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I have two data frames with same column but different values, out of which some are same and some are different. I want to compare both columns and keep the common values.

df1 :

  A B C
  1 1 1
  2 4 6
  3 7 9
  4 9 0
  6 0 1

df2 :

  A D E
  1 5 7
  5 6 9
  2 3 5
  7 6 8
  3 7 0

This is what I am expecting after comparison

df2 :

  A D E
  1 5 7
  2 3 5
  3 7 0
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  • Perhaps this can be useful? stackoverflow.com/questions/26921943/… Commented Jul 26, 2019 at 11:53
  • I guess you want to use something like merging df1 and df2 based on the column A Commented Jul 26, 2019 at 11:56
  • I don't want to merge them, I want to save the common points only. Commented Jul 26, 2019 at 12:20

1 Answer 1

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You can use pd.Index.intersection() to find the matching columns and do a inner merge finally reindex() to keep df2.columns:

match=df2.columns.intersection(df1.columns).tolist() #finds matching cols in both df
df2.merge(df1,on=match).reindex(df2.columns,axis=1) #merge and reindex to df2.columns

   A  D  E
0  1  5  7
1  2  3  5
2  3  7  0
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3 Comments

Using this, I am getting the merged values and not the common ones.
@Bhavishya what is the difference? can you elaborate, this gets exactly what you posted as desired answer
I have around 250k entries in df1 and around 1.1m entries in df2, so after keeping just the common values I should get less than or equal to 250k entries in df2 but I'm getting around 1m entries instead.

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