# Make working with datetimes in Python simpler and more powerful.
Created to be used in a project, this package is published to github for ease of management and installation across different modules.
## Installation
Install from `PyPi`
```bash
pip install cytimes
```
Install from `github`
```bash
pip install git+https://github.com/AresJef/cyTimes.git
```
## Compatibility
Supports Python 3.10 and above.
## Features
`cyTimes` introduces two classes that simplify and enhance working with datetimes:
- `Pydt` (Python datetime.datetime)
- `Pddt` (Pandas DatetimeIndex)
Both provide similar functionalities:
- Direct drop-in replacements (subclasses) for standard Python `datetime` and Pandas `DatetimeIndex`.
- Cython-optimized for high-performance parsing, creation, and manipulation.
- Well-documented methods with type annotations.
- Flexible constructors accepting multiple input formats (strings, datetime objects, timestamps, etc.).
- Rich conversion options (ISO strings, ordinals, timestamps, and more).
- Comprehensive manipulation for precise datetime fields adjustments (years, quarters, months, days, time).
- Direct calendar information insights (e.g., days in month, leap years).
- Extended timezone-related capabilities.
- Supports adding or subtracting deltas, and compute deltas against datetime-like object(s).
## `Pydt` Usage
The `Pydt` class operates similarly to Python’s native `datetime.datetime`, with added methods and improvements.
### Construction
```python
from cytimes import Pydt
import datetime, numpy as np
Pydt(1970, 1, 1, tzinfo="UTC")
>>> 1970-01-01 00:00:00+0000
Pydt.parse("1970 Jan 1 00:00:01 PM")
>>> 1970-01-01 12:00:01
Pydt.now()
>>> 2024-12-06 10:37:25.619593
Pydt.utcnow()
>>> 2024-12-06 09:37:36.743159+0000
Pydt.combine("1970-01-01", "00:00:01")
>>> 1970-01-01 00:00:01
Pydt.fromordinal(1)
>>> 0001-01-01 00:00:00
Pydt.fromseconds(1)
>>> 1970-01-01 00:00:01
Pydt.fromicroseconds(1)
>>> 1970-01-01 00:00:00.000001
Pydt.fromtimestamp(1, datetime.UTC)
>>> 1970-01-01 00:00:01+0000
Pydt.utcfromtimestamp(1)
>>> 1970-01-01 00:00:01+0000
Pydt.fromisoformat("1970-01-01T00:00:01")
>>> 1970-01-01 00:00:01
Pydt.fromisocalendar(1970, 1, 4)
>>> 1970-01-01 00:00:00
Pydt.fromdate(datetime.date(1970, 1, 1))
>>> 1970-01-01 00:00:00
Pydt.fromdatetime(datetime.datetime(1970, 1, 1))
>>> 1970-01-01 00:00:00
Pydt.fromdatetime64(np.datetime64(1, "s"))
>>> 1970-01-01 00:00:01
Pydt.strptime("00:00:01 1970-01-01", "%H:%M:%S %Y-%m-%d")
>>> 1970-01-01 00:00:01
```
### Conversion
```python
from cytimes import Pydt
dt = Pydt(1970, 1, 1, tzinfo="CET")
dt.ctime()
>>> "Thu Jan 1 00:00:00 1970"
dt.strftime("%Y-%m-%d %H:%M:%S %Z")
>>> "1970-01-01 00:00:00 CET"
dt.isoformat()
>>> "1970-01-01T00:00:00+01:00"
dt.timetuple()
>>> (1970, 1, 1, 0, 0, 0, 3, 1, 0)
dt.toordinal()
>>> 719163
dt.seconds()
>>> 0.0
dt.microseconds()
>>> 0
dt.timestamp()
>>> -3600.0
dt.date()
>>> 1970-01-01
dt.time()
>>> 00:00:00
