Append 到 Dataframe 的列 1 基于 Dataframe 中的匹配列值 2

Question

I have two dataframes, df1 and df2 . The first of these dataframes tracks the locations (ie, ZIP codes) of specific individuals at different time points:

ID	ZIP 1	ZIP 2	ZIP 3
1	55333	N/A	55316
2	55114	27265	27265
3	55744	55744	N/A

The second dataframe contains several columns of data pertaining to every ZIP code in the country (many of which do not appear in df1 ):

ZIP	State	Tier
01001	MA	1
...	...	...
27265	NC	2
55114	MN	4
55316	MN	7
55333	MN	5
55744	MN	3

I would like to merge these dataframes and append the variable names from df2 to the ends of the corresponding ZIP/time point variable in df1 to end up with something like this (Note: I removed the ZIP 3 variable for simplicity; I'd still like to append the relevant State and Tier data, as shown for ZIP 1 and ZIP 2):

ID	ZIP 1	ZIP 2	ZIP 1 State	ZIP 2 State	ZIP 1 Tier	ZIP 2 Tier
1	55333	N/A	MN	N/A	5	N/A
2	55114	27265	MN	NC	4	2
3	55744	55744	MN	MN	3	3

The closest solution I have come up with is to create multiple "merged" dataframes by merging on each individual ZIP code variable in df1 . This is obviously less than ideal, and does not resolve the variable naming issue either.

merged = pd.merge(df1, df2, left_on = 'ZIP 1', right_on = 'ZIP', how = 'left')
merged2 = pd.merge(df1, df2, left_on = 'ZIP 2', right_on = 'ZIP', how = 'left')
merged3 = pd.merge(df1, df2, left_on = 'ZIP 3', right_on = 'ZIP', how = 'left')

Any guidance would be much appreciated: :-)

Answer 1

Try something like this:

dfs = df1.set_index('ID').stack().rename('ZIP').reset_index().drop('level_1', axis=1)
dfm = dfs.merge(df2)
df_out =dfm.set_index(['ID', dfm.groupby('ID').cumcount() +1]).unstack()
df_out.columns = [f'{i} {j}' for i, j in df_out.columns]
print(df_out)

Output:

    ZIP 1  ZIP 2  ZIP 3 State 1 State 2 State 3 Tier 1 Tier 2 Tier 3
ID                                                                  
1   55333  55316    NaN      MN      MN     NaN      5      7    NaN
2   55114  27265  27265      MN      NC      NC      4      2      2
3   55744  55744    NaN      MN      MN     NaN      3      3    NaN