
f = make_incrementor(42)
f(0)
>>> 42
f(1)
>>> 43
So make_incrementor creates a function that uses n in it's results. You could have a function that would increment a parameter by 2 like so:
f2 = make_incrementor(2)
f2(3)
>>> 5
This is a very powerful idea in functional programming and functional programming languages like lisp & scheme.
Hope this helps.

Lambdas are not anonymous functions. Lambdas are anonymous expressions.
They're accessed like functions, but they're not the same thing. Functions allow complex tasks: flow control, variable declarations and lists of statements containing expressions. Expressions are merely one part of a function, and that's what lambdas give you. They're severely limited compared to functions.
Python does not support anonymous functions. For examples of languages that do, see Javascript and Lua.
(Note: It's correct to call lambdas anonymous functions in functional languages, where the mathematical definition of "function" is used, but in procedural languages the word has a very different meaning than in mathematics.)

Lambda functions are not a Python specific concept, but are a general programming term for anonymous function, i.e. functions without a name. In Python they are commonly used where you need to pass a simple function as a parameter to another function.
The sort method on lists takes a parameter key which is a function that is used to calculate the value the list is sorted on. Imagine you are sorting a list of two element tuples, and you want to sort the list based on the first element. You need to pass a function to key which returns the first element. You could do this:
def first_element(x):
return x[0]
my_list.sort(key=first_element)
or, much more concisely you can do:
my_list.sort(key=lambda x: x[0])

The lambda construct is a shorter way to define a simple function that calculates a single expression. The def statement can be inconvenient and make the code longer, broken up and harder to read through. The functions created by the two are virtually the same by the way they work, the difference is that lambda is limited to a single expression the value of which is returned and that def assigns a name to the function and adds it to the local variables by that same name. Also lambda can be used in an expression directly, while def is a statement.
def f(x, y):
return x + y
Would give you almost the same result as
f = lambda x, y: x + y
And you can use it directly in an expression
g(5, 6, helper=lambda x, y: x + y)
which with def would be less concise
def helper_function(x + y):
return x + y
g(5, 6, helper=helper_function)

lambda is a way of defining an anonymous in-line function to accomplish some task versus defining it with the def keyword and calling it. Think of it as a shorthand you can use when you won't need to reuse a function. Anywhere you use lambda, you could substitute an explicitly called function: it's use is completely optional and left to the programmer as a matter of coding style.
Example code (without lambda):
def numSquared(num):
return num**2
for i in range(1,11):
print numSquared(i)
Example code (with lambda):
for i in range(1,11):
print (lambda x: x**2)(i)
A good beginner's discussion of lambda:
DiveIntPython.org - Lambda

lambda allows you define simple, unnamed functions inline with your code, e.g. as an argument. This is useful if you plan to use the function only once, and therefore don't want to clutter your code with a named function.
Let's say you have a list of numbers (thelist), and you want to get a list of the same length, with each number doubled. Rather than defining a times_two function, you could do something like this:
map(lambda x: x * 2, thelist)
lambda also makes Currying more convenient.

The first linked returned by googling "python lambda" points to a page whose first paragraphs answer your question. I could copy & paste the content it, but I think it's unfair :)

It's an inline anonymous function.

lambda is an anonymous function, usually used for something quick that needs computing.
Example (This is used in a LexYacc command parser):
assign_command = lambda dictionary, command: lambda command_function: dictionary.setdefault(command, command_function)
This is a decorator. Put before a command we'd have from the LexYacc parser:
#assign_command(_command_handlers, 'COMMAND')
This essentially builds a python script from the LexYacc language defined.
Or, a bit simpler:
`x = lambda x: return x > 0 and x & (x-1) == 0

Lambda can be interpreted intuitively as algebra function.
Suppose a basic algebra y = x**2 + 1
if typed directly,
>>> y = x**2 + 1
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'x' is not defined
error occurs and reports that variable 'x' should be defined firstly.
nevertheless,'x' is unknown, how can it be pre-defined.
Lambda helps:
python
>>> y = lambda x: x**2 + 1 # no errors reports
>>> y(3)
10
That's core about lambda,
It's in programming language to write an inline function.

