Why are assignments not allowed in Python's `lambda` expressions? - python

This is not a duplicate of Assignment inside lambda expression in Python, i.e., I'm not asking how to trick Python into assigning in a lambda expression.
I have some λ-calculus background. Considering the following code, it
looks like Python is quite willing to perform side-effects in lambda
expressions:
#!/usr/bin/python
def applyTo42(f):
return f(42)
def double(x):
return x * 2
class ContainsVal:
def __init__(self, v):
self.v = v
def store(self, v):
self.v = v
def main():
print('== functional, no side effects')
print('-- print the double of 42')
print(applyTo42(double))
print('-- print 1000 more than 42')
print(applyTo42(lambda x: x + 1000))
print('-- print c\'s value instead of 42')
c = ContainsVal(23)
print(applyTo42(lambda x: c.v))
print('== not functional, side effects')
print('-- perform IO on 42')
applyTo42(lambda x: print(x))
print('-- set c\'s value to 42')
print(c.v)
applyTo42(lambda x: c.store(x))
print(c.v)
#print('== illegal, but why?')
#print(applyTo42(lambda x: c.v = 99))
if __name__ == '__main__':
main()
But if I uncomment the lines
print('== illegal, but why?')
print(applyTo42(lambda x: c.v = 99))
I'll get
SyntaxError: lambda cannot contain assignment
Why not? What is the deeper reason behind this?
As the code demonstrates, it cannot be about “purity” in a
functional sense.
The only explanation I can imagine is that assignemts do not
return anything, not even None. But that sounds lame and would
be easy to fix (one way: make lambda expressions return None if
body is a statement).
Not an answer:
Because it's defined that way (I want to know why it's defined that way).
Because it's in the grammar (see above).
Use def if you need statements (I did not ask for how to get
statements into a function).
“This would change syntax / the language / semantics” would be ok as an answer if you can come up with an example of such a change, and why it would be bad.

The entire reason lambda exists is that it's an expression.1 If you want something that's like lambda but is a statement, that's just def.
Python expressions cannot contain statements. This is, in fact, fundamental to the language, and Python gets a lot of mileage out of that decision. It's the reason indentation for flow control works instead of being clunky as in many other attempts (like CoffeeScript). It's the reason you can read off the state changes by skimming the first object in each line. It's even part of the reason the language is easy to parse, both for the compiler and for human readers.2
Changing Python to have some way to "escape" the statement-expression divide, except maybe in a very careful and limited way, would turn it into a completely different language, and one that no longer had many of the benefits that cause people to choose Python in the first place.
Changing Python to make most statements expressions (like, say, Ruby) would again turn it into a completely different language without Python's current benefits.
And if Python did make either of those changes, then there'd no longer be a reason for lambda in the first place;2,3 you could just use def statements inside an expression.
What about changing Python to instead make assignments expressions? Well, it should be obvious that would break "you can read off the state changes by skimming the first object in each line". Although Guido usually focuses on the fact that if spam=eggs is an error more often than a useful thing.
The fact that Python does give you ways to get around that when needed, like setattr or even explicitly calling __setitem__ on globals(), doesn't mean it's something that should have direct syntactic support. Something that's very rarely needed doesn't deserve syntactic sugar—and even more so for something that's unusual enough that it should raise eyebrows and/or red flags when it actually is done.
1. I have no idea whether that was Guido's understanding when he originally added lambda back in Python 1.0. But it's definitely the reason lambda wasn't removed in Python 3.0.
2. In fact, Guido has, multiple times, suggested that allowing an LL(1) parser that humans can run in their heads is sufficient reason for the language being statement-based, to the point that other benefits don't even need to be discussed. I wrote about this a few years ago if anyone's interested.
3. If you're wondering why so many languages do have a lambda expression despite already having def: In many languages, ranging from C++ to Ruby, function aren't first-class objects that can be passed around, so they had to invent a second thing that is first-class but works like a function. In others, from Smalltalk to Java, functions don't even exist, only methods, so again, they had to invent a second thing that's not a method but works like one. Python has neither of those problems.
4. A few languages, like C# and JavaScript, actually had perfectly working inline function definitions, but added some kind of lambda syntax as pure syntactic sugar, to make it more concise and less boilerplatey. That might actually be worth doing in Python (although every attempt at a good syntax so far has fallen flat), but it wouldn't be the current lambda syntax, which is nearly as verbose as def.

There is a syntax problem: an assignment is a statement, and the body of a lambda can only have expressions. Python's syntax is designed this way1. Check it out at https://docs.python.org/3/reference/grammar.html.
There is also a semantics problem: what does each statement return?
I don't think there is interest in changing this, as lambdas are meant for very simple and short code. Moreover, a statement would allow sequences of statements as well, and that's not desirable for lambdas.
It could be also fixed by selectively allowing certain statements in the lambda body, and specifying the semantics (e.g. an assignment returns None, or returns the assigned value; the latter makes more sense to me). But what's the benefit?
Lambdas and functions are interchangeable. If you really have a use-case for a particular statement in the body of a lambda, you can define a function that executes it, and your specific problem is solved.
Perhaps you can create a syntactic macro to allow that with MacroPy3 (I'm just guessing, as I'm a fan of the project, but still I haven't had the time to dive in it).
For example MacroPy would allow you to define a macro that transforms f[_ * _] into lambda a, b: a * b, so it should not be impossible to define the syntax for a lambda that calls a function you defined.
1 A good reason to not change it is that it would cripple the syntax, because a lambda can be in places where expressions can be. And statements should not. But that's a very subjective remark of my own.