dt.timetz()
>>> 00:00:00
```
### Manipulation
```python
from cytimes import Pydt
dt = Pydt(1970, 2, 2, 2, 2, 2, 2, "CET")
# . replace
dt.replace(year=2007, microsecond=1, tzinfo="UTC")
>>> 2007-02-02 02:02:02.000001+0000
# . year
dt.to_curr_year(3, 15)
>>> 1970-03-15 02:02:02.000002+0100
dt.to_prev_year("Feb", 30)
>>> 1969-02-28 02:02:02.000002+0100
dt.to_next_year("十二月", 31)
>>> 1971-12-31 02:02:02.000002+0100
dt.to_year(100, "noviembre", 30)
>>> 2070-11-30 02:02:02.000002+0100
# . quarter
dt.to_curr_quarter(3, 15)
>>> 1970-03-15 02:02:02.000002+0100
dt.to_prev_quarter(3, 15)
>>> 1969-12-15 02:02:02.000002+0100
dt.to_next_quarter(3, 15)
>>> 1970-06-15 02:02:02.000002+0100
dt.to_quarter(100, 3, 15)
>>> 1995-03-15 02:02:02.000002+0100
# . month
dt.to_curr_month(15)
>>> 1970-02-15 02:02:02.000002+0100
dt.to_prev_month(15)
>>> 1970-01-15 02:02:02.000002+0100
dt.to_next_month(15)
>>> 1970-03-15 02:02:02.000002+0100
dt.to_month(100, 15)
>>> 1978-06-15 02:02:02.000002+0200
# . weekday
dt.to_monday()
>>> 1970-02-02 02:02:02.000002+0100
dt.to_sunday()
>>> 1970-02-08 02:02:02.000002+0100
dt.to_curr_weekday(4)
>>> 1970-02-06 02:02:02.000002+0100
dt.to_prev_weekday(4)
>>> 1970-01-30 02:02:02.000002+0100
dt.to_next_weekday(4)
>>> 1970-02-13 02:02:02.000002+0100
dt.to_weekday(100, 4)
>>> 1972-01-07 02:02:02.000002+0100
# . day
dt.to_yesterday()
>>> 1970-02-01 02:02:02.000002+0100
dt.to_tomorrow()
>>> 1970-02-03 02:02:02.000002+0100
dt.to_day(100)
>>> 1970-05-13 02:02:02.000002+0100
# . date&time
dt.to_first_of("Y")
>>> 1970-01-01 02:02:02.000002+0100
dt.to_last_of("Q")
>>> 1970-03-31 02:02:02.000002+0100
dt.to_start_of("M")
>>> 1970-02-01 00:00:00+0100
dt.to_end_of("W")
>>> 1970-02-08 23:59:59.999999+0100
# . round / ceil / floor
dt.round("h")
>>> 1970-02-02 02:00:00+0100
dt.ceil("m")
>>> 1970-02-02 02:03:00+0100
dt.floor("s")
>>> 1970-02-02 02:02:02+0100
```
### Calendar Information
```python
from cytimes import Pydt
dt = Pydt(1970, 2, 2, tzinfo="UTC")
# . iso
dt.isocalendar()
>>> {'year': 1970, 'week': 6, 'weekday': 1}
dt.isoyear()
>>> 1970
dt.isoweek()
>>> 6
dt.isoweekday()
>>> 1
# . year
dt.is_leap_year()
>>> False
dt.is_long_year()
>>> True
dt.leap_bt_year(2007)
>>> 9
dt.days_in_year()
>>> 365
dt.days_bf_year()
>>> 719162
dt.days_of_year()
>>> 33
dt.is_year(1970)
>>> True
# . quarter
dt.days_in_quarter()
>>> 90
dt.days_bf_quarter()
>>> 0
dt.days_of_quarter()
>>> 33
dt.is_quarter(1)
>>> True
# . month
dt.days_in_month()
>>> 28
dt.days_bf_month()
>>> 31
dt.days_of_month()
>>> 2
dt.is_month("Feb")
>>> True
dt.month_name("es")
>>> "febrero"
# . weekday
dt.is_weekday("Monday")
>>> True
# . day
dt.is_day(2)
>>> True
dt.day_name("fr")
>>> "lundi"
# . date&time
dt.is_first_of("Y")
>>> False
dt.is_last_of("Q")
>>> False
dt.is_start_of("M")
>>> False
dt.is_end_of("W")
>>> False
```
### Timezone Operation
```python
from cytimes import Pydt
dt = Pydt(1970, 1, 1, tzinfo="UTC")
dt.is_local()
>>> False