Lambda is an anonymous function in Python programming language, instead of bearing a def statement in the front it is simply called and written lambda.
def mult2(x):
return x*2
print(mult2(2))
# returns 4
Exactly same you can do with a lambda function.
lam = lambda x : x*2
print(lam(2))
# returns 4
For more details you can look up in this blog post.
http://friendlypython.herokuapp.com/2017/1/anonymous-function-lambda/ or here

I would like to explain this with a little layman approach in terms of the usage perspective, since all the technical points are already covered.
A lambda function is just like any other function, only thing that makes it different (other than the syntax and the fact that it can't be reused), is that it is used when we want to quickly write a function which takes an argument and just returns a value using only one expression.
For instance:
If all that a function does is take a argument and add 1 to it then using a lambda function is a better approach than a normal function.
But if your function requires more than one line of processing before returning the value, then we need to use a normal function definition and then call it.

How to create multiple functions in a for loop?

I want to create multiple functions namly func1, func2, ..., funci, in a for loop that all take different definitions. I have tested the following methods but they throw me errors. How is the proper way to do it:
What I want:
for i in range(1, 10):
func1(x, args1):
do something
func2(x, args2):
do something
What I did but didn't work:
for ii in range(1,10):
def globals()["func{}".format(ii)](t, "args{}".format(ii))
for ii in range(1,10):
def "func{}".format(ii)(t, "args{}".format(ii))
For the question I have I found only the following link which does not work for my purpose.
How do I loop through functions with similar names in python?

I think this answer using inspect's signature function does what you want. If it doesn't, or you think it's too complicated, the only way I can think of is to use eval:
for i in range(1, 10):
eval(f"""def func{i}(arg{i}):
print(arg{i})""")
Please read here why you shouldn't do that.

It's not clear what exactly you're trying to accomplish here.
This is a simple example of a way to define a list of functions dynamically:
fns = [lambda:char for char in string.ascii_lowercase]
(obviously, I've defined some very simple functions to focus on the creation aspect)
Based on discussion in the comments thread, I believe what the OP is looking for is to pass a list of functions to an optimizing function (specifically, scipy.optimize_least_squares)
The most important thing to know here is that the names of the functions in the local namespace do not matter, what matters is that the functions are passed to the optimizing function with the correct parameter names. (they will then have the correct names in the namespace of the called function, which is what you want)
However, if you want to dynamically generate the keyword arguments for a function call, the easiest way to do that is to create a dict and then use the ** operator to unpack it:
kwargs = some_func_returning_a_dict_of_kwargs()
result = scipy.optimize_least_squares(**kwargs)

Use a dict and put lambda functions as value with arguments, e.g:
d = {}
for i in range(0,101):
f = "func{0}".format(i)
if f not in d:
d[f] = lambda x, args: x * args
print(d['func1'](10,10))
print(d['func2'](10,20))
print(d['func3'](10,30))
This will give you each key calling that function with different arguments.
you don't need to call the args{0}.format(i) to give you different name, you can keep track of them with your keys. this is preferable way when readability counts.

Pythonic way to initialise function parameters

Which is more pythonic?
A
def my_function(arg, p=0):
while arg:
# do something
value = arg.pop()
# save value with a name which uses p for indexing
p+=1
or B
def my_function(arg):
p = 0
while arg:
# do something
value = arg.pop()
# save value with a name which uses p for indexing
p+=1
A part of me think its silly to include p as an argument to the function incase someone sets it to a weird a value. But at the same time I don't like having p=0 clutter up a function which is already very complicated.

Don't clutter up function parameters with locals. If it is not a parameter the caller should use, don't add it to your function signature.
In other words, you don't want anyone using help(my_function) and be surprised at what p might be for.
This isn't a hard and fast rule, of course; some critical-path functions can be made faster by using locals, so sometimes you'll see something like:
some_global = expensive_function()
def foo(bar, baz, _some_local=some_global):
# code using _some_local instead of some_global
to make use of faster local name lookups. The _ at the start of the argument then tells you that the name is really an internal implementation detail you should not rely on.

It depends on if p always has to start at 0 or not. If it does, then definitely go with option B. Don't give users an opportunity to mess with your code if it's not necessary.