My answer is based on chepner's comment above and doesn't draw from any other credible or official source, however I think that it will be useful.
If assignment was allowed in lambda expressions, then the error of confusing == (equality test) with = (assignment) would have more chances of escaping into the wild.
Example:
>>> # Correct use of equality test
... list(filter(lambda x: x==1, [0, 1, 0.0, 1.0, 0+0j, 1+0j]))
[1, 1.0, (1+0j)]
>>> # Suppose that assignment is used by mistake instead of equality testing
... # and the return value of an assignment expression is always None
... list(filter(lambda x: None, [0, 1, 0.0, 1.0, 0+0j, 1+0j]))
[]
>>> # Suppose that assignment is used by mistake instead of equality testing
... # and the return value of an assignment expression is the assigned value
... list(filter(lambda x: 1, [0, 1, 0.0, 1.0, 0+0j, 1+0j]))
[0, 1, 0.0, 1.0, 0j, (1+0j)]

As long as exec() (and eval()) is allowed inside lambda, you can do assignments inside lambda:
q = 3
def assign(var_str, val_str):
exec("global " + var_str + "; " +
var_str + " = " + val_str)
lambda_assign = lambda var_str, val_str: assign(var_str, val_str)
q ## gives: 3
lambda_assign("q", "100")
q ## gives: 100
## what would such expression be a win over the direct:
q = 100
## ? `lambda_assign("q", "100")` will be for sure slower than
## `q = 100` isn't it?
q_assign = lambda v: assign("q", v)
q_assign("33")
q ## 33
## but do I need lambda for q_assign?
def q_assign(v): assign("q", v)
## would do it, too, isn't it?
But since lambda expressions allow only 1 expression to be defined inside their body (at least in Python ...), what would be the point of to allow an assignment inside a lambda? Its net effect would be to assign directly (without using any lambda) q = 100, isn't it?
It would be even faster than doing it over a defined lambda, since you have at least one function lookup and execution less to execute ...

There's not really any deeper reasons, it has nothing to do with lambda or functional language designs, it's just to avoid programmers from mixing = and == operators, which is a very common mistake in other languages
IF there's more to this story, I assume like MAYBE because python bdfl GVR has expressed his unloving sides to lambda and other functional features and attempted(and conceded) to remove them from python 3 altogether https://www.artima.com/weblogs/viewpost.jsp?thread=98196
At the time of this writing the core devs were seen having a heated discussions recently on whether to include a limited name binding expression assignment, the debate is still on going so perhaps someday we may see it in lambda(unlikely)
As you said it yourself it is definitely not about side effects or purity, they just don't want lambda to be more than a single expression... ... ...
With that said, here's something about multi expressions assignments in lambda, read on if you're interested
It is not at all impossible in python, in fact it was sometimes necessary to capture variable and sidestep late bindings by (ab)using kwargs(keyword arguments)
edit:
code example
f = lambda x,a=1: (lambda c = a+2, b = a+1: (lambda e = x,d = c+1: print(a,b,c,d,e))())()
f("w")
# output 1 2 3 4 w
# expression assignment through an object's method call
if let(a=1) .a > 0 and let(b=let.a+1) .b != 1 and let(c=let.b+let.a) .c:
print(let.a, let.b, let.c)
# output 1 2 3

As it stands, Python was designed as a statement-based language. Therefore assignment and other name bindings are statements, and do not have any result.
The Python core developers are currently discussing PEP 572, which would introduce a name-binding expression.

I think all the fellows answered this already. We use mostly lambdas function when we just want to:
-create some simple functions that do the work perfectly in a specific place(most of the time hidden inside some other big functions
-The lambda function does not have a name
-Can be used with some other built-ins functions such as map, list and so forth ...
>>> Celsius = [39.2, 36.5, 37.3, 37.8]
>>> Fahrenheit = map(lambda x: (float(9)/5)*x + 32, Celsius) # mapping the list here
>>> print Fahrenheit
[102.56, 97.700000000000003, 99.140000000000001, 100.03999999999999]
Please visit this webpage , this could be useful.Keep it up !!!
https://www.python-course.eu/lambda.php

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What will be the values of the lambda var in the following function syntax? [duplicate]

