dately 2.1.4

A comprehensive Python library for advanced date and time manipulation.

Dately Library: Comprehensive Date and Time Handling in Python

The dately module is a comprehensive library designed for advanced date and time manipulation. It offers extensive capabilities to handle various date and time formats, ensuring compatibility across different systems and data structures. The module provides tools for validating, extracting, and transforming date and time information with a focus on performance and accuracy.

Table of Contents

1. Why Choose Dately?
2. Key Features
3. Solving Windows Date Formatting Issues
4. Usage Examples - Working with Date Strings
   • Importing the Module
   • Extracting Datetime Components
   • Detecting Datetime Formats
   • Converting Dates
   • Converting Dates in Dictionaries
   • Replacing Datestring
   • Replacing Datetimestring
5. Working with Time Zones
   • Retrieving Time Zone Information
   • Time Zone Operations

Why Choose Dately?

• Windows Optimized: Specifically addresses inconsistencies in Python's date formatting on the Windows operating system.
• Comprehensive Functionalities: Supports date parsing, detection, extraction, and conversion for various input types, including date strings, datetime objects, pandas Series, and NumPy arrays.
• High Performance: Leverages both high-level Python and low-level C code (via Cython) for efficient operations.

Key Features

1. Date and Time Format Detection:

   • Automatically detect various date and time formats from strings, ensuring seamless parsing and conversion.
   • Supports a wide range of date formats, including standard and unique custom formats.

2. Timezone Management:

   • Provides detailed information for specific time zones.
   • Converts time from one time zone to another.
   • Retrieves the current time for specific time zones.
   • Categorizes time zones by country, offset, and daylight saving time observance.

3. String Manipulation and Validation:

   • Extract specific components (year, month, day, hour, minute, second, timezone) from datetime strings.
   • Validate and replace parts of datetime strings to ensure accuracy and consistency.
   • Strip time and timezone information from datetime strings when needed.

4. Performance Optimizations:

   • Utilizes Cython to enhance performance for computationally intensive tasks.
   • Interfaces with underlying C code to perform high-speed string operations and date validations.

Solving Windows Date Formatting Issues

A key aspect of this module is addressing inconsistencies in Python's date formatting on the Windows operating system. The module specifically targets the handling of the hyphen-minus (-) in date format specifiers. This flag, used to remove leading zeros from formatted output (e.g., turning '01' into '1' for January), works reliably on Unix-like systems but does not function as intended on Windows.

To solve this problem on Windows, the dately module introduces a workaround using regular expressions. It utilizes a detection function to determine the format string and then examines each date component for leading zeros through an extract_date_component function and a subsequent has_leading_zero check. Depending on the presence of leading zeros, the module adjusts the format string-replacing %m with %-m where applicable-to emulate the behavior expected from the hyphen-minus on Unix-like systems.

This method ensures that users on Windows achieve consistent date formatting, effectively compensating for the lack of native support for the hyphen-minus in date specifiers on this system.

Overall, dately is a powerful utility for anyone needing precise and flexible date and time handling in their applications, making it easier to manage, format, and validate date and time data consistently and efficiently.

Usage Examples - Working with Date Strings

Importing the Module

# Import module
import dately as dtly

# Additional imports for examples
import pandas as pd
import numpy as np

# Set variables
datestring = "2023-06-21"
datestring_list = [
    '2023-06-21', '2024-06-21', '2024-07-21', '2024-08-20',
    '2024-09-19', '2024-10-19', '2024-11-18', '2024-12-18',
    '2025-01-17', '2025-02-16', '2025-03-18', '2025-04-17'
]
datestring_array = np.array(datestring_list)
datestring_series = pd.Series(datestring_list)

Extracting Datetime Components

Single Date String

print(dtly.dt.extract_datetime_component(datestring, "year"))
# Output: '2023'

print(dtly.dt.extract_datetime_component(datestring, "day"))
# Output: '21'

print(dtly.dt.extract_datetime_component(datestring, "month"))
# Output: '06'

