How to convert a string to a number if it has commas in it as thousands separators?

There are several ways to parse numbers with thousands separators. And I doubt that the way described by @unutbu is the best in all cases. That's why I list other ways too.

1. The proper place to call setlocale() is in __main__ module. It's global setting and will affect the whole program and even C extensions (although note that LC_NUMERIC setting is not set at system level, but is emulated by Python). Read caveats in documentation and think twice before going this way. It's probably OK in single application, but never use it in libraries for wide audience. Probably you shoud avoid requesting locale with some particular charset encoding, since it might not be available on some systems.

2. Use one of third party libraries for internationalization. For example PyICU allows using any available locale wihtout affecting the whole process (and even parsing numbers with particular thousands separators without using locales):

   NumberFormat.createInstance(Locale('en_US')).parse("1,000,000").getLong()

3. Write your own parsing function, if you don't what to install third party libraries to do it "right way". It can be as simple as int(data.replace(',', '')) when strict validation is not needed.

Replace the commas with empty strings, and turn the resulting string into an int or a float.

>>> a = '1,000,000'
>>> int(a.replace(',' , ''))
1000000
>>> float(a.replace(',' , ''))
1000000.0

I got locale error from accepted answer, but the following change works here in Finland (Windows XP):

import locale
locale.setlocale( locale.LC_ALL, 'english_USA' )
print locale.atoi('1,000,000')
# 1000000
print locale.atof('1,000,000.53')
# 1000000.53

This works:

(A dirty but quick way)

>>> a='-1,234,567,89.0123'
>>> "".join(a.split(","))
'-123456789.0123'

I tried this. It goes a bit beyond the question: You get an input. It will be converted to string first (if it is a list, for example from Beautiful soup); then to int, then to float.

It goes as far as it can get. In worst case, it returns everything unconverted as string.

def to_normal(soupCell):
    ''' converts a html cell from beautiful soup to text, then to int, then to float: as far as it gets.
    US thousands separators are taken into account.
    needs import locale'''
    
    locale.setlocale( locale.LC_ALL, 'english_USA' ) 

    output = unicode(soupCell.findAll(text=True)[0].string)
    try: 
        return locale.atoi(output)
    except ValueError: 
        try: return locale.atof(output)
        except ValueError:
            return output

If you're using pandas and you're trying to parse a CSV that includes numbers with a comma for thousands separators, you can just pass the keyword argument thousands=',' like so:

df = pd.read_csv('your_file.csv', thousands=',')

The most pythonic solution would be int("1,000,000".replace(',', ''))

But if you will not want to use .replace() and lets say also not want to import anything.

string = '1,000,000,000'
# string_w_float = '1,000,000,000.5'

no_comma = ''
for num in string:
    # if num == '.':
    #   break       # own code here for after decimal
    try:
        no_comma += str(int(num))
    except:
        pass
print(int(no_comma))

>>> import locale
>>> locale.setlocale(locale.LC_ALL, "")
'en_US.UTF-8'
>>> print locale.atoi('1,000,000')
1000000
>>> print locale.atof('1,000,000.53')
1000000.53

this is done on Linux in US.

A little late, but the babel library has parse_decimal and parse_number which do exactly what you want:

from babel.numbers import parse_decimal, parse_number
parse_decimal('10,3453', locale='es_ES')
>>> Decimal('10.3453')
parse_number('20.457', locale='es_ES')
>>> 20457
parse_decimal('10,3453', locale='es_MX')
>>> Decimal('103453')

You can also pass a Locale class instead of a string:

from babel import Locale
parse_decimal('10,3453', locale=Locale('es_MX'))
>>> Decimal('103453')

Not the shortest solution, but for the sake of completeness and maybe interesting if you want to rely on an existing function that has been proven a million times: you can leverage pandas by injecting your number as StringIO to its read_csv() function (it has a C backend, so the conversion functionality cannot be leveraged directly - as far as I know).

>>> float(pd.read_csv(StringIO("1,000.23"), sep=";", thousands=",", header=None)[0])
1000.23