Replace missing and inconstant value, Pythons

Question

having the following example:

 import pandas as pd
df = pd.DataFrame({ 'Column A' : ['null',20,30,40,'null'],'Column B' : [100,'null',30,50,'null']});

I need a Python function that takes two columns and compare them:

1. In case one column is a missing value, we fill it from the other column.

2. In case both values are 'NULL', we Keep 'NULL'.

3. In case of different values (inconsistent) replace both values with 'NULL'

4. return with one attribute

the data should look like this after running the function.

this is what I did so far, I need help implementing step number 3

def myFunction(firAttribute,secAttribute):
    x=df.ix[:,[firAttribute,secAttribute]]
    x['new']=x[firAttribute].fillna(x[secAttribute])
    x['new2']=x[secAttribute].fillna(x[firAttribute])
    x['new'] =x['new'].fillna(x['new2'])
    return x['new'] 

Answer 1

You can first replace null to NaN, then combine_first NaN between columns and last use boolean indexing for matching different columns values and fill them NaN:

import pandas as pd
import numpy as np

df = pd.DataFrame({ 'Column A' : ['null',20,30,40,'null'],
                    'Column B' : [100,'null',30,50,'null']});
print df
  Column A Column B
0     null      100
1       20     null
2       30       30
3       40       50
4     null     null

#replace null to NaN
df = df.replace("null", np.nan)
print df
   Column A  Column B
0       NaN       100
1        20       NaN
2        30        30
3        40        50
4       NaN       NaN

df['Column A'] = df['Column A'].combine_first(df['Column B'])
df['Column B'] = df['Column B'].combine_first(df['Column A'])
print df
   Column A  Column B
0       100       100
1        20        20
2        30        30
3        40        50
4       NaN       NaN

#inconsistent values replace to NaN
df[df['Column A'] != df['Column B']] = np.nan
print df
   Column A  Column B
0       100       100
1        20        20
2        30        30
3       NaN       NaN
4       NaN       NaN