CODEWITHSUNDEEP
pythonpython-3.xpandasdataframedata-science
ayush
ayush

Reputation: 25

Merge two pandas dataframes in python with a common column and with condition on string

I have two dfs in python :

df1

    folder_name   name
0   f1          aa
1   g1          bb


df2   
    name        icon
0   aa          i1
1   bb          i2
2   aadoq       i3
3   bbaddd      i4

Desired output:

df   
    folder_name  name    icon
0   f1           aa      i1
1   g1           bb      i2
2   f1           aadoq   i3
3   g1           bbaddd  i4

I tried merging them but it seemed wrong 

pd.merge(df1,df2,on='name',how='right')

i am getting :

      folder_name  name    icon
  0   f1           aa      i1
  1   g1           bb      i2
  2   NAN          aadoq   i3
  3   NAN          bbaddd  i4

So if the prefix string in df2 name column matches any name column itme in df1 i want that folder name to be there for that name column in output

Upvotes: 1

Views: 184

Answers (2)

Raj006
Raj006

Reputation: 562

Based on your question, here are the prerequisites I got

  1. Python (I used version 3, but should not be much different for version 2)
  2. Pandas data frames
  3. The folder name is not restricted to the length=2

Here is my Python code. I used the Python regex module. I selected the "name" list from both dataframes, checked if any name in dataframe 1 matches the name in dataframe 2 (match in python regex means to match from the beginning of the string). Created a new list called MappedName based on these criteria where there is a match use the matched name value from dataframe 1 if not, use the name value from dataframe 2. Added this list as a new column to dataframe 2. Used the 'name' column from dataframe 1 and 'MappedName' column from dataframe 2 for merge criteria.

I added one extra data point to the dataframe 2 to show what happens when there is no match with the regular expressions.

from pandas import DataFrame
import re

df1=DataFrame({'folder_name':['f1','g1'],'name':['aa','bb']})
df2=DataFrame({'name':['aa','bb','aadoq','bbaddd','ding'],'icon':['i1','i2','i3','i4','i5']})
df1_name_list=df1['name']
df2_name_list=df2['name']
MappedName=[]
for name2 in df2_name_list:
    for name1 in df1_name_list:
        if re.match(name1,name2):
            name2=name1
            break
    MappedName.append(name2)
df2['MappedName']=MappedName
df3=df1.merge(df2,left_on='name',right_on='MappedName',how='right').drop(['name_x','MappedName'],axis=1)
df4=df1.merge(df2,left_on='name',right_on='MappedName').drop(['name_x','MappedName'],axis=1)

print ('\ndf1\n',df1)
print ('\ndf2\n',df2)
print ('\ndf3\n',df3)
print ('\ndf4\n',df4)

The result looks like below

df1
   folder_name name
0          f1   aa
1          g1   bb

df2
      name icon MappedName
0      aa   i1         aa
1      bb   i2         bb
2   aadoq   i3         aa
3  bbaddd   i4         bb
4    ding   i5       ding

df3
   folder_name  name_y icon
0          f1      aa   i1
1          f1   aadoq   i3
2          g1      bb   i2
3          g1  bbaddd   i4
4         NaN    ding   i5

df4
   folder_name  name_y icon
0          f1      aa   i1
1          f1   aadoq   i3
2          g1      bb   i2
3          g1  bbaddd   i4

If you want NaN when you merge and there is no match, use the df3 example otherwise, use df4. For large datasets (millions of data points), this might not be efficient code.

Upvotes: 1

G. Anderson
G. Anderson

Reputation: 5955

I would explicitly add a column for prefix, merge, then remove the column after: (Note, this only works if all the data is as you posted, with a 2-character prefix)

df1=pd.DataFrame({'folder_name':['f1','d1'],'name':['aa','bb']})
df2=pd.DataFrame({'name':['aa','bb','aaq','bbdfg'],'icon':[1,2,3,4]})

df2['prefix']=df2['name'].str[:2]

    name    icon    prefix
0   aa      1       aa
1   bb      2       bb
2   aaq     3       aa
3   bbdfg   4       bb

df3=df2.merge(df1, left_on='prefix', right_on='name').drop(['name_y','prefix'], axis=1)

df3

    name_x  icon    folder_name
0   aa      1       f1
1   aaq     3       f1
2   bb      2       d1
3   bbdfg   4       d1

Upvotes: 1

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