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Generate all combinations from multiple lists in python

I have a dataset which contains multiple lists each having different number of elements. For eg.
list1 = ['a','b','c']
list2 = ['d','e']
list3 = ['f','g','h','i']
I want to generate all possible unique combinations from these lists with the following conditions:
The number of elements in one combination should be 5
Minimum of 1 element from each list.
How would I go about doing this?

I think this will do it (a lot of these steps could be combined but keeping them in to show steps)
Create a new list with all items in
list4 = list1 + list2 + list3
And another list to iterate through them to find all 5 combinations (you didn't specify about order or replacement so have a read here and change as necessary)
list5 = list(itertools.combinations(list4, 5))
Then remove the items that don't have an element from each list.
[i for i in list5 if any(j in list1 for j in i) and any(j in list2 for j in i) and any(j in list3 for j in i)]
So in one line it could be:
[i for i in itertools.combinations(list1 + list2 + list3, 5) if any(j in list1 for j in i) and any(j in list2 for j in i) and any(j in list3 for j in i)]

I support #Ghost Ops's answer.
itertools.product gave me the result I was looking for & in the easiest way.
Example:
from itertools import product
Suit = [u"\u2666", u"\u2665", u"\u2663", u"\u2660"]
Card = ['A','K','Q','J','10','9','8','7','6','5','4','3','2']
FullDeck = list(product(Card, Suit))
for i in FullDeck:
print(i)
Which Returns:
('A', '♦')
('A', '♥')
('A', '♣')
('A', '♠')
('K', '♦')
('K', '♥')
('K', '♣')
('K', '♠')
('Q', '♦')
('Q', '♥')
('Q', '♣')
('Q', '♠')
('J', '♦')
('J', '♥')
('J', '♣')
('J', '♠')
('10', '♦')
('10', '♥')
('10', '♣')
('10', '♠')
('9', '♦')
('9', '♥')
('9', '♣')
('9', '♠')
('8', '♦')
('8', '♥')
('8', '♣')
('8', '♠')
('7', '♦')
('7', '♥')
('7', '♣')
('7', '♠')
('6', '♦')
('6', '♥')
('6', '♣')
('6', '♠')
('5', '♦')
('5', '♥')
('5', '♣')
('5', '♠')
('4', '♦')
('4', '♥')
('4', '♣')
('4', '♠')
('3', '♦')
('3', '♥')
('3', '♣')
('3', '♠')
('2', '♦')
('2', '♥')
('2', '♣')
('2', '♠')

List Comprehension Appending Index with Number Range on List of Lists

I am looking for a list comprehension in adding a range of numbers based on index position in a list of lists while keeping the second index alone.
search_keys = [('1', 'B'), ('1', 'K'), ('1', 'Y')]
Desired Result:
new_search_keys = [('11', 'B'), ('12', 'B'), ('13', 'B'), ('14', 'B'), ('15', 'B')...
...('11', 'K'), ('12', 'K'), ('13', 'K'), ('14', 'K'), ('15', 'K')...
...('11', 'Y'), ('12', 'Y'), ('13', 'Y'), ('14', 'Y'), ('15', 'Y')...]
My attempt brings a TypeError: 'type' object is not subscriptable:
new_search_keys = [search_keys[i][0] + list(range[1,9]) for i in range(len(search_keys))]

This should solve your problem:-
new_search_keys = [(search_keys[i][0] +str(j), search_keys[i][1]) for i in range(len(search_keys)) for j in range(1,9)]
Output:-
[('11', 'B'), ('12', 'B'), ('13', 'B'), ('14', 'B'), ('15', 'B'), ('16', 'B'), ('17', 'B'), ('18', 'B'), ('11', 'K'), ('12', 'K'), ('13', 'K'), ('14', 'K'), ('15', 'K'), ('16', 'K'), ('17', 'K'), ('18', 'K'), ('11', 'Y'), ('12', 'Y'), ('13', 'Y'), ('14', 'Y'), ('15', 'Y'), ('16', 'Y'), ('17', 'Y'), ('18', 'Y')]

