Combine two dataframes in Pandas to generate many to many relationship - pandas

Question

I have two lists, say
customers = ['a', 'b', 'c']
accounts = [1, 2, 3, 4, 5, 6, 7, 8, 9]
I want to generate a Pandas dataframe so:
All customers and accounts are used
There is a many to many relationship between customers and accounts (one customer 'may' have multiple accounts and an account 'may' be owned by multiple customers
I want the many to many relationship to be random. That is, some customers will have one account and some will have more than one. Similarly, some accounts will be owned by just one customers and others by more than one.
Something like,
Customer
Account
a
1
a
2
b
2
c
3
a
4
b
4
c
4
b
5
b
6
b
7
b
8
a
9
Since I am generating random data, in the worst case scenario, I can generate way too many accounts and discard the unused ones if the code is easier (essentially relaxing the requirement 1 above).
I am using sample (n=20, replace=True) to generate 20 records in both dataframes and then merging them into one based on the index. Is there an out of the box API or library to do this or is my code the recommended way?
customers = ['a', 'b', 'c']
accounts = [1, 2, 3, 4, 5, 6, 7, 8, 9]
customers_df = pd.DataFrame(data=customers)
customers_df = customers_df.sample (n=20, replace=True)
customers_df['new_index'] = range (20)
customers_df.set_index ('new_index', inplace=True)
accounts_df = pd.DataFrame (data=accounts)
accounts_df = accounts_df.sample (n=20, replace=True)
accounts_df['new_index'] = range (20)
accounts_df.set_index ('new_index', inplace=True)
combined_df = pd.merge (customers_df, accounts_df, on='new_index')
print (combined_df)
Edit: Modified the question and added sample code I have tried.

Answer 1

One way to accomplish this is to collect the set of all possible relationships with a cartesian product, then select from that list before building your dataframe:
import itertools
import random
customers = ['a', 'b', 'c']
accounts = [1, 2, 3, 4, 5, 6, 7, 8, 9]
possible_associations = [ca for ca in itertools.product(customers, accounts)]
df = pd.DataFrame.from_records(random.choices(possible_associations, k=20), columns=['customers', 'accounts']).sort_values(['customers','accounts'])
print(df)
Output
customers accounts
0 a 2
3 a 2
15 a 2
18 a 4
16 a 5
14 a 7
7 a 8
12 a 8
1 a 9
2 b 5
9 b 5
8 b 8
11 b 8
19 c 2
17 c 3
5 c 4
4 c 5
6 c 5
13 c 5
10 c 7

Answer 2

To have a repeatable test result, start with np.random.seed(1) (in the target
version drop it).
Then proceed as follows:
Create a list of probabilities - how many accounts can have a customer, e.g.:
prob = [0.5, 0.25, 0.15, 0.09, 0.01]
Generate a Series stating how many owners shall have each account:
cnt = pd.Series(np.random.choice(range(1, len(prob) + 1), size=len(accounts),
p=prob), name='Customer')
Its name is Customer, because it will be the source to create just
Customer column.
For my sample probablities and generator seeding the result is:
0 1
1 2
2 1
3 1
4 1
5 1
6 1
7 1
8 1
Name: Customer, dtype: int32
(the left column is the index, the right - actual values).
Because your data sample contains only 9 accounts, the result does
not contain "greater" number of owners. But in your target version,
with more accounts, there will be accounts with greater numbers of
owners.
Generate the result - cust_acct DataFrame, defining the assignment of customers
to accounts:
cust_acct = cnt.apply(lambda x: np.random.choice(customers, x, replace=False))\
.explode().to_frame().join(pd.Series(accounts, name='Account')).reset_index(drop=True)
The result, for your sample data and my seeding and probabilities, is:
Customer Account
0 b 1
1 a 2
2 b 2
3 b 3
4 b 4
5 c 5
6 b 6
7 c 7
8 a 8
9 b 9
Of course, you can assume different proabilities in prob.
You can also choose other "top" number of owners (the number of
entries in prob).
In this case no change in code is needed, because the range of values in
the first np.random.choice is set to accomodete to the lenght of prob.
Note: Because your sample data contains only 3 customers,
under different generator seeding there can occur ValueError: Cannot
take a larger sample than population when 'replace=False'.
The reason is that if the number of owners for some account was > 3 then just
this error occurs.
But with your target data, with greater number of customers, this error
will not occur.