如果第一个 dataframe 中的列的数据存在于 python 中另一个 dataframe 的任何列中，则合并两个数据帧

Question

I have two data frames I need to merge. The first one is:

page            value
shoes           554
sneakers        226
sandals         114
boots           821
T-shirt         213
mobile-phone    284
laptop          361

The second data frame is:

path1            path2            path3              path4
fashion          footwear         shoes-and-other    shoes
fashion          footwear         shoes-and-other    sneakers
fashion          footwear         sandals            NaN
fashion          footwear         shirts             T-shirt
electronic       devices          mobile-and-tablet  mobile-phone 
electronic       devices          laptop             NaN 

My expected output will be:

path1        path2      path3              path4        page         value
fashion      footwear   shoes-and-other    shoes        shoes        554
fashion      footwear   shoes-and-other    sneakers     sneakers     226
fashion      footwear   sandals            NaN          sandals      114
fashion      footwear   shirts             T-shirt      T-shirt      213
electronic   devices    mobile-and-tablet  mobile-phone mobile-phone 284 
electronic   devices    laptop             NaN          laptop       361

I want to join these two data frames if any of the page strings in the first data frame exists in the path1 or path2 , or path3 , or path4 columns of the second data frame. Notice that page of the first data frame might be matched with path1 of the second data frame and I have a variety of situations.

Is there a simple pythonic way?

Answer 1

Let us try where with ffill create the merge key, then merge

df1['page'] = df1.where(df1.isin(df.page.tolist())).ffill(1).iloc[:,-1]
df1 = df1.merge(df, how='left')
df1
Out[131]: 
        path1     path2              path3         path4          page  value
0     fashion  footwear    shoes-and-other         shoes         shoes    554
1     fashion  footwear    shoes-and-other      sneakers      sneakers    226
2     fashion  footwear            sandals           NaN       sandals    114
3     fashion  footwear             shirts       T-shirt       T-shirt    213
4  electronic   devices  mobile-and-tablet  mobile-phone  mobile-phone    284
5  electronic   devices             laptop           NaN        laptop    361