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I'm trying to figure out Python lambdas. Is lambda one of those "interesting" language items that in real life should be forgotten?
I'm sure there are some edge cases where it might be needed, but given the obscurity of it, the potential of it being redefined in future releases (my assumption based on the various definitions of it) and the reduced coding clarity - should it be avoided?
This reminds me of overflowing (buffer overflow) of C types - pointing to the top variable and overloading to set the other field values. It feels like sort of a techie showmanship but maintenance coder nightmare.
Are you talking about lambda expressions? Like
lambda x: x**2 + 2*x - 5
Those things are actually quite useful. Python supports a style of programming called functional programming where you can pass functions to other functions to do stuff. Example:
mult3 = filter(lambda x: x % 3 == 0, [1, 2, 3, 4, 5, 6, 7, 8, 9])
sets mult3 to [3, 6, 9], those elements of the original list that are multiples of 3. This is shorter (and, one could argue, clearer) than
def filterfunc(x):
return x % 3 == 0
mult3 = filter(filterfunc, [1, 2, 3, 4, 5, 6, 7, 8, 9])
Of course, in this particular case, you could do the same thing as a list comprehension:
mult3 = [x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9] if x % 3 == 0]
(or even as range(3,10,3)), but there are many other, more sophisticated use cases where you can't use a list comprehension and a lambda function may be the shortest way to write something out.
Returning a function from another function
>>> def transform(n):
... return lambda x: x + n
...
>>> f = transform(3)
>>> f(4)
7
This is often used to create function wrappers, such as Python's decorators.
Combining elements of an iterable sequence with reduce()
>>> reduce(lambda a, b: '{}, {}'.format(a, b), [1, 2, 3, 4, 5, 6, 7, 8, 9])
'1, 2, 3, 4, 5, 6, 7, 8, 9'
Sorting by an alternate key
>>> sorted([1, 2, 3, 4, 5, 6, 7, 8, 9], key=lambda x: abs(5-x))
[5, 4, 6, 3, 7, 2, 8, 1, 9]
I use lambda functions on a regular basis. It took me a while to get used to them, but eventually I came to understand that they're a very valuable part of the language.
lambda is just a fancy way of saying function. Other than its name, there is nothing obscure, intimidating or cryptic about it. When you read the following line, replace lambda by function in your mind:
>>> f = lambda x: x + 1
>>> f(3)
4
It just defines a function of x. Some other languages, like R, say it explicitly:
> f = function(x) { x + 1 }
> f(3)
4
You see? It's one of the most natural things to do in programming.
The two-line summary:
Closures: Very useful. Learn them, use them, love them.
Python's lambda keyword: unnecessary, occasionally useful. If you find yourself doing anything remotely complex with it, put it away and define a real function.
A lambda is part of a very important abstraction mechanism which deals with higher order functions. To get proper understanding of its value, please watch high quality lessons from Abelson and Sussman, and read the book SICP
These are relevant issues in modern software business, and becoming ever more popular.
I doubt lambda will go away.
See Guido's post about finally giving up trying to remove it. Also see an outline of the conflict.
You might check out this post for more of a history about the deal behind Python's functional features:
http://python-history.blogspot.com/2009/04/origins-of-pythons-functional-features.html
Curiously, the map, filter, and reduce functions that originally motivated the introduction of lambda and other functional features have to a large extent been superseded by list comprehensions and generator expressions. In fact, the reduce function was removed from list of builtin functions in Python 3.0. (However, it's not necessary to send in complaints about the removal of lambda, map or filter: they are staying. :-)
My own two cents: Rarely is lambda worth it as far as clarity goes. Generally there is a more clear solution that doesn't include lambda.
lambdas are extremely useful in GUI programming. For example, lets say you're creating a group of buttons and you want to use a single paramaterized callback rather than a unique callback per button. Lambda lets you accomplish that with ease:
for value in ["one","two","three"]:
b = tk.Button(label=value, command=lambda arg=value: my_callback(arg))
b.pack()
(Note: although this question is specifically asking about lambda, you can also use functools.partial to get the same type of result)
The alternative is to create a separate callback for each button which can lead to duplicated code.
In Python, lambda is just a way of defining functions inline,
a = lambda x: x + 1
print a(1)
and..
def a(x): return x + 1
print a(1)
..are the exact same.
There is nothing you can do with lambda which you cannot do with a regular function—in Python functions are an object just like anything else, and lambdas simply define a function:
>>> a = lambda x: x + 1
>>> type(a)
<type 'function'>
I honestly think the lambda keyword is redundant in Python—I have never had the need to use them (or seen one used where a regular function, a list-comprehension or one of the many builtin functions could have been better used instead)
For a completely random example, from the article "Python’s lambda is broken!":
To see how lambda is broken, try generating a list of functions fs=[f0,...,f9] where fi(n)=i+n. First attempt:
>>> fs = [(lambda n: i + n) for i in range(10)]
>>> fs[3](4)
13
I would argue, even if that did work, it's horribly and "unpythonic", the same functionality could be written in countless other ways, for example:
>>> n = 4
>>> [i + n for i in range(10)]
[4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
Yes, it's not the same, but I have never seen a cause where generating a group of lambda functions in a list has been required. It might make sense in other languages, but Python is not Haskell (or Lisp, or ...)
Please note that we can use lambda and still achieve the desired
results in this way :
>>> fs = [(lambda n,i=i: i + n) for i in range(10)]
>>> fs[3](4)
7
Edit:
There are a few cases where lambda is useful, for example it's often convenient when connecting up signals in PyQt applications, like this:
w = PyQt4.QtGui.QLineEdit()
w.textChanged.connect(lambda event: dothing())
Just doing w.textChanged.connect(dothing) would call the dothing method with an extra event argument and cause an error. Using the lambda means we can tidily drop the argument without having to define a wrapping function.
I find lambda useful for a list of functions that do the same, but for different circumstances.
Like the Mozilla plural rules:
plural_rules = [
lambda n: 'all',
lambda n: 'singular' if n == 1 else 'plural',
lambda n: 'singular' if 0 <= n <= 1 else 'plural',
...
]
# Call plural rule #1 with argument 4 to find out which sentence form to use.
plural_rule[1](4) # returns 'plural'
If you'd have to define a function for all of those you'd go mad by the end of it.
Also, it wouldn't be nice with function names like plural_rule_1, plural_rule_2, etc. And you'd need to eval() it when you're depending on a variable function id.
Pretty much anything you can do with lambda you can do better with either named functions or list and generator expressions.
Consequently, for the most part you should just one of those in basically any situation (except maybe for scratch code written in the interactive interpreter).
I've been using Python for a few years and I've never run in to a case where I've needed lambda. Really, as the tutorial states, it's just for syntactic sugar.
