Python: Change values in a pandas DataFrame column based on multiple conditions in Python

Question

I have 2 pandas dataframes df_x and df_y and I want to update a ‘SCORE’ column that they both have in common. I want to update the score column with a score of 100 if the following conditions are met:

Age <= 45 AND column Banned != 1 OR column chargeback !=1

My trial below did not work. Any inputs?

if df_x.AGE_DAYS <= 45 and (df_x.BANNED != 1 or df_x.CHARGEBACK != 1):
    df_x['SCORE'] = 100
 
if df_y.AGE_DAYS <= 45 and (df_y.BANNED != 1 or df_y.CHARGEBACK != 1):
    df_y['SCORE'] = 100

Outcome: Basically, the 'Score' column should update the existing value with a 100 or do nothing at all if the above described criteria are met.

To set up a sample testing enviornment:

import pandas as pd
df = pd.DataFrame([[1, 1, 0], [100, 0,0], [46, 1, 0]], columns=['AGE_DAYS', 'Banned', 'Chargeback'])

print(df)

Desired output: Updated score column added to show that score values outside the criteria specified not changed. Only values changed if they meet the criteria of this search!

AGE BANNED  CHARGEBACK  SCORE   "UPDATED SCORE"
45    1        0         75      75
33    0        0         45      **100**
44    0        0         77      **100**
235   0        1         75      75
43    1        0         88      88
21    0        0         23      **100**
1     0        0         56      **100**
432   1        1         12      12

You are expected to add a sample of input data and your expected output as text, You can look at How to make good reproducible pandas examples — ThePyGuy
– ThePyGuy, Commented Jun 18, 2021 at 23:57
how are you getting 100 in updated score bcz there Age <= 45 doesn't satisfying? — Anurag Dabas
– Anurag Dabas, Commented Jun 19, 2021 at 3:05

Anurag Dabas · Accepted Answer · 2021-06-19 03:17:12Z

1

Try:

c=(df['AGE']<=45) & df[['BANNED','CHARGEBACK']].ne(1).all(1)
#OR(both conditions are same so choose any one)
c=(((df['BANNED'].ne(1)) & (df['CHARGEBACK'].ne(1)))) & (df['AGE']<=45)
#your condition
#also notice that you need & in place of | in your condition that you posted in question

Finally use mask() method:

df['UPDATED SCORE']=df['SCORE'].mask(c,100)

OR

you can also use numpy's where() method for this:

#import numpy as np
df['UPDATED SCORE']=np.where(c,100,df['SCORE'])

edited Jun 19, 2021 at 3:17

answered Jun 19, 2021 at 3:11

Anurag Dabas

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Comments

Ade_1 · Accepted Answer · 2021-06-19 00:00:03Z

0

you can try using apply

df_x['SCORE'] = df_x.apply(lambda x:100 if x['AGE_DAYS'] <= 45 and (x['BANNED'] != 1 or x['CHARGEBACK'] != 1)

df_y['SCORE'] = df_y.apply(lambda x: 100 if x['AGE_DAYS'] <= 45 and (x['BANNED'] != 1 or x['CHARGEBACK'] != 1)

answered Jun 19, 2021 at 0:00

Ade_1

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2 Comments

Blackdynomite Over a year ago

I seem to get a syntax error here. I tried to debug but idk why it would return "an invalid syntax starting at df_y portion"

Ade_1 Over a year ago

hmm strange can you paste the traceback?

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