How to remove rows in a Pandas dataframe if the same row exists in another dataframe?

Question

I have two dataframes:

 df1 = row1;row2;row3  df2 = row4;row5;row6;row2 

I want my output dataframe to only contain the rows unique in df1, i.e.:

df_out = row1;row3 

How do I get this most efficiently?

This code does what I want, but using 2 for-loops:

a = pd.DataFrame({0:[1,2,3],1:[10,20,30]}) b = pd.DataFrame({0:[0,1,2,3],1:[0,1,20,3]})  match_ident = [] for i in range(0,len(a)):     found=False     for j in range(0,len(b)):         if a[0][i]==b[0][j]:             if a[1][i]==b[1][j]:                 found=True     match_ident.append(not(found))  a = a[match_ident] 

Answer 1

You an use merge with parameter indicator and outer join, query for filtering and then remove helper column with drop:

DataFrames are joined on all columns, so on parameter can be omit.

print (pd.merge(a,b, indicator=True, how='outer')          .query('_merge=="left_only"')          .drop('_merge', axis=1))    0   1 0  1  10 2  3  30