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Anonymous functions are great for single-use functions. And they have a really fun name, lambdas!
lambda, like def, is the keyword that marks a new function. Lambda functions don't have to have a name, though.
Lambdas can't contain new lines (outside of containers) or assignments.
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Anonymous functions are functions that
don't have a name, hence being anonymous.
0:00
I say that, but it's not a 100% true.
0:05
You can give anonymous functions a name
in Python, but you don't have to and
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usually won't.
0:11
We call anonymous functions lambda's,
borrowed from mathematics lambda calculus.
0:12
Remember that whole,
programming is done with expressions bit?
0:17
We've come full circle.
0:20
Let's jump over to Workspaces and
see how to use lambdas and
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how they can be applied to
our existing functionality.
0:25
So lambdas are anonymous
functions in Python.
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They let us just write a function
somewhere that we need one, and
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we don't wanna reference
that function again.
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We can give lambdas names, so they're not
anonymous, but that's a little weird.
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Most people don't do that.
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And lambdas are really only
meant to be one line long, and
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lambdas can't contain assignments,
so we can't do a equals five.
0:45
You might be thinking,
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well that's an awful lot of rules,
why do we even wanna have lambdas then?
0:50
It's because they're really useful for
little one off problems.
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They're very handy for,
I don't wanna write a function,
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I just need to throw this thing in there,
so let me do that.
1:00
For instance, all these places that
we've been writing functions up here,
1:05
with the exception of the maps.
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So, this map here, the title case,
and this map here, the sales price.
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We have to do assignment in both of these.
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So we can't do these as lambdas.
1:18
But we can do our functions and
our reduces and stuff as lambdas.
1:20
So let's see about that.
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Let's look at this one.
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We just did this total one, right?
1:25
Total equals reduce add_book_prices,
so let's repeat that.
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So let's do total, and
it's gonna be reduced, and
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now I haven't written a function, but my
function up there just did a + b, right?
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So, we're going to do lambda, and
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we're going to take two numbers,
x and y, just like in algebra class.
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And we're gonna add x and y.
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And lambda's automatically return
the last value that they calculate.
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They have an implicit return, so
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you can pretend that there's
the return keyword right there.
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But you don't wanna put that in.
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Okay, so this is our lambda.
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So we have lambda.
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These are the arguments
that the lambda takes.
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In this case our lambda takes
two arguments, x and y.
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A colon cuz we're starting a block,
but we're not indenting the block.
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Lambdas are a little bit strange, huh?
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And then this is the body of our function,
just x plus y.
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And that gets returned automatically.
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So now it's a reduce so
we have our function and
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then we have our list of values right?
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So let's say b.price for b in BOOKS So
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when we run this we should get 225,
so we did.
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So, we got the same amount of work but
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we didn't have to write a function
just to add two things together.
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So, that's pretty cool.
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Let's do our long_books again.
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So, it's a filter and lambda is x.
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Now lambda can be anything,
we're gonna say book.
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Lambda book.
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Cuz we're gonna get a book, right?
2:54
So book.number_of_pages.
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[INAUDIBLE] equal to 600 and
we're gonna do this out of BOOKS.
2:59
All right, so
now since I want to be able to print,
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let's do len(list(long_books)).
3:09
So how many long books do we have?
3:13
We have 12 long books.
3:16
If I remember correctly,
that was the number before.
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So lambdas are really handy for
being able to stick in places that we
3:19
don't wanna have to write a function
that's only gonna get used one time.
3:24
We could do,
good_deals = filter (lambda book:
3:30
book.price <= let's say 5.99, all right,
3:35
we'll do 6, so
if the book's than $6 this time.
3:41
Out of books and
we're gonna print len(listgood_deals)) and
3:47
let's see if there
are any good cheap books.
3:52
There's 6, cool.
3:55
So 6 books that are nice and cheap.
3:57
So again lambdas are just something
that you use instead of having to
4:00
write a function for
something that you're only gonna do once.
4:04
That's where lambdas come
in really really handy.
4:07
You might have heard the expression,
when all you have is a hammer,
4:11
everything looks like a nail.
4:13
Well, lambdas are often a big
hammer in the Python world.
4:15
You learn about them, and then you want
to use them in all sorts of places and
4:18
situations.
4:21
Most of the time, a lambda is the wrong
choice unless you're sure that you're only
4:23
going to need a particular
bit of functionality once and
4:27
only in one location.
4:29
Writing our full functions might be a bit
more trouble and take up more room, but
4:30
it will usually serve you
better in the long run.
4:34
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