[Pandas] Help with finding only non-matching rows

[LowEnd]

Hello, new here and new to python. I am trying to write a function which will display only non-matched and/or missing rows between two datasets. Some of the datasets will be in the thousands of rows. Closest I can get thus far is with two pandas dataframes using merge, which shows all rows but any advice to only get non-matched or a better approach?

Example:

1 2 3 4 5 6 7

import pandas as pd data1=[('id1','desc1'),('id2','desc2'),('id3','desc3')] data2=[('id1','wrong description'),('id2','desc2')] df1=pd.DataFrame(data1,columns=['ID','DESCRIPTION']) df2=pd.DataFrame(data2,columns=['ID','DESCRIPTION']) diff=pd.merge(df1,df2,how='outer',on=['ID'],suffixes=('_SRC','_TGT'),indicator=True) print(diff)

Output:
    ID DESCRIPTION_SRC    DESCRIPTION_TGT
0  id1           desc1  wrong description
1  id2           desc2              desc2
2  id3           desc3                NaN

How would I get similar result with only row 0 and row 2?

Thank you.

Yoriz write Nov-11-2021, 09:29 PM:
Please post all code, output and errors (in their entirety) between their respective tags. Refer to BBCode help topic on how to post. Use the "Preview Post" button to make sure the code is presented as you expect before hitting the "Post Reply/Thread" button.

[jefsummers]

1 2 3 4 5 6 7 8

import pandas as pd data1=[('id1','desc1'),('id2','desc2'),('id3','desc3')] data2=[('id1','wrong description'),('id2','desc2')] df1=pd.DataFrame(data1,columns=['ID','DESCRIPTION']) df2=pd.DataFrame(data2,columns=['ID','DESCRIPTION']) print(pd.merge(df1,df2, indicator=True, how='outer')          .query('_merge=="left_only"')          .drop('_merge', axis=1))

Output:
  ID DESCRIPTION
0  id1       desc1
2  id3       desc3

Does this work?

[LowEnd]

Thank you so much! It is closer, but I am wanting to capture the differences between source and target. I am wanting output something like:

Output:
    ID DESCRIPTION_SRC    DESCRIPTION_TGT
0  id1           desc1  wrong description
2  id3           desc3                NaN

[jefsummers]

1 2 3 4 5 6 7 8 9 10

import pandas as pd data1=[('id1','desc1'),('id2','desc2'),('id3','desc3')] data2=[('id1','wrong description'),('id2','desc2')] df1=pd.DataFrame(data1,columns=['ID','DESCRIPTION']) df2=pd.DataFrame(data2,columns=['ID','DESCRIPTION']) df1a = df1.copy() df1a['Desc2']=df2['DESCRIPTION'] df1a['Different'] = (df1a['DESCRIPTION'] != df1a['Desc2']) df1a = df1a[df1a.Different == True] df1a

Output:
ID	DESCRIPTION	Desc2	Different
0	id1	desc1	wrong description	True
2	id3	desc3	NaN	True

You can then strip away the Different column.