Why use lambda functions?

I can find lots of stuff showing me what a lambda function is, and how the syntax works and what not. But other than the "coolness factor" (I can make a function in middle a call to another function, neat!) I haven't seen something that's overwelmingly compelling to say why I really need/want to use them.
It seems to be more of a stylistic or structual choice in most examples I've seen. And kinda breaks the "Only one correct way to do something" in python rule. How does it make my programs, more correct, more reliable, faster, or easier to understand? (Most coding standards I've seen tend to tell you to avoid overly complex statements on a single line. If it makes it easier to read break it up.)

Here's a good example:
def key(x):
return x[1]
a = [(1, 2), (3, 1), (5, 10), (11, -3)]
a.sort(key=key)
versus
a = [(1, 2), (3, 1), (5, 10), (11, -3)]
a.sort(key=lambda x: x[1])
From another angle: Lambda expressions are also known as "anonymous functions", and are very useful in certain programming paradigms, particularly functional programming, which lambda calculus provided the inspiration for.
http://en.wikipedia.org/wiki/Lambda_calculus

The syntax is more concise in certain situations, mostly when dealing with map et al.
map(lambda x: x * 2, [1,2,3,4])
seems better to me than:
def double(x):
return x * 2
map(double, [1,2,3,4])
I think the lambda is a better choice in this situation because the def double seems almost disconnected from the map that is using it. Plus, I guess it has the added benefit that the function gets thrown away when you are done.
There is one downside to lambda which limits its usefulness in Python, in my opinion: lambdas can have only one expression (i.e., you can't have multiple lines). It just can't work in a language that forces whitespace.
Plus, whenever I use lambda I feel awesome.

For me it's a matter of the expressiveness of the code. When writing code that people will have to support, that code should tell a story in as concise and easy to understand manner as possible. Sometimes the lambda expression is more complicated, other times it more directly tells what that line or block of code is doing. Use judgment when writing.
Think of it like structuring a sentence. What are the important parts (nouns and verbs vs. objects and methods, etc.) and how should they be ordered for that line or block of code to convey what it's doing intuitively.

Lambda functions are most useful in things like callback functions, or places in which you need a throwaway function. JAB's example is perfect - It would be better accompanied by the keyword argument key, but it still provides useful information.
When
def key(x):
return x[1]
appears 300 lines away from
[(1,2), (3,1), (5,10), (11,-3)].sort(key)
what does key do? There's really no indication. You might have some sort of guess, especially if you're familiar with the function, but usually it requires going back to look. OTOH,
[(1,2), (3,1), (5,10), (11,-3)].sort(lambda x: x[1])
tells you a lot more.
Sort takes a function as an argument
That function takes 1 parameter (and "returns" a result)
I'm trying to sort this list by the 2nd value of each of the elements of the list
(If the list were a variable so you couldn't see the values) this logic expects the list to have at least 2 elements in it.
There's probably some more information, but already that's a tremendous amount that you get just by using an anonymous lambda function instead of a named function.
Plus it doesn't pollute your namespace ;)

Yes, you're right — it is a structural choice. It probably does not make your programs more correct by just using lambda expressions. Nor does it make them more reliable, and this has nothing to do with speed.
It is only about flexibility and the power of expression. Like list comprehension. You can do most of that defining named functions (possibly polluting namespace, but that's again purely stylistic issue).
It can aid to readability by the fact, that you do not have to define a separate named function, that someone else will have to find, read and understand that all it does is to call a method blah() on its argument.
It may be much more interesting when you use it to write functions that create and return other functions, where what exactly those functions do, depends on their arguments. This may be a very concise and readable way of parameterizing your code behaviour. You can just express more interesting ideas.
But that is still a structural choice. You can do that otherwise. But the same goes for object oriented programming ;)

Ignore for a moment the detail that it's specifically anonymous functions we're talking about. functions, including anonymous ones, are assignable quantities (almost, but not really, values) in Python. an expression like
map(lambda y: y * -1, range(0, 10))
explicitly mentions four anonymous quantities: -1, 0, 10 and the result of the lambda operator, plus the implied result of the map call. it's possible to create values of anonymous types in some languages. so ignore the superficial difference between functions and numbers. the question when to use an anonymous function as opposed to a named one is similar to a question of when to put a naked number literal in the code and when to declare a TIMES_I_WISHED_I_HAD_A_PONY or BUFFER_SIZE beforehand. there are times when it's appropriate to use a (numeric, string or function) literal, and there are times when it's more appropriate to name such a thing and refer to it through its name.
see eg. Allen Holub's provocative, thought-or-anger-provoking book on Design Patterns in Java; he uses anonymous classes quite a bit.

