Filter out nulls and garbage values in pandas-CodePudding

I have a dataframe as shown:

Name    Email                 Phone
Mohan   [email protected]     AAASWS
Shubham [email protected]   1234567889
Sohail  [email protected]    
Suresh  [email protected]    9821345123
Kashi   [email protected]     Kshgatr

Now the phone column should contain phone numbers of individuals.

But here it is observed that it contains some missing values and also some garbage string values

I want to filter these values and label them as "Rectify" in the resultant Dataframe

Hence df should be as given

Name    Email                 Phone
Mohan   [email protected]     Rectify
Shubham [email protected]   1234567889
Sohail  [email protected]  Rectify
Suresh  [email protected]    9821345123
Kashi   [email protected]     Rectify

CodePudding user response：

Use to_numeric and fill NaN values

pd.to_numeric(df.Phone, errors='coerce').fillna('Rectify')