cytimes 0.1.12.7

Easy management of python datetime & pandas time Series

Easy management of python datetime & pandas time Series.

Created to be used in a project, this package is published to github for ease of management and installation across different modules.

Features

Provides two classes for datetime and pandas time series management.

• pydt (Python Datetime)
• pddt (Pandas Time Series)

Both provide similar functionalities:

• Parse time string
• Access in different data types
• Conversion to int/float (ordinal, total_seconds, timestamp, etc.)
• Calender properties (days_in_month, weekday, etc.)
• Day manipulation (next_week, to_week, etc.)
• Month manipulation (next_month, to_month, etc.)
• Quarter manipulation (next_quarter, to_quarter, etc.)
• Year manipulation (next_year, to_year, etc.)
• Timezone manipulation (tz_localize, tz_convert, etc.)
• Frequency manipulation (round, ceil, floor, etc.)
• Delta adjustment (Equivalent to adding relativedelta and pandas.DateOffset)
• Replace adjustment (Equivalent to datetime.replace and custom pandas.Series.replace)

Installation

Install from PyPi

pip install cytimes

Install from github

pip install git+https://github.com/AresJef/cyTimes.git

Compatibility

Only support for python 3.10 and above.

Usage (pydt)

import datetime, numpy as np, pandas as pd
from cytimes import pydt

# Parse time string
pt = pydt('2021-01-01 00:00:00')
# 2021-01-01T00:00:00
pt = pydt("2021 Jan 1 11:11 AM")
# 2021-01-01T11:11:00

# dateimte/date/time
pt = pydt(datetime.datetime(2021, 1, 1, 0, 0, 0))
# 2021-01-01T00:00:00
pt = pydt(datetime.date(2021, 1, 1))
# 2021-01-01T00:00:00
pt = pydt(datetime.time(12, 0, 0)) # date defaults to today. Can change through default arugment.
# 2023-09-01T12:00:00

# pandas.Timestamp
pt = pydt(pd.Timestamp("2021-01-01 00:00:00"))
# 2021-01-01T00:00:00

# numpy.datetime64
pt = pydt(np.datetime64("2021-01-01 00:00:00"))
# 2021-01-01T00:00:00

# Access in different data types
pt.dt # -> datetime.datetime
pt.date # -> datetime.date
pt.time # -> datetime.time
pt.timetz # -> datetime.time with timezone
pt.ts # -> pandas.Timestamp
pt.dt64 # -> numpy.datetime64
...

# Conversion to int/float
pt.ordinal # -> int
pt.timestamp # -> float
...

# Calender properties
pt.is_leap_year # -> bool
pt.days_bf_year # -> int
pt.days_in_month # -> int
pt.weekday # -> int
pt.isocalendar # -> tuple

# Day manipulation
pt.monday # -> pydt (monday of the week)
pt.tuesday # -> pydt (tuesday of the week)
pt.next_week("monday") # -> pydt (next monday)
pt.to_week(3, "Mon") # -> pydt (three weeks later monday )
...

# Month manipulation
pt.month_lst # -> pydt (last day of the month)
pt.next_month(3) # -> pydt (next month 3rd day)
pt.to_month(3, 31) # -> pydt (three months later last day)
...

# Quarter manipulation
pt.quarter_1st # -> pydt (first day of the quarter)
pt.next_quarter(2, 10) # -> pydt (next quarter 2nd month 10th day)
pt.to_quarter(2, 10, 15) # -> pydt (two quarters later 2nd month 15th day)
...

# Year manipulation
pt.year_lst # -> pydt (last day of the year)
pt.next_year("jan", 1) # -> pydt (next year jan 1st)
pt.to_year(2, "feb", 29) # -> pydt (two years later feb last day)
...

# Timezone manipulation
pt.tz_localize("UTC") # -> pydt (UTC time)
pt.tz_switch(targ_tz="CET", base_tz="UTC") # -> pydt (Setting base timezone to UTC and convert to CET)
...

# Frequency manipulation
pt.round("H") # -> pydt (round to hour)
...

# Delta adjustment
pt.delta(days=1) # -> pydt (add one day)

# Replace adjustment
pt.replace(year=2022) # -> pydt (replace year to 2022)

Usage (pddt)

pddt accepts list and pandas.Series as argument instead of str/datetime comparing to pydt. Properties and methods are similar to pydt, except pddt is designed to work with pandas.Series[datetime64].

Acknowledgements

cyTimes is based on several open-source repositories.

• numpy
• pandas

cyTimes makes modification of the following open-source repositories:

• dateutil

This package created a Cythonized version of dateutil.parser (cyparser) and dateutil.relativedelta (cytimedelta). As a result, these two modules in this package have sacrificed flexibility and readability in exchange for enhancements in performance. All credits go to the original authors and contributors of dateutil.