Displaying Custom Business Hour Start Times in 24h Format with Python Pandas

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πŸ’‘ Problem Formulation: In data analysis, especially when dealing with financial or business data, it’s often necessary to represent time within custom business hours. Python’s Pandas library provides a powerful BusinessHour offset object. However, displaying the exact start time of these customized business hours in a 24-hour format can be a challenge. This article showcases methods to achieve this by transforming a BusinessHour object into a human-readable start time.

Method 1: Using the BusinessHour Attributes

This method focuses on accessing built-in attributes of the Pandas BusinessHour offset object. The start time of custom business hours can be retrieved using the start attribute, which holds the starting hour and minute information. After extracting these details, we can format them into a 24-hour time representation using standard Python string formatting techniques.

Here’s an example:

import pandas as pd

# Define business hours
bh = pd.offsets.BusinessHour(start='09:00', end='17:00')

# Get start time in 24h format
start_time = f"{bh.start.hour:02d}:{bh.start.minute:02d}"

print(start_time)

Output:

09:00

This snippet creates a BusinessHour object representing a typical 9 to 5 workday. Using the start attribute, we extract the hour and minute and format them as a string in 24-hour notation. The f"{variable:02d}" syntax ensures that single-digit hours are prefixed with a zero.

Method 2: Utilizing the .strftime() Method

Python’s datetime objects come with a .strftime() method, which can format date-time objects into readable strings. We can combine a dummy date with our business hour’s start time and apply .strftime() with a format directive to display the time in 24-hour format.

Here’s an example:

from datetime import datetime
import pandas as pd

# Define business hours
bh = pd.offsets.BusinessHour(start='09:00')

# Create a dummy date with the start time
dummy_date = datetime(2000, 1, 1, bh.start.hour, bh.start.minute)

# Format to 24h time
start_time_24h = dummy_date.strftime("%H:%M")

print(start_time_24h)

Output:

09:00

In this code, a datetime object representing an arbitrary date with the business hour’s start time is created. The strftime("%H:%M") function is then used to format this datetime into a 24-hour string representation of the start time.

Method 3: Python String Slice

The third method makes use of Python’s inherent string slicing capability. By utilizing the default string representation of the BusinessHour start attribute, we can directly slice the string to extract the start time. This is a quick and straight-to-the-point method with minimal coding required.

Here’s an example:

import pandas as pd

# Define business hours
bh = pd.offsets.BusinessHour(start='09:00')

# Get start time as a string and slice in 24h format
start_time_24h = str(bh.start)[:-3]

print(start_time_24h)

Output:

09:00

This piece of code simply converts the start attribute of the BusinessHour object into a string format and slices off the unnecessary parts, leaving the hour and minute in 24h format.

Method 4: Using Regular Expressions

Regular expressions are a powerful tool for string matching and extraction. We can use them to search for the time pattern within the string representation of the BusinessHour’s start attribute. This method is particularly useful if the format of the time string is irregular or complicated.

Here’s an example:

import pandas as pd
import re

# Define business hours
bh = pd.offsets.BusinessHour(start='09:00')

# Use regex to find the time pattern
match = re.search(r"\d{2}:\d{2}", str(bh.start))

start_time_24h = match.group() if match else "Time not found"

print(start_time_24h)

Output:

09:00

By applying a regular expression to the string version of the start time, we extract the time in HH:MM format. The re.search() function looks for the first occurrence that fits the pattern, which in this case, is the 24-hour start time of our business hours.

Bonus One-Liner Method 5: Using the strftime() Function Directly on start

The last method offers a concise one-liner approach by directly applying the strftime() function to the start attribute within a list comprehension to format the business hour’s start time.

Here’s an example:

import pandas as pd

# Define business hours
bh = pd.offsets.BusinessHour(start='09:00')

# One-liner to get the start time in 24h format
start_time_24h = bh.start.strftime('%H:%M')

print(start_time_24h)

Output:

09:00

This compact code snippet applies the strftime() function directly to the start attribute, creating a clean and efficient one-liner to get the business hour start time in 24-hour format.

Summary/Discussion

  • Method 1: Built-in Attributes. Straightforward for users familiar with string formatting. Limited if additional transformations are needed.
  • Method 2: Using .strftime() with datetime. Versatile and uses standard datetime formatting. Slightly more verbose and requires a dummy date.
  • Method 3: String Slice. Quick and easy for well-formed strings. Fragile if the format changes or isn’t consistent.
  • Method 4: Regular Expressions. Robust against irregular time string formats. Can be overkill for simple time strings and runs slower than other methods.
  • Method 5: Direct strftime() One-liner. Efficient and succinct for quick operations. May lack readability for those unfamiliar with the strftime() function.