Why use Arrow over built-in modules?

Python’s standard library and some other low-level modules have near-complete date, time and timezone functionality, but don’t work very well from a usability perspective:

• Too many modules: datetime, time, calendar, dateutil, pytz and more

• Too many types: date, time, datetime, tzinfo, timedelta, relativedelta, etc.

• Timezones and timestamp conversions are verbose and unpleasant

• Timezone naivety is the norm

• Gaps in functionality: ISO 8601 parsing, timespans, humanization

Features

• Fully-implemented, drop-in replacement for datetime

• Support for Python 3.8+

• Timezone-aware and UTC by default

• Super-simple creation options for many common input scenarios

• shift method with support for relative offsets, including weeks

• Format and parse strings automatically

• Wide support for the ISO 8601 standard

• Timezone conversion

• Support for dateutil, pytz, and ZoneInfo tzinfo objects

• Generates time spans, ranges, floors and ceilings for time frames ranging from microsecond to year

• Humanize dates and times with a growing list of contributed locales

• Extensible for your own Arrow-derived types

• Full support for PEP 484-style type hints

Quick Start

$ pip install -U arrow

>>> import arrow
>>> arrow.get('2013-05-11T21:23:58.970460+07:00')
<Arrow [2013-05-11T21:23:58.970460+07:00]>

>>> utc = arrow.utcnow()
>>> utc
<Arrow [2013-05-11T21:23:58.970460+00:00]>

>>> utc = utc.shift(hours=-1)
>>> utc
<Arrow [2013-05-11T20:23:58.970460+00:00]>

>>> local = utc.to('US/Pacific')
>>> local
<Arrow [2013-05-11T13:23:58.970460-07:00]>

>>> local.timestamp()
1368303838.970460

>>> local.format()
'2013-05-11 13:23:58 -07:00'

>>> local.format('YYYY-MM-DD HH:mm:ss ZZ')
'2013-05-11 13:23:58 -07:00'

>>> local.humanize()
'an hour ago'

>>> local.humanize(locale='ko-kr')
'한시간 전'

Documentation

For full documentation, please visit arrow.readthedocs.io.

Contributing

Contributions are welcome for both code and localizations (adding and updating locales). Begin by gaining familiarity with the Arrow library and its features. Then, jump into contributing:

1. Find an issue or feature to tackle on the issue tracker. Issues marked with the “good first issue” label may be a great place to start!

2. Fork this repository on GitHub and begin making changes in a branch.

3. Add a few tests to ensure that the bug was fixed or the feature works as expected.

4. Run the entire test suite and linting checks by running one of the following commands: tox && tox -e lint,docs (if you have tox installed) OR make build39 && make test && make lint (if you do not have Python 3.9 installed, replace build39 with the latest Python version on your system).

5. Submit a pull request and await feedback 😃.

If you have any questions along the way, feel free to ask them here.

Support Arrow

Open Collective is an online funding platform that provides tools to raise money and share your finances with full transparency. It is the platform of choice for individuals and companies to make one-time or recurring donations directly to the project. If you are interested in making a financial contribution, please visit the Arrow collective.