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Input：
S T W U
0 A A 1 Undirected
1 A B 0 Undirected
2 A C 1 Undirected
3 B A 0 Undirected
4 B B 1 Undirected
5 B C 1 Undirected
6 C A 1 Undirected
7 C B 1 Undirected
8 C C 1 Undirected
Output：
S T W U
1 A B 0 Undirected
2 A C 1 Undirected
3 B A 0 Undirected
5 B C 1 Undirected
6 C A 1 Undirected
7 C B 1 Undirected
For column S and T ,rows(0,4,8) have same values. I want to drop these rows.
Trying:
I used df.drop_duplicates(['S','T'] but failed, how could I get the results.
604
By using pandas. DataFrame. drop_duplicates() method you can drop/remove/delete duplicate rows from DataFrame. Using this method you can drop duplicate rows on selected multiple columns or all columns.
Use drop() method to delete rows based on column value in pandas DataFrame, as part of the data cleansing, you would be required to drop rows from the DataFrame when a column value matches with a static value or on another column value.
To remove duplicates of only one or a subset of columns, specify subset as the individual column or list of columns that should be unique. To do this conditional on a different column's value, you can sort_values(colname) and specify keep equals either first or last .
You need boolean indexing:
print (df['S'] != df['T'])
0 False
1 True
2 True
3 True
4 False
5 True
6 True
7 True
8 False
dtype: bool
df = df[df['S'] != df['T']]
print (df)
S T W U
1 A B 0 Undirected
2 A C 1 Undirected
3 B A 0 Undirected
5 B C 1 Undirected
6 C A 1 Undirected
7 C B 1 Undirected
Or query:
df = df.query("S != T")
print (df)
S T W U
1 A B 0 Undirected
2 A C 1 Undirected
3 B A 0 Undirected
5 B C 1 Undirected
6 C A 1 Undirected
7 C B 1 Undirected
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