In Dataframe, remove parentheses and dash from phone number and also take care about international p-CodePudding

In data frame, how to remove unnecessary thing from Contact number

Id Phone
1  ( 1)123-456-7890
2  (123)-(456)-(7890)
3  123-456-7890

Final Output

Id  Phone
1   1234567890
2   1234567890
3   1234567890

CodePudding user response：

I would use a regex with str.replace here:

df['Phone2'] = df['Phone'].str.replace(r'^(?:\(\ \d \))|\D', '', regex=True)

output:

   Id               Phone      Phone2
0   1    ( 1)123-456-7890  1234567890
1   2  (123)-(456)-(7890)  1234567890
2   3        123-456-7890  1234567890

regex:

^(?:\(\ \d \)) # match a ( 0) leading identifier
|              # OR
\D             # match a non-digit

regex demo

notes on the international prefix:

This might be important to keep.

Keep the prefixes:

df['Phone2'] = df['Phone'].str.replace(r'[^ \d]', '', regex=True)

output:

   Id               Phone          Phone2
0   1    ( 1)123-456-7890     11234567890
1   2  (123)-(456)-(7890)      1234567890
2   3        123-456-7890      1234567890
3   4  ( 380)123-456-7890   3801234567890

Only drop a specific prefix (here 1):

df['Phone2'] = df['Phone'].str.replace(r'^(?:\(\ 1\))|[^ \d]', '', regex=True)
# or, more flexible
df['Phone2'] = df['Phone'].str.replace(r'(?:\ 1\D)|[^ \d]', '', regex=True)

output:

   Id               Phone          Phone2
0   1    ( 1)123-456-7890      1234567890
1   2  (123)-(456)-(7890)      1234567890
2   3        123-456-7890      1234567890
3   4  ( 380)123-456-7890   3801234567890