I really searched for this one, because I am almost certain some variation has been asked before but I couldn't put in the correct terms into Google to get a result that matches what I am trying to do. Generally seems like people are looking for the total combinations without constraints.
I am trying to do the following:
Given a list like this:
[1, 1, 2, 2, 3, 3] group it into as many groups of [1, 2, 3] as possible
So
[1, 1, 2, 2, 3, 3] -> [[1, 2, 3], [1, 2, 3]]
[1, 1, 2, 3, 3] -> [[1, 2, 3], [1, 3]]
[1, 1, 3, 3, 5] -> [[1, 3, 5], [1, 3]]
[1, 4, 4, 7] -> [[1, 4, 7], [4]]
Notes:
Input will always be sorted, but the values of these numbers is not known, so it will need to work in general sense.
The idea is I have objects with certain attributes that need to be grouped together to create a different object, but sometimes I am given repeats (and potentially incomplete repeats) -- ie, I used to think that the attributes of my objects will always just be [1, 2, 3] but turns out sometimes I can get [1, 1, 2, 2, 3, 3] and I need a way to break that into two [1, 2, 3] lists to create an intermediate object downstream.
You can use zip_longest and groupby from itertools:
from itertools import zip_longest, groupby
def f(l):
z = zip_longest(*[list(g) for _, g in groupby(l)])
return [[j for j in i if j is not None] for i in z]
Usage:
>>> f([1, 1, 2, 2, 3, 3])
[[1, 2, 3], [1, 2, 3]]
>>> f([1, 1, 2, 3, 3])
[[1, 2, 3], [1, 3]]
>>> f([1, 1, 3, 3, 5])
[[1, 3, 5], [1, 3]]
>>> f([1, 4, 4, 7])
[[1, 4, 7], [4]]
# Update
>>> f(sorted([1, 1, 2, 2, 3, 3, 1, 2]))
[[1, 2, 3], [1, 2, 3], [1, 2]]
# Update 2
>>> f([1, 1, 1, 2, 2, 2, 3, 3])
[[1, 2, 3], [1, 2, 3], [1, 2]]
Update
Alternative version suggested by #cards using filterfalse:
from itertools import zip_longest, groupby, filterfalse
def f(l):
z = zip_longest(*[list(g) for _, g in groupby(l)])
return [list(filterfalse(lambda j: j is None, i)) for i in z]
Related
This is what i have-
def merge_list(mylist)
list1 = []
one_len = len(mylist)
two_len = len(mylist[0][0])
for index in range(two_len):
combine_list = []
for index2 in range(one_len):
combine_list.extend([a[index] for a in mylist[
index2]])
list1.append(combine_list)
return list1
But i have a problem with the output-
for example:
input-
mylist=[[[1, 1, 1], [1, 1, 1], [1, 1, 1], [1, 1, 1]], [[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]], [[3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]]
in short-
[[[1]*3]*4, [[2]*3]*4, [[3]*3]*4]
the output is -
[[[1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]]]
and not -
[[[1, 2, 3], [1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3], [1, 2, 3]], [[1, 2, 3], [1, 2, 3], [1, 2, 3]]]
in short-
[[[1, 2, 3]]*3]*4
I would be happy to solve the problem and advise how to shorten the code.
In order to fix your code, you just need to update the return statement like this:
return [list1]
Though there is still a hidden problem when your inner lists lengths are longer than one element. Check your outputs with sample inputs like [[[2,1], [3,1]]].
One further - more compact - solution may be obtained by concatenating the inner lists within a cycle, then recreate the number of dimensions you need, moving the complexity from O(n^2) to O(n).
mylist = [[[2,1]],[[3,1]]]
def merge_list(mylist):
l_out = []
for l in mylist:
l_out += l[0]
return [[l_out]]
EDIT: In case of more complex inputs, you can extract first all elements and eventually fix the dimensions. This will still bring O(n^2) complexity though:
def merge_list(lst):
elements = [[] for _ in range(len(mylist[0]))]
for l1 in mylist:
for idx, l2 in enumerate(l1):
elements[idx] += l2
return [elements]
merge_list(mylist)
Input:
[[[1], [1]], [[2], [2]], [[3], [3]]]
Output:
[[[1, 2, 3], [1, 2, 3]]]
I am breaking my head around this small issue which I am sure can (and should) be solved recursively.
