How to operate conditional calculation between columns in pandas dataframe?

debugcn Published at Dev

Wookeun Lee

Here is a simple dataframe

import pandas as pd
import numpy as np
dates = pd. date_range(' 20130101' , periods=14)
data = pd.DataFrame({'a':[1,0,0,1,0,0,0,1,1,0,0,1,0,0],'b':[0,0,1,0,0,1,0,0,0,0,1,0,1,0]},index=dates)

Now I'd like to add column 'c', with the following conditions all together.

if a = 1, c = 1
if b = 1, c = 0
if a = 0 and b = 0, c = c.shift(1) constraint : there exists no cases of a = 1 and b = 1 at the same time.

This is a simple question, but very hard to solve...

Any good idea?

jezrael

IIUC you need:

data['c'] = np.where(data.a == 1, 1,
            np.where(data.b == 1, 0, np.nan))
print (data)
            a  b    c
2013-01-01  1  0  1.0
2013-01-02  0  0  NaN
2013-01-03  0  1  0.0
2013-01-04  1  0  1.0
2013-01-05  0  0  NaN
2013-01-06  0  1  0.0
2013-01-07  0  0  NaN
2013-01-08  1  0  1.0
2013-01-09  1  0  1.0
2013-01-10  0  0  NaN
2013-01-11  0  1  0.0
2013-01-12  1  0  1.0
2013-01-13  0  1  0.0
2013-01-14  0  0  NaN

Then I am not sure if need bfill or ffill:

data['c'] = data['c'].bfill()
print (data)
            a  b    c
2013-01-01  1  0  1.0
2013-01-02  0  0  0.0
2013-01-03  0  1  0.0
2013-01-04  1  0  1.0
2013-01-05  0  0  0.0
2013-01-06  0  1  0.0
2013-01-07  0  0  1.0
2013-01-08  1  0  1.0
2013-01-09  1  0  1.0
2013-01-10  0  0  0.0
2013-01-11  0  1  0.0
2013-01-12  1  0  1.0
2013-01-13  0  1  0.0
2013-01-14  0  0  NaN

data['c'] = data['c'].ffill()
print (data)
            a  b    c
2013-01-01  1  0  1.0
2013-01-02  0  0  1.0
2013-01-03  0  1  0.0
2013-01-04  1  0  1.0
2013-01-05  0  0  1.0
2013-01-06  0  1  0.0
2013-01-07  0  0  0.0
2013-01-08  1  0  1.0
2013-01-09  1  0  1.0
2013-01-10  0  0  1.0
2013-01-11  0  1  0.0
2013-01-12  1  0  1.0
2013-01-13  0  1  0.0
2013-01-14  0  0  0.0

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