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I'm doing Advent of Code and until this point I had no problem to solve issues on my own until day 18 which got me. I want to solve it on my own but I feel like I can't even start however the task itself is not difficult. It's kinda long to elaborate but the point is the following:
I have to implement nested pairs, so every item of a pair can be an another pair and so on, for example:
[[[[[9,8],1],2],3],4]
[[3,[2,[1,[7,3]]]],[6,[5,[4,[3,2]]]]]
I have to do different operations on these pairs, such as deleting pairs on a very deep level and use its values at higher level. I know Python handles tuples really great but I have yet to find a solution for recursive (?) traversal, deletion and "saving" values from the "deep". It's not a wise solution to delete items during iterating. Shall I use a different approach or some custom data structure for a task like this? I don't need an exact solution just some generic guidance.
I haven't read problem 18 from advent of code, but here is an example answer to your question with a list version and a tuple version.
Imagine I have nested pairs, and I want to write a function that delete all the deepest pairs and replaced them with a single number being the sum of the two numbers in the pair. For example:
input -> output
(1, 2) -> 3
((1, 2), 3) -> (3, 3)
(((1,2), (3,4)), ((5,6), (7,8))) -> ((3, 7), (11, 15))
((((((1, 2), 3), 4), 5), 6), 7) -> (((((3, 3), 4), 5), 6), 7)
Here is a version with immutable tuples, that returns a new tuple:
def updated(p):
result, _ = updated_helper(p)
return result
def updated_helper(p):
if isinstance(p, tuple) and len(p) == 2:
a, b = p
new_a, a_is_number = updated_helper(a)
new_b, b_is_number = updated_helper(b)
if a_is_number and b_is_number:
return a+b, False
else:
return (new_a, new_b), False
else:
return p, True
Here is a version with mutable lists, that returns nothing useful but mutates the list:
def update(p):
if isinstance(p, list) and len(p) == 2:
a, b = p
a_is_number = update(a)
b_is_number = update(b)
if isinstance(a, list) and len(a) == 1:
p[0] = a[0]
if isinstance(b, list) and len(b) == 1:
p[1] = b[0]
if a_is_number and b_is_number:
p[:] = [a+b]
return False
else:
return True
Note how I used a substantive, updated, and a verb, update, to highlight the different logic between these two similar functions. Function update performs an action (modifying a list), whereas updated(p) doesn't perform any action, but is the updated pair.
Testing:
print( updated( (((1,2), (3,4)), ((5,6), (7,8))) ) )
# ((3, 7), (11, 15))
l = [[[1,2], [3,4]], [[5,6], [7,8]]]
update(l)
print(l)
# [[3, 7], [11, 15]]
Is there a faster way to write this, the function takes a list and a value to find the pairs of numeric values in that list that sum to N without duplicates I tried to make it faster by using sets instead of using the list itself (however I used count() which I know is is linear time) any suggestions I know there is probably a way
def pairsum_n(list1, value):
set1 = set(list1)
solution = {(min(i, value - i) , max(i, value - i)) for i in set1 if value - i in set1}
solution.remove((value/2,value/2)) if list1.count(value/2) < 2 else None
return solution
"""
Example: value = 10, list1 = [1,2,3,4,5,6,7,8,9]
pairsum_n = { (1,9), (2,8), (3,7), (4,6) }
Example: value = 10, list2 = [5,6,7,5,7,5,3]
pairsum_n = { (5,5), (3,7) }
"""
Your approach is quite good, it just needs a few tweaks to make it more efficient. itertools is convenient, but it's not really suitable for this task because it produces so many unwanted pairs. It's ok if the input list is small, but it's too slow if the input list is large.
We can avoid producing duplicates by looping over the numbers in order, stopping when i >= value/2, after using a set to get rid of dupes.
def pairsum_n(list1, value):
set1 = set(list1)
list1 = sorted(set1)
solution = []
maxi = value / 2
for i in list1:
if i >= maxi:
break
j = value - i
if j in set1:
solution.append((i, j))
return solution
Note that the original list1 is not modified. The assignment in this function creates a new local list1. If you do actually want (value/2, value/2) in the output, just change the break condition.
