CODEWITHSUNDEEP
pythonpandasdataframe
parth hirpara
parth hirpara

Reputation: 80

In Dataframe, remove parentheses and dash from phone number and also take care about international prefix

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

df

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

Upvotes: 1

Views: 491

Answers (1)

mozway
mozway

Reputation: 262284

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

Upvotes: 4

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