Would someone please let me know how to apply the function with 2 parameters into DataFrame? I have tried a lot of solution but still not successful. Here is my code below.

import pandas as pd

df=pd.DataFrame({'tran_amt_lcy':[40,500,60],'tran_amt_usd':[30,40,50],'client_id':['2001','2033','2045']})

df.dtypes

def test_func(col1,col2):
    if col1>30 & col2<500:
        tran_status='approved'
    else:
        tran_status='declined'
    return tran_status

df['tran_stat']=df.apply(lambda x:test_func(df['tran_amt_usd'],df['tran_amt_lcy']),axis=1)

The error message still pop up as ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I don’t know why it is still failed. Does anyone tell me the possible way?

Thanks a lot.

>Solution :

For binary condition, you can use numpy.where:

import numpy as np

# Boolean mask
m = (df['tran_amt_usd'] > 30) & (df['tran_amt_lcy'] < 500)
df['tran_stat'] = np.where(m, 'approved', 'declined')
print(df)

# Output
   tran_amt_lcy  tran_amt_usd client_id tran_stat
0            40            30      2001  declined
1           500            40      2033  declined
2            60            50      2045  approved

There are many post which explain this error. In fact, Python can’t compare a list (or a Series) to a scalar value. In your case, you try to evaluate:

([30, 40, 50] > 30) & ([40, 500, 60] < 100)

Update

Do this with a def function

def test_func(col1, col2):
    m = (col1 > 30) & (col2 < 500)
    return np.where(m, 'approved', 'declined')

# You don't need apply here
df['tran_stat'] = test_func(df['tran_amt_usd'], df['tran_amt_lcy'])