Python Pandas: Extracting Start Time in 24h Format with CustomBusinessHour

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πŸ’‘ Problem Formulation: Working with time series data in Python’s Pandas library often requires manipulating and extracting specific time-related information. Here, we address how to derive the start time of a custom business hour in a 24-hour format using the CustomBusinessHour offset object. For instance, if you have a business that operates from 10:00 to 19:00 and you want to display the starting time (10:00) in a 24-hour format, you need to follow certain methods to achieve that using Pandas.

Method 1: Using CustomBusinessHour Attributes

This method involves directly accessing the attributes of the CustomBusinessHour object to retrieve the start time. CustomBusinessHour objects have a start attribute which represents the beginning of the business hour window in a timedelta object relative to midnight.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour

business_hours = CustomBusinessHour(start='10:00', end='19:00')
start_time = (business_hours.start.hour, business_hours.start.minute)
formatted_start_time = f'{start_time[0]:02d}:{start_time[1]:02d}'

print(formatted_start_time)

Output:

10:00

This snippet creates a CustomBusinessHour object with specified start and end times. It then accesses the hour and minute attributes of the start attribute, formats them to a string in 24-hour format, and prints the start time.

Method 2: Leveraging strftime() for Formatting

Utilizing Python’s datetime formatting capabilities, the strftime() method can convert the timedelta object to a properly formatted string. This approach is more direct and leverages well-known datetime formatting techniques.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
from datetime import datetime

business_hours = CustomBusinessHour(start='10:00', end='19:00')
# Creating a reference datetime object for midnight
ref_datetime = datetime(2000, 1, 1)
start_time = (ref_datetime + business_hours.start).strftime('%H:%M')

print(start_time)

Output:

10:00

This code snippet uses a reference datetime object at midnight and adds the start timedelta to it. It then formats the resulting datetime object to a string in 24-hour format with strftime('%H:%M').

Method 3: Combining Time with Today’s Date

Here, the concept is to combine the custom business start time with today’s date to create a datetime object. The datetime.combine() function is employed for this purpose. While slightly more complex, this method mimics real-world scenarios where business hours typically correspond to a specific date.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
from datetime import datetime, time

business_hours = CustomBusinessHour(start='10:00', end='19:00')
start_time_obj = time(business_hours.start.hour, business_hours.start.minute)
start_time_with_date = datetime.combine(datetime.today(), start_time_obj).strftime('%H:%M')

print(start_time_with_date)

Output:

10:00

This approach uses the time() constructor to create a time object from the CustomBusinessHour offset, which is then combined with the current date to form a complete datetime object. The final start time is extracted and formatted using strftime().

Method 4: Using Pandas to_datetime() for Conversion

Pandas provides a method for converting scalar, array-like, or list-like objects into a datetime-like Timestamp object. This can accept a time object and, although it may seem indirect, it takes advantage of native Pandas functions for conversion.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour
import pandas as pd
from datetime import time

business_hours = CustomBusinessHour(start='10:00', end='19:00')
start_time_obj = time(business_hours.start.hour, business_hours.start.minute)
start_time = pd.to_datetime(start_time_obj, format='%H:%M').time()

print(start_time.strftime('%H:%M'))

Output:

10:00

In this method, a time object representing the start time is converted into a Pandas Timestamp object using the pd.to_datetime() function. The format parameter ensures that the time is interpreted correctly. Finally, the strftime() method formats the output.

Bonus One-Liner Method 5: Utilizing a Lambda Function

Sometimes a quick one-liner is all that is needed. This concise approach uses a lambda function combined with the attributes of CustomBusinessHour to immediately format and return the start time in the needed format.

Here’s an example:

from pandas.tseries.offsets import CustomBusinessHour

business_hours = CustomBusinessHour(start='10:00', end='19:00')
formatted_start_time = (lambda bh: f"{bh.start.hour:02d}:{bh.start.minute:02d}")(business_hours)

print(formatted_start_time)

Output:

10:00

This compact example introduces a lambda function that takes a CustomBusinessHour object, accesses the start attribute, formats the hour and minute, and returns the string representation of the start time, all in one line of code.

Summary/Discussion

  • Method 1: Direct Attribute Access. This method is straightforward and easy to understand. However, it may not be as familiar to those who don’t work with timedelta objects regularly.
  • Method 2: Strftime Formatting. It is a common method familiar to many and applies standard datetime formatting. This method might be unnecessarily complex for simple time extractions.
  • Method 3: Combining Time with Date. It mirrors common real-life uses where business hours are linked with dates. It is overkill if you only need the time without any date context.
  • Method 4: Pandas Conversion. Utilizes Pandas’ powerful datetime conversion functions for those comfortable with the library. However, this could be seen as using a sledgehammer to crack a nut for simple scenarios.
  • Method 5: Lambda Function. Offers a quick, in-line solution for those preferring conciseness over clarity. This method may be less readable for users not familiar with lambda functions.