Given the table above, I wish to go through each row under "Contact" and check if Missing. If the row has Missing, use corresponding "Old Contact" values inplace of the text 'Missing'. If old contact is empty, then leave it as 'Missing'

Desired table:

df['Contact'] = df['Contact'].apply(
    lambda x: df['Old Contact'] if "Missing" in x else x)

the line above gives me the whole column of 'Old Contact' where there is Missing. I'm not sure how to use index here to get what I want. Thanks in advance!

CodePudding user response：

use mask

df['Contact'].mask(df['Contact'].eq('Missing'), df['Old Contact'].fillna('Missing'))

output:

0      234255
1      343556
2      984567
3     Missing
4      845665
5      343556
Name: Contact, dtype: object

make reult to Contact column

CodePudding user response：

I haven't used pandas in a while, so I'm sure there's a better solution, but a brute force method could be:

for idx in df.index:
    if (df.iloc[idx]['Contact']=='Missing'):
        if len(df.iloc[idx]['Old Contact'].strip()):
            df.iloc[idx]['Contact']=df.iloc[idx]['Old Contact']

CodePudding user response：

Using .where and .assign

df = df.assign(
    Contact=df["Contact"].where(df["Contact"].ne("Missing"), df["Old Contact"]).fillna("Missing")
).fillna("")
print(df)

    Contact Old Contact
0    234255    987778.0
1    343556    987877.0
2  984567.0    984567.0
3   Missing            
4  845665.0    845665.0
5    343556    789998.0

CodePudding user response：

You have two conditions you are checking. You can check both conditions in a multiple conditions if statement (https://www.geeksforgeeks.org/check-multiple-conditions-in-if-statement-python/) while looping through each entry in df['Contact'].

for idx in df.index:
    if (df.iloc[idx]['Contact']=='Missing') & (df.iloc[idx]['Old Contact']!=''):
        df.iloc[idx]['Contact']=df.iloc[idx]['Old Contact']
    elif (df.iloc[idx]['Contact']=='Missing') & (df.iloc[idx]['Old Contact']==''):
        df.iloc[idx]['Contact']='Missing'
    else: pass

Output:

df

    Contact Old Contact
0   234255  987778
1   343556  987877
2   984567  984567
3   Missing 
4   845665  845665
5   343556  789998