dt.is_utc()
>>> True
dt.is_dst()
>>> False
dt.tzname()
>>> "UTC"
dt.utcoffset()
>>> 0:00:00
dt.utcoffset_seconds()
>>> 0
dt.dst()
>>> None
dt.astimezone("CET")
>>> 1970-01-01 01:00:00+0100
dt.tz_localize(None)
>>> 1970-01-01 00:00:00
dt.tz_convert("CET")
>>> 1970-01-01 01:00:00+0100
dt.tz_switch("CET")
>>> 1970-01-01 01:00:00+0100
```
### Arithmetic
```python
from cytimes import Pydt
dt = Pydt(1970, 1, 1, tzinfo="UTC")
dt.add(years=1, weeks=1, microseconds=1)
>>> 1971-01-08 00:00:00.000001+0000
dt.sub(quarters=1, days=1, seconds=1)
>>> 1969-09-29 23:59:59+0000
dt.diff("2007-01-01 01:01:01+01:00", "s")
>>> -1167609662
```
### Comparison
```python
from cytimes import Pydt
dt = Pydt(1970, 1, 1)
dt.is_past()
>>> True
dt.is_future()
>>> False
dt.closest("1970-01-02", "2007-01-01")
>>> 1970-01-02 00:00:00
dt.farthest("1970-01-02", "2007-01-01")
>>> 2007-01-01 00:00:00
```
## `Pddt` Usage
`Pddt` extends similar functionalities to Pandas `DatetimeIndex`, making it behave more like native Python `datetime.datetime`, but for arrays of datetime values. It supports:
- Vectorized parsing, creation, and manipulation.
- Most of the same methods and properties as `Pydt` (see examples above), adapted for datetime-arrays.
- Automatic handling of out-of-range datetimes in nanoseconds by downcasting to microsecond precision `'us'` to avoid overflow.
### Handling Nanosecond Overflow
By default, `DatetimeIndex` uses nanosecond precision `'ns'`, which cannot represent datetimes outside the range 1677-09-21 to 2262-04-11. Pddt automatically downcasts to microseconds `us` when encountering out-of-range datetimes, sacrificing nanosecond precision to allow a broader range support.
```python
from cytimes import Pddt
Pddt(["9999-01-01 00:00:00+00:00", "9999-01-02 00:00:00+00:00"])
>>> Pddt(['9999-01-01 00:00:00+00:00', '9999-01-02 00:00:00+00:00'],
dtype='datetime64[us, UTC]', freq=None)
```
Downcasting mechanism also automacially applies to all methods that modify the datetimes, resulting values out of the `'ns'` range:
```python
from cytimes import Pddt
pt = Pddt(["1970-01-01 00:00:00+00:00", "1970-01-02 00:00:00+00:00"])
# Pddt(['1970-01-01 00:00:00+00:00', '1970-01-02 00:00:00+00:00'],
# dtype='datetime64[ns, UTC]', freq=None)
pt.to_year(1000, "Feb", 30)
>>> Pddt(['2970-02-28 00:00:00+00:00', '2970-02-28 00:00:00+00:00'],
dtype='datetime64[us, UTC]', freq=None)
```
### Acknowledgements
cyTimes is based on several open-source repositories.
- [babel](https://github.com/python-babel/babel)
- [numpy](https://github.com/numpy/numpy)
- [pandas](https://github.com/pandas-dev/pandas)
cyTimes is built on the following open-source repositories:
- [dateutil](https://github.com/dateutil/dateutil)
Class <'Parser'> and <'Delta'> in this package are the cythonized version of <'dateutil.parser'> and <'dateutil.relativedelta'>. Credit and thanks go to the original authors and contributors of the `dateutil` library.
