Skip to main content

Quickly convert strings to number types.

Project description

https://travis-ci.org/SethMMorton/fastnumbers.svg?branch=master

Convert strings to numbers quickly.

This module is a Python C extension that will convert strings to numbers much faster than can be done using pure Python; numeric types can also be converted to other numeric types.

Additionally, the user has control over what happens in the event that the input string cannot be converted to a number:

  • the input can be returned as-is (this is the default behavior)

  • the input can be passed to a user-given key function then returned

  • a ValueError can be raised (like the built-in float or int)

  • a default value can be returned

Examples

fastnumbers contains functions that are fast C implementations similar to the following Pure Python function:

def fast_float(input, default=None, raise_on_invalid=False, key=None, inf=None, nan=None):
    import math
    try:
        x = float(input)
    except ValueError:
        if raise_on_invalid:
            raise
        elif key is not None:
            return key(input)
        return default if default is not None else input
    else:
        if inf is not None and math.isinf(x):
            return inf
        elif nan is not None and math.isnan(x):
            return nan
        else:
            return x

In addition to fast_float, there are also fast_real, fast_int, fast_forceint, isreal, isfloat, isint, and isintlike - please see the API Documentation for full details.

Some example usage:

>>> from fastnumbers import fast_float
>>> # Convert string to a float
>>> fast_float('56.07')
56.07
>>> # Unconvertable string returned as-is by default
>>> fast_float('bad input')
'bad input'
>>> # Unconvertable strings can trigger a default value
>>> fast_float('bad input', default=0)
0
>>> # 'default' is also the first optional positional arg
>>> fast_float('bad input', 0)
0
>>> # Integers are converted to floats
>>> fast_float(54)
54.0
>>> # One can ask inf or nan to be substituted with another value
>>> fast_float('nan')
nan
>>> fast_float('nan', nan=0.0)
0.0
>>> fast_float(float('nan'), nan=0.0)
0.0
>>> fast_float('56.07', nan=0.0)
56.07
>>> # The default built-in float behavior can be triggered with
>>> # "raise_on_invalid" set to True.
>>> fast_float('bad input', raise_on_invalid=True) #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
  ...
ValueError: invalid literal for float(): bad input
>>> # A key function can be used to return an alternate value for invalid input
>>> fast_float('bad input', key=len)
9
>>> fast_float(54, key=len)
54.0
>>> # Single unicode characters can be converted.
>>> fast_float(u'\u2164')  # Roman numeral 5 (V)
5.0
>>> fast_float(u'\u2466')  # 7 enclosed in a circle
7.0

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.

from timeit import timeit
float_try = '''\
def float_try(input):
    """Typical approach to this problem."""
    try:
        return float(input)
    except ValueError:
        return input
'''

float_re = '''\
import re
float_match = re.compile(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?$').match
def float_re(input):
    """Alternate approach to this problem."""
    try:
        if float_match(input):
            return float(input)
        else:
            return input
    except TypeError:
        return float(input)
'''

print('Invalid input:')
print("Try:", timeit('float_try("invalid")', float_try))
print("re:", timeit('float_re("invalid")', float_re))
print("fast", timeit('fast_float("invalid")', 'from fastnumbers import fast_float'))
print()
print('Valid input:')
print("try:", timeit('float_try("56.07")', float_try))
print("re:", timeit('float_re("56.07")', float_re))
print("fast", timeit('fast_float("56.07")', 'from fastnumbers import fast_float'))

The results will be similar to below, but vary based on your system:

Invalid input:
Try: 2.27156710625
re: 0.570491075516
fast 0.173984050751

Valid input:
try: 0.378665924072
re: 1.08740401268
fast 0.204708099365

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

Author

Seth M. Morton

History

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

03-19-2016 v. 0.7.4

  • Added the “coerce” option to fast_real.

03-08-2016 v. 0.7.3

  • Newline is now considered to be whitespace (for consistency with the builtin float and int).

03-07-2016 v. 0.7.2

  • Fixed overflow bug in exponential parts of floats.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

fastnumbers-0.7.4.zip (63.2 kB view details)

Uploaded Source

fastnumbers-0.7.4.tar.gz (46.9 kB view details)

Uploaded Source

File details

Details for the file fastnumbers-0.7.4.zip.

File metadata

  • Download URL: fastnumbers-0.7.4.zip
  • Upload date:
  • Size: 63.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for fastnumbers-0.7.4.zip
Algorithm Hash digest
SHA256 4308986ad6d907b0e6c6511d289cab59cec85d33f920536e51df8a73aa448fcc
MD5 352e5e152a9000753ff86d4f156f3a12
BLAKE2b-256 32e78ad3968fb049d3b8d3f189e47d3a1c5246b60fdf215d0866ca2bbf43370a

See more details on using hashes here.

File details

Details for the file fastnumbers-0.7.4.tar.gz.

File metadata

  • Download URL: fastnumbers-0.7.4.tar.gz
  • Upload date:
  • Size: 46.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for fastnumbers-0.7.4.tar.gz
Algorithm Hash digest
SHA256 8c151b132754bf1ce9e192af3bc5ab39c4a57f73ad01f2d4828c3ac51cdee3c1
MD5 f9f67abcc82f701d54f6a0bca8febc83
BLAKE2b-256 c57bed67cbdf367029151d8e3090dda3b34a266c99d9dd5a322ff54a7bd8aba6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page