Here's a slightly more compact version.
def pairsum_n(list1, value):
set1 = set(list1)
solution = []
for i in sorted(set1):
j = value - i
if i >= j:
break
if j in set1:
solution.append((i, j))
return solution
It's possible to condense this further, eg using itertools.takewhile, but it will be harder to read and there won't be any improvement in efficiency.
Try this, running time O(nlogn):
v = [1, 2, 3, 4, 5, 6, 7, 8, 9]
l = 0
r = len(v)-1
def myFunc(v, value):
ans = []
% this block search for the pair (value//2, value//2)
if value % 2 == 0:
c = [i for i in v if i == value // 2]
if len(c) >= 2:
ans.append((c[0], c[1]))
v = list(set(v))
l = 0
r = len(v)-1
v.sort()
while l<len(v) and r >= 0 and l < r:
if v[l] + v[r] == value:
ans.append((v[l], v[r]))
l += 1
r -= 1
elif v[l] + v[r] < value:
l += 1
else:
r -= 1
return list(set(ans))
It is called the Two pointers technique and it works as follows. First of all, sort the array. This imposes a minimum running time of O(nlogn). Then set two pointers, one pointing at the start of the array l and other pointing at its last element r (pointers name are for left and right).
Now, look at the list. If the sum of the values returned at position l and r is lower than the value we are looking for, then we need to increment l. If it's greater, we need to decrement r.
If v[l] + v[r] == value than we can increment/decrement both l or r since in any case we want to skip the combination of values (v[l], v[r]) as we don't want duplicates.
Timings: this is actually slower then the other 2 solutions. Due to the amount of combinations produced but not actually needed it gets worse the bigger the lists are.
You can use itertools.combinations to produce the 2-tuple-combinations for you.
Put them into a set if they match your value, then return as set/list:
from itertools import combinations
def pairsum_n(list1, value):
"""Returns the unique list of pairs of combinations of numbers from
list1 that sum up `value`. Reorders the values to (min_value,max_value)."""
result = set()
for n in combinations(list1, 2):
if sum(n) == value:
result.add( (min(n),max(n)) )
return list(result)
# more ugly one-liner:
# return list(set(((min(n),max(n)) for n in combinations(list1,2) if sum(n)==value)))
data = [1,2,3,4,5,6,6,5,4,3,2,1]
print(pairsum_n(data,7))
Output:
[(1, 6), (2, 5), (3, 4)]
Fun little thing, with some sorting overhead you can get all at once:
def pairsum_n2(data, count_nums=2):
"""Generate a dict with all count_nums-tuples from data. Key into the
dict is the sum of all tuple-values."""
d = {}
for n in (tuple(sorted(p)) for p in combinations(data,count_nums)):
d.setdefault(sum(n),set()).add(n)
return d
get_all = pairsum_n2(data,2) # 2 == number of numbers to combine
for k in get_all:
print(k," -> ", get_all[k])
Output:
3 -> {(1, 2)}
4 -> {(1, 3), (2, 2)}
5 -> {(2, 3), (1, 4)}
6 -> {(1, 5), (2, 4), (3, 3)}
7 -> {(3, 4), (2, 5), (1, 6)}
2 -> {(1, 1)}
8 -> {(2, 6), (4, 4), (3, 5)}
9 -> {(4, 5), (3, 6)}
10 -> {(5, 5), (4, 6)}
11 -> {(5, 6)}
12 -> {(6, 6)}
And then just access the one you need via:
print(get_all.get(7,"Not possible")) # {(3, 4), (2, 5), (1, 6)}
print(get_all.get(17,"Not possible")) # Not possible
Have another solution, it's alot faster then the one I just wrote, not as fast as #PM 2Ring's answer:
def pairsum_n(list1, value):
set1 = set(list1)
if list1.count(value/2) < 2:
set1.remove(value/2)
return set((min(x, value - x) , max(x, value - x)) for x in filterfalse(lambda x: (value - x) not in set1, set1))
Hello fellow stackoverflowers, I am practising my Python with an example question given to me (actually a Google interview practice question) and ran into a problem I did not know how to a) pose properly (hence vague title), b) overcome.