List of Date Strings

print(dtly.dt.extract_datetime_component(datestring_list, "year"))
# Output: ['2023', '2024', '2024', '2024', '2024', '2024', '2024', '2024', '2025', '2025', '2025', '2025']

print(dtly.dt.extract_datetime_component(datestring_list, "day"))
# Output: ['21', '21', '21', '20', '19', '19', '18', '18', '17', '16', '18', '17']

print(dtly.dt.extract_datetime_component(datestring_list, "month"))
# Output: ['06', '06', '07', '08', '09', '10', '11', '12', '01', '02', '03', '04']

NumPy Array of Date Strings

print(dtly.dt.extract_datetime_component(datestring_array, "year"))
# Output: array(['2023', '2024', '2024', '2024', '2024', '2024', '2024', '2024', '2025', '2025', '2025', '2025'], dtype=object)

print(dtly.dt.extract_datetime_component(datestring_array, "day"))
# Output: array(['21', '21', '21', '20', '19', '19', '18', '18', '17', '16', '18', '17'], dtype=object)

print(dtly.dt.extract_datetime_component(datestring_array, "month"))
# Output: array(['06', '06', '07', '08', '09', '10', '11', '12', '01', '02', '03', '04'], dtype=object)

Pandas Series of Date Strings

print(dtly.dt.extract_datetime_component(datestring_series, "year"))
# Output:
# 0     2023
# 1     2024
# 2     2024
# 3     2024
# 4     2024
# 5     2024
# 6     2024
# 7     2024
# 8     2025
# 9     2025
# 10    2025
# 11    2025
# dtype: object

print(dtly.dt.extract_datetime_component(datestring_series, "day"))
# Output:
# 0     21
# 1     21
# 2     21
# 3     20
# 4     19
# 5     19
# 6     18
# 7     18
# 8     17
# 9     16
# 10    18
# 11    17
# dtype: object

print(dtly.dt.extract_datetime_component(datestring_series, "month"))
# Output:
# 0     06
# 1     06
# 2     07
# 3     08
# 4     09
# 5     10
# 6     11
# 7     12
# 8     01
# 9     02
# 10    03
# 11    04
# dtype: object

Detecting Datetime Formats

Single Date String

print(dtly.dt.detect_date_format(datestring))
# Output: '%Y-%m-%d'

List of Date Strings

print(dtly.dt.detect_date_format(datestring_list))
# Output: ['%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d']

NumPy Array of Date Strings

print(dtly.dt.detect_date_format(datestring_array))
# Output: array(['%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d', '%Y-%m-%d'], dtype=object)

Pandas Series of Date Strings

print(dtly.dt.detect_date_format(datestring_series))
# Output:
# 0     %Y-%m-%d
# 1     %Y-%m-%d
# 2     %Y-%m-%d
# 3     %Y-%m-%d
# 4     %Y-%m-%d
# 5     %Y-%m-%d
# 6     %Y-%m-%d
# 7     %Y-%m-%d
# 8     %Y-%m-%d
# 9     %Y-%m-%d
# 10    %Y-%m-%d
# 11    %Y-%m-%d
# dtype: object

Converting Dates

# Converting a single date string
print(dtly.dt.convert_date(datestring, to_format='%m.%Y/%d %I:%M %p', delta=1))
# Output: '06.2023/22 12:00 AM'

# Converting a list of date strings
print(dtly.dt.convert_date(datestring_list, to_format='%Y/%m/%d %I:%M:%S %p'))
# Output: ['2023/06/21 12:00:00 AM', '2024/06/21 12:00:00 AM', '2024/07/21 12:00:00 AM', '2024/08/20 12:00:00 AM', '2024/09/19 12:00:00 AM', '2024/10/19 12:00:00 AM', '2024/11/18 12:00:00 AM', '2024/12/18 12:00:00 AM', '2025/01/17 12:00:00 AM', '2025/02/16 12:00:00 AM', '2025/03/18 12:00:00 AM', '2025/04/17 12:00:00 AM']

