- Python style guide recommends using list comprehensions instead of map/reduce
- String formatting using percent operator is obsolete, consider using format() method
the code you need is this simple one-liner
output = [" this string contains {} and {}".format(x, y) for (x, y) in matrix]
- Python style guide recommends using list comprehensions instead of map/reduce
- String formatting using percent operator is obsolete, consider using format() method
the code you need is this simple one-liner
output = [" this string contains {} and {}".format(x, y) for (x, y) in matrix]
You have a couple of issues, these structures aren't nested deeply enough to warrant the nested loops.
You need 1 map for each level of list you wish to process, so if you want to process a list, you need a map, if you want to process a list of lists, you need 2 and so on.
In this case you most likely only want to process the top level (effectively this is because you want each list in the top level to become a sentence).
def sentence( x, y):
return " this string contains %s and %s" % (x,y)
matrix = [['a','b'],['c','d']]
output = map(lambda a: sentence(a[0],a[1]), matrix)
# Print the top level
for i in output:
print(i)
Videos
I'm looking to expand my knowledge on the subject these upcoming days and was hoping someone could shed some clarity. Lambda escapes me sometimes because I THINK I understand, but then when I look at it in practice I get the cold fingers.
What I was wondering is if there was any similarity between the two? They look to me that they accomplish similar tasks. I'm not sure if they're the same thing or can be made into the same thing or if they're different? I was also looking at both of them and wondering if they're pretty much synonymous with a "for" loop?
Hello Pythonistas!
I've been on a Python journey recently, and I've found myself fascinated by the power and flexibility of Lambda functions. These anonymous functions have not only made my code more efficient and concise, but they've also opened up a new way of thinking about data manipulation when used with Python's built-in functions like Map, Filter, and Reduce.
Lambda functions are incredibly versatile. They can take any number of arguments, but can only have one expression. This makes them perfect for small, one-time-use functions that you don't want to give a name.
Here's a simple example of a Lambda function that squares a number:
square = lambda x: x ** 2
print(square(5)) # Output: 25
But the real power of Lambda functions comes when you use them with functions like Map, Filter, and Reduce. For instance, you can use a Lambda function with `map()` to square all numbers in a list:
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x ** 2, numbers))
print(squared) # Output: [1, 4, 9, 16, 25]
You can also use a Lambda function with `filter()` to get all the even numbers from a list:
numbers = [1, 2, 3, 4, 5]
even = list(filter(lambda x: x % 2 == 0, numbers))
print(even) # Output: [2, 4]
And finally, you can use a Lambda function with `reduce()` to get the product of all numbers in a list:
from functools import reduce
numbers = [1, 2, 3, 4, 5]
product = reduce(lambda x, y: x * y, numbers)
print(product) # Output: 120
Understanding and using Lambda functions, especially in conjunction with Map, Filter, and Reduce, has significantly improved my data manipulation skills in Python. If you haven't explored Lambda functions yet, I highly recommend giving them a try!
Happy coding!
You could nest the power() function in the main() function:
def main():
def power(x):
return x(r)
funcs = [square, cube]
for r in range(5):
value = map(power, funcs)
print value
so that r is now taken from the surrounding scope again, but is not a global. Instead it is a closure variable instead.
However, using a lambda is just another way to inject r from the surrounding scope here and passing it into the power() function:
def power(r, x):
return x(r)
def main():
funcs = [square, cube]
for r in range(5):
value = map(lambda x: power(r, x), funcs)
print value
Here r is still a non-local, taken from the parent scope!
You could create the lambda with r being a default value for a second argument:
def power(r, x):
return x(r)
def main():
funcs = [square, cube]
for r in range(5):
value = map(lambda x, r=r: power(r, x), funcs)
print value
Now r is passed in as a default value instead, so it was taken as a local. But for the purposes of your map() that doesn't actually make a difference here.
Currying is another option. Because a function of two arguments is the same as a function of one argument that returns another function that takes the remaining argument, you can write it like this:
def square(x):
return (x**2)
def cube(x):
return (x**3)
def power(r):
return lambda(x): x(r) # This is where we construct our curried function
def main():
funcs = [square, cube]
for y in range(5):
value = map(power(y), funcs) # Here, we apply the first function
# to get at the second function (which
# was constructed with the lambda above).
print value
if __name__ == "__main__":
main()
To make the relation a little more explicit, a function of the type (a, b) -> c (a function that takes an argument of type a and an argument of type b and returns a value of type c) is equivalent to a function of type a -> (b -> c).
Extra stuff about the equivalence
If you want to get a little deeper into the math behind this equivalence, you can see this relationship using a bit of algebra. Viewing these types as algebraic data types, we can translate any function a -> b to ba and any pair (a, b) to a * b. Sometimes function types are called "exponentials" and pair types are called "product types" because of this connection. From here, we can see that
c(a * b) = (cb)a
and so,
(a, b) -> c ~= a -> (b -> c)
There are several different answers I can give here, from your specific question to more general concerns. So from most specific to most general:
Q. Can you put multiple statements in a lambda?
A. No. But you don't actually need to use a lambda. You can put the statements in a def instead. i.e.:
def second_lowest(l):
l.sort()
return l[1]
map(second_lowest, lst)
Q. Can you get the second lowest item from a lambda by sorting the list?
A. Yes. As alex's answer points out, sorted() is a version of sort that creates a new list, rather than sorting in-place, and can be chained. Note that this is probably what you should be using - it's bad practice for your map to have side effects on the original list.
Q. How should I get the second lowest item from each list in a sequence of lists?
A. sorted(l)[1] is not actually the best way for this. It has O(N log(N)) complexity, while an O(n) solution exists. This can be found in the heapq module.
>>> import heapq
>>> l = [5,2,6,8,3,5]
>>> heapq.nsmallest(l, 2)
[2, 3]
So just use:
map(lambda x: heapq.nsmallest(x,2)[1], list_of_lists)
It's also usually considered clearer to use a list comprehension, which avoids the lambda altogether:
[heapq.nsmallest(x,2)[1] for x in list_of_lists]
Putting the expressions in a list may simulate multiple expressions:
E.g.:
lambda x: [f1(x), f2(x), f3(x), x+1]
This will not work with statements.