The question is: For an array of numbers (given or random) find unique pairs of numbers within the array which when summed give a given number. E.G: find the pairs of numbers in the array below which add to 6.
[1 2 4 5 11]
So in the above case:
[1,5] and [2,4]
The code I have written is:
from secrets import *
i = 10
x = randbelow(10)
number = randbelow(100) #Generate a random number to be the sum that we are after#
if number == 0:
pass
else:
number = number
array = []
while i>0: #Generate a random array to use#
array.append(x)
x = x + randbelow(10)
i -= 1
print("The following is a randomly generated array:\n" + str(array))
print("Within this array we are looking for a pair of numbers which sum to " + str(number))
for i in range(0,10):
for j in range(0,10):
if i == j or i>j:
pass
else:
elem_sum = array[i] + array[j]
if elem_sum == number:
number_one = array[i]
number_two = array[j]
print("A pair of numbers within the array which satisfy that condition is: " + str(number_one) + " and " + str(number_two))
else:
pass
If no pairs are found, I want the line "No pairs were found". I was thinking a try/except, but wasn't sure if it was correct or how to implement it. Also, I'm unsure on how to stop repeated pairs appearing (unique pairs only), so for example if I wanted 22 as a sum and had the array:
[7, 9, 9, 13, 13, 14, 23, 32, 41, 45]
[9,13] would appear twice
Finally forgive me if there are redundancies/the code isn't written very efficiently, I'm slowly learning so any other tips would be greatly appreciated!
Thanks for reading :)
You can simply add a Boolean holding the answer to "was at least one pair found?".
initialize it as found = false at the beginning of your code.
Then, whenever you find a pair (the condition block that holds your current print command), just add found = true.
after all of your search (the double for loop`), add this:
if not found:
print("No pairs were found")
Instead of actually comparing each pair of numbers, you can just iterate the list once, subtract the current number from the target number, and see if the remainder is in the list. If you convert the list to a set first, that lookup can be done in O(1), reducing the overall complexity from O(n²) to just O(n). Also, the whole thing can be done in a single line with a list comprehension:
>>> nums = [1, 2, 4, 5, 11]
>>> target = 6
>>> nums_set = set(nums)
>>> pairs = [(n, target-n) for n in nums_set if target-n in nums_set and n <= target/2]
>>> print(pairs)
[(1, 5), (2, 4)]
For printing the pairs or some message, you can use the or keyword. x or y is interpreted as x if x else y, so if the result set is empty, the message is printed, otherwise the result set itself.
>>> pairs = []
>>> print(pairs or "No pairs found")
No pairs found
Update: The above can fail, if the number added to itself equals the target, but is only contained once in the set. In this case, you can use a collections.Counter instead of a set and check the multiplicity of that number first.
>>> nums = [1, 2, 4, 5, 11, 3]
>>> nums_set = set(nums)
>>> [(n, target-n) for n in nums_set if target-n in nums_set and n <= target/2]
[(1, 5), (2, 4), (3, 3)]
>>> nums_counts = collections.Counter(nums)
>>> [(n, target-n) for n in nums_counts if target-n in nums_counts and n <= target/2 and n != target-n or nums_counts[n] > 1]
[(1, 5), (2, 4)]
List your constraints first!
numbers added must be unique
only 2 numbers can be added
the length of the array can be arbitrary
the number to be summed to can be arbitrary
& Don't skip preprocessing! Reduce your problem-space.
2 things off the bat:
Starting after your 2 print statements, the I would do array = list(set(array)) to reduce the problem-space to [7, 9, 13, 14, 23, 32, 41, 45].
