Add multiple columns to MultiIndex dataframe from multiple scalar values

Given the following MultiIndex df

| foo     |        |
| one     | two    |
| ------- | ------ |
| "12345" | "1235" |
| "12345" | "1345" |

I would like to append more columns that are each all filled with the same value, but this value is different for different columns. I have these values stored the following way as a MultiIndex pandas Series se:

|bar | 0  | 2 |
|    | 1  | 3 |
 ...  ...  ...
|    | 99 | 7 |

The result would look like this:

| foo      |          | bar |     | ... |     |
| one      | two      | 0   | 1   | ... | 99  |
| -------- | -------- | --- | --- | ... | --- |
| "12345"  | "1235"   | 2   | 3   | ... | 7   |
| "12345"  | "1345"   | 2   | 3   | ... | 7   |

I have found this very ugly solution to my problem...

for i in range(len(se)):
    df["bar", i] = se[i]

... that also gives me a warning:

PerformanceWarning: DataFrame is highly fragmented. This is usually the result of callingframe.insertmany times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use newframe = frame.copy()

Have been trying to find the solution to this for a while now, thanks in advance for useful answers!

IIUC, you can use :

tmp = se.to_frame().T

out = df.join(tmp.loc[tmp.index.repeat(len(df))].reset_index(drop=True))

Output :

print(out)

     foo       bar      
     one   two   0  1 99
0  12345  1235   2  3  7
1  12345  1345   2  3  7