How to remove rows from a table that are repeated in another table?

Question

Python

Good afternoon!
Problem essence: there is a big table and the small table. The small table contains individual rows from the large one.
"Large":
col1 col2 col3
0 A 1 5
1 B 2 6
2 C 3 7
3 D 4 8
4 C 3 102

"Small":
col1 col2 col3
0 C 3 7

How to remove rows from a large table that are repeated in both? (in this case it is the string "C 3 7").
It should look like this:

col1 col2 col3
0 A 1 5
1 B 2 6
2 D 4 8
3 C 3 102

It is desirable to do this without loops, since real tables contain hundreds of thousands of rows and many repeating values.

Thank you very much!

Answer 1

import pandas as pd

df1 = pd.DataFrame({'col1':['A', 'B', 'C', 'D', 'C'], 'col2': [1,2,3,4,3], 'col3': [5,6,7,8,102]})
df1

df2 = pd.DataFrame({'col1':['C'], 'col2': [3], 'col3': [7]})
df2


df_new = pd.merge(df1, df2, how='outer', indicator=True)
df_new.loc[df_new['_merge'] == 'left_only'].drop('_merge', axis=1)

PS
Learn to google in English (in Google)
I'm not a super connoisseur of pandas, but even an extremely stupid and obvious question in Google in the form of "how to delete rows from table from other table pandas" results in a link on the first line, from where this is taken solution
https://stackoverflow.com/questions/39880627/in-pa...

Answer 2

convert dataframes into lists, more precisely into a two-dimensional array format, i.e. a list of lists, and make a compression sheet with a condition, then convert it back with the same column names as they were.

new_list=[x for x in df1.values.tolist() if x not in df2.values.tolist()]
df3=pandas.Dataframe.from_records(data=new_list, columns=df1.columns.values())
df3
Out[217]: 
  col1  col2  col3
0    A     1     5
1    B     2     6
2    D     4     8
3    C     3   102

Or as it was written above through the union in the dataframes themselves. See what works faster