Lambda, while useful in certain situations, has a large potential for abuse. lambda's almost always make code more difficult to read. And while it might feel satisfying to fit all your code onto a single line, it will suck for the next person who has to read your code.
Direct from PEP8
"One of Guido's key insights is that code is read much more often than it is written."

It is definitely true that abusing lambda functions often leads to bad and hard-to-read code. On the other hand, when used accurately, it does the opposite. There are already great answers in this thread, but one example I have come across is:
def power(n):
return lambda x: x**n
square = power(2)
cubic = power(3)
quadruple = power(4)
print(square(10)) # 100
print(cubic(10)) # 1000
print(quadruple(10)) # 10000
This simplified case could be rewritten in many other ways without the use of lambda. Still, one can infer how lambda functions can increase readability and code reuse in perhaps more complex cases and functions with this example.

Lambdas are anonymous functions (function with no name) that can be assigned to a variable or that can be passed as an argument to another function. The usefulness of lambda will be realized when you need a small piece of function that will be run once in a while or just once. Instead of writing the function in global scope or including it as part of your main program you can toss around few lines of code when needed to a variable or another function. Also when you pass the function as an argument to another function during the function call you can change the argument (the anonymous function) making the function itself dynamic. Suppose if the anonymous function uses variables outside its scope it is called closure. This is useful in callback functions.

One use of lambda function which I have learned, and where is not other good alternative or at least looks for me best is as default action in function parameter by
parameter=lambda x: x
This returns the value without change, but you can supply one function optionally to perform a transformation or action (like printing the answer, not only returning)
Also often it is useful to use in sorting as key:
key=lambda x: x[field]
The effect is to sort by fieldth (zero based remember) element of each item in sequence. For reversing you do not need lambda as it is clearer to use
reverse=True
Often it is almost as easy to do new real function and use that instead of lambda. If people has studied much Lisp or other functional programming, they also have natural tendency to use lambda function as in Lisp the function definitions are handled by lambda calculus.

Lambdas are objects, not methods, and they cannot be invoked in the same way that methods are.
for e.g
succ = ->(x){ x+1 }
succ mow holds a Proc object, which we can use like any other:
succ.call(2)
gives us an output = 3

I want to point out one situation other than list-processing where the lambda functions seems the best choice:
from tkinter import *
from tkinter import ttk
def callback(arg):
print(arg)
pass
root = Tk()
ttk.Button(root, text = 'Button1', command = lambda: callback('Button 1 clicked')).pack()
root.mainloop()
And if we drop lambda function here, the callback may only execute the callback once.
ttk.Button(root, text = 'Button1', command = callback('Button1 clicked')).pack()

Another point is that python does not have switch statements. Combining lambdas with dicts can be an effective alternative. e.g.:
switch = {
'1': lambda x: x+1,
'2': lambda x: x+2,
'3': lambda x: x+3
}
x = starting_val
ans = expression
new_ans = switch[ans](x)

In some cases it is much more clear to express something simple as a lambda. Consider regular sorting vs. reverse sorting for example:
some_list = [2, 1, 3]
print sorted(some_list)
print sorted(some_list, lambda a, b: -cmp(a, b))
For the latter case writing a separate full-fledged function just to return a -cmp(a, b) would create more misunderstanding then a lambda.

