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df1 = pandas.DataFrame(data = {'col1' : [1, 2, 3, 4, 5], 'col2' : [10, 11, 12, 13, 14]}) df2 = pandas.DataFrame(data = {'col1' : [1, 2, 3], 'col2' : [10, 11, 12]}) records_df2 = set([tuple(row) for row in df2.values]) in_df2_mask = np.array([tuple(row) in records_df2 for row in df1.values]) result = df1[~in_df2_mask] How about this:
df1 = pandas.DataFrame(data = {'col1' : [1, 2, 3, 4, 5], 'col2' : [10, 11, 12, 13, 14]}) df2 = pandas.DataFrame(data = {'col1' : [1, 2, 3], 'col2' : [10, 11, 12]}) records_df2 = set([tuple(row) for row in df2.values]) in_df2_mask = np.array([tuple(row) in records_df2 for row in df1.values]) result = df1[~in_df2_mask] How about this:
df1 = pandas.DataFrame(data = {'col1' : [1, 2, 3, 4, 5], 'col2' : [10, 11, 12, 13, 14]}) df2 = pandas.DataFrame(data = {'col1' : [1, 2, 3], 'col2' : [10, 11, 12]}) records_df2 = set([tuple(row) for row in df2.values]) in_df2_mask = np.array([tuple(row) in records_df2 for row in df1.values]) result = df1[~in_df2_mask] How about this:
df1 = pandas.DataFrame(data = {'col1' : [1, 2, 3, 4, 5], 'col2' : [10, 11, 12, 13, 14]}) df2 = pandas.DataFrame(data = {'col1' : [1, 2, 3], 'col2' : [10, 11, 12]}) records_df2 = set([tuple(row) for row in df2.values]) in_df2_mask = np.array([tuple(row) in records_df2 for row in df1.values]) result = df1[~in_df2_mask]