Compare value of specific column and add a value to another | Pandas | Python

Question

I have a dataframe with 6 columns like that:

c1      c2             c3      c4           c5      c6
C875    DOID_3263       1       9.65E-18    1       unknown
C783    DOID_4064       1       4.80E-17    1       unknown
C372    DOID_0050084    0.996   0.00429     0.996   unknown
C43     DOID_936        0.0457  0.954       0.954   known

Column c5 represent the max value between c3 and c4, I want to add a column after c6 to compare if the max value in c5 come from c3 put 0 if it come from c4 put 1

So, at the end the final result will be like this:

c1      c2              c3      c4          c5      c6       c7
C875    DOID_3263       1       9.65E-18    1       known    0
C783    DOID_4064       1       4.80E-17    1       unknown  0
C372    DOID_0050084    0.996   0.00429     0.996   unknown  0
C43     DOID_936        0.0457  0.954       0.954   known    1

Any help, please?

Brian · Accepted Answer · 2018-08-24 03:14:55Z

1

First, take the max of the two columns

df['c5'] = np.maximum(df['c3'], df['c4'])

If the max is equal to 'c4' put a 1, else 0 (which implies it came from 'c3' under this paradigm).

df['c7'] = (df['c5'] == df['c4']).astype(int)

answered Aug 24, 2018 at 3:14

Brian

1,5951 gold badge11 silver badges19 bronze badges

Sign up to request clarification or add additional context in comments.

Comments

rafaelc · Accepted Answer · 2018-08-24 03:15:22Z

1

Using idxmax yields the name of the columns right away

df[['c3','c4']].idxmax(1)

0    c3
1    c3
2    c3
3    c4
dtype: object

Can always map if need 0 or 1

df[['c3','c4']].idxmax(1).map({'c3': 0, 'c4':1})

0    0
1    0
2    0
3    1
dtype: int64

answered Aug 24, 2018 at 3:15

rafaelc

59.4k15 gold badges64 silver badges87 bronze badges

1 Comment

Brian Over a year ago

Nice solution, I had no idea idmax could be used in this fashion.

BENY · Accepted Answer · 2018-08-24 03:01:54Z

0

Using select from numpy

s1=df.c3==df.c5
s2=df.c4==df.c5
df['c7']=np.select([s1,s2],[0,1])
df
Out[670]: 
     c1            c2      c3            c4     c5       c6  c7
0  C875     DOID_3263  1.0000  9.650000e-18  1.000  unknown   0
1  C783     DOID_4064  1.0000  4.800000e-17  1.000  unknown   0
2  C372  DOID_0050084  0.9960  4.290000e-03  0.996  unknown   0
3   C43      DOID_936  0.0457  9.540000e-01  0.954    known   1

answered Aug 24, 2018 at 3:01

BENY

324k22 gold badges176 silver badges250 bronze badges

Collectives™ on Stack Overflow

Compare value of specific column and add a value to another | Pandas | Python

3 Answers 3

Comments

1 Comment

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

3 Answers 3

Comments

1 Comment

Comments

Your Answer

Sign up or log in

Post as a guest

Related