pandas merge df many to many without duplicates

Question

suppose i have two df like below:

import pandas as pd

data_dic = {
    \"a\": [0,0,1,2],
    \"b\": [3,3,4,5],
    \"c\": [6,7,8,9]
}
df1 = pd.DataFrame(dat         


        

Answer 1

Use GroupBy.cumcount for counter columns in both DataFrames with merge by added column:

df1['g'] = df1.groupby(['a','b']).cumcount()
df2['g'] = df2.groupby(['a','b']).cumcount()

df = pd.merge(df1, df2, on=['a', 'b', 'g'] , how='inner')
print (df)
   a  b  c  g   d
0  0  3  6  0  10
1  0  3  7  1  10
2  1  4  8  0  12
3  2  5  9  0  13

Difference with another solutions the best see in changed data in second df second 10 to 11 - it correct merge by first duplicate pair a, b from df1 with first a, b pais from second, similar for all duplicates and also for unique pairs:

data_dic = {
    "a": [0,0,1,2],
    "b": [3,3,4,5],
    "d": [10,11,12,13]
}
df2 = pd.DataFrame(data_dic)


df1['g'] = df1.groupby(['a','b']).cumcount()
df2['g'] = df2.groupby(['a','b']).cumcount()

df = pd.merge(df1, df2, on=['a', 'b', 'g'] , how='inner')
print (df)

   a  b  c  g   d
0  0  3  6  0  10
1  0  3  7  1  11
2  1  4  8  0  12
3  2  5  9  0  13