fastnumbers 0.2.0

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.

If you don’t want to return the input as-is for invalid input, you can either set raise_on_invalid or default to some value:

>>> from fastnumbers import safe_float
>>> safe_float('bad input', raise_on_invalid=True) #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
  ...
ValueError: invalid literal for float(): bad input
>>> safe_float('bad input', default=0.0)
0.0

NOTE: If you need locale-dependent conversions, supply the fastnumbers function of your choice to locale.atof.

import locale
locale.setlocale(locale.LC_ALL, 'de_DE.UTF-8')
print(atof('468,5', func=fast_float))  # Prints 468.5

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

These are the last three entries of the changelog. See the package documentation for the complete changelog.