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ballpark-1.4.0
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مشاهده بیشترتوضیحات
Better human-readable numbers.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ballpark-1.4.0 |
| نام | ballpark |
| نسخه کتابخانه | 1.4.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Stijn Debrouwere |
| ایمیل نویسنده | stijn@debrouwere.org |
| آدرس صفحه اصلی | https://github.com/debrouwere/python-ballpark/ |
| آدرس اینترنتی | https://pypi.org/project/ballpark/ |
| مجوز | ISC |
Ballpark
========
When people think of human-readable numbers, they think of rounding to
two decimal places and adding a thousands separator. 12,214.17 is
already quite an improvement over 12214.16666667. But standard formats
for human-readable numbers still have various flaws:
- even with a thousands separator, at a glance you might easily mistake
a billion for a trillion
- even when rounding, an amount like 12,214.17 dollars is a lot of
number noise for communicating 12.2K
- scientific notation leads to exponents like ``1.22e4`` which are hard
to interpret because we're used to working with thousands, millions
and billions – orders of magnitudes that are multiples of three
- when comparing multiple measurements of the same underlying variable,
like the yearly sales numbers for 2010-2015, it's annoying to have
some numbers in thousands and other numbers in millions – you want
consistency so that digits in the same position are of the same
magnitude
``python-ballpark`` introduces *business notation*, an offshoot of
`engineering
notation <https://en.wikipedia.org/wiki/Engineering_notation>`__, for
producing better human-readable numbers.
Install with ``pip install ballpark`` or ``pip3 install ballpark``.
What it looks like
~~~~~~~~~~~~~~~~~~
+---------------------+-----------------------+-----------------+-----------------+
| numbers | rounded | engineering | **business |
| | | notation | notation** |
+=====================+=======================+=================+=================+
| 11234.22, | 11,234.22, | 11.2E+3, | 11K, 233K, |
| 233000.55, | 233,000.55, | 233E+3, 1.18E+6 | 1,180K |
| 1175125.2 | 1,175,125.2 | | |
+---------------------+-----------------------+-----------------+-----------------+
| 111, 1111.23, | 111, 1,111.23, | 111, 1.11E+3, | 0.11K, 1.11K, |
| 1175125.234 | 1,175,125.23 | 1.18E+6 | 1,180.00K |
+---------------------+-----------------------+-----------------+-----------------+
How to use it
~~~~~~~~~~~~~
.. code:: python
>>> from ballpark import human, scientific, engineering, business, ballpark
>>> business([11234.22, 233000.55, 1175125.2])
['11K', '233K', '1,180K']
>>>
>>> # business notation is also aliased as `ballpark`
>>> ballpark([11234.22, 233000.55, 1175125.2])
['11K', '233K', '1,180K']
>>>
>>> # or use the shortcut functions
>>> from ballpark import H, S, E, B
>>> B([11234.22, 233000.55, 1175125.2])
['11K', '233K', '1,180K']
>>>
>>> # all notations accept single numbers too, but then we can't guarantee
>>> # that all numbers will have the same prefix (kilo, mega etc.)
>>> [B(value) for value in [11234.22, 233000.55, 1175125.2]]
['11.2K', '233K', '1.18M']
How it works
~~~~~~~~~~~~
.. code:: python
business(values, precision=3, prefix=True, prefixes=SI, statistic=median)
- **precision:** the amount of significant digits. When necessary,
``business`` will round beyond the decimal sign as well: in the
example above, ``1175125.2`` was turned into ``1,180K`` rather than
``1,175K`` to retain only 3 significant digits.
- **prefix:** whether to use SI prefixes like m (milli), K (kilo) and
so on instead of scientific exponents like E+03.
- **prefixes:** a mapping of orders of magnitude to prefixes, e.g.
``{-3: 'm', 3: 'K'}``, allowing you to customize the prefixes, for
example using B for billion instead of T for tera.
- **statistic:** a function to produce the reference number. The
reference number determines the order of magnitude and precision for
the entire group of numbers, so that for example when the reference
number is 23.3K, smaller numbers like 1.1K won't gain a decimal place
and larger numbers like 1,180K won't jump an order of magnitude to
1.18M. The median often works well, but if you want more precision
for small outliers, try ``ballpark.statistics.Q1`` or even Python's
builtin ``min``.
نحوه نصب
نصب پکیج whl ballpark-1.4.0:
pip install ballpark-1.4.0.whl
نصب پکیج tar.gz ballpark-1.4.0:
pip install ballpark-1.4.0.tar.gz