Using if else case in Python to have a derived column-CodePudding

I have a query in my code, where am trying to use if else case to derive a value in a data frame,

import pandas as pd
import numpy as np
c=15
s={'yr':[2014,2014,2014,2014],'value':[10,20,20,50]}
p=pd.DataFrame(data=s)

if (p['value'])>= c:
    p['qty']=c-p['value']
else:
    p['value']

I am getting the error in the above code-

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Basically this should be my expected output-

    yr    value qty
0   2014    10  10
1   2014    20  5
2   2014    20  5
3   2014    50  35

How should I solve this error?

CodePudding user response：

You can select certain rows with the loc statement:

# initialize the qty column
df['qty'] = df['value']
# adjust qty where qty is larger than c
df.loc[df['qty'] > c, 'qty'] -= c

CodePudding user response：

if expects a boolean value (True/False) but (p['value'])>= c is a Series, so you're getting that error. One way to get the desired output is to use mask:

p['qty'] = p['value'].mask(lambda x: x>=c, p['value']-c)

Another option is to use numpy.where:

import numpy as np
p['qty'] = np.where(p['value']>=c, p['value']-c, p['value'])

Output:

     yr  value  qty
0  2014     10   10
1  2014     20    5
2  2014     20    5
3  2014     50   35

CodePudding user response：

You're trying to operate element-wise on a column, that usually requires iteration or applying a function

p['qty'] = p['value'].apply(lambda x: c - x if x >= c else x)

CodePudding user response：

Solution using np.where, assuming your intended calculation is p['value']-c rather than c-p['value']:

p['qty'] = np.where(p['value'] >= c, p['value']-c, p['value'])

Result:

     yr  value  qty
0  2014     10   10
1  2014     20    5
2  2014     20    5
3  2014     50   35