Displaying Custom Business Hours End Time in 24h Format Using Python Pandas

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πŸ’‘ Problem Formulation: When working with time series data in financial analytics or similar fields, it’s often necessary to calculate the end time of a custom business hour, considering a specific business start time and duration. The objective is to use Python’s pandas library along with its CustomBusinessHour offset object to display the closing time of this custom business period in a 24-hour format. For instance, if the custom business hour starts at 09:00 AM and spans for 8 hours, the desired output is 17:00 in 24-hour notation.

Method 1: Utilizing CustomBusinessHour with the .rollforward() Function

This method involves creating a CustomBusinessHour offset object with specified starting time and duration, then using the .rollforward() method to find the end time. It accurately calculates the end of the business period even when the start time is non-standard and accounts for weekends and holidays if provided.

Here’s an example:

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

# Define custom business hours: 9am to 5pm
cbh = CustomBusinessHour(start='09:00', end='17:00')

# Starting point
start_time = pd.Timestamp('2023-03-01 09:00')

# Calculate end time
end_time = cbh.rollforward(start_time + cbh)

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

Output:

17:00

This snippet first imports pandas and the CustomBusinessHour class. It then defines a business hour from 9 AM to 5 PM. Utilizing a pandas Timestamp, we specify the start time, and with the .rollforward() method, we calculate the end time by adding the CustomBusinessHour duration to the start time and then rolling forward to the nearest business hour, which correctly gives us the end time formatted in 24-hour notation.

Method 2: Using the .apply() Function on a DatetimeIndex

This method creates a DatetimeIndex with a single start time and then applies the CustomBusinessHour offset through the pandas .apply() function. This approach is particularly flexible because it can handle arrays of start times and can easily be incorporated into a dataframe.

Here’s an example:

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

# Instantiate the CustomBusinessHour object
cbh = CustomBusinessHour(start='09:00', end='17:00')

# Create datetime index with our start time
dt_index = pd.DatetimeIndex(['2023-03-01 09:00'])

# Apply the CustomBusinessHour offset
end_times = dt_index + cbh

print(end_times.strftime('%H:%M')[0])

Output:

17:00

The code demonstrates the creation of a DatetimeIndex with the start time. The CustomBusinessHour offset is then applied to this index to calculate the end times. Using the strftime() method, we convert the resulting Timestamps to strings in a 24-hour format. This type of approach ensures that the code can be easily expanded to multiple start times if necessary.

Method 3: Defining a Function to Calculate End Time

Defining a custom function encapsulates the logic for calculating the end time given a start time. This function will create a CustomBusinessHour for each call and return the end time. It offers reusability and a clear separation of concerns within the codebase.

Here’s an example:

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

def calculate_end_time(start_time_str):
    cbh = CustomBusinessHour(start='09:00', end='17:00')
    start_time = pd.Timestamp(start_time_str)
    end_time = start_time + cbh
    return end_time.strftime('%H:%M')

print(calculate_end_time('2023-03-01 09:00'))

Output:

17:00

This code defines a function called calculate_end_time() that takes a string representation of the start time, converts it to a Timestamp, and then calculates the end time using the CustomBusinessHour. The result is then formatted into a 24-hour format using strftime(). This method is highly modular and makes the calculation easily accessible throughout a larger program.

Bonus One-Liner Method 4: Lambda Function with map()

This one-liner approach combines a lambda function with the map() method to apply the CustomBusinessHour offset to a start time or a list of start times. It is perfect for quick operations or inline calculations.

Here’s an example:

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

cbh = CustomBusinessHour(start='09:00', end='17:00')

# Assuming we have a Series of start times
start_times = pd.Series(pd.Timestamp('2023-03-01 09:00'))

# Applying CustomBusinessHour using map() and a lambda function
end_times = start_times.map(lambda x: (x + cbh).strftime('%H:%M'))

print(end_times.iloc[0])

Output:

17:00

This compact snippet takes advantage of a lambda function used in conjunction with the map() method to apply the addition of the CustomBusinessHour offset to each element in a pandas Series of start times. The lambda expression handles the addition and formatting seamlessly, providing a quick and clean one-liner solution for calculating end times.

Summary/Discussion

  • Method 1: Utilizing CustomBusinessHour with .rollforward(). Strengths: Explicit handling of business hours. Weaknesses: Requires creation of a Timestamp object before applying the rollforward method.
  • Method 2: .apply() Function on a DatetimeIndex. Strengths: Applicable to arrays of times and DataFrame integration. Weaknesses: Slightly more complex setup for beginners.
  • Method 3: Defining a Function. Strengths: Reusable and easily maintained. Weaknesses: Needs a custom function for a task that could be inline.
  • Bonus Method 4: Lambda Function with map(). Strengths: Ideal for inline calculations and Series. Weaknesses: Less readable for complex operations and those not familiar with lambda functions.