# Converting a NumPy array of date strings
print(dtly.dt.convert_date(datestring_array, to_format='%y/%m-%d %H:%M'))
# Output: array(['23/06-21 00:00', '24/06-21 00:00', '24/07-21 00:00', '24/08-20 00:00', '24/09-19 00:00', '24/10-19 00:00', '24/11-18 00:00', '24/12-18 00:00', '25/01-17 00:00', '25/02-16 00:00', '25/03-18 00:00', '25/04-17 00:00'], dtype=object)

# Converting a Pandas Series of date strings
print(dtly.dt.convert_date(datestring_series, to_format='%Y.%m.%d'))
# Output:
# 0     2023.06.21
# 1     2024.06.21
# 2     2024.07.21
# 3     2024.08.20
# 4     2024.09.19
# 5     2024.10.19
# 6     2024.11.18
# 7     2024.12.18
# 8     2025.01.17
# 9     2025.02.16
# 10    2025.03.18
# 11    2025.04.17
# dtype: object

Converting Dates in Dictionaries

# Sample dictionary with dates
sample_dict = {
    "event": {
        "name": "Annual Conference",
        "dates": {
            "start_date": "2024-01-15",
            "end_date": "2024-01-20"
        },
        "registration": {
            "open_date": "2023-11-01",
            "close_date": "2023-12-30"
        }
    },
    "meetings": [
        {
            "title": "Planning Meeting",
            "meeting_date": "2023-10-01"
        },
        {
            "title": "Review Meeting",
            "meeting_date": "2023-10-15"
        }
    ],
    "webinars": [
        {
            "topic": "Introduction to the Event",
            "session_dates": [
                "2023-11-10",
                "2023-11-17"
            ]
        }
    ],
    "workshops": {
        "sessions": [
            {
                "session_name": "Workshop 1",
                "date": "01/01/2024"
            },
            {
                "session_name": "Workshop 2",
                "date": "2024-01-18"
            }
        ]
    }
}

# Converting dates in a dictionary
converted_dict = dtly.dt.convert_date(sample_dict, to_format='%Y/%m', dict_keys=["meeting_date", "date", "session_dates"])
print(converted_dict)
# Output:
# {'event': {'name': 'Annual Conference', 'dates': {'start_date': '2024-01-15', 'end_date': '2024-01-20'}, 'registration': {'open_date': '2023-11-01', 'close_date': '2023-12-30'}}, 'meetings': [{'title': 'Planning Meeting', 'meeting_date': '2023/10'}, {'title': 'Review Meeting', 'meeting_date': '2023/10'}], 'webinars': [{'topic': 'Introduction to the Event', 'session_dates': ['2023/11', '2023/11']}], 'workshops': {'sessions': [{'session_name': 'Workshop 1', 'date': '2024/01'}, {'session_name': 'Workshop 2', 'date': '2024/01'}]}}

Replacing Datestring

# Replacing year in a single date string
print(dtly.dt.replace_datestring(datestring, year=2021))
# Output: '2021-06-21'

# Replacing month in a single date string
print(dtly.dt.replace_datestring(datestring, month="5"))
# Output: '2023-5-21'

# Replacing day in a list of date strings
print(dtly.dt.replace_datestring(datestring_list, day=6))
# Output: ['2023-06-6', '2024-06-6', '2024-07-6', '2024-08-6', '2024-09-6', '2024-10-6', '2024-11-6', '2024-12-6', '2025-01-6', '2025-02-6', '2025-03-6', '2025-04-6']

# Replacing day in a Pandas Series of date strings
print(dtly.dt.replace_datestring(datestring_series, day="02"))
# Output:
# 0     2023-06-02
# 1     2024-06-02
# 2     2024-07-02
# 3     2024-08-02
# 4     2024-09-02
# 5     2024-10-02
# 6     2024-11-02
# 7     2024-12-02
# 8     2025-01-02
# 9     2025-02-02
# 10    2025-03-02
# 11    2025-04-02
# dtype: object