Lambda function it's a non-bureaucratic way to create a function.
That's it. For example, let's supose you have your main function and need to square values. Let's see the traditional way and the lambda way to do this:
Traditional way:
def main():
...
...
y = square(some_number)
...
return something
def square(x):
return x**2
The lambda way:
def main():
...
square = lambda x: x**2
y = square(some_number)
return something
See the difference?
Lambda functions go very well with lists, like lists comprehensions or map. In fact, list comprehension it's a "pythonic" way to express yourself using lambda. Ex:
>>>a = [1,2,3,4]
>>>[x**2 for x in a]
[1,4,9,16]
Let's see what each elements of the syntax means:
[] : "Give me a list"
x**2 : "using this new-born function"
for x in a: "into each element in a"
That's convenient uh? Creating functions like this. Let's rewrite it using lambda:
>>> square = lambda x: x**2
>>> [square(s) for x in a]
[1,4,9,16]
Now let's use map, which is the same thing, but more language-neutral. Maps takes 2 arguments:
(i) one function
(ii) an iterable
And gives you a list where each element it's the function applied to each element of the iterable.
So, using map we would have:
>>> a = [1,2,3,4]
>>> squared_list = map(lambda x: x**2, a)
If you master lambdas and mapping, you will have a great power to manipulate data and in a concise way. Lambda functions are neither obscure nor take away code clarity. Don't confuse something hard with something new. Once you start using them, you will find it very clear.
I can't speak to python's particular implementation of lambda, but in general lambda functions are really handy. They're a core technique (maybe even THE technique) of functional programming, and they're also very useuful in object-oriented programs. For certain types of problems, they're the best solution, so certainly shouldn't be forgotten!
I suggest you read up on closures and the map function (that links to python docs, but it exists in nearly every language that supports functional constructs) to see why it's useful.
One of the nice things about lambda that's in my opinion understated is that it's way of deferring an evaluation for simple forms till the value is needed. Let me explain.
Many library routines are implemented so that they allow certain parameters to be callables (of whom lambda is one). The idea is that the actual value will be computed only at the time when it's going to be used (rather that when it's called). An (contrived) example might help to illustrate the point. Suppose you have a routine which which was going to do log a given timestamp. You want the routine to use the current time minus 30 minutes. You'd call it like so
log_timestamp(datetime.datetime.now() - datetime.timedelta(minutes = 30))
Now suppose the actual function is going to be called only when a certain event occurs and you want the timestamp to be computed only at that time. You can do this like so
log_timestamp(lambda : datetime.datetime.now() - datetime.timedelta(minutes = 30))
Assuming the log_timestamp can handle callables like this, it will evaluate this when it needs it and you'll get the timestamp at that time.
There are of course alternate ways to do this (using the operator module for example) but I hope I've conveyed the point.
Update: Here is a slightly more concrete real world example.
Update 2: I think this is an example of what is called a thunk.
As stated above, the lambda operator in Python defines an anonymous function, and in Python functions are closures. It is important not to confuse the concept of closures with the operator lambda, which is merely syntactic methadone for them.
When I started in Python a few years ago, I used lambdas a lot, thinking they were cool, along with list comprehensions. However, I wrote and have to maintain a big website written in Python, with on the order of several thousand function points. I've learnt from experience that lambdas might be OK to prototype things with, but offer nothing over inline functions (named closures) except for saving a few key-stokes, or sometimes not.
Basically this boils down to several points:
it is easier to read software that is explicitly written using meaningful names. Anonymous closures by definition cannot have a meaningful name, as they have no name. This brevity seems, for some reason, to also infect lambda parameters, hence we often see examples like lambda x: x+1
it is easier to reuse named closures, as they can be referred to by name more than once, when there is a name to refer to them by.
it is easier to debug code that is using named closures instead of lambdas, because the name will appear in tracebacks, and around the error.
That's enough reason to round them up and convert them to named closures. However, I hold two other grudges against anonymous closures.
The first grudge is simply that they are just another unnecessary keyword cluttering up the language.
The second grudge is deeper and on the paradigm level, i.e. I do not like that they promote a functional-programming style, because that style is less flexible than the message passing, object oriented or procedural styles, because the lambda calculus is not Turing-complete (luckily in Python, we can still break out of that restriction even inside a lambda). The reasons I feel lambdas promote this style are:
There is an implicit return, i.e. they seem like they 'should' be functions.
They are an alternative state-hiding mechanism to another, more explicit, more readable, more reusable and more general mechanism: methods.
I try hard to write lambda-free Python, and remove lambdas on sight. I think Python would be a slightly better language without lambdas, but that's just my opinion.
Lambdas are actually very powerful constructs that stem from ideas in functional programming, and it is something that by no means will be easily revised, redefined or removed in the near future of Python. They help you write code that is more powerful as it allows you to pass functions as parameters, thus the idea of functions as first-class citizens.
Lambdas do tend to get confusing, but once a solid understanding is obtained, you can write clean elegant code like this:
squared = map(lambda x: x*x, [1, 2, 3, 4, 5])
The above line of code returns a list of the squares of the numbers in the list. Ofcourse, you could also do it like:
def square(x):
return x*x
squared = map(square, [1, 2, 3, 4, 5])
It is obvious the former code is shorter, and this is especially true if you intend to use the map function (or any similar function that takes a function as a parameter) in only one place. This also makes the code more intuitive and elegant.
Also, as #David Zaslavsky mentioned in his answer, list comprehensions are not always the way to go especially if your list has to get values from some obscure mathematical way.
From a more practical standpoint, one of the biggest advantages of lambdas for me recently has been in GUI and event-driven programming. If you take a look at callbacks in Tkinter, all they take as arguments are the event that triggered them. E.g.
def define_bindings(widget):
widget.bind("<Button-1>", do-something-cool)
def do-something-cool(event):
#Your code to execute on the event trigger
Now what if you had some arguments to pass? Something as simple as passing 2 arguments to store the coordinates of a mouse-click. You can easily do it like this:
def main():
# define widgets and other imp stuff
x, y = None, None
widget.bind("<Button-1>", lambda event: do-something-cool(x, y))
def do-something-cool(event, x, y):
x = event.x
y = event.y
#Do other cool stuff
