1. The fastnumbers module

Super-fast and clean conversions to numbers.

Please see the Timing Documentation for timing details. Check out the API.

1.1. Quick Description

The below examples showcase the fast_float() function, which is a fast conversion functions with error-handling. Please see the API Documentation for other functions that are available from fastnumbers.

>>> from fastnumbers import fast_float, float as fnfloat
>>> # Convert string to a float
>>> fast_float('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)
>>> # 'default' is also the first optional positional arg
>>> fast_float('bad input', 0)
>>> # Integers are converted to floats
>>> fast_float(54)
>>> # One can ask inf or nan to be substituted with another value
>>> fast_float('nan')
>>> fast_float('nan', nan=0.0)
>>> fast_float(float('nan'), nan=0.0)
>>> fast_float('56.07', nan=0.0)
>>> # The default built-in float behavior can be triggered with
>>> # "raise_on_invalid" set to True.
>>> fast_float('bad input', raise_on_invalid=True) 
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)
>>> fast_float(54, key=len)
>>> # Single unicode characters can be converted.
>>> fast_float(u'\u2164')  # Roman numeral 5 (V)
>>> fast_float(u'\u2466')  # 7 enclosed in a circle

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

1.2. How Is fastnumbers So Fast?

CPython goes to great lengths to ensure that your string input is converted to a number correctly (you can prove this to yourself by examining the source code for integer conversions and for float conversions), but this extra effort is only needed for very large integers or for floats with many digits or large exponents. For integers, if the result could fit into a C long then a naive algorithm of < 10 lines of C code is sufficient. For floats, if the number does not require high precision or does not have a large exponent (such as “-123.45e6”) then a short naive algorithm is also possible.

These naive algorithms are quite fast, but the performance improvement comes at the expense of being unsafe (no protection against overflow or round-off errors). fastnumbers uses a heuristic to determine if the input can be safely converted with the much faster naive algorithm. These heuristics are extremely conservative - if there is any chance that the naive result would not give exactly the same result as the built-in functions then it will fall back on CPython’s conversion function. For this reason, fastnumbers is aways at least as fast as CPython’s built-in float and int functions, and oftentimes is significantly faster because most real-world numbers pass the heuristic.

1.3. Installation

Installation of fastnumbers is ultra-easy. Simply execute from the command line:

$ pip install fastnumbers

You can also download the source from https://pypi.org/project/fastnumbers/, or browse the git repository at https://github.com/SethMMorton/fastnumbers.

If you choose to install from source (will need a C compiler and the Python headers), you can unzip the source archive and enter the directory, and type:

$ python setup.py install

If you want to build this documentation, enter:

$ python setup.py build_sphinx

fastnumbers requires python version 2.7 or greater (this includes python 3.x). Unit tests are only run on 2.7 and >= 3.4.

1.4. How to Run Tests

Please note that fastnumbers is NOT set-up to support python setup.py test.

The recommended way to run tests with with tox. Suppose you want to run tests for Python 3.6 - you can run tests by simply executing the following:

$ tox -e py36

tox will create virtual a virtual environment for your tests and install all the needed testing requirements for you.

If you want to run testing on all of Python 2.7, 3.4, 3.5, 3.6, and 3.7 you can simply execute

$ tox

If you do not wish to use tox, you can install the testing dependencies and run the tests manually using pytest - fastnumbers contains a Pipfile for use with pipenv that makes it easy for you to install the testing dependencies:

$ pipenv install --skip-lock --dev
$ pipenv install --skip-lock -e .
$ pipenv run pytest

fastnumbers uses pytest to run its tests.