Lambdas allow you to create functions on the fly. Most of the examples I've seen don't do much more than create a function with parameters passed at the time of creation rather than execution. Or they simplify the code by not requiring a formal declaration of the function ahead of use.
A more interesting use would be to dynamically construct a python function to evaluate a mathematical expression that isn't known until run time (user input). Once created, that function can be called repeatedly with different arguments to evaluate the expression (say you wanted to plot it). That may even be a poor example given eval(). This type of use is where the "real" power is - in dynamically creating more complex code, rather than the simple examples you often see which are not much more than nice (source) code size reductions.

you master lambda, you master shortcuts in python.Here is why:
data=[(lambda x:x.text)(x.extract()) for x in soup.findAll('p') ]
^1 ^2 ^3 ^4
here we can see 4 parts of the list comprehension:
1: i finally want this
2: x.extract will perform some operation on x, here it pop the element from soup
3: x is the list iterable which is passed to the input of lambda at 2 along with extract operation
4: some arbitary list
i had found no other way to use 2 statements in lambda, but with this
kind of pipe-lining we can exploit the infinite potential of lambda.
Edit: as pointed out in the comments, by juanpa, its completely fine to use x.extract().text but the point was explaining the use of lambda pipe, ie passing the output of lambda1 as input to lambda2. via (lambda1 y:g(x))(lambda2 x:f(x))

Which is more preferable to use: lambda functions or nested functions ('def')?

I mostly use lambda functions but sometimes use nested functions that seem to provide the same behavior.
Here are some trivial examples where they functionally do the same thing if either were found within another function:
Lambda function
>>> a = lambda x : 1 + x
>>> a(5)
6
Nested function
>>> def b(x): return 1 + x
>>> b(5)
6
Are there advantages to using one over the other? (Performance? Readability? Limitations? Consistency? etc.)
Does it even matter? If it doesn't then does that violate the Pythonic principle:
There should be one-- and preferably only one --obvious way to do it..

If you need to assign the lambda to a name, use a def instead. defs are just syntactic sugar for an assignment, so the result is the same, and they are a lot more flexible and readable.
lambdas can be used for use once, throw away functions which won't have a name.
However, this use case is very rare. You rarely need to pass around unnamed function objects.
The builtins map() and filter() need function objects, but list comprehensions and generator expressions are generally more readable than those functions and can cover all use cases, without the need of lambdas.
For the cases you really need a small function object, you should use the operator module functions, like operator.add instead of lambda x, y: x + y
If you still need some lambda not covered, you might consider writing a def, just to be more readable. If the function is more complex than the ones at operator module, a def is probably better.
So, real world good lambda use cases are very rare.

Practically speaking, to me there are two differences:
The first is about what they do and what they return:
def is a keyword that doesn't return anything and creates a 'name' in the local namespace.
lambda is a keyword that returns a function object and does not create a 'name' in the local namespace.
Hence, if you need to call a function that takes a function object, the only way to do that in one line of python code is with a lambda. There's no equivalent with def.
In some frameworks this is actually quite common; for example, I use Twisted a lot, and so doing something like
d.addCallback(lambda result: setattr(self, _someVariable, result))
is quite common, and more concise with lambdas.
The second difference is about what the actual function is allowed to do.
A function defined with 'def' can contain any python code
A function defined with 'lambda' has to evaluate to an expression, and can thus not contain statements like print, import, raise, ...
For example,
def p(x): print x
works as expected, while
lambda x: print x
is a SyntaxError.
Of course, there are workarounds - substitute print with sys.stdout.write, or import with __import__. But usually you're better off going with a function in that case.

In this interview, Guido van Rossum says he wishes he hadn't let 'lambda' into Python:
"Q. What feature of Python are you least pleased with?
Sometimes I've been too quick in accepting contributions, and later realized that it was a mistake. One example would be some of the functional programming features, such as lambda functions. lambda is a keyword that lets you create a small anonymous function; built-in functions such as map, filter, and reduce run a function over a sequence type, such as a list.
In practice, it didn't turn out that well. Python only has two scopes: local and global. This makes writing lambda functions painful, because you often want to access variables in the scope where the lambda was defined, but you can't because of the two scopes. There's a way around this, but it's something of a kludge. Often it seems much easier in Python to just use a for loop instead of messing around with lambda functions. map and friends work well only when there's already a built-in function that does what you want.
IMHO, Iambdas can be convenient sometimes, but usually are convenient at the expense of readibility. Can you tell me what this does:
str(reduce(lambda x,y:x+y,map(lambda x:x**x,range(1,1001))))[-10:]
I wrote it, and it took me a minute to figure it out. This is from Project Euler - i won't say which problem because i hate spoilers, but it runs in 0.124 seconds :)

