Third column based on first two columns-CodePudding

I have a Dataframe with two columns named volume qty and volume price. I want to create a third column based on above mentioned columns.

If both the volume qty and volume price columns have value or both are blank or empty then third column should have value "YES". If one of columns as value and another is empty or blank then I want third column should have value "NO" Eg

     volume qty  volume price   column x
        20            100          YES 
                                   YES
        30                         NO
                      200          NO

Is there way I can achieve this with any inbuild functions.

CodePudding user response：

You can use numpy.select:

Case 1: When columns have empty(NaN) values:

In [152]: df
Out[152]: 
   volume qty  volume price
0        20.0         100.0
1         NaN           NaN
2        30.0           NaN
3         NaN         200.0

In [152]: import numpy as np

In [153]: conds = [df['volume qty'].notna() & df['volume price'].notna(), df['volume qty'].isna() & df['volume price'].isna(), df['volume qty'].isna() | df['volume price'].isna()]

In [154]: choices = ['YES', 'YES', 'NO']

In [156]: df['column x'] = np.select(conds, choices)

In [157]: df
Out[157]: 
   volume qty  volume price column x
0        20.0         100.0      YES
1         NaN           NaN      YES
2        30.0           NaN       NO
3         NaN         200.0       NO

Case 2: When columns have blank values:

In [167]: df
Out[167]: 
  volume qty volume price
0         20          100
1                        
2         30             
3                     200

In [164]: conds = [~df['volume qty'].eq('') & ~df['volume price'].eq(''), df['volume qty'].eq('') & df['volume price'].eq(''), df['volume qty'].eq('') | df['volume price'].eq('')]

In [165]: choices = ['YES', 'YES', 'NO']

In [168]: df['column x'] = np.select(conds, choices)

In [169]: df
Out[169]: 
  volume qty volume price column x
0         20          100      YES
1                              YES
2         30                    NO
3                     200       NO