CODEWITHSUNDEEP
pythonpandasdataframeapply
FeBludger
FeBludger

Reputation: 27

Check if string in a column, then return value from another column at the same index

Contact Old Contact
234255 987778
343556 987877
Missing 984567
Missing
Missing 845665
343556 789998

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:

Contact Old Contact
234255 987778
343556 987877
984567 984567
Missing
845665 845665
343556 789998 
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!

Upvotes: 2

Views: 1086

Answers (4)

Python16367225
Python16367225

Reputation: 131

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.index.

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

Upvotes: 0

Jason Baker
Jason Baker

Reputation: 3708

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

Upvotes: 0

RufusVS
RufusVS

Reputation: 4127

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']

Upvotes: 0

Panda Kim
Panda Kim

Reputation: 13212

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

Upvotes: 1

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