Now you can argue that this can be done using global variables, but do you really want to bang your head worrying about memory management and leakage especially if the global variable will just be used in one particular place? That would be just poor programming style.
In short, lambdas are awesome and should never be underestimated. Python lambdas are not the same as LISP lambdas though (which are more powerful), but you can really do a lot of magical stuff with them.
Lambdas are deeply linked to functional programming style in general. The idea that you can solve problems by applying a function to some data, and merging the results, is what google uses to implement most of its algorithms.
Programs written in functional programming style, are easily parallelized and hence are becoming more and more important with modern multi-core machines.
So in short, NO you should not forget them.
First congrats that managed to figure out lambda. In my opinion this is really powerful construct to act with. The trend these days towards functional programming languages is surely an indicator that it neither should be avoided nor it will be redefined in the near future.
You just have to think a little bit different. I'm sure soon you will love it. But be careful if you deal only with python. Because the lambda is not a real closure, it is "broken" somehow: pythons lambda is broken
I'm just beginning Python and ran head first into Lambda- which took me a while to figure out.
Note that this isn't a condemnation of anything. Everybody has a different set of things that don't come easily.
Is lambda one of those 'interesting' language items that in real life should be forgotten?
No.
I'm sure there are some edge cases where it might be needed, but given the obscurity of it,
It's not obscure. The past 2 teams I've worked on, everybody used this feature all the time.
the potential of it being redefined in future releases (my assumption based on the various definitions of it)
I've seen no serious proposals to redefine it in Python, beyond fixing the closure semantics a few years ago.
and the reduced coding clarity - should it be avoided?
It's not less clear, if you're using it right. On the contrary, having more language constructs available increases clarity.
This reminds me of overflowing (buffer overflow) of C types - pointing to the top variable and overloading to set the other field values...sort of a techie showmanship but maintenance coder nightmare..
Lambda is like buffer overflow? Wow. I can't imagine how you're using lambda if you think it's a "maintenance nightmare".
A useful case for using lambdas is to improve the readability of long list comprehensions.
In this example loop_dic is short for clarity but imagine loop_dic being very long. If you would just use a plain value that includes i instead of the lambda version of that value you would get a NameError.
>>> lis = [{"name": "Peter"}, {"name": "Josef"}]
>>> loop_dic = lambda i: {"name": i["name"] + " Wallace" }
>>> new_lis = [loop_dic(i) for i in lis]
>>> new_lis
[{'name': 'Peter Wallace'}, {'name': 'Josef Wallace'}]
Instead of
>>> lis = [{"name": "Peter"}, {"name": "Josef"}]
>>> new_lis = [{"name": i["name"] + " Wallace"} for i in lis]
>>> new_lis
[{'name': 'Peter Wallace'}, {'name': 'Josef Wallace'}]
I use lambdas to avoid code duplication. It would make the function easily comprehensible
Eg:
def a_func()
...
if some_conditon:
...
call_some_big_func(arg1, arg2, arg3, arg4...)
else
...
call_some_big_func(arg1, arg2, arg3, arg4...)
I replace that with a temp lambda
def a_func()
...
call_big_f = lambda args_that_change: call_some_big_func(arg1, arg2, arg3, args_that_change)
if some_conditon:
...
call_big_f(argX)
else
...
call_big_f(argY)
I started reading David Mertz's book today 'Text Processing in Python.' While he has a fairly terse description of Lambda's the examples in the first chapter combined with the explanation in Appendix A made them jump off the page for me (finally) and all of a sudden I understood their value. That is not to say his explanation will work for you and I am still at the discovery stage so I will not attempt to add to these responses other than the following:
I am new to Python
I am new to OOP
Lambdas were a struggle for me
Now that I read Mertz, I think I get them and I see them as very useful as I think they allow a cleaner approach to programming.
He reproduces the Zen of Python, one line of which is Simple is better than complex. As a non-OOP programmer reading code with lambdas (and until last week list comprehensions) I have thought-This is simple?. I finally realized today that actually these features make the code much more readable, and understandable than the alternative-which is invariably a loop of some sort. I also realized that like financial statements-Python was not designed for the novice user, rather it is designed for the user that wants to get educated. I can't believe how powerful this language is. When it dawned on me (finally) the purpose and value of lambdas I wanted to rip up about 30 programs and start over putting in lambdas where appropriate.
I can give you an example where I actually needed lambda serious. I'm making a graphical program, where the use right clicks on a file and assigns it one of three options. It turns out that in Tkinter (the GUI interfacing program I'm writing this in), when someone presses a button, it can't be assigned to a command that takes in arguments. So if I chose one of the options and wanted the result of my choice to be:
print 'hi there'
Then no big deal. But what if I need my choice to have a particular detail. For example, if I choose choice A, it calls a function that takes in some argument that is dependent on the choice A, B or C, TKinter could not support this. Lamda was the only option to get around this actually...
I use it quite often, mainly as a null object or to partially bind parameters to a function.
Here are examples:
to implement null object pattern:
{
DATA_PACKET: self.handle_data_packets
NET_PACKET: self.handle_hardware_packets
}.get(packet_type, lambda x : None)(payload)
for parameter binding:
let say that I have the following API
def dump_hex(file, var)
# some code
pass
class X(object):
#...
def packet_received(data):
# some kind of preprocessing
self.callback(data)
#...
Then, when I wan't to quickly dump the recieved data to a file I do that:
dump_file = file('hex_dump.txt','w')
X.callback = lambda (x): dump_hex(dump_file, x)
...
dump_file.close()
I use lambda to create callbacks that include parameters. It's cleaner writing a lambda in one line than to write a method to perform the same functionality.
For example:
import imported.module
def func():
return lambda: imported.module.method("foo", "bar")
as opposed to:
import imported.module
def func():
def cb():
return imported.module.method("foo", "bar")
return cb
I'm a python beginner, so to getter a clear idea of lambda I compared it with a 'for' loop; in terms of efficiency.
Here's the code (python 2.7) -
import time
start = time.time() # Measure the time taken for execution
def first():
squares = map(lambda x: x**2, range(10))
# ^ Lambda
end = time.time()
elapsed = end - start
print elapsed + ' seconds'
return elapsed # gives 0.0 seconds
def second():
lst = []
for i in range(10):
lst.append(i**2)
# ^ a 'for' loop
end = time.time()
elapsed = end - start
print elapsed + ' seconds'
return elapsed # gives 0.0019998550415 seconds.
print abs(second() - first()) # Gives 0.0019998550415 seconds!(duh)
Lambda is a procedure constructor. You can synthesize programs at run-time, although Python's lambda is not very powerful. Note that few people understand that kind of programming.