Raw data
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"keywords": "cytimes, parser, datetime, pandas, Series, DatetimeIndex",
"author": "Jiefu Chen",
"author_email": "keppa1991@163.com",
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"platform": null,
"description": "# Make working with datetimes in Python simpler and more powerful.\n\nCreated to be used in a project, this package is published to github for ease of management and installation across different modules.\n\n## Installation\n\nInstall from `PyPi`\n\n```bash\npip install cytimes\n```\n\nInstall from `github`\n\n```bash\npip install git+https://github.com/AresJef/cyTimes.git\n```\n\n## Compatibility\n\nSupports Python 3.10 and above.\n\n## Features\n\n`cyTimes` introduces two classes that simplify and enhance working with datetimes:\n\n- `Pydt` (Python datetime.datetime)\n- `Pddt` (Pandas DatetimeIndex)\n\nBoth provide similar functionalities:\n\n- Direct drop-in replacements (subclasses) for standard Python `datetime` and Pandas `DatetimeIndex`.\n- Cython-optimized for high-performance parsing, creation, and manipulation.\n- Well-documented methods with type annotations.\n- Flexible constructors accepting multiple input formats (strings, datetime objects, timestamps, etc.).\n- Rich conversion options (ISO strings, ordinals, timestamps, and more).\n- Comprehensive manipulation for precise datetime fields adjustments (years, quarters, months, days, time).\n- Direct calendar information insights (e.g., days in month, leap years).\n- Extended timezone-related capabilities.\n- Supports adding or subtracting deltas, and compute deltas against datetime-like object(s).\n\n## `Pydt` Usage\n\nThe `Pydt` class operates similarly to Python\u2019s native `datetime.datetime`, with added methods and improvements.\n\n### Construction\n\n```python\nfrom cytimes import Pydt\nimport datetime, numpy as np\n\nPydt(1970, 1, 1, tzinfo=\"UTC\")\n>>> 1970-01-01 00:00:00+0000\nPydt.parse(\"1970 Jan 1 00:00:01 PM\")\n>>> 1970-01-01 12:00:01\nPydt.now()\n>>> 2024-12-06 10:37:25.619593\nPydt.utcnow()\n>>> 2024-12-06 09:37:36.743159+0000\nPydt.combine(\"1970-01-01\", \"00:00:01\")\n>>> 1970-01-01 00:00:01\nPydt.fromordinal(1)\n>>> 0001-01-01 00:00:00\nPydt.fromseconds(1)\n>>> 1970-01-01 00:00:01\nPydt.fromicroseconds(1)\n>>> 1970-01-01 00:00:00.000001\nPydt.fromtimestamp(1, datetime.UTC)\n>>> 1970-01-01 00:00:01+0000\nPydt.utcfromtimestamp(1)\n>>> 1970-01-01 00:00:01+0000\nPydt.fromisoformat(\"1970-01-01T00:00:01\")\n>>> 1970-01-01 00:00:01\nPydt.fromisocalendar(1970, 1, 4)\n>>> 1970-01-01 00:00:00\nPydt.fromdate(datetime.date(1970, 1, 1))\n>>> 1970-01-01 00:00:00\nPydt.fromdatetime(datetime.datetime(1970, 1, 1))\n>>> 1970-01-01 00:00:00\nPydt.fromdatetime64(np.datetime64(1, \"s\"))\n>>> 1970-01-01 00:00:01\nPydt.strptime(\"00:00:01 1970-01-01\", \"%H:%M:%S %Y-%m-%d\")\n>>> 1970-01-01 00:00:01\n```\n\n### Conversion\n\n```python\nfrom cytimes import Pydt\n\ndt = Pydt(1970, 1, 1, tzinfo=\"CET\")\n\ndt.ctime()\n>>> \"Thu Jan 1 00:00:00 1970\"\ndt.strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n>>> \"1970-01-01 00:00:00 CET\"\ndt.isoformat()\n>>> \"1970-01-01T00:00:00+01:00\"\ndt.timetuple()\n>>> (1970, 1, 1, 0, 0, 0, 3, 1, 