For n=1000 here's some timeit's of calling a function vs a lambda:
In [11]: def f(a, b):
return a * b
In [12]: g = lambda x, y: x * y
In [13]: %%timeit -n 100
for a in xrange(n):
for b in xrange(n):
f(a, b)
....:
100 loops, best of 3: 285 ms per loop
In [14]: %%timeit -n 100
for a in xrange(n):
for b in xrange(n):
g(a, b)
....:
100 loops, best of 3: 298 ms per loop
In [15]: %%timeit -n 100
for a in xrange(n):
for b in xrange(n):
(lambda x, y: x * y)(a, b)
....:
100 loops, best of 3: 462 ms per loop

More preferable: lambda functions or nested functions (def)?
There is one advantage to using a lambda over a regular function: they are created in an expression.
There are several drawbacks:
no name (just '<lambda>')
no docstrings
no annotations
no complex statements
They are also both the same type of object. For those reasons, I generally prefer to create functions with the def keyword instead of with lambdas.
First point - they're the same type of object
A lambda results in the same type of object as a regular function
>>> l = lambda: 0
>>> type(l)
<class 'function'>
>>> def foo(): return 0
...
>>> type(foo)
<class 'function'>
>>> type(foo) is type(l)
True
Since lambdas are functions, they're first-class objects.
Both lambdas and functions:
can be passed around as an argument (same as a regular function)
when created within an outer function become a closure over that outer functions' locals
But lambdas are, by default, missing some things that functions get via full function definition syntax.
A lamba's __name__ is '<lambda>'
Lambdas are anonymous functions, after all, so they don't know their own name.
>>> l.__name__
'<lambda>'
>>> foo.__name__
'foo'
Thus lambda's can't be looked up programmatically in their namespace.
This limits certain things. For example, foo can be looked up with serialized code, while l cannot:
>>> import pickle
>>> pickle.loads(pickle.dumps(l))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
_pickle.PicklingError: Can't pickle <function <lambda> at 0x7fbbc0464e18>:
attribute lookup <lambda> on __main__ failed
We can lookup foo just fine - because it knows its own name:
>>> pickle.loads(pickle.dumps(foo))
<function foo at 0x7fbbbee79268>
Lambdas have no annotations and no docstring
Basically, lambdas are not documented. Let's rewrite foo to be better documented:
def foo() -> int:
"""a nullary function, returns 0 every time"""
return 0
Now, foo has documentation:
>>> foo.__annotations__
{'return': <class 'int'>}
>>> help(foo)
Help on function foo in module __main__:
foo() -> int
a nullary function, returns 0 every time
Whereas, we don't have the same mechanism to give the same information to lambdas:
>>> help(l)
Help on function <lambda> in module __main__:
<lambda> lambda (...)
But we can hack them on:
>>> l.__doc__ = 'nullary -> 0'
>>> l.__annotations__ = {'return': int}
>>> help(l)
Help on function <lambda> in module __main__:
<lambda> lambda ) -> in
nullary -> 0
But there's probably some error messing up the output of help, though.
Lambdas can only return an expression
Lambdas can't return complex statements, only expressions.
>>> lambda: if True: 0
File "<stdin>", line 1
lambda: if True: 0
^
SyntaxError: invalid syntax
Expressions can admittedly be rather complex, and if you try very hard you can probably accomplish the same with a lambda, but the added complexity is more of a detriment to writing clear code.
We use Python for clarity and maintainability. Overuse of lambdas can work against that.
The only upside for lambdas: can be created in a single expression
This is the only possible upside. Since you can create a lambda with an expression, you can create it inside of a function call.
Creating a function inside a function call avoids the (inexpensive) name lookup versus one created elsewhere.
However, since Python is strictly evaluated, there is no other performance gain to doing so aside from avoiding the name lookup.
For a very simple expression, I might choose a lambda.
I also tend to use lambdas when doing interactive Python, to avoid multiple lines when one will do. I use the following sort of code format when I want to pass in an argument to a constructor when calling timeit.repeat:
import timeit
def return_nullary_lambda(return_value=0):
return lambda: return_value
def return_nullary_function(return_value=0):
def nullary_fn():
return return_value
return nullary_fn
And now:
>>> min(timeit.repeat(lambda: return_nullary_lambda(1)))
0.24312214995734394
>>> min(timeit.repeat(lambda: return_nullary_function(1)))
0.24894469301216304
I believe the slight time difference above can be attributed to the name lookup in return_nullary_function - note that it is very negligible.
Conclusion
Lambdas are good for informal situations where you want to minimize lines of code in favor of making a singular point.
Lambdas are bad for more formal situations where you need clarity for editors of code who will come later, especially in cases where they are non-trivial.
We know we are supposed to give our objects good names. How can we do so when the object has no name?
For all of these reasons, I generally prefer to create functions with def instead of with lambda.