Do different types of recursion have different memory complexities? [duplicate]

I have the following piece of code which fails with the following error:
RuntimeError: maximum recursion depth exceeded
I attempted to rewrite this to allow for tail recursion optimization (TCO). I believe that this code should have been successful if a TCO had taken place.
def trisum(n, csum):
if n == 0:
return csum
else:
return trisum(n - 1, csum + n)
print(trisum(1000, 0))
Should I conclude that Python does not do any type of TCO, or do I just need to define it differently?
No, and it never will since Guido van Rossum prefers to be able to have proper tracebacks:
Tail Recursion Elimination (2009-04-22)
Final Words on Tail Calls (2009-04-27)
You can manually eliminate the recursion with a transformation like this:
>>> def trisum(n, csum):
... while True: # Change recursion to a while loop
... if n == 0:
... return csum
... n, csum = n - 1, csum + n # Update parameters instead of tail recursion
>>> trisum(1000,0)
500500
I published a module performing tail-call optimization (handling both tail-recursion and continuation-passing style): https://github.com/baruchel/tco
Optimizing tail-recursion in Python
It has often been claimed that tail-recursion doesn't suit the Pythonic way of coding and that one shouldn't care about how to embed it in a loop. I don't want to argue with
this point of view; sometimes however I like trying or implementing new ideas
as tail-recursive functions rather than with loops for various reasons (focusing on the
idea rather than on the process, having twenty short functions on my screen in the same
time rather than only three "Pythonic" functions, working in an interactive session rather than editing my code, etc.).
Optimizing tail-recursion in Python is in fact quite easy. While it is said to be impossible
or very tricky, I think it can be achieved with elegant, short and general solutions; I even
think that most of these solutions don't use Python features otherwise than they should.
Clean lambda expressions working along with very standard loops lead to quick, efficient and
fully usable tools for implementing tail-recursion optimization.
As a personal convenience, I wrote a small module implementing such an optimization
by two different ways. I would like to discuss here about my two main functions.
The clean way: modifying the Y combinator
The Y combinator is well known; it allows to use lambda functions in a recursive
manner, but it doesn't allow by itself to embed recursive calls in a loop. Lambda
calculus alone can't do such a thing. A slight change in the Y combinator however
can protect the recursive call to be actually evaluated. Evaluation can thus be delayed.
Here is the famous expression for the Y combinator:
lambda f: (lambda x: x(x))(lambda y: f(lambda *args: y(y)(*args)))
With a very slight change, I could get:
lambda f: (lambda x: x(x))(lambda y: f(lambda *args: lambda: y(y)(*args)))
Instead of calling itself, the function f now returns a function performing the
very same call, but since it returns it, the evaluation can be done later from outside.
My code is:
def bet(func):
b = (lambda f: (lambda x: x(x))(lambda y:
f(lambda *args: lambda: y(y)(*args))))(func)
def wrapper(*args):
out = b(*args)
while callable(out):
out = out()
return out
return wrapper
The function can be used in the following way; here are two examples with tail-recursive
versions of factorial and Fibonacci:
>>> from recursion import *
>>> fac = bet( lambda f: lambda n, a: a if not n else f(n-1,a*n) )
>>> fac(5,1)
120
>>> fibo = bet( lambda f: lambda n,p,q: p if not n else f(n-1,q,p+q) )
>>> fibo(10,0,1)
55
Obviously recursion depth isn't an issue any longer:
>>> bet( lambda f: lambda n: 42 if not n else f(n-1) )(50000)
42
This is of course the single real purpose of the function.
Only one thing can't be done with this optimization: it can't be used with a
tail-recursive function evaluating to another function (this comes from the fact
that callable returned objects are all handled as further recursive calls with
no distinction). Since I usually don't need such a feature, I am very happy
with the code above. However, in order to provide a more general module, I thought
a little more in order to find some workaround for this issue (see next section).
Concerning the speed of this process (which isn't the real issue however), it happens
to be quite good; tail-recursive functions are even evaluated much quicker than with
the following code using simpler expressions:
def bet1(func):
def wrapper(*args):
out = func(lambda *x: lambda: x)(*args)
while callable(out):
out = func(lambda *x: lambda: x)(*out())
return out
return wrapper
I think that evaluating one expression, even complicated, is much quicker than
evaluating several simple expressions, which is the case in this second version.
I didn't keep this new function in my module, and I see no circumstances where it
could be used rather than the "official" one.
Continuation passing style with exceptions
Here is a more general function; it is able to handle all tail-recursive functions,
including those returning other functions. Recursive calls are recognized from
other return values by the use of exceptions. This solutions is slower than the
previous one; a quicker code could probably be written by using some special
values as "flags" being detected in the main loop, but I don't like the idea of
using special values or internal keywords. There is some funny interpretation
of using exceptions: if Python doesn't like tail-recursive calls, an exception
