First common element from two lists

Question

x = [8,2,3,4,5]
y = [6,3,7,2,1]

How to find out the first common element in two lists (in this case, "2") in a concise and elegant way? Any list can be empty or there can be no common elements - in this case None is fine.

I need this to show python to someone who is new to it, so the simpler the better.

UPD: the order is not important for my purposes, but let's assume I'm looking for the first element in x that also occurs in y.

Answer 1

This should be straight forward and almost as effective as it gets (for more effective solution check Ashwini Chaudharys answer and for the most effective check jamylaks answer and comments):

result = None
# Go trough one array
for i in x:

    # The element repeats in the other list...
    if i in y:

        # Store the result and break the loop
        result = i
        break

Or event more elegant would be to encapsulate the same functionality to function_{using PEP 8 like coding style conventions}:

def get_first_common_element(x,y):
    ''' Fetches first element from x that is common for both lists
        or return None if no such an element is found.
    '''
    for i in x:
        if i in y:
            return i

    # In case no common element found, you could trigger Exception
    # Or if no common element is _valid_ and common state of your application
    # you could simply return None and test return value
    # raise Exception('No common element found')
    return None

And if you want all common elements you can do it simply like this:

>>> [i for i in x if i in y]
[1, 2, 3]

Answer 2

A sort is not the fastest way of doing this, this gets it done in O(N) time with a set (hash map).

>>> x = [8,2,3,4,5]
>>> y = [6,3,7,2,1]
>>> set_y = set(y)
>>> next((a for a in x if a in set_y), None)
2

Or:

next(ifilter(set(y).__contains__, x), None)

This is what it does:

>>> def foo(x, y):
        seen = set(y)
        for item in x:
            if item in seen:
                return item
        else:
            return None


>>> foo(x, y)
2

To show the time differences between the different methods (naive approach, binary search an sets), here are some timings. I had to do this to disprove the suprising number of people that believed binary search was faster...:

from itertools import ifilter
from bisect import bisect_left

a = [1, 2, 3, 9, 1, 1] * 100000
b = [44, 11, 23, 9, 10, 99] * 10000

c = [1, 7, 2, 4, 1, 9, 9, 2] * 1000000 # repeats early
d = [7, 6, 11, 13, 19, 10, 19] * 1000000

e = range(50000) 
f = range(40000, 90000) # repeats in the middle

g = [1] * 10000000 # no repeats at all
h = [2] * 10000000

from random import randrange
i = [randrange(10000000) for _ in xrange(5000000)] # some randoms
j = [randrange(10000000) for _ in xrange(5000000)]

def common_set(x, y, ifilter=ifilter, set=set, next=next):
    return next(ifilter(set(y).__contains__, x), None)
    pass

def common_b_sort(x, y, bisect=bisect_left, sorted=sorted, min=min, len=len):
    sorted_y = sorted(y)
    for a in x:
        if a == sorted_y[min(bisect_left(sorted_y, a),len(sorted_y)-1)]:
            return a
    else:
        return None

def common_naive(x, y):
    for a in x:
        for b in y:
            if a == b: return a
    else:
        return None

from timeit import timeit
from itertools import repeat
import threading, thread

print 'running tests - time limit of 20 seconds'

for x, y in [('a', 'b'), ('c', 'd'), ('e', 'f'), ('g', 'h'), ('i', 'j')]:
    for func in ('common_set', 'common_b_sort', 'common_naive'):        
        try:
            timer = threading.Timer(20, thread.interrupt_main)   # 20 second time limit
            timer.start()
            res = timeit(stmt="print '[', {0}({1}, {2}), ".format(func, x, y),
                         setup='from __main__ import common_set, common_b_sort, common_naive, {0}, {1}'.format(x, y),
                         number=1)
        except:
            res = "Too long!!"
        finally:
            print '] Function: {0}, {1}, {2}. Time: {3}'.format(func, x, y, res)
            timer.cancel()

The test data was:

a = [1, 2, 3, 9, 1, 1] * 100000
b = [44, 11, 23, 9, 10, 99] * 10000

c = [1, 7, 2, 4, 1, 9, 9, 2] * 1000000 # repeats early
d = [7, 6, 11, 13, 19, 10, 19] * 1000000

e = range(50000) 
f = range(40000, 90000) # repeats in the middle

g = [1] * 10000000 # no repeats at all
h = [2] * 10000000

from random import randrange
i = [randrange(10000000) for _ in xrange(5000000)] # some randoms
j = [randrange(10000000) for _ in xrange(5000000)]

Results:

running tests - time limit of 20 seconds
[ 9 ] Function: common_set, a, b. Time: 0.00569520707241
[ 9 ] Function: common_b_sort, a, b. Time: 0.0182240340602
[ 9 ] Function: common_naive, a, b. Time: 0.00978832505249
[ 7 ] Function: common_set, c, d. Time: 0.249175872911
[ 7 ] Function: common_b_sort, c, d. Time: 1.86735751332
[ 7 ] Function: common_naive, c, d. Time: 0.264309220865
[ 40000 ] Function: common_set, e, f. Time: 0.00966861710078
[ 40000 ] Function: common_b_sort, e, f. Time: 0.0505980508696
[ ] Function: common_naive, e, f. Time: Too long!!
[ None ] Function: common_set, g, h. Time: 1.11300018578
[ None ] Function: common_b_sort, g, h. Time: 14.9472068377
[ ] Function: common_naive, g, h. Time: Too long!!
[ 5411743 ] Function: common_set, i, j. Time: 1.88894859542
[ 5411743 ] Function: common_b_sort, i, j. Time: 6.28617268396
[ 5411743 ] Function: common_naive, i, j. Time: 1.11231867458

