What should be the code for lambda function?

This is python code for dataframe

from pandas import DataFrame
import pandas as pd
names = {'First_name': ['Hanah', 'Ria', 'Jay', 'Bholu', 'Sachin'],
'Status':['Hanah', 'Ria', 'Jay', 'Bholu', 'Sachin'],
"charge":[10,11,12,13,14]}
df = pd.DataFrame(names)

This is the lambda function for generating output

df['Status'] = df['First_name'].apply(lambda x: [df["charge"]] if x == 'Ria' else 'Not Found')
print(df)

The output generated is this:

df['Status'] = df['First_name'].apply(lambda x: [df["charge"]] if x == 'Ria' else 'Not Found')
print(df)

The output is

  First_name                  Status  charge
0      Hanah               Not Found      10
1        Ria  [[10, 11, 12, 13, 14]]      11
2        Jay               Not Found      12
3      Bholu               Not Found      13
4     Sachin               Not Found      14

But I want to generate the output

  First_name                  Status  charge
0      Hanah               Not Found      10
1        Ria                      11      11
2        Jay               Not Found      12
3      Bholu               Not Found      13
4     Sachin               Not Found      14

So as in second row it is Ria so I have to fetch the result of charge(column) in second row and put it in status(column)

Solution

if you want use apply and lambda func, apply lambda func to all dataframe with axis=1

df['Status'] = df.apply(lambda x: x['charge'] if x['First_name'] == 'Ria' else 'Not Found', axis=1)

df

    First_name  Status  charge
0   Hanah       Not Found   10
1   Ria         11          11
2   Jay         Not Found   12
3   Bholu       Not Found   13
4   Sachin      Not Found   14

but we can use mask instead apply

cond1 = df['First_name'].ne('Ria')
df.assign(Status=df['charge'].mask(cond1, 'Not Found'))

result:

    First_name  Status  charge
0   Hanah       Not Found   10
1   Ria         11          11
2   Jay         Not Found   12
3   Bholu       Not Found   13
4   Sachin      Not Found   14