my df:
No A B C D
1 1 1 0 1
2 1 1 1 0
3 1 0 1 1
4 1 1 1 1
I would like to perform A or B or C or D and write the result in a column.
No A B C D Result
1 1 1 0 1 1
2 1 1 1 0 1
3 1 0 1 1 1
4 1 1 1 1 1
...
so the idea is, the result is '1' even if there is one '1' present either in A,B,C or D.
One idea is use DataFrame.any for test at least one 1 per rows or use max per rows:
#if No is column
df['Result'] = df.iloc[:, 1:].any(axis=1).astype(int)
#if No is index
#df['Result'] = df.any(axis=1).astype(int)
If some another columns:
df['Result'] = df[['A','B','C','D']].any(axis=1).astype(int)
Or:
df['Result'] = df[['A','B','C','D']].max(axis=1)
print (df)
No A B C D Result
0 1 1 1 0 1 1
1 2 1 1 1 0 1
2 3 1 0 1 1 1
3 4 1 1 1 1 1
Try using:
df['Result'] = df.drop('No', axis=1).max(1)
print(df)
Output:
No A B C D Result
0 1 1 1 0 1 1
1 2 1 1 1 0 1
2 3 1 0 1 1 1
3 4 1 1 1 1 1
To add to the list of funny ways:
df_a['Result'] = df_a.eval('A + B + C').astype(bool)
The advantage is that eval doesn't make intermediate tables in the memory. If you need explicit int instead of bool, you can cast it, of course:
df_a['Result'] = df_a.eval('A + B + C').astype(bool).astype(int)
Strangely, no one mentioned the use of simple | operator.
Answer to your question
df['Result'] = df['A'] | df['B'] | df['C'] | df['D']
Similarly, if you want to perform other operations like AND
df['Result'] = df['A'] & df['B'] & df['C'] & df['D']
