Remove all rows from pandas dataframe based on a second dataframe

I’m relatively new to pandas, so forgive the possibly simple question. I have two dataframes, one containing all of my data, in this case filenames, and the other containing filenames I want to remove.

What I would like to do is remove all of the rows in the master dataframe, where the filename appears in the second dataframe. There are several thousand different filenames, so I’m looking for some kind of generalisation of df = df[df["filename"].str.contains("A") == False] to take a dataframe, where there are also lots of duplicate values.

master_df = pd.DataFrame({'filename': ['A', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'C'],
                          'label': [0, 0, 0, 1, 0, 1, 0, 1, 1]})

files_to_remove = pd.DataFrame({'filename': ['A', 'A', 'A', 'A', 'C', 'C', 'C'],
                                'label': [0, 0, 0, 1, 0, 1, 1]})

desired_result = pd.DataFrame({'filename': ['B', 'B'],
                                'label': [0, 1]})

Thanks for the help!

>Solution :

try like this :

import pandas as pd

master_df = pd.DataFrame({'filename': ['A', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'C'],
                          'label': [0, 0, 0, 1, 0, 1, 0, 1, 1]})

files_to_remove = pd.DataFrame({'filename': ['A', 'A', 'A', 'A', 'C', 'C', 'C'],
                                'label': [0, 0, 0, 1, 0, 1, 1]})


print (master_df[~master_df.filename.isin(files_to_remove.filename)])

Output :

  filename  label
4        B      0
5        B      1