cytimes 2.1.1

Make working with datetimes in Python simpler and more powerful.

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

pip install cytimes

Install from github

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

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

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

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

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

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

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

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.

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:

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
• numpy
• pandas

cyTimes is built on the following open-source repositories:

• 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.