0)\ndt.toordinal()\n>>> 719163\ndt.seconds()\n>>> 0.0\ndt.microseconds()\n>>> 0\ndt.timestamp()\n>>> -3600.0\ndt.date()\n>>> 1970-01-01\ndt.time()\n>>> 00:00:00\ndt.timetz()\n>>> 00:00:00\n```\n\n### Manipulation\n\n```python\nfrom cytimes import Pydt\n\ndt = Pydt(1970, 2, 2, 2, 2, 2, 2, \"CET\")\n\n# . replace\ndt.replace(year=2007, microsecond=1, tzinfo=\"UTC\")\n>>> 2007-02-02 02:02:02.000001+0000\n\n# . year\ndt.to_curr_year(3, 15)\n>>> 1970-03-15 02:02:02.000002+0100\ndt.to_prev_year(\"Feb\", 30)\n>>> 1969-02-28 02:02:02.000002+0100\ndt.to_next_year(\"\u5341\u4e8c\u6708\", 31)\n>>> 1971-12-31 02:02:02.000002+0100\ndt.to_year(100, \"noviembre\", 30)\n>>> 2070-11-30 02:02:02.000002+0100\n\n# . quarter\ndt.to_curr_quarter(3, 15)\n>>> 1970-03-15 02:02:02.000002+0100\ndt.to_prev_quarter(3, 15)\n>>> 1969-12-15 02:02:02.000002+0100\ndt.to_next_quarter(3, 15)\n>>> 1970-06-15 02:02:02.000002+0100\ndt.to_quarter(100, 3, 15)\n>>> 1995-03-15 02:02:02.000002+0100\n\n# . month\ndt.to_curr_month(15)\n>>> 1970-02-15 02:02:02.000002+0100\ndt.to_prev_month(15)\n>>> 1970-01-15 02:02:02.000002+0100\ndt.to_next_month(15)\n>>> 1970-03-15 02:02:02.000002+0100\ndt.to_month(100, 15)\n>>> 1978-06-15 02:02:02.000002+0200\n\n# . weekday\ndt.to_monday()\n>>> 1970-02-02 02:02:02.000002+0100\ndt.to_sunday()\n>>> 1970-02-08 02:02:02.000002+0100\ndt.to_curr_weekday(4)\n>>> 1970-02-06 02:02:02.000002+0100\ndt.to_prev_weekday(4)\n>>> 1970-01-30 02:02:02.000002+0100\ndt.to_next_weekday(4)\n>>> 1970-02-13 02:02:02.000002+0100\ndt.to_weekday(100, 4)\n>>> 1972-01-07 02:02:02.000002+0100\n\n# . day\ndt.to_yesterday()\n>>> 1970-02-01 02:02:02.000002+0100\ndt.to_tomorrow()\n>>> 1970-02-03 02:02:02.000002+0100\ndt.to_day(100)\n>>> 1970-05-13 02:02:02.000002+0100\n\n# . date&time\ndt.to_first_of(\"Y\")\n>>> 1970-01-01 02:02:02.000002+0100\ndt.to_last_of(\"Q\")\n>>> 1970-03-31 02:02:02.000002+0100\ndt.to_start_of(\"M\")\n>>> 1970-02-01 00:00:00+0100\ndt.to_end_of(\"W\")\n>>> 1970-02-08 23:59:59.999999+0100\n\n# . round / ceil / floor\ndt.round(\"h\")\n>>> 1970-02-02 02:00:00+0100\ndt.ceil(\"m\")\n>>> 1970-02-02 02:03:00+0100\ndt.floor(\"s\")\n>>> 1970-02-02 02:02:02+0100\n```\n\n### Calendar Information\n\n```python\nfrom cytimes import Pydt\n\ndt = Pydt(1970, 2, 2, tzinfo=\"UTC\")\n\n# . iso\ndt.isocalendar()\n>>> {'year': 1970, 'week': 6, 'weekday': 1}\ndt.isoyear()\n>>> 1970\ndt.isoweek()\n>>> 6\ndt.isoweekday()\n>>> 1\n\n# . year\ndt.is_leap_year()\n>>> False\ndt.is_long_year()\n>>> True\ndt.leap_bt_year(2007)\n>>> 9\ndt.days_in_year()\n>>> 365\ndt.days_bf_year()\n>>> 719162\ndt.days_of_year()\n>>> 33\ndt.is_year(1970)\n>>> True\n\n# . quarter\ndt.days_in_quarter()\n>>> 90\ndt.days_bf_quarter()\n>>> 0\ndt.days_of_quarter()\n>>> 33\ndt.is_quarter(1)\n>>> True\n\n# . month\ndt.days_in_month()\n>>> 28\ndt.days_bf_month()\n>>> 31\ndt.days_of_month()\n>>> 2\ndt.is_month(\"Feb\")\n>>> True\ndt.month_name(\"es\")\n>>> \"febrero\"\n\n# . weekday\ndt.is_weekday(\"Monday\")\n>>> True\n\n# . day\ndt.is_day(2)\n>>> True\ndt.day_name(\"fr\")\n>>> \"lundi\"\n\n# . date&time\ndt.is_first_of(\"Y\")\n>>> False\ndt.is_last_of(\"Q\")\n>>> False\ndt.is_start_of(\"M\")\n>>> False\ndt.is_end_of(\"W\")\n>>> False\n```\n\n### Timezone Operation\n\n```python\nfrom