Python pandas: concat two DataFrames with different number of rows by duplication

Question

I have a DataFrame, namely df , like:

    Name  Subject Score
0    Tom        A    91
1    Bob        B    92
2    Ali        C    93

and a dictionary like:

exam_info = {
    "exam_date": "2021-04-01",
    "advisor":   "Jim",
}

My goal DataFrame is to insert exam_date and advisor into each row of df , resulting like:

      exam_date advisor Name  Subject Score
0    2021-04-01     Jim  Tom        A    91
1    2021-04-01     Jim  Bob        B    92
2    2021-04-01     Jim  Ali        C    93

I know following code can be working:

df.insert(0, 'advisor', exam_info['advisor'])
df.insert(0, 'exam_date', exam_info['exam_date'])

but in the real project, I have a number of df s to insert and the real exam_info dictionary is also quite lengthy, thus there will be a bunch of blocks of df.insert(..) in the code, which is not so elegant.

I also tried to change exam_info into a helper DataFrame df_helper like:

    exam_date   advisor
0  2021-04-01       Jim

and then use pd.concat([df_helper, df], axis=1) , but the resulting DataFrame will only have exam_date and advisor in the first row, with the corresponding columns in other rows are all Nan , like:

      exam_date advisor Name  Subject Score
0    2021-04-01     Jim  Tom        A    91
1           NaN     NaN  Bob        B    92
2           NaN     NaN  Ali        C    93

Please advice if any more elegant ways to concat both DataFrames so that the NaN s are filled up with correct values.

Answer 1

Do you want this?

df = pd.concat([df, pd.DataFrame([xam_info])],axis=1).fillna(method='ffill')