should be raised when a tail-recursive call does occur, and the Pythonic way will be
to catch the exception in order to find some clean solution, which is actually what
happens here...
class _RecursiveCall(Exception):
def __init__(self, *args):
self.args = args
def _recursiveCallback(*args):
raise _RecursiveCall(*args)
def bet0(func):
def wrapper(*args):
while True:
try:
return func(_recursiveCallback)(*args)
except _RecursiveCall as e:
args = e.args
return wrapper
Now all functions can be used. In the following example, f(n) is evaluated to the
identity function for any positive value of n:
>>> f = bet0( lambda f: lambda n: (lambda x: x) if not n else f(n-1) )
>>> f(5)(42)
42
Of course, it could be argued that exceptions are not intended to be used for intentionally
redirecting the interpreter (as a kind of goto statement or probably rather a kind of
continuation passing style), which I have to admit. But, again,
I find funny the idea of using try with a single line being a return statement: we try to return
something (normal behaviour) but we can't do it because of a recursive call occurring (exception).
Initial answer (2013-08-29).
I wrote a very small plugin for handling tail recursion. You may find it with my explanations there: https://groups.google.com/forum/?hl=fr#!topic/comp.lang.python/dIsnJ2BoBKs
It can embed a lambda function written with a tail recursion style in another function which will evaluate it as a loop.
The most interesting feature in this small function, in my humble opinion, is that the function doesn't rely on some dirty programming hack but on mere lambda calculus: the behaviour of the function is changed to another one when inserted in another lambda function which looks very like the Y combinator.
The word of Guido is at http://neopythonic.blogspot.co.uk/2009/04/tail-recursion-elimination.html
I recently posted an entry in my Python History blog on the origins of
Python's functional features. A side remark about not supporting tail
recursion elimination (TRE) immediately sparked several comments about
what a pity it is that Python doesn't do this, including links to
recent blog entries by others trying to "prove" that TRE can be added
to Python easily. So let me defend my position (which is that I don't
want TRE in the language). If you want a short answer, it's simply
unpythonic. Here's the long answer:
CPython does not and will probably never support tail call optimization based on Guido van Rossum's statements on the subject.
I've heard arguments that it makes debugging more difficult because of how it modifies the stack trace.
Try the experimental macropy TCO implementation for size.
Besides optimizing tail recursion, you can set the recursion depth manually by:
import sys
sys.setrecursionlimit(5500000)
print("recursion limit:%d " % (sys.getrecursionlimit()))
There is no built-in tail recursion optimization in Python. However, we can "rebuild" the function through the Abstract Syntax Tree( AST), eliminating the recursion there and replacing it with a loop. Guido was absolutely right, this approach has some limitations, so I can't recommend it for use.
However, I still wrote (rather as a training example) my own version of the optimizer, and you can even try how it works.
Install this package via pip:
pip install astrologic
Now you can run this sample code:
from astrologic import no_recursion
counter = 0
#no_recursion
def recursion():
global counter
counter += 1
if counter != 10000000:
return recursion()
return counter
print(recursion())
This solution is not stable, and you should never use it in production. You can read about some significant restrictions on the page in github (in Russian, sorry). However, this solution is quite "real", without interrupting the code and other similar tricks.
A tail call can never be optimized to a jump in Python. An optimization is a program transformation that preserves the program's meaning. Tail-call elimination doesn't preserve the meaning of Python programs.
One problem, often mentioned, is that tail-call elimination changes the call stack, and Python allows for runtime introspection of the stack. But there is another problem that is rarely mentioned. There is probably a lot of code like this in the wild:
def map_file(path):
f = open(path, 'rb')
return mmap.mmap(f.fileno())
The call to mmap.mmap is in tail position. If it were replaced by a jump, then the current stack frame would be discarded before control was passed to mmap. The current stack frame contains the only reference to the file object, so the file object could (and in CPython would) be freed before mmap is called, which would close the file descriptor, invalidating it before mmap sees it.
At best, the code would fail with an exception. At worst, the file descriptor could be reused in another thread, causing mmap to map the wrong file. So this "optimization" would be a potentially disastrous thing to unleash on the huge body of existing Python code.
The Python spec guarantees that such problems won't occur, so you can be sure that no conformant implementation will ever convert return f(args) into a jump—unless, perhaps, it has a sophisticated static analysis engine that can prove that discarding an object early will have no observable consequences in this case.
None of that would prevent Python from adding a syntax for explicit tail calls with jump semantics, such as
return from f(args)
That wouldn't break code that didn't use it, and it would probably be useful for autogenerated code and some algorithms. GvR is no longer BDFL, so it might happen, but I wouldn't hold my breath.

