Quickly convert strings to number types.
Project description
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.
History
08-12-2014 v. 0.1.3
Fixed bug where ‘e’ and ‘E’ were incorrectly identified as a valid float/int and converted to 0. This bug only applied to the fast_* and is* functions.
08-12-2014 v. 0.1.2
Fixed bug where ‘+’ and ‘-’ were incorrectly identified as a valid float/int and converted to 0. This bug only applied to the fast_* and is* functions.
Fixed bug where ‘safe_forceint’ did not handle ‘nan’ correctly.
08-11-2014 v. 0.1.1
‘fastnumbers’ now understands ‘inf’ and ‘nan’.
08-10-2014 v. 0.1.0
Initial release of ‘fastnumbers’.
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