# split list in sublists based on length of first element.
list = [3, 1, 2, 3, 4, 1, 2, 3, 4]
#* #*
# *number of elements of the sublist
It is better shown than explained, the above should result to:
[[1, 2, 3], [1, 2, 3, 4]]
The lists I am processing always respect this logic, the first element is always the length of the following n elements.
EDIT:
Based on some of the suggestions, I simply added a yield to get it done lazily.
def split(ls):
"""
func that given a list extracts sub lists with the length indicated by the first element
[2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4] => [[1, 2], [1, 2, 3], [1, 2, 3, 4]]
"""
res = []
while ls:
dim = ls[0]
yield ls[1:dim + 1]
ls = ls[dim + 1:]
return res
>>> list(split([2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4]))
[[1, 2], [1, 2, 3], [1, 2, 3, 4]]
Simple slicing will do:
>>> a = [3, 1, 2, 3, 4, 1, 2, 3, 4]
>>> c = []
>>> while len(a) :
... b = a[0]
... c.append( a[1:b+1] )
... a = a[b+1:]
...
>>> c
[[1, 2, 3], [1, 2, 3, 4]]
Here is a way to achieve what you want:
numbers = [3, 1, 2, 3, 4, 1, 2, 3, 4]
result = []
def split_list(list_):
first = list_.pop(0)
result.append(list_[:first])
if len(list_[first:]) > 0:
split_list(list_[first:])
split_list(numbers)
print(result)
You can use itertools.islice here.
>>> from itertools import islice
>>> lst = [3, 1, 2, 3, 4, 1, 2, 3, 4]
>>> def chunks(lst):
... t=iter(lst)
... c=next(t,None)
... while c:
... yield list(islice(t,None,c))
... c=next(t,None)
...
>>> list(chunks(lst))
[[1, 2, 3], [1, 2, 3, 4]]
I've edited my answer as inspired by someone else's in this thread. This doesn't consume the original array and uses recursion instead of looping.
numbers = [3, 1, 2, 3, 4, 1, 2, 3, 4, 3,1,1,1,1]
def do_take(numbers: []):
result = []
to_take = numbers[0]
result.append(numbers[1:to_take + 1])
if len(numbers) > to_take:
result.append(do_take(numbers[to_take + 1:]))
return result
print(do_take(numbers))
print(numbers)
Results in the following output:
# python /tmp/breakup.py
[[1, 2, 3], [[1, 2, 3, 4], [[1, 1, 1], [[]]]]]
[3, 1, 2, 3, 4, 1, 2, 3, 4, 3, 1, 1, 1, 1]
Suppose I have the following array:
a = [[1, 4, 2, 3]
[3, 1, 5, 4]
[4, 3, 1, 2]]
What I'd like to do is impose a maximum value on the array, but have that maximum vary by row. For instance if I wanted to limit the 1st and 3rd row to a maximum value of 3, and the 2nd row to a value of 4, I could create something like:
[[1, 3, 2, 3]
[3, 1, 4, 4]
[3, 3, 1, 2]
Is there any better way than just looping over each row individually and setting it with 'nonzero'?
With numpy.clip (using the method version here):
a.clip(max=np.array([3, 4, 3])[:, None]) # np.clip(a, ...)
# array([[1, 3, 2, 3],
# [3, 1, 4, 4],
# [3, 3, 1, 2]])
Generalized:
def clip_2d_rows(a, maxs):
maxs = np.asanyarray(maxs)
if maxs.ndim == 1:
maxs = maxs[:, np.newaxis]
return np.clip(a, a_min=None, a_max=maxs)
You might be safer using the module-level function (np.clip) rather than the class method (np.ndarray.clip). The former uses a_max as a parameter, while the latter uses the builtin max as a parameter which is never a great idea.
