What's a Zip Join? Have You Ever Heard of That, or a Pairwise Join

What's a zip join? Have you ever heard of that, or a pairwise join?

Zip joins are only meaningful when talking about ordered sets. Instead of joining based on the value of a column, you are joining based on the row number.

Table1

[λ]  [color] 
400  violet 
415  indigo 
475  blue   
510  green  
570  yellow 
590  orange 
650  red    

Table2

[flame]  [element]
green    boron
yellow   sodium
white    magnesium
red      calcium
blue     indium

Table1 INNER JOIN Table2 ON [color] = [flame] : only matching rows

[λ]  [color]  [flame]  [element]
475  blue     blue     indium 
510  green    green    boron
570  yellow   yellow   sodium
650  red      red      calcium

Table1 OUTER JOIN Table2 ON [color] = [flame] : all rows, matched where possible

[λ]  [color]  [flame]  [element]
400  violet   NULL     NULL
415  indigo   NULL     NULL
475  blue     blue     indium
510  green    green    boron
570  yellow   yellow   sodium
590  orange   NULL     NULL
650  red      red      calcium
NULL NULL     white    magnesium

Table1 "zip joined" to Table2 : all rows, regardless of match

[λ]  [color]  [flame]  [element]
400  violet   green    boron
415  indigo   yellow   sodium
475  blue     white    magnesium
510  green    red      calcium
570  yellow   blue     indium
590  orange   NULL     NULL
650  red      NULL     NULL

Zip joins are combining the data like a zipper, pairing the first row from one table with the first row from the other, second paired with second, etc. It's not actually looking at that data. They can be generated very quickly, but they won't mean anything unless there is some meaningful order already present in your data or if you just want to generate random pairings

python3 join lists that have same value in list of lists

This can be seen as a graph problem in which you merge subgraphs and need to find the connected components.

Here is your graph:

networkx

Using networkx you can do:

import networkx as nx
from itertools import chain, pairwise
# python < 3.10 use this recipe for pairwise instead
# from itertools import tee
# def pairwise(iterable):
#     a, b = tee(iterable)
#     next(b, None)
#     return zip(a, b)

G = nx.Graph()
G = nx.from_edgelist(chain.from_iterable(pairwise(e) for e in l))
G.add_nodes_from(set.union(*map(set, l))) # adding single items

list(nx.connected_components(G))

output:

[{1, 2, 3, 4}, {5, 6, 7, 8, 9}]

python

Now, you can use pure python to perform the same thing, finding the connected components and merging them.

An example code is nicely described in this post (archive.org link for long term).

In summary, the first step is building the list of neighbors, then a recursive function is used to join the neighbors of neighbors keeping track of the already seen ones.

from collections import defaultdict 
  
#merge function to  merge all sublist having common elements. 
def merge_common(lists): 
    neigh = defaultdict(set) 
    visited = set() 
    for each in lists: 
        for item in each: 
            neigh[item].update(each) 
    def comp(node, neigh = neigh, visited = visited, vis = visited.add): 
        nodes = set([node]) 
        next_node = nodes.pop 
        while nodes: 
            node = next_node() 
            vis(node) 
            nodes |= neigh[node] - visited 
            yield node 
    for node in neigh: 
        if node not in visited: 
            yield sorted(comp(node))

example:

merge_common(l)
# [[1, 2, 3, 4], [5, 6, 7, 8, 9]]

Python 3: pairwise iterating through list

Use zip(*[iter(it)] * 2), as seen in this answer.

it = [1,2,3,4,5,6]
for x, y in zip(*[iter(it)] * 2):
    print(x, y)

Return Alternating Letters With the Same Length From two Strings

str.join with zip is possible, since zip only iterates pairwise up to the shortest iterable. You can combine with itertools.chain to flatten an iterable of tuples:

from itertools import chain

def one_each(st, dum):
    return ''.join(chain.from_iterable(zip(st, dum)))

x = one_each("bofa", "BOFAAAA")

print(x)

bBoOfFaA

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