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pandas.tseries.offsets.Hour.is_quarter_end: Demystifying the Non-Existent Method


Data Offsets in pandas

pandas provides the Offset class and its subclasses like Hour to represent time-based increments for working with dates and times. These offsets allow you to efficiently shift dates or time series data by a specific amount of time.

Hour Offset

The Hour offset represents a time difference of one hour. You can use it to move a date or datetime object forward or backward by a certain number of hours. For instance:

import pandas as pd

date = pd.to_datetime('2024-07-07 10:00')
one_hour_later = date + pd.offsets.Hour(1)  # Moves forward by 1 hour
print(one_hour_later)  # Output: 2024-07-07 11:00:00

Hour.is_quarter_end Method (Not Actually Available)

However, the Hour offset class does not have a method called is_quarter_end. This method is likely intended for the QuarterEnd offset, which deals with quarter-based time increments.

QuarterEnd Offset (For Checking Quarter Ends)

The QuarterEnd offset represents the end of a quarter (March-May, June-August, September-November, December-February). It has a method named is_quarter_end that you can use to check if a specific date or datetime object falls on a quarter end.

import pandas as pd

date = pd.to_datetime('2024-06-30')  # June 30th, a quarter end
quarter_end_check = pd.offsets.QuarterEnd().is_quarter_end(date)
print(quarter_end_check)  # Output: True

date = pd.to_datetime('2024-07-05')  # Not a quarter end
quarter_end_check = pd.offsets.QuarterEnd().is_quarter_end(date)
print(quarter_end_check)  # Output: False
  • QuarterEnd.is_quarter_end: Checks if a date/datetime object falls on a quarter end.
  • QuarterEnd offset: Represents the end of a quarter.
  • Hour.is_quarter_end (non-existent): Not a valid method for Hour.
  • Hour offset: Represents an hour-based time difference.

Example 1: Moving Dates Forward by Hours and Checking for Quarter End

import pandas as pd

# Create a starting date
date = pd.to_datetime('2024-06-28')  # Friday, June 28th

# Move forward 4 hours (within the same day)
four_hours_later = date + pd.offsets.Hour(4)
print(four_hours_later)  # Output: 2024-06-28 16:00:00

# Check if the new date is still in the same quarter
is_same_quarter = pd.offsets.QuarterEnd().is_quarter_end(date) == pd.offsets.QuarterEnd().is_quarter_end(four_hours_later)
print(is_same_quarter)  # Output: True (both dates are in Q2 2024)

# Move forward to the next day (potentially crossing a quarter boundary)
next_day = date + pd.offsets.Day(1)
print(next_day)  # Output: 2024-06-29 (Saturday, June 29th)

# Check if the next day is in the same quarter
is_same_quarter = pd.offsets.QuarterEnd().is_quarter_end(date) == pd.offsets.QuarterEnd().is_quarter_end(next_day)
print(is_same_quarter)  # Output: False (June 29th is in Q2, June 30th is the end of Q2)
  • Moving to the next day (next_day) might cross a quarter boundary. We use QuarterEnd().is_quarter_end to verify that they are not in the same quarter (June 29th is in Q2, June 30th is the end of Q2).
  • We create a starting date (date) and move it forward by 4 hours using Hour(4). The is_same_quarter check confirms that both dates are still within the same quarter (Q2 2024).
import pandas as pd

# Create a starting date within a quarter (e.g., July 1st)
date = pd.to_datetime('2024-07-01')  # Monday, July 1st (Q3)

# Get the next quarter end date (September 30th)
next_quarter_end = pd.to_datetime('2024-09-30')

# Calculate the number of hours needed to reach the next quarter end
hours_to_next_quarter = (next_quarter_end - date).total_seconds() / 3600

# Print the number of hours (adjust for daylight saving time if needed)
print(f"Hours to next quarter end: {hours_to_next_quarter:.2f}")  # Example output: 2160.00 (assuming no daylight saving time)
  • We calculate the total seconds between the dates and convert them to hours using total_seconds() / 3600. The result represents the number of hours needed to reach the next quarter end.
  • We define the next quarter end date (next_quarter_end).
  • We start with a date within a quarter (date).

Using QuarterEnd Offset

  • Use the is_quarter_end method on your hourly datetime object with the QuarterEnd offset. This checks if the hourly datetime falls on a quarter end.
  • Create a QuarterEnd offset object.
import pandas as pd

date = pd.to_datetime('2024-06-30 10:00')  # Hourly datetime
quarter_end_check = pd.offsets.QuarterEnd().is_quarter_end(date)
print(quarter_end_check)  # True (June 30th is a quarter end)

date = pd.to_datetime('2024-07-01 12:00')  # Not a quarter end
quarter_end_check = pd.offsets.QuarterEnd().is_quarter_end(date)
print(quarter_end_check)  # False

Vectorized Check with floor and DatetimeIndex

  • Check if the resulting values are equal to the quarter number for the last day of the quarter. This can be done using comparison or boolean indexing.
  • Use np.floor (or a similar function) to ensure you get whole quarter numbers.
  • Use integer division by 3 (number of months in a quarter) to get the quarter number for each hour.
  • Convert your hourly datetime data to a DatetimeIndex if it's not already one.
import pandas as pd
import numpy as np

data = pd.to_datetime(['2024-06-30 10:00', '2024-07-01 12:00', '2024-09-30 18:00'])
quarter_numbers = np.floor(data.month / 3)
is_quarter_end = quarter_numbers == data.dt.month.max() / 3
print(is_quarter_end)  # True, False, True
  • Performance
    For large datasets, the vectorized approach using DatetimeIndex operations might be faster. However, readability might be slightly lower.
  • Clarity
    The QuarterEnd offset with is_quarter_end is more readable for simple checks.