With masking -
In [50]: row_lims = np.array([3,4,3])
In [51]: np.where(a > row_lims[:,None], row_lims[:,None], a)
Out[51]:
array([[1, 3, 2, 3],
[3, 1, 4, 4],
[3, 3, 1, 2]])
With
>>> a
array([[1, 4, 2, 3],
[3, 1, 5, 4],
[4, 3, 1, 2]])
Say you have
>>> maxs = np.array([[3],[4],[3]])
>>> maxs
array([[3],
[4],
[3]])
What about doing
>>> a.clip(max=maxs)
array([[1, 3, 2, 3],
[3, 1, 4, 4],
[3, 3, 1, 2]])
My task is to calculate the k-permutations from the updated List by new element
without recalculating the k-permutations already gotten from the previous state of the list. Example:
liste = [1, 2, 3]
3-permutations are:
[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]
The updated list:
liste = [1, 2, 3, 4]
I would like to obtain directly 3-permutations[1, 2, 3, 4]-3-permutations[1, 2, 3]
without recalculating 3-permutations[1, 2, 3]
Calculate directly the new permutations:
[1, 2, 4], [1, 3, 4], [1, 4, 2], [1, 4, 3], [2, 1, 4], [2, 3, 4], [2, 4, 1],
[2, 4, 3], [3, 1, 4], [3, 2, 4], [3, 4, 1], [3, 4, 2], [4, 1, 2], [4, 1, 3],
[4, 2, 1], [4, 2, 3], [4, 3, 1], [4, 3, 2]
Thanks
Computing first the cartesian product {0,1,2,3}x{0,1,2}x{0,1} and taking the nth element of list (1,2,3,4).
r=[]
for prd in itertools.product([[0,1,2,3],[0,1,2],[0,1]]):
l=[1,2,3,4]
r0=[]
for i in prd:
r0 += l[i]
del l[i]
r += r0
EDIT: original answer gives the 3-permutations of [1,2,3,4]
following command answers specifically to question, see how it can be generalized
[list(j) for i in itertools.combinations([1,2,3],2) for j in itertools.permutations(list(i)+[4])]
next case, maybe one of ?
[list(j) for i in itertools.combinations([1,2,3],2) for j in itertools.permutations(list(i)+[4,5])]
[list(j) for i in itertools.combinations([1,2,3,4],3) for j in itertools.permutations(list(i)+[4,5])]
try saving the existing permutations to a list, then do:
if newPermutation not in listOfExistingPermutations:
listOfExistingPermutations.append(newPermutation)
or something along those lines
I have a list of lists like this:
i = [[1, 2, 3], [2, 4, 5], [1, 2, 3], [2, 4, 5]]
I would like to get a list containing "unique" lists (based on their elements) like:
o = [[1, 2, 3], [2, 4, 5]]
I cannot use set() as there are non-hashable elements in the list. Instead, I am doing this:
o = []
for e in i:
if e not in o:
o.append(e)
Is there an easier way to do this?
You can create a set of tuples, a set of lists will not be possible because of non hashable elements as you mentioned.
>>> l = [[1, 2, 3], [2, 4, 5], [1, 2, 3], [2, 4, 5]]
>>> set(tuple(i) for i in l)
{(1, 2, 3), (2, 4, 5)}
i = [[1, 2, 3], [2, 4, 5], [1, 2, 3], [2, 4, 5]]
print([ele for ind, ele in enumerate(i) if ele not in i[:ind]])
[[1, 2, 3], [2, 4, 5]]
If you consider [2, 4, 5] to be equal to [2, 5, 4] then you will need to do further checks
You can convert each element to a tuple and then insert it in a set.
Here's some code with your example:
tmp = set()
a = [[1, 2, 3], [2, 4, 5], [1, 2, 3], [2, 4, 5]]
for i in a:
tmp.add(tuple(i))
tmp will be like this:
{(1, 2, 3), (2, 4, 5)}
Here's another way to do it:
I = [[1, 2, 3], [2, 4, 5], [1, 2, 3], [2, 4, 5]]
mySet = set()
for j in range(len(I)):
mySet = mySet | set([tuple(I[j])])
print(mySet)