Assuming that all the numbers in question will be positive, I would discard numbers above number. :
array = [x for x in array if x < number]
giving [7, 9, 9, 13, 13, 14]
Combine the last 2 steps into a list comprehension and then use that as array:
smaller_array = [x for x in list(set(array)) if x < number]
which gives array == [7, 9, 13, 14]
After these two steps, you can do a bunch of stuff. I'm fully aware that I haven't answered your question, but from here you got this. ^this is the kind of stuff I'd assume google wants to see.
I'm trying to write a recursive function that will look for matching items in a list of tuples, and distribute them into groups. An example list could be:
l = [(1,2),(2,3),(4,6),(3,5),(7,9),(6,8)]
And the intent is to end with three sublists in the form of:
l = [(1,2),(2,3),(3,5)],[(4,6),(6,8)],[7,9]]
This appears to be an ideal situation for a recursive function, but I haven't made it work yet. Here is what I have written so far:
count = 0
def network(index_list, tuples_list, groups, count):
if len(index_list) > 0:
i = 0
for j in range (len(index_list)):
match = set(tuples_list[groups[count][i]]) & set(tuples_list[index_list[j]])
if len(match) > 0:
groups[count].append(index_list[j])
index_list.pop(j)
i += 1
else:
count += 1
groups.append([])
groups[count].append(index_list[0])
network(index_list, tuples_list, groups, count)
else:
return groups
Also I'm pretty certain this question is different than the one marked duplicate. I'm looking for a recursive solution that keeps all of the tuples intact, in this case by pushing around their indices with append and pop. I'm positive there's an elegant and recursive solution to this problem.
I'm not sure recursion is helpful here. It was an interesting thought experiment, so I tried it out, but my recursive function only ever needs one run-through.
Modified based on comment to only match the first two data points of a tuple
import copy
#Inital 1-item groups
def groupTuples(tupleList):
groups = []
for t in tupleList:
groups.append([t])
return groups
#Merge two groups
def splice(list1, list2, index):
for item in list2:
list1.insert(index, item)
index += 1
return list1
def network(groups = []):
#Groups get modified by reference, need a shallow copy for reference
oldGroups = copy.copy(groups)
for group in groups:
for index, secondGroup in enumerate(groups):
if group == secondGroup:
#Splice can get caught in a loop if passed the same item twice.
continue
#Last item of First Group matches First item of Second Group
if group[-1][1] == secondGroup[0][0]:
splice(group, secondGroup, len(group))
groups.pop(index)
#First item of First Group matches Last item of Second Group
elif group[0][0] == secondGroup[-1][1]:
splice(group, secondGroup, 0)
groups.pop(index)
if groups == oldGroups:
return groups #No change.
else:
return network(groups)
l = [(1,2,0),(2,3,1),(4,6,2),(3,5,3),(1,7,4),(7,9,5),(6,8,6),(12, 13,7),(5, 12,8)]
result = network(groupTuples(l))
print result
Result: [[(1, 2, 0), (2, 3, 1), (3, 5, 3), (5, 12, 8), (12, 13, 7)], [(1, 7, 4), (7, 9, 5)], [(4, 6, 2), (6, 8, 6)]]
It does run the matching algorithm over the dataset multiple times, but I've yet to see a configuration of data where the second iteration yields any changes, so not sure recursion is necessary here.
I have a sumranges() function, which sums all the ranges of consecutive numbers found in a tuple of tuples. To illustrate:
def sumranges(nums):
return sum([sum([1 for j in range(len(nums[i])) if
nums[i][j] == 0 or
nums[i][j - 1] + 1 != nums[i][j]]) for
i in range(len(nums))])
>>> nums = ((1, 2, 3, 4), (1, 5, 6), (19, 20, 24, 29, 400))
>>> print sumranges(nums)
7
As you can see, it returns the number of ranges of consecutive digits within the tuple, that is: len((1, 2, 3, 4), (1), (5, 6), (19, 20), (24), (29), (400)) = 7. The tuples are always ordered.
My problem is that my sumranges() is terrible. I hate looking at it. I'm currently just iterating through the tuple and each subtuple, assigning a 1 if the number is not (1 + previous number), and summing the total. I feel like I am missing a much easier way to accomplish my stated objective. Does anyone know a more pythonic way to do this?
