- 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- String formatting using percent operator is obsolete, consider using format() method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- String formatting using percent operator is obsolete, consider using format() method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
because the lambda function needs to be created len(data) times, thus inefficient.
Not true, in the example the lambda definition is evaluated only once at compile time and not len(data) times - there is no need to assign it to a name for performance reasons. Look at Sergey's answer, he proves lambda is not expensive at all for this case.
If you do want to give it a name for the sake of clarity, you should just use a def statement instead. Assigning a lambda to a name is considered bad style: according to PEP-8 Programming Recommendations you should "Always use a def statement instead of an assignment statement that binds a lambda expression directly to an identifier". Quoting from the official style guide:
Yes:
def f(x): return 2*x
No:
f = lambda x: 2*x:
The only difference between lambda and the one-line def is that def will give it a name (probably an extra LOAD_CONST):
>>> import dis
>>> def _(x):
return f(x, 30)
>>> dis.dis(_)
2 0 LOAD_GLOBAL 0 (f)
2 LOAD_FAST 0 (x)
4 LOAD_CONST 1 (30)
6 CALL_FUNCTION 2
8 RETURN_VALUE
>>> dis.dis(lambda x: f(x, 30))
1 0 LOAD_GLOBAL 0 (f)
2 LOAD_FAST 0 (x)
4 LOAD_CONST 1 (30)
6 CALL_FUNCTION 2
8 RETURN_VALUE
As you can see above, both forms compile to the same bytecode.
The lisp inspired functions map, filter and reduce always felt a bit alien in Python. Since the introduction of list comprehensions (at version 2.0 IINM) they became the idiomatic way to achieve the same result. So this:
new_data = map(lambda x: f(x, 30), data)
Is often written as:
new_data = [f(x, 30) for x in data]
If data is big and you are just iterating over it, generator expressions trade memory for cpu:
for value in (f(x, 30) for x in data):
do_something_with(value)
The lispy constructs like map, filter and reduce are likely to be retired (moved to the functools module) and I recommend the use of list comprehensions and generator expressions in new code.
Last, Python is surprisingly counterintuitive regarding performance. You should always profile in order to put your beliefs about performance in check.
Bottom line: never worry about "optimizing" a damn thing until you have profiled it and know for sure it's a relevant bottleneck.
Lambda creates only once when map calls
In [20]: l = list(range(100000))
In [21]: %timeit list(map(lambda x: x * 2, l))
100 loops, best of 3: 13.8 ms per loop
In [22]: g = lambda x: x * 2
In [23]: %timeit list(map(g, l))
100 loops, best of 3: 13.8 ms per loop
As you can see, the execution time is not changed.
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!
Why? What am I missing? I understand map returns an iterator which is supposed to save memory instead of just holding the entire list right away. But when is my super heavy
The map function is what's commonly known in programmer terminology as lazy. It won't do any work unless it has to. This is more broadly known as in functional programming as lazy evaluation. Rather than immediately compute any values, the map function instead returns what's known as an iterator. By doing this, it's delegating the job of compute the values it was given back to you.
A single value can be computed by the iterator using next:
>>> arr = [1, 2, 3]
>>> it = map(lambda x: print(x), arr)
>>> next(it)
1
>>>
However, when you casted the map iterator to be a list, you were forcing map to compute all of its values and thus call your function:
>>> it = map(lambda x: print(x), arr)
>>> list(it)
1
2
3
4
[None, None, None, None]
>>>
As you said, you have to switch your mindset to use functional programming. One of the key concepts of functional programming is lazy evaluation, which is the default policy in languages such as Haskell.
The purpose of this policy is to save both time and memory, by calculating something only when it is needed. In that sense Python generators are also close to the functional paradigm.
If you want to execute it as soon as possible, you shouldn't write it in a functional style and fix it with list() if you don't care about the results. Using a loop is totally ok.