How to get the unique values from column on string data with file handling in python

this is how my data looks :ALL THESE DATA ARE IN SINGLE COLUMN
#columnA
(8,8)
(6,7)(7,7)(7,6)
(2,12)(12,3)(3,4)(4,12)(12,12)
(14,14)
(1,1)(1,12)(12,2)(2,2)(2,4)
(6,8)(8,8)(8,12)
(6,6)(6,3)(3,14)
(1,14)(14,14)(14,1)(1,1)(1,2)
(1,1)(1,14)
(2,2)(2,15)(15,5)(5,5)(5,16)
(1,11)(11,1)(1,2)(2,2)(2,14)
(5,5)(5,1)
(12,2)(2,2)(2,10)(10,10)
(9,9)(9,4)(4,4)
(13,13)
(1,1)
(11,14)(14,14)
for i in range(len(data)):
x.iloc[i]=data.iloc[i].unique()
output is to replace the duplicate values and find unique:

How about storing all the data in a list and find the set of the list.
for instance, if I have a list of tupples or list of lists in the form:
list = [(1,1), (2,3), (2,3), (1,3), (1,3), (1,1)]
set_of_list = set(list)
>>> {(1,3), (2,3), (1,1)}

IIUC, use pandas.Series.str.findall with explode and unique:
uniq = df['columnA'].str.findall("(\d+),(\d+)").explode().unique()
uniq
Output:
array([('8', '8'), ('6', '7'), ('7', '7'), ('7', '6'), ('2', '12'),
('12', '3'), ('3', '4'), ('4', '12'), ('12', '12'), ('14', '14'),
('1', '1'), ('1', '12'), ('12', '2'), ('2', '2'), ('2', '4'),
('6', '8'), ('8', '12'), ('6', '6'), ('6', '3'), ('3', '14'),
('1', '14'), ('14', '1'), ('1', '2'), ('2', '15'), ('15', '5'),
('5', '5'), ('5', '16'), ('1', '11'), ('11', '1'), ('2', '14'),
('5', '1'), ('2', '10'), ('10', '10'), ('9', '9'), ('9', '4'),
('4', '4'), ('13', '13'), ('11', '14')], dtype=object)

Unable to get all the elements in ImmutableMltiDict in Flask

I have an ImmutableMultiDict([('1', 'a'), ('2', 'a'), ('3', 'a'), ('4', 'a'), ('5', 'a'), ('6', 'a'), ('7', 'a'), ('8', 'a'), ('9', 'a'), ('10', 'a')]).
When I run the following code:-
f = request.form
app.logger.debug(f) # This gives out ImmutableMultiDict([('1', 'a'), ('2', 'a'), ('3', 'a'), ('4', 'a'), ('5', 'a'), ('6', 'a'), ('7', 'a'), ('8', 'a'), ('9', 'a'), ('10', 'a')])
for i in f:
app.logger.debug(i) # This gives out 1,2,3,4,5,6,7,8,9,10 in each iteration of the loop.
return 'hi'
I expected the output :
('1','a') in the first iteration, then ('2', 'a') in the next etc.
Do you have any idea what went wrong?
Thanks in advance!!

Dict-like things in Python give you the keys when you iterate over them. To get what you're looking for, use .items():
for i in f.items():
app.logger.debug(i)

Finding cycles in graph results in empty array

I am trying to find all cycles in a directed graph, I am using networks library for this. The program executes normally and results an empty array.
I am not sure how the networks library works but my use case graphs may have discontinuities in them.
I have attached the code below
import networkx as nx
G.add_nodes_from(['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18'])
G.add_edges_from([('1', '2'), ('1', '8'), ('1', '9'), ('1', '10'), ('1', '11'), ('1', '14'), ('1', '15'), ('1', '18'), ('2', '3'), ('2', '8'), ('2', '18'), ('3', '4'), ('3', '7'), ('3', '17'), ('4', '5'), ('4', '6'), ('4', '7'), ('4', '12'), ('4', '13'), ('4', '16'), ('4', '17'), ('5', '6'), ('5', '10'), ('5', '12'), ('6', '7'), ('6', '9'), ('7', '8'), ('8', '9'), ('9', '10'), ('10', '11'), ('11', '12'), ('11', '14'), ('12', '13'), ('13', '14'), ('13', '16'), ('14', '15'), ('15', '16'), ('15', '18'), ('16', '17'), ('17', '18')])
print(list(nx.simple_cycles(G)))
There are supposed to be a few cycles in my example.
Please let me know what am I doing wrong here.

print(list(nx.find_cycle(G, source=list of nodes)))