Best way to clean phone numbers if they have different country codes

What I ideally want is data in this particular format. I've shown some below to get an idea

Country Code	Phone_number	Corrected Phone Number
GB	0131 496 0902	+441314960902
GB	0141 4960760	+441414960760
GB	+44117 4960589	+441174960589
US	191.040.0076x2520	+11910400076x2520
DE	05532 56994	+49553256994
GB	01514960233
GB	(0118)4960720	+441184960720
GB	+44(0)114 4960045
DE	+49(0)1743095110
GB	+44(0)1632 960 720
GB	+44117 496 0381

I haven't filled out all the corrected numbers but what i was attempted was, first to use regex to take out any parenthesis and dots from all phone numbers then replacing the 0 with the country code which will be taken from a dictionary i.e

{
    GB : +44
    DE : +49
    US : +1
}

This hadn't worked for me since matching the parenthesis pattern, caused some issues.

Solution

The logic may change with rules of phone number format; but a logic that fits your requirement is as follows:

Remove special chars other than digits, + and letters used for extension e.g. x, ext (following keeps all alphabets).
Prefix ISD code by matching country code.
Remove 0 that follows ISD code.

df = pd.DataFrame(data=[["GB","0131 496 0902"],["GB","0141 4960760"],["GB","+44117 4960589"],["US","191.040.0076x2520"],["DE","05532 56994"],["GB","01514960233"],["GB","(0118)4960720"],["GB","+44(0)114 4960045"],["DE","+49(0)1743095110"],["GB","+44(0)1632 960 720"],["GB","+44117 496 0381"]], columns=["Country Code","Phone_number"])
isd_code_map = { "GB": "+44", "DE": "+49", "US": "+1" }

def correct_phone_number(row):
  import re
  # Remove special chars other than digits, `+` and letters used for extension e.g. `x`, `ext` (following keeps all alphabets).
  result = re.sub("[^A-Za-z\d\+]", "", row["Phone_number"])
  
  # Prefix ISD code by matching country code.
  if not result.startswith(isd_code_map[row["Country Code"]]):
    result = isd_code_map[row["Country Code"]] + result

  # Remove `0` that follows ISD code.
  if result.startswith(isd_code_map[row["Country Code"]] + "0"):
    result = result.replace(isd_code_map[row["Country Code"]] + "0", isd_code_map[row["Country Code"]])
  return result

df["Corrected Phone Number"] = df.apply(correct_phone_number, axis=1)

print(df)

Output:

   Country Code        Phone_number Corrected Phone Number
0            GB       0131 496 0902          +441314960902
1            GB        0141 4960760          +441414960760
2            GB      +44117 4960589          +441174960589
3            US   191.040.0076x2520      +11910400076x2520
4            DE         05532 56994           +49553256994
5            GB         01514960233          +441514960233
6            GB       (0118)4960720          +441184960720
7            GB   +44(0)114 4960045          +441144960045
8            DE    +49(0)1743095110          +491743095110
9            GB  +44(0)1632 960 720          +441632960720
10           GB     +44117 496 0381          +441174960381