使用两列连接，在四个其他数据帧中填充一个pandas数据帧中的列

Question

The end result Pandas Data Frame needs to look something like this.

        aggregate_FID   jurisdiction    FID       name           rate
2217    750             municipal       405       Auburn         0.093
2218    751             municipal       81        Bonney Lake    0.088
2219    752             municipal       405       Auburn         0.093
2220    753             municipal       171       Steilacoom     0.094
2221    754             municipal       235       Lakewood       0.094
2222    755             municipal       176       Fircrest       0.094
2223    750             state           1         Washington     0.065
2224    751             state           1         Washington     0.065

The starting point is a Data Frame with this structure.

        aggregate_FID   jurisdiction    FID
2217    750             municipal       405
2218    751             municipal       81
2219    752             municipal       405
2220    753             municipal       171
2221    754             municipal       235
2222    755             municipal       176
2223    750             state           1
2224    751             state           1

...and multiple data frames I need to use for populating the name and tax rate fields.

    FID name        rate    jurisdiction
0   1   Waterville  0.082   municipal
1   2   Riverside   0.081   municipal
2   3   Pierce HBZ  0.079   municipal
3   4   Cle Elum    0.080   municipal
4   5   Pacific     0.095   municipal

    FID name        rate    jurisdiction
0   1   Washington  0.065   state

I need to match up the latter data frames with the first based on the jurisdiction and FID columns, and populate the name and rate columns. I have managed to create a single data frame merging with one of the latter data frames using...

df_merge = pd.merge(left=df_aggregate, right=df_jurisdiction, how='left', on=['FID', 'jurisdiction'])

...but this only works for one of the tables. Unfortunately I need to do this for as little as one, but as many as seven tables. This has been a pain for over two days now. Please feel free to ask for more clarification if I have not been clear enough in my question, and thank you in advance for your help.

Answer 1

You can concatenate all the jurisdiction tables first and then use merge . It would look something like this.

j_all = pd.concat([j1, j2, j3, j4, j5, j6, j7])
df_merge = pd.merge(left=df_aggregate, right=j_all, how='left', on=['FID', 'jurisdiction'])