forcedtypes

Force crappy data into Python type. Yeah, I know It sounds strange but It could be useful sometimes. Ohh, did I mention that It is working with Python 2.7 & 3.3+.

Supported types:

• t.Bool -> bool
• t.Int -> long in PY2, int in PY3
• t.Float -> float
• t.Date -> datetime.date
• t.Datetime -> datetime.datetime
• t.Str -> unicode in PY2, str in PY3

How to install

You can use easy_install or pip to install the package.

$ easy_install forcedtypes

Examples

Let's see some examples for this awesomeness.

>>> import forcedtypes as t

1. Date parsing

>>> t.Date('3/4/15')
datetime.date(2015, 3, 4)
>>> t.Date('3/4/15', dayfirst=True)
datetime.date(2015, 4, 3)

2. String to boolean

>>> t.Bool('yes') == t.Bool('t') == t.Bool(1.0) == True
True

3. Float with unique separator

If you want to convert 3,400 to float It will give you error:

>>> float('3,400')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: could not convert string to float: '3,400'

The forcedtypes package makes it easier.

>>> t.Float('3,400')
3.4
>>> t.Float('3,400', locale='de_DE')
3.4
>>> t.Float('3,400', locale='en_US')
3400.0

4. Eliminate empty strings

>>> t.Str('     ') is None
True

5. Use for type enforcement

If you have a file like this one,

1;3.4;Item 1.
3;55.6;Item 2.
4;44,5;
6;77.5;Item 4.

You can use this script ...

import forcedtypes as t

with open('file.csv','r', encoding='utf-8') as fd:
    for line in fd.readlines():
        id, price, name = map(lambda x,: x[0](x[1]), 
        	zip([int, t.Float, t.Str], line.split(';')))
        print(repr(id), repr(price), repr(name))

... to get the correct result:

1 3.4 'Item 1.\n'
3 55.6 'Item 2.\n'
4 44.5 None
6 77.5 'Item 4.'

6. Type conversion based on rule

You can create a new type based on additional attribute.

>>> ft = t.new(t.Float, locale='en_US')
>>> ft('3,4')
34.0
>>> ft('3.4')
3.4
>>> ft('3,400.43')
3400.43