Edit: I have benchmarked all the answers given thus far. Thanks to all of you for your answers.
The benchmarking code is as follows, using a sample size of 100K:
from time import time
from random import randrange
nums = [sorted(list(set(randrange(1, 10) for i in range(10)))) for
j in range(100000)]
for func in sumranges, alex, matt, redglyph, ephemient, ferdinand:
start = time()
result = func(nums)
end = time()
print ', '.join([func.__name__, str(result), str(end - start) + ' s'])
Results are as follows. Actual answer shown to verify that all functions return the correct answer:
sumranges, 250281, 0.54171204567 s
alex, 250281, 0.531121015549 s
matt, 250281, 0.843333005905 s
redglyph, 250281, 0.366822004318 s
ephemient, 250281, 0.805964946747 s
ferdinand, 250281, 0.405596971512 s
RedGlyph does edge out in terms of speed, but the simplest answer is probably Ferdinand's, and probably wins for most pythonic.
My 2 cents:
>>> sum(len(set(x - i for i, x in enumerate(t))) for t in nums)
7
It's basically the same idea as descriped in Alex' post, but using a set instead of itertools.groupby, resulting in a shorter expression. Since sets are implemented in C and len() of a set runs in constant time, this should also be pretty fast.
Consider:
>>> nums = ((1, 2, 3, 4), (1, 5, 6), (19, 20, 24, 29, 400))
>>> flat = [[(x - i) for i, x in enumerate(tu)] for tu in nums]
>>> print flat
[[1, 1, 1, 1], [1, 4, 4], [19, 19, 22, 26, 396]]
>>> import itertools
>>> print sum(1 for tu in flat for _ in itertools.groupby(tu))
7
>>>
we "flatten" the "increasing ramps" of interest by subtracting the index from the value, turning them into consecutive "runs" of identical values; then we identify and could the "runs" with the precious itertools.groupby. This seems to be a pretty elegant (and speedy) solution to your problem.
Just to show something closer to your original code:
def sumranges(nums):
return sum( (1 for i in nums
for j, v in enumerate(i)
if j == 0 or v != i[j-1] + 1) )
The idea here was to:
avoid building intermediate lists but use a generator instead, it will save some resources
avoid using indices when you already have selected a subelement (i and v above).
The remaining sum() is still necessary with my example though.
Here's my attempt:
def ranges(ls):
for l in ls:
consec = False
for (a,b) in zip(l, l[1:]+(None,)):
if b == a+1:
consec = True
if b is not None and b != a+1:
consec = False
if consec:
yield 1
'''
>>> nums = ((1, 2, 3, 4), (1, 5, 6), (19, 20, 24, 29, 400))
>>> print sum(ranges(nums))
7
'''
It looks at the numbers pairwise, checking if they are a consecutive pair (unless it's at the last element of the list). Each time there's a consecutive pair of numbers it yields 1.
This could probably be put together in a more compact form, but I think clarity would suffer:
def pairs(seq):
for i in range(1,len(seq)):
yield (seq[i-1], seq[i])
def isadjacent(pair):
return pair[0]+1 == pair[1]
def sumrange(seq):
return 1 + sum([1 for pair in pairs(seq) if not isadjacent(pair)])
def sumranges(nums):
return sum([sumrange(seq) for seq in nums])
nums = ((1, 2, 3, 4), (1, 5, 6), (19, 20, 24, 29, 400))
print sumranges(nums) # prints 7
You could probably do this better if you had an IntervalSet class because then you would scan through your ranges to build your IntervalSet, then just use the count of set members.
Some tasks don't always lend themselves to neat code, particularly if you need to write the code for performance.
There is a formula for this, the sum of the first n numbers, 1+ 2+ ... + n = n(n+1) / 2 . Then if you want to have the sum of i-j then it is (j(j+1)/2) - (i(i+1)/2) this I am sure simplifies but you can work that out. It might not be pythonic but it is what I would use.