cytimes import Pydt\n\ndt = Pydt(1970, 1, 1, tzinfo=\"UTC\")\n\ndt.is_local()\n>>> False\ndt.is_utc()\n>>> True\ndt.is_dst()\n>>> False\ndt.tzname()\n>>> \"UTC\"\ndt.utcoffset()\n>>> 0:00:00\ndt.utcoffset_seconds()\n>>> 0\ndt.dst()\n>>> None\ndt.astimezone(\"CET\")\n>>> 1970-01-01 01:00:00+0100\ndt.tz_localize(None)\n>>> 1970-01-01 00:00:00\ndt.tz_convert(\"CET\")\n>>> 1970-01-01 01:00:00+0100\ndt.tz_switch(\"CET\")\n>>> 1970-01-01 01:00:00+0100\n```\n\n### Arithmetic\n\n```python\nfrom cytimes import Pydt\n\ndt = Pydt(1970, 1, 1, tzinfo=\"UTC\")\n\ndt.add(years=1, weeks=1, microseconds=1)\n>>> 1971-01-08 00:00:00.000001+0000\ndt.sub(quarters=1, days=1, seconds=1)\n>>> 1969-09-29 23:59:59+0000\ndt.diff(\"2007-01-01 01:01:01+01:00\", \"s\")\n>>> -1167609662\n```\n\n### Comparison\n\n```python\nfrom cytimes import Pydt\n\ndt = Pydt(1970, 1, 1)\n\ndt.is_past()\n>>> True\ndt.is_future()\n>>> False\ndt.closest(\"1970-01-02\", \"2007-01-01\")\n>>> 1970-01-02 00:00:00\ndt.farthest(\"1970-01-02\", \"2007-01-01\")\n>>> 2007-01-01 00:00:00\n```\n\n## `Pddt` Usage\n\n`Pddt` extends similar functionalities to Pandas `DatetimeIndex`, making it behave more like native Python `datetime.datetime`, but for arrays of datetime values. It supports:\n\n- Vectorized parsing, creation, and manipulation.\n- Most of the same methods and properties as `Pydt` (see examples above), adapted for datetime-arrays.\n- Automatic handling of out-of-range datetimes in nanoseconds by downcasting to microsecond precision `'us'` to avoid overflow.\n\n### Handling Nanosecond Overflow\n\nBy default, `DatetimeIndex` uses nanosecond precision `'ns'`, which cannot represent datetimes outside the range 1677-09-21 to 2262-04-11. Pddt automatically downcasts to microseconds `us` when encountering out-of-range datetimes, sacrificing nanosecond precision to allow a broader range support.\n\n```python\nfrom cytimes import Pddt\n\nPddt([\"9999-01-01 00:00:00+00:00\", \"9999-01-02 00:00:00+00:00\"])\n>>> Pddt(['9999-01-01 00:00:00+00:00', '9999-01-02 00:00:00+00:00'],\n dtype='datetime64[us, UTC]', freq=None)\n```\n\nDowncasting mechanism also automacially applies to all methods that modify the datetimes, resulting values out of the `'ns'` range:\n\n```python\nfrom cytimes import Pddt\n\npt = Pddt([\"1970-01-01 00:00:00+00:00\", \"1970-01-02 00:00:00+00:00\"])\n# Pddt(['1970-01-01 00:00:00+00:00', '1970-01-02 00:00:00+00:00'],\n# dtype='datetime64[ns, UTC]', freq=None)\npt.to_year(1000, \"Feb\", 30)\n>>> Pddt(['2970-02-28 00:00:00+00:00', '2970-02-28 00:00:00+00:00'],\n dtype='datetime64[us, UTC]', freq=None)\n```\n\n### Acknowledgements\n\ncyTimes is based on several open-source repositories.\n\n- [babel](https://github.com/python-babel/babel)\n- [numpy](https://github.com/numpy/numpy)\n- [pandas](https://github.com/pandas-dev/pandas)\n\ncyTimes is built on the following open-source repositories:\n\n- [dateutil](https://github.com/dateutil/dateutil)\n\n Class <'Parser'> and <'Delta'> in this package are the cythonized version of <'dateutil.parser'> and <'dateutil.relativedelta'>. Credit and thanks go to the original authors and contributors of the `dateutil` library.\n",
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