CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
HazimoRa3d
HazimoRa3d

Reputation: 527

Compare multiple row values

I want to compare for each row, values of A with the other columns

The problem is more complex but i tried to simplify it in this table:

     A    B    C  D
0  1.3  1.0  1.1  1
1  2.5  2.9  2.6  3
2  3.1  3.0  3.2  2

The result should look like this:

Here in index 0: 1.3 is larger than the values in B,C and D, then we return 1, otherwise it is 0

     A    B    C  D  result
0  1.3  1.0  1.1  1       1
1  2.5  2.9  2.6  3       0
2  3.1  3.0  3.2  2       0

Thank you in advance

Upvotes: 1

Views: 505

Answers (4)

Joe T. Boka
Joe T. Boka

Reputation: 6589

You can use idxmax:

df['result'] = (df.idxmax(axis=1)== 'A').astype(int)

Output:

    A    B    C  D  result
0  1.3  1.0  1.1  1       1
1  2.5  2.9  2.6  3       0
2  3.1  3.0  3.2  2       0

Upvotes: 1

Dror Paz
Dror Paz

Reputation: 387

if you know the column names do:

df['results']=(df.loc[:,'A']>df.loc[:,'B':'D'].max(axis=1)).astype(int)

if you know want to work with columns order you can do:

df['results']=(df.iloc[:,0]>df.iloc[:,1:].max(axis=1)).astype(int)

on your example those will give the same output. the "astype(int)" at the end converts the boolean value to 0/1.

Upvotes: 0

jezrael
jezrael

Reputation: 862511

You can use gt or le for compare, then any or all for get at least one True or all Trues and last cast boolean mask to int:

df['result'] = (~df[['B','C','D']].gt(df.A, axis=0).any(1)).astype(int)
print (df)
     A    B    C  D  result
0  1.3  1.0  1.1  1       1
1  2.5  2.9  2.6  3       0
2  3.1  3.0  3.2  2       0

Another solution:

df['result'] = df[['B','C','D']].le(df.A, axis=0).all(1).astype(int)
print (df)
     A    B    C  D  result
0  1.3  1.0  1.1  1       1
1  2.5  2.9  2.6  3       0
2  3.1  3.0  3.2  2       0

Upvotes: 2

piRSquared
piRSquared

Reputation: 294218

Use assign to create new column
Use df.le(df.A, 0) to compare column 'A' to all other columns
Use all(1) to find where True for all columns
Use astype(int) to make it 1 or 0

df.assign(result=df.lt(df.A, 0).all(1).astype(int))

     A    B    C  D  result
0  1.3  1.0  1.1  1       1
1  2.5  2.9  2.6  3       0
2  3.1  3.0  3.2  2       0

Upvotes: 5

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