CODEWITHSUNDEEP
pythonpython-3.xpandasmerge
John Doe
John Doe

Reputation: 10203

Merge DataFrame using `contains` (not a full match !)

I have been struggling with this problem all week. I have two DataFrames as follows:

df1:

Account| ID              | Name
--------------------------------------
B36363 | 2019001         | John
G47281 | 2019002;2018101 | Alice;Emma
H46291 | 2019001         | John

df2:

Account   | Col_B   | Col_C
-----------------------------
B36363-0  | text_b1 | text_c1
01_G47281 | text_b2 | text_c2
X_H46291  | text_b3 | text_c3
II_G47281 | text_b4 | text_C4

I want to merge these DataFrames on Account when df2.Account contains df1.Account (not the full match with a normal merge/join!)

Desired output:

df3:

Account   | Col_B   | Col_C   | ID              | Name
--------------------------------------------------------------
B36363-0  | text_b1 | text_c1 | 2019001         | John
01_G47281 | text_b2 | text_c3 | 2019002;2018101 | Alice;Emma
X_H46291  | text_b3 | text_c3 | 2019001         | John
II_G47281 | text_b4 | text_C4 | 2019002;2018101 | Alice;Emma

I have no sample code because I have no idea how to handle this. A normal merge/join goes well, but not if I want to use contains. Thank you very much in advance

Upvotes: 2

Views: 122

Answers (3)

anky
anky

Reputation: 75100

You can try str.extract with join():

d=df1.set_index('Account').agg(list,axis=1).to_dict()
p='({})'.format('|'.join(df1.Account))
#'(B36363|G47281|H46291)'
m=pd.DataFrame(df2.Account.str.extract(p,expand=False).map(d).fillna('').tolist()
               ,columns=['ID','Name'],index=df2.index)
df2.join(m)

     Account    Col_B    Col_C               ID        Name
1   B36363-0  text_b1  text_c1          2019001        John
2  01_G47281  text_b2  text_c2  2019002;2018101  Alice;Emma
3   X_H46291  text_b3  text_c3          2019001        John
4  II_G47281  text_b4  text_C4  2019002;2018101  Alice;Emma

Upvotes: 3

Andy L.
Andy L.

Reputation: 25259

Try str.extract on df2.Account and set the result to index of df2 and join

pat1 = '('+'|'.join(df1.Account)+')'
s = df2.Account.str.extract(pat1, expand=False)
df2.set_index(s).join(df1.set_index('Account')).reset_index(drop=True)

Out[644]:
     Account    Col_B    Col_C               ID        Name
0   B36363-0  text_b1  text_c1          2019001        John
1  01_G47281  text_b2  text_c2  2019002;2018101  Alice;Emma
2  II_G47281  text_b4  text_C4  2019002;2018101  Alice;Emma
3   X_H46291  text_b3  text_c3          2019001        John

Another way is using merge

df2.assign(Account2=df2.Account.str.extract(pat1, expand=False)) \
   .merge(df1, left_on='Account2', right_on='Account', suffixes=('', 'y')) \
   .drop(['Account2', 'Accounty'], 1)

Out[645]:
     Account    Col_B    Col_C               ID        Name
0   B36363-0  text_b1  text_c1          2019001        John
1  01_G47281  text_b2  text_c2  2019002;2018101  Alice;Emma
2  II_G47281  text_b4  text_C4  2019002;2018101  Alice;Emma
3   X_H46291  text_b3  text_c3          2019001        John

Upvotes: 2

Erfan
Erfan

Reputation: 42916

Using my fuzzy_merge function:

from fuzzywuzzy import fuzz
from fuzzywuzzy import process

df3 = fuzzy_merge(df2, df1, 'Account', 'Account', threshold=80)\
     .merge(df1, left_on='matches', right_on='Account', suffixes=['', '_2'])\
     .drop(columns=['matches', 'Account_2'])

Output

     Account    Col_B    Col_C               ID        Name
0   B36363-0  text_b1  text_c1          2019001        John
1  01_G47281  text_b2  text_c2  2019002;2018101  Alice;Emma
2  II_G47281  text_b4  text_C4  2019002;2018101  Alice;Emma
3   X_H46291  text_b3  text_c3          2019001        John

Fuzzy_merge function from linked answer:

def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2):
    """
    df_1 is the left table to join
    df_2 is the right table to join
    key1 is the key column of the left table
    key2 is the key column of the right table
    threshold is how close the matches should be to return a match
    limit is the amount of matches will get returned, these are sorted high to low
    """
    s = df_2[key2].tolist()

    m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit))    
    df_1['matches'] = m

    m2 = df_1['matches'].apply(lambda x: ', '.join([i[0] for i in x if i[1] >= threshold]))
    df_1['matches'] = m2

    return df_1

Upvotes: 2

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