This gives you an idea of how it will scale for larger inputs, O(N) vs O(N log N) vs O(N^2)

Answer 3

One liner, using next to take the first item from a generator:

x = [8,2,3,4,5]
y = [6,3,7,2,1]

first = next((a for a in x if a in y), None)

Or more efficiently since set.__contains__ is faster than list.__contains__:

set_y = set(y)
first = next((a for a in x if a in set_y), None)

Or more efficiently but still in one line (don't do this):

first = next((lambda set_y: a for a in x if a in set_y)(set(y)), None)

Answer 4

Using a for loops with in will result in a O(N^2) complexity, but you can sort y here and use binary search to improve the time complexity to O(NlogN).

def binary_search(lis,num):
    low=0
    high=len(lis)-1
    ret=-1  #return -1 if item is not found
    while low<=high:
        mid=(low+high)//2
        if num<lis[mid]:
            high=mid-1
        elif num>lis[mid]:
            low=mid+1
        else:
            ret=mid
            break

    return ret

x = [8,2,3,4,5]
y = [6,3,7,2,1]
y.sort()

for z in x:
    ind=binary_search(y,z)
    if ind!=-1
        print z
        break

output: 2

Using the bisect module to perform the same thing as above:

import bisect

x = [8,2,3,4,5]
y = [6,3,7,2,1]
y.sort()

for z in x:
    ind=bisect.bisect(y,z)-1  #or use `ind=min(bisect.bisect_left(y, z), len(y) - 1)`
    if ind!=-1 and y[ind] ==z:
        print z      #prints 2
        break     

Answer 5

I assume you want to teach this person Python, not just programming. Therefore I do not hesitate to use zip instead of ugly loop variables; it's a very useful part of Python and not hard to explain.

def first_common(x, y):
    common = set(x) & set(y)
    for current_x, current_y in zip(x, y):
        if current_x in common:
            return current_x
        elif current_y in common:
            return current_y

print first_common([8,2,3,4,5], [6,3,7,2,1])

If you really don't want to use zip, here's how to do it without:

def first_common2(x, y):
    common = set(x) & set(y)
    for i in xrange(min(len(x), len(y))):
        if x[i] in common:
            return x[i]
        elif y[i] in common:
            return y[i]

And for those interested, this is how it extends to any number of sequences:

def first_common3(*seqs):
    common = set.intersection(*[set(seq) for seq in seqs])
    for current_elements in zip(*seqs):
        for element in current_elements:
            if element in common:
                return element

Finally, please note that, in contrast to some other solutions, this works as well if the first common element appears first in the second list.

I just noticed your update, which makes for an even simpler solution:

def first_common4(x, y):
    ys = set(y) # We don't want this to be recreated for each element in x
    for element in x:
        if element in ys:
            return element

The above is arguably more readable than the generator expression.

Too bad there is no built-in ordered set. It would have made for a more elegant solution.

Answer 6

Using for loops seems easiest to explain to someone new.

for number1 in x:
    for number2 in y:
        if number1 == number2:
            print number1, number2
            print x.index(number1), y.index(number2)
            exit(0)
print "No common numbers found."

NB Not tested, just out of my head.

Answer 7

This one uses sets. It returns the first common element or None if no common element.

def findcommon(x,y):
    common = None
    for i in range(0,max(len(x),len(y))):
        common = set(x[0:i]).intersection(set(y[0:i]))
        if common: break
    return list(common)[0] if common else None

Answer 8

def first_common_element(x,y):
    common = set(x).intersection(set(y))
    if common:
        return x[min([x.index(i)for i in common])]

Answer 9

Just for fun (probably not efficient), another version using itertools:

from itertools import dropwhile, product
from operator import __ne__

def accept_pair(f):
    "Make a version of f that takes a pair instead of 2 arguments."
    def accepting_pair(pair):
        return f(*pair)
    return accepting_pair

def get_first_common(x, y):
    try:
        # I think this *_ unpacking syntax works only in Python 3
        ((first_common, _), *_) = dropwhile(
            accept_pair(__ne__),
            product(x, y))
    except ValueError:
        return None
    return first_common

x = [8, 2, 3, 4, 5]
y = [6, 3, 7, 2, 1]
print(get_first_common(x, y))  # 2
y = [6, 7, 1]
print(get_first_common(x, y))  # None

It is simpler, but not as fun, to use lambda pair: pair[0] != pair[1] instead of accept_pair(__ne__).

Answer 10

Use set - this is the generic solution for arbitrary number of lists:

def first_common(*lsts):
    common = reduce(lambda c, l: c & set(l), lsts[1:], set(lsts[0]))
    if not common:
        return None
    firsts = [min(lst.index(el) for el in common) for lst in lsts]
    index_in_list = min(firsts)
    trgt_lst_index = firsts.index(index_in_list)
    return lsts[trgt_lst_index][index_in_list]

An afterthought - not an effective solution, this one reduces redundant overhead

def first_common(*lsts):
    common = reduce(lambda c, l: c & set(l), lsts[1:], set(lsts[0]))
    if not common:
        return None
    for lsts_slice in itertools.izip_longest(*lsts):
        slice_intersection = common.intersection(lsts_slice)
        if slice_intersection:
            return slice_intersection.pop()