Join series with repeated index on dataframe where column values are equal to the index in the series

Say I have the series an dataframe like:

import pandas as pd
s = pd.Series([10,20,11,12,30,34],
    index=["red","red","blue","blue","green","green"])
s.index.name="numbers"

df = pd.DataFrame({
    "color":["red","green","blue","blue","red","green"],
    "id":[1,2,3,4,5,6]})

I want to add the values in s to the column in df in the same order as they appear where the index of s is equal to df["color"] i.e

pd.some_function(df,s,left_on="color",right_index=True)

color   id    numbers
red      1      10
green    2      30
blue     3      11
blue     4      12
red      5      20
green    6      34

I have tried pd.merge, pd.join etc. but I simply cannot make it work (without looping over df, filtered by color, add the data from s and then concat it at the end)

>Solution :

You can use groupby.cumcount to set up a unique key for the merge:

idx1 = s.groupby(level=0).cumcount()
# [0, 1, 0, 1, 0, 1]
idx2 = df.groupby('color').cumcount()
# [0, 0, 0, 1, 1, 1]

s.index.name="color"
out = (df
   .merge(s.reset_index(name='number'),
          left_on=['color', idx2], right_on=['color', idx1])
   .drop(columns='key_1')
)

variant:

s.index.name="color"
out = (df
   .assign(idx=df.groupby('color').cumcount())
   .merge(s.reset_index(name='number')
           .assign(idx=s.groupby(level=0).cumcount().values),
          left_on=['color', 'idx'], right_on=['color', 'idx'])
    .drop(columns='idx')
)

output:

   color  id  number
0    red   1      10
1  green   2      30
2   blue   3      11
3   blue   4      12
4    red   5      20
5  green   6      34