Performance:
Creating a function with lambda is slightly faster than creating it with def. The difference is due to def creating a name entry in the locals table. The resulting function has the same execution speed.
Readability:
Lambda functions are somewhat less readable for most Python users, but also much more concise in some circumstances. Consider converting from using non-functional to functional routine:
# Using non-functional version.
heading(math.sqrt(v.x * v.x + v.y * v.y), math.atan(v.y / v.x))
# Using lambda with functional version.
fheading(v, lambda v: math.sqrt(v.x * v.x + v.y * v.y), lambda v: math.atan(v.y / v.x))
# Using def with functional version.
def size(v):
return math.sqrt(v.x * v.x + v.y * v.y)
def direction(v):
return math.atan(v.y / v.x)
deal_with_headings(v, size, direction)
As you can see, the lambda version is shorter and "easier" in the sense that you only need to add lambda v: to the original non-functional version to convert to the functional version. It's also a lot more concise. But remember, a lot of Python users will be confused by the lambda syntax, so what you lose in length and real complexity might be gained back in confusion from fellow coders.
Limitations:
lambda functions can only be used once, unless assigned to a variable name.
lambda functions assigned to variable names have no advantage over def functions.
lambda functions can be difficult or impossible to pickle.
def functions' names must be carefully chosen to be reasonably descriptive and unique or at least otherwise unused in scope.
Consistency:
Python mostly avoids functional programming conventions in favor of procedural and simpler objective semantics. The lambda operator stands in direct contrast to this bias. Moreover, as an alternative to the already prevalent def, the lambda function adds diversity to your syntax. Some would consider that less consistent.
Pre-existing functions:
As noted by others, many uses of lambda in the field can be replaced by members of the operator or other modules. For instance:
do_something(x, y, lambda x, y: x + y)
do_something(x, y, operator.add)
Using the pre-existing function can make code more readable in many cases.
The Pythonic principle: “There should be one—and preferably only one—obvious way to do it”
That's similar to the single source of truth doctrine. Unfortunately, the single-obvious-way-to-do-it principle has always been more an wistful aspiration for Python, rather than a true guiding principal. Consider the very-powerful array comprehensions in Python. They are functionally equivalent to the map and filter functions:
[e for e in some_array if some_condition(e)]
filter(some_array, some_condition)
lambda and def are the same.
It's a matter of opinion, but I would say that anything in the Python language intended for general use which doesn't obviously break anything is "Pythonic" enough.

I agree with nosklo's advice: if you need to give the function a name, use def. I reserve lambda functions for cases where I'm just passing a brief snippet of code to another function, e.g.:
a = [ (1,2), (3,4), (5,6) ]
b = map( lambda x: x[0]+x[1], a )

While agreeing with the other answers, sometimes it's more readable. Here's an example where lambda comes in handy, in a use case I keep encountering of an N dimensional defaultdict.Here's an example:
from collections import defaultdict
d = defaultdict(lambda: defaultdict(list))
d['Foo']['Bar'].append(something)
I find it more readable than creating a def for the second dimension. This is even more significant for higher dimensions.

