Super-fast and clean conversions to numbers.
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
>>> from fastnumbers import fast_float, float as fnfloat >>> # 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) 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
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
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
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
int functions, and oftentimes is significantly faster
because most real-world numbers pass the heuristic.
fastnumbers is ultra-easy. Simply execute from the
$ pip install 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
If you do not wish to use
tox, you can install the testing dependencies and run the
tests manually using pytest -
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