Replacing Datetimestring

# Replacing time components in a single date string
print(dtly.dt.replace_timestring(datestring))
# Output: '2023-06-21 15:17:47.50691'

print(dtly.dt.replace_timestring(datestring, hour=13))
# Output: '2023-06-21 13:17:47.56700'

print(dtly.dt.replace_timestring(datestring, hour="02"))
# Output: '2023-06-21 02:17:47.63773'

print(dtly.dt.replace_timestring(datestring, hour="02", minute=11))
# Output: '2023-06-21 02:11:47.69779'

print(dtly.dt.replace_timestring(datestring, hour="02", minute=10, second=44))
# Output: '2023-06-21 02:10:44.75777'

print(dtly.dt.replace_timestring(datestring, hour="02", minute=10, second=44, microsecond=1))
# Output: '2023-06-21 02:10:44.00001'

print(dtly.dt.replace_timestring(datestring, hour="02", minute=10, second=44, microsecond=1, time_indicator="AM"))
# Output: '2023-06-21 02:10:44.00001 AM'

# Replacing time components in an ISO date string
iso_datestring = "2023-06-21T12:30:00Z"
print(dtly.dt.replace_timestring(iso_datestring, hour=2, minute=10, second=44, microsecond=1))
# Output: '2023-06-21T02:10:44.000001+00:00'

print(dtly.dt.replace_timestring(iso_datestring, hour=2, minute=10, second=44, microsecond=1, tzinfo=3))
# Output: '2023-06-21T02:10:44.000001+03:00'

Working with Time Zones

Retrieving Time Zone Information

# Get the list of country codes
dtly.TimeZoner.CountryCodes
# Output: ['AD', 'AE', 'AF', 'AG', 'AI', 'AL', 'AM', 'AO', 'AQ', 'AR', 'AS', 'AT', 'AU', 'AW', 'AX'.....]

# Get the list of country names
dtly.TimeZoner.CountryNames
# Output: ['Afghanistan', 'Aland Islands', 'Albania', 'Algeria', 'American Samoa', 'Andorra', 'Angola', 'Anguilla', 'Antarctica'.....]

# Get the list of time zones
dtly.TimeZoner.Zones
# Output: ['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Bamako', 'Africa/Bangui'.....] 

# Get time zones by country
dtly.TimeZoner.ZonesByCountry
# Output: {'CI': ['Africa/Abidjan'], 'GH': ['Africa/Accra'], 'ET': ['Africa/Addis_Ababa'].....]}

# Get time zones by DST observance
dtly.TimeZoner.ObservesDST
# Output: {'observes_dst': ['Africa/Casablanca', 'Africa/Ceuta', 'Africa/El_Aaiun'.....]}

# Get time zones by offset
dtly.TimeZoner.Offsets
# Output: {'+00:00': ['Africa/Abidjan', 'Africa/Accra', 'Africa/Bamako'.....], '+03:00': ['Africa/Addis_Ababa', 'Africa/Asmara'.....], '-09:00': ['America/Adak', 'Pacific/Gambier'.....], '-08:00': ['America/Anchorage'.....]}

Time Zone Operations

# Retrieve detailed information for a specific time zone
dtly.TimeZoner.FilterZoneDetail('America/Denver')
# Output: {'countryCode': 'US', 'countryName': 'United States', 'Offset': '-06:00', 'UTC offset (STD)': '-07:00', 'UTC offset (DST)': '-06:00', 'Abbreviation (STD)': 'MST', 'Abbreviation (DST)': 'MDT'}

# Get the current time for a specific time zone
dtly.TimeZoner.CurrentTimebyZone('Australia/Adelaide')
# Output: '2024-07-02T04:32:05.642329+09:30'

# Convert time from one time zone to another
from_zone = 'Africa/Ceuta'
to_zone = 'America/Anchorage'
dtly.TimeZoner.ConvertTimeZone(from_zone, to_zone, year=2024, month=5, day=22, hour=12, minute=13, second=22)
# Output: [{'countryCode': 'ES', 'countryName': 'Spain', 'zoneName': 'Africa/Ceuta', 'gmtOffset': 7200, 'timestamp': 1719865256}, {'countryCode': 'US', 'countryName': 'United States', 'zoneName': 'America/Anchorage', 'gmtOffset': -28800, 'timestamp': 1719829256}]