The primary use of lambda has always been for simple callback functions, and for map, reduce, filter, which require a function as an argument. With list comprehensions becoming the norm, and the added allowed if as in:
x = [f for f in range(1, 40) if f % 2]
it's hard to imagine a real case for the use of lambda in daily use. As a result, I'd say, avoid lambda and create nested functions.

An important limitation of lambdas is that they cannot contain anything besides an expression. It's nearly impossible for a lambda expression to produce anything besides trivial side effects, since it cannot have anywhere near as rich a body as a def'ed function.
That being said, Lua influenced my programming style toward the extensive use of anonymous functions, and I litter my code with them. On top of that, I tend to think about map/reduce as abstract operators in ways I don't consider list comprehensions or generators, almost as If I'm deferring an implementation decision explicitly by using those operators.
Edit: This is a pretty old question, and my opinions on the matter have changed, somewhat.
First off, I am strongly biased against assigning a lambda expression to a variable; as python has a special syntax just for that (hint, def). In addition to that, many of the uses for lambda, even when they don't get a name, have predefined (and more efficient) implementations. For instance, the example in question can be abbreviated to just (1).__add__, without the need to wrap it in a lambda or def. Many other common uses can be satisfied with some combination of the operator, itertools and functools modules.

Computation time.
Function without name.
To achieve One function and many use functionality.
Considering a simple example,
# CREATE ONE FUNCTION AND USE IT TO PERFORM MANY OPERATIONS ON SAME TYPE OF DATA STRUCTURE.
def variousUse(a,b=lambda x:x[0]):
return [b(i) for i in a]
dummyList = [(0,1,2,3),(4,5,6,7),(78,45,23,43)]
variousUse(dummyList) # extract first element
variousUse(dummyList,lambda x:[x[0],x[2],x[3]]) # extract specific indexed element
variousUse(dummyList,lambda x:x[0]+x[2]) # add specific elements
variousUse(dummyList,lambda x:x[0]*x[2]) # multiply specific elements

If you are just going to assign the lambda to a variable in the local scope, you may as well use def because it is more readable and can be expanded more easily in the future:
fun = lambda a, b: a ** b # a pointless use of lambda
map(fun, someList)
or
def fun(a, b): return a ** b # more readable
map(fun, someList)

One use for lambdas I have found... is in debug messages.
Since lambdas can be lazily evaluated you can have code like this:
log.debug(lambda: "this is my message: %r" % (some_data,))
instead of possibly expensive:
log.debug("this is my message: %r" % (some_data,))
which processes the format string even if the debug call does not produce output because of current logging level.
Of course for it to work as described the logging module in use must support lambdas as "lazy parameters" (as my logging module does).
The same idea may be applied to any other case of lazy evaluation for on demand content value creation.
For example this custom ternary operator:
def mif(condition, when_true, when_false):
if condition:
return when_true()
else:
return when_false()
mif(a < b, lambda: a + a, lambda: b + b)
instead of:
def mif(condition, when_true, when_false):
if condition:
return when_true
else:
return when_false
mif(a < b, a + a, b + b)
with lambdas only the expression selected by the condition will be evaluated, without lambdas both will be evaluated.
Of course you could simply use functions instead of lambdas, but for short expressions lambdas are (c)leaner.

I agree with nosklo. By the way, even with a use once, throw away function, most of the time you just want to use something from the operator module.
E.G :
You have a function with this signature : myFunction(data, callback function).
You want to pass a function that add 2 elements.
Using lambda :
myFunction(data, (lambda x, y : x + y))
The pythonic way :
import operator
myFunction(data, operator.add)
Or course this is a simple example, but there is a lot of stuff the operator module provides, including the items setters / getters for list and dict. Really cool.

A major difference is that you can not use def functions inline, which is in my opinion the most convenient use case for a lambda function. For example when sorting a list of objects:
my_list.sort(key=lambda o: o.x)
I would therefore suggest keeping the use of lambdas to this kind of trivial operations, which also do not really benefit from the automatic documentation provided by naming the function.

lambda is useful for generating new functions:
>>> def somefunc(x): return lambda y: x+y
>>> f = somefunc(10)
>>> f(2)
12
>>> f(4)
14