format phone number in csv using pandas

Question

Python/pandas n00b. I have code that is processing event data stored in csv files. Data from df[\"CONTACT PHONE NUMBER\"] is outputting the phone number as `5555551

Answer 1

I think phone numbers should be stored as a string.
When reading the csv you can ensure this column is read as a string:

pd.read_csv(filename, dtype={"CONTACT PHONE NUMBER": str})

You can use the string methods, naively adding:

In [11]: s = pd.Series(['5554443333', '1114445555', np.nan, '123'])  # df["CONTACT PHONE NUMBER"]

# phone_nos = '(' + s.str[:3] + ')' + s.str[3:7] + '-' + s.str[7:11]

Edit: as Noah answers in a related question, you can do this more directly/efficiently using str.replace:

In [12]: phone_nos = s.str.replace('^(\d{3})(\d{3})(\d{4})$', r'(\1)\2-\3')

In [13]: phone_nos
Out[13]:
0    (555)4443-333
1    (111)4445-555
2              NaN
3              123
dtype: object

But there is a problem here as you have a malformed number, not precisely 10 digits, so you could NaN those:

In [14]: s.str.contains('^\d{10}$')  # note: NaN is truthy
Out[14]:
0     True
1     True
2      NaN
3    False
dtype: object

In [15]: phone_nos.where(s.str.contains('^\d{10}$'))
Out[15]:
0    (555)4443-333
1    (111)4445-555
2              NaN
3              NaN
dtype: object

Now, you might like to inspect the bad formats you have (maybe you have to change your output to encompass them, e.g. if they included a country code):

In [16]: s[~s.str.contains('^\d{10}$').astype(bool)]
Out[16]:
3    123
dtype: object