Python / Pandas - 在使用多列的合并期间将“空字符串”视为匹配项

Question

I'm trying to merge 2 dataframes on multiple columns: ['Unit','Geo','Region'] . And, the condition is: When a value from right_df encounters an 'empty string' on left_df , it should consider as a match.

eg.,when first row of right_df joins with first row of left_df , we have a empty string for column: 'Region' . So,need to consider the empty string as a match to 'AU' and get the final result 'DE".

left_df = pd.DataFrame({'Unit':['DEV','DEV','DEV','DEV','DEV','TEST1','TEST2','ACCTEST1','ACCTEST1','ACCTEST1'],
                    'Geo':['AP','JAPAN','NA','Europe','Europe','','','AP','Europe','NA'],
                    'Region':['','','','France','BENELUX','','','','',''],
                    'Resp':['DE','FG','BO','MD','KR','PM','NJ','JI','HN','FG']})


right_df = pd.DataFrame({'Unit':['DEV','DEV','DEV','DEV','TEST1','TEST2','ACCTEST1','DEV','ACCTEST1','TEST1','TEST2','DEV','TEST1','TEST2'],
                    'Geo':['AP','JAPAN','AP','NA','AP','Europe','Europe','Europe','AP','JAPAN','AP','Europe','Europe','Europe'],
                    'Region':['AU','JAPAN','ISA','USA','AU/NZ','France','CEE','France','ISA','JAPAN','ISA','BENELUX','CEE','CEE']})    

I tried with the below code but it works only if the 'empty strings' have values. I'm struggling to add a condition saying 'consider empty string as a match' or 'ignore if right_df encounters empty string and continue with available match'. Would appreciate for any help. Thanks!!

result_df = pd.merge(left_df, right_df, how='inner', on=['Unit','Geo','Region'])

Answer 1

Looks like there's some mismatch in your mapping, however you can use update method to handle empty strings:

# replace empty strings with nan
left_df = left_df.replace('', np.nan)

# replace np.nan with values from other dataframe
left_df.update(right_df, overwrite=False)

# merge
df = pd.merge(left_df, right_df, how='right', on=['Unit','Geo','Region'])

Hope this gives you some idea.

Answer 2

Use DataFrame.merge inside a list comprehension and perform the left merge operations in the following order:

1. Merge right_df with left_df on columns Unit , Geo and Region and select column Resp .

2. Merge right_df with left_df (drop duplicate values in Unit and Geo) on columns Unit , Geo and select column Resp .

3. Merge right_df with left_df (drop duplicate values in Unit) on column Unit and select column Resp .

Then use functools.reduce with a reducing function Series.combine_first to combine the all the series in the list s and assign this result to Resp column in right_df .

from functools import reduce

c = ['Unit', 'Geo', 'Region']
s = [right_df.merge(left_df.drop_duplicates(c[:len(c) - i]), 
              on=c[:len(c) - i], how='left')['Resp'] for i in range(len(c))]
right_df['Resp'] = reduce(pd.Series.combine_first, s)

Result:

print(right_df)

        Unit     Geo   Region Resp
0        DEV      AP       AU   DE
1        DEV   JAPAN    JAPAN   FG
2        DEV      AP      ISA   DE
3        DEV      NA      USA   BO
4      TEST1      AP    AU/NZ   PM
5      TEST2  Europe   France   NJ
6   ACCTEST1  Europe      CEE   HN
7        DEV  Europe   France   MD
8   ACCTEST1      AP      ISA   JI
9      TEST1   JAPAN    JAPAN   PM
10     TEST2      AP      ISA   NJ
11       DEV  Europe  BENELUX   KR
12     TEST1  Europe      CEE   PM
13     TEST2  Europe      CEE   NJ