Functional python programming and conditionals

I'm trying to write a python function in a functional way. The problem is I don't know, how to transform an if conditional into a functional style. I have two variables: A and C, which I want to check for the following conditions:
def function():
if(A==0): return 0
elif(C!=0): return 0
elif(A > 4): return 0
else: someOtherFunction()
I looked at the lambda shortcircuiting, but I couldn't get it to work.
I thank you in advance for your help!
From the link you posted:
FP either discourages or outright disallows statements,
and instead works with the evaluation of expressions
So instead of if-statements, you could use a conditional expression:
def function():
return (0 if ((A == 0) or (C != 0) or (A > 4)) else
someOtherFunction())
or, (especially useful if there were many different values):
def function():
return (0 if A == 0 else
0 if C != 0 else
0 if A > 4 else
someOtherFunction())
By the way, the linked article proposes
(<cond1> and func1()) or (<cond2> and func2()) or (func3())
as a short-curcuiting equivalent to
if <cond1>: func1()
elif <cond2>: func2()
else: func3()
The problem is they are not equivalent! The boolean expression fails to return the right value when <cond1> is Truish but func1() is Falsish (e.g. False or 0 or None). (Or similarly when <cond2> is Truish but func2 is Falsish.)
(<cond1> and func1())
is written with the intention of evaluating to func1() when <cond1> is Truish, but when func1() is Falsish, (<cond1> and func1()) evaluates to False, so the entire expression is passed over and Python goes on to evaluate (<cond2> and func2()) instead of short-circuiting.
So here is a bit of interesting history. In 2005,
Raymond Hettinger found a similar hard-to-find bug in type(z)==types.ComplexType and z.real or z when z = (0+4j) because z.real is Falsish. Motivated by a desire to save us from similar bugs, the idea of using a less error-prone syntax (conditional expressions) was born.
There's nothing non-"functional style" in your current code! who said conditionals are not functional anyway? Practically all functional languages have a conditional operator of some sort, for instance the cond special form in Lisp.
I'd take issue with the code if it were using the assignment operator, or mutating state in some way (say, appending to a list) but as it is, the function in the question is already in a "functional style" - there are no state changes.
Perhaps you meant something like this?
return A != 0 and C == 0 and A <= 4 and someOtherFunction()
The above will return False if either A == 0 or C != 0 or A > 4, in all other cases it will return the value of calling someOtherFunction(). And by the way, False can be assumed to evaluate to 0 (for example, 42 + False == 42), so the semantics in the code in the question will be preserved from the caller's point of view.
Notice that you're taking the information in the link out of context. There's absolutely no need to use a lambda for this, the article is only explaining how to get around an inherent limitation of lambdas in Python, which is that you can't return statements inside (like if-elif-else) - only expressions are allowed, but you can fake them with boolean operators. In the context of a normal function by all means, use conditionals.
Although Peter Norvig is a really great guy, his website is pretty hard to search.
I remember reading about Can I do the equivalent of (test ? result : alternative) in Python? on his site a while back during some research before a functional Python talk.
I'm not going to sway you one way or the other in light of my findings, but you should still go and read the section about ternary conditional operators in a functional style.
def if_(test, result, alternative=None):
"If test is true, 'do' result, else alternative. 'Do' means call if callable."
if test:
if callable(result): result = result()
return result
else:
if callable(alternative): alternative = alternative()
return alternative
Just use it as you have it.
Python does not have the syntax and library built-ins to make it easy to program all functions to directly return a single expression. That's not the most important part of functional style anyway, the most important part is making sure that your functions maintain referential integrity. Basically this means that whenever you supply them with the same input values they return the same output.
So when trying to program functionally in Python, I do not refrain from using statements entirely. I use a block of local variable assignments as an equivalent of let ... in ... from Haskell. I use an if/elif/else chain as an equivalent of a case expression from Haskell. And often there are built-in types which do not provide an adequate interface to create new "modified" versions of them rather than updating them in-place, so you instead have to implement such operations with an explicit copy operation and then using mutations on the new copy.
Python allows you to implement functional-style designs directly. You can easily structure your program as a whole bunch of functions which explicitly pass state around and don't have side effects, so you can design your high level algorithms in a very similar way that you would in a functional programming language. Nearly every programming language supports functional programming in this sense, if you're prepared to stomach the boilerplate necessary to fake first class functions. Since Python has first class functions, you don't even have to put up with boilerplate.
But that's as far as Python goes in supporting functional programming. It does't really support implementing functions as single referentially-transparent expressions. But that doesn't really matter. In a language that doesn't enforce or track purity, you get pretty much all the benefits of functional programming that you can by simply designing your program as a bunch of referentially transparent functions, and then how you implement those functions doesn't actually matter as long as the interface is kept referentially transparent.

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

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