Remove one dataframe from another with Pandas

Question

I have two dataframes of different size (df1 nad df2). I would like to remove from df1 all the rows which are stored within df2.

So if I have df2 equals to:

     A  B
0  wer  6
1  tyu  7

And df1 equals to:

     A  B  C
0  qwe  5  a
1  wer  6  s
2  wer  6  d
3  rty  9  f
4  tyu  7  g
5  tyu  7  h
6  tyu  7  j
7  iop  1  k

The final result should be like so:

     A  B  C
0  qwe  5  a
1  rty  9  f
2  iop  1  k

I was able to achieve my goal by using a for loop but I would like to know if there is a better and more elegant and efficient way to perform such operation.

Here is the code I wrote in case you need it: import pandas as pd

df1 = pd.DataFrame({'A' : ['qwe', 'wer', 'wer', 'rty', 'tyu', 'tyu', 'tyu', 'iop'],
                    'B' : [    5,     6,     6,     9,     7,     7,     7,     1],
                    'C' : ['a'  ,   's',   'd',   'f',   'g',   'h',   'j',   'k']})

df2 = pd.DataFrame({'A' : ['wer', 'tyu'],
                    'B' : [    6,     7]})

for i, row in df2.iterrows():
    df1 = df1[(df1['A']!=row['A']) & (df1['B']!=row['B'])].reset_index(drop=True)

Answer 1

Use merge with outer join with filter by query, last remove helper column by drop:

df = pd.merge(df1, df2, on=['A','B'], how='outer', indicator=True)
       .query("_merge != 'both'")
       .drop('_merge', axis=1)
       .reset_index(drop=True)
print (df)
     A  B  C
0  qwe  5  a
1  rty  9  f
2  iop  1  k