How to add 91 to all the values in a column of a pandas data frame?

Question

Consider my data frame as like this

S.no	Phone Number
1	9955290232
2	8752837492
3	9342832245
4	919485928837
5	917482482938
6	98273642733

I want the values in "Phone number" column to prefixed with 91 If the value has 91 already then, proceed to the next value.

My output

S.no	Phone Number
1	919955290232
2	918752837492
3	919342832245
4	919485928837
5	917482482938
6	919827364273

How could this be done?

Answer 1

Simplest would be comvert to string, add 91 to the beginning and slice to last 12 digits:

df['New Phone Number'] = df['Phone Number'].astype(str).radd("91").str[-12:]

Answer 2

Series.str.replace

df['Phone Number'].astype(str).str.replace(r'^(?!91)', '91')

---

0     919955290232
1     918752837492
2     919342832245
3     919485928837
4     917482482938
5    9198273642733
Name: Phone Number, dtype: object

Answer 3

With your shown samples, you could try following code. This makes sure that it catches 10 digits phone numbers only(valid by number of digits).

df['Phone Number'].astype(str).replace({r'^(?!91)(\d{10})$' : r'91\1'}, regex=True)

Online demo for above regex

Simple explanation would be: changing DataFrame's Phone Number column's value to string and using replace to check if its not starting from 91 and followed by 10 digits then add 91 to it.

Answer 4

You can apply() on each element in Series.

def add(x):
    if '91' in str(x):
        return str(x)
    else:
        return '91'+str(x)

df['Phone Number'] = df['Phone Number'].apply(add)

Or with lambda in one line

df['Phone Number'] = df['Phone Number'].apply(lambda x: str(x) if '91' in str(x) else '91' + str(x) )

Answer 5

You can also do this by boolean masking astype() method and startswith() method:

mask=df['Phone Number'].astype(str).str.startswith('91')

Finally use:

df.loc[~mask,'Phone Number']='91'+df.loc[~mask,'Phone Number'].astype(str)

Now If you print df you will get:

0     919955290232
1     918752837492
2     919342832245
3     919485928837
4     917482482938
5     9198273642733
Name: Phone Number, dtype: object

Answer 6

You can try this out....one of way to do it where df is your dataframe name

def add_91(x):
    return '91'+str(x)

df['Phone Number'] = df['Phone Number'].apply(lambda x:add_91(x))