fastnumbers 0.1.0

Quickly convert strings to number types.

Convert strings to numbers quickly.

• Source Code: https://github.com/SethMMorton/fastnumbers

• Downloads: https://pypi.python.org/pypi/fastnumbers

• Documentation: http://pythonhosted.org//fastnumbers/

This module is a Python C extension that will convert strings to numbers much faster than can be done using pure Python. Additionally, if the string cannot be converted, the string is returned as-is instead of returning a ValueError.

To achieve this, the module makes some assumptions about the input type (input is int (or long), float, or str (or unicode)), and otherwise a TypeError is raised.

Examples

It is probably easiest to illustrate fastnumbers in use rather than describe it:

>>> from fastnumbers import safe_float
>>> def float_no_raise(input):
...     try:
...         return float(input)
...     except ValueError:
...         return input
...
>>> safe_float('56.07')
56.07
>>> float_no_raise('56.07') == safe_float('56.07')
True
>>> safe_float('bad input')
'bad input'
>>> float_no_raise('bad input') == safe_float('bad input')
True
>>> safe_float(54)
54.0
>>> float_no_raise(54) == safe_float(54)
True

If you really need speed, there are fast versions of the conversion functions:

>>> from fastnumbers import fast_float
>>> fast_float('56.07')
56.07
>>> safe_float('56.07') == fast_float('56.07')
True

The difference between safe_float and fast_float is that the fast version uses an extremely fast implementation of atof under the hood that does not do overflow or underflow checking, and also can lose precision around the 12th decimal place for extreme exponents; for the majority of cases, the results will be identical.

Timing

Just how much faster is fastnumbers than a pure python implementation? Below are the timing results for the *_float functions; please see the Timing Documentation for details into all timing results.

import re
from timeit import timeit
float_match = re.compile(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?$').match
float_try = '''\
def float_try(input):
    """Typical approach to this problem."""
    try:
        return float(input)
    except ValueError:
        return input
'''

float_re = '''\
def float_re(input):
    """Alternate approach to this problem."""
    try:
        if float_match(x):
            return float(x)
        else:
            return x
    except TypeError:
        return float(x)
'''

print('Invalid input:')
print(timeit('float_try("invalid")', float_try))
print(timeit('float_re("invalid")', float_re))
print(timeit('safe_float("invalid"), 'from fastnumbers import safe_float'))
print(timeit('fast_float("invalid"), 'from fastnumbers import fast_float'))
print()
print('Valid input:')
print(timeit('float_try("56.07")', float_try))
print(timeit('float_re("56.07")', float_re))
print(timeit('safe_float("56.07"), 'from fastnumbers import safe_float'))
print(timeit('fast_float("56.07"), 'from fastnumbers import fast_float'))

The results will be similar to the below, by vary on the system you are on:

Invalid input:
2.28478188515
0.601616001129
0.543533372879
0.185416555405

Valid input:
0.774985694885
1.7571870327
0.584108567238
0.275424480438

As you can see, in all cases fastnumbers beats the pure python implementations.

Full Suite of Functions

In addition to safe_float and fast_float mentioned above, there are also

• safe_real

• safe_int

• safe_forceint

• fast_real

• fast_int

• fast_forceint

• isreal

• isfloat

• isint

• isintlike

Please see the API Documentation for full details.

Author

Seth M. Morton

History

08-11-2014 v, 0.1.1

• ‘fastnumbers’ now understands ‘inf’ and ‘nan’.

08-10-2014 v. 0.1.0

• Initial release of ‘fastnumbers’.