I'm trying to find the same number of occurrences in both data frames This is a follow-up question for [my previous question][1] <br>I got 2 data frames df1=pd.DataFrame([[1,None],[1,None,],[1,None],[1,'item_a'],[2,'item_a'],[2,'item_b'],[2,'item_f'],[3,'item_e'],[3,'item_e'],[3,'item_g'],[3,'item_h']],columns=['id','A']) df2=pd.DataFrame([[1,'item_a'],[1,'item_b'],[1,'item_c'],[1,'item_d'],[2,'item_a'],[2,'item_b'],[2,'item_c'],[2,'item_d'],[3,'item_e'],[3,'item_f'],[3,'item_g'],[3,'item_h']],columns=['id','A']) <br> df1 id A 0 1 None 1 1 None 2 1 None 3 1 item_a # id 1 has 1 occurrences in total in df1 4 2 item_a 5 2 item_b 6 2 item_f #id 2 has 3 occurrences in total in df1 7 3 item_e 8 3 item_e 9 3 item_g 10 3 item_h #id3 has 4 ccurrences in total in df1 df2 id A 0 1 item_a 1 1 item_b 2 1 item_c 3 1 item_d 4 2 item_a 5 2 item_b 6 2 item_c 7 2 item_d 8 3 item_e 9 3 item_f 10 3 item_g 11 3 item_h <br>I got an answer on how to find similarities by using previous result: d=pd.merge(df1,df2,how='inner') id A 3 1 item_a # id 1 has 1 occurrences in total in d 4 2 item_a 5 2 item_b # id 2 has 2 occurrences in total in d(does not match df1) 7 3 item_e 8 3 item_e 9 3 item_g 10 3 item_h #id 3 has 4 occurrences in total in d What I've tried to find same number of occurrences in both data frames: <br>`d[d['id'].value_counts()==df1['id'].value_counts()]` <br>`Which gave me an error:Can only compare identically-labeled Series objects` <br>I've also tried different things using rename to put a column name for value_counts and merge them but failed. My desired output for this question: id count 0 1 1 1 3 4 [1]: https://datascience.stackexchange.com/questions/76738/compare-similarities-between-two-data-frames-using-more-than-one-column-in-each/76750#76750