> What can lambda do that normal function definitions cannot?
> Is it quicker to execute/less memory intensive?
> Or is it just quicker to type and easier to think about?
Notational convenience. Think about how, in arithmetic expressions,
how we're not forced to give explicit names to all the subexpressions:
#########################
def hypotenuse(a, b):
return ((a * a) + (b * b))**0.5
#########################
Imagine a world where we have to give explicit names to all of the
subexpressions:
###################
def hypotenuse(a, b):
tmp1 = a * a
tmp2 = b * b
tmp3 = tmp1 + tmp2
tmp4 = tmp3**0.5
return tmp4
####################
Does this look funny to you? Why?
Sometimes we don't care what something is named: we just want to use
the value. lambda's use is motivated by the same idea: sometimes, we
want to use a function value without having to give a name. Here's a
toy example.
####################################
def deepmap(f, datum):
"""Deeply applies f across the datum."""
if type(datum) == list:
return [deepmap(f, x) for x in datum]
else:
return f(datum)
####################################
If we wanted to apply a squaring on all the numbers in the nested list
(while still maintaining the nested structure):
[42, 43, [44, [45]]]
then we can use deepmap by feeding in a square function to it.
##############################
def square(x):
return x * x
deepmap(square, [42, 43, [44, [45]]])
###############################
An alternative way to express the above is:
deepmap(lambda x: x *x, [42, 43, [44, [45]]])
Here, we avoid having to first give a name to the function value we're
passing to deepmap.
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