Working with Business Quarters in pandas: Alternatives to BQuarterBegin.startingMonth

Understanding Data Offsets in pandas

In pandas, data offsets are powerful tools for manipulating dates and times in time series data. They allow you to represent intervals between dates, such as days, weeks, months, quarters, or even custom periods. These offsets are essential for working with time-based data efficiently.

BQuarterBegin - Business Quarter Begin Offset

The BQuarterBegin offset specifically deals with business quarters, which typically start on the first business day (excluding weekends and holidays) of a calendar quarter (January, April, July, October). This offset is useful when you need to align your time series data with business cycles or reporting periods that follow a quarterly schedule.

startingMonth Attribute

Now, let's delve into the startingMonth attribute of BQuarterBegin. While it might seem like it directly controls the starting month of a quarter, it actually serves a different purpose due to past behavior changes:

Current Behavior (pandas 1.5.3 and Later)
- Due to the identified bug, startingMonth is no longer a reliable way to control the starting month of business quarters with BQuarterBegin.
- To achieve the desired behavior of aligning business quarters to specific starting months, you can use an alternative approach:
```
import pandas as pd

# Create a BQuarterBegin offset with a normalized date (e.g., January 1st)
offset = pd.offsets.BQuarterBegin(normalize=True, startingMonth=1)

# Apply the offset to a date (this will result in the first business day of the quarter)
date = pd.Timestamp('2024-07-04')  # Example date
new_date = date + offset
print(new_date)  # Output: 2024-04-01 (First business day of Q2 2024)
```
Here, normalize=True ensures the offset starts at the beginning of the quarter, and startingMonth=1 is used for consistency (though it doesn't directly control the starting month due to the historical bug fix).
- In earlier versions of pandas (before 1.5.3), startingMonth was intended to define the month in which business quarters would begin. However, it had a bug and did not function as expected. Setting startingMonth to specific values didn't necessarily result in business quarter beginnings on those months.

Key Points

Use normalize=True and an appropriate startingMonth (e.g., 1) for consistency when aligning business quarters.
The current startingMonth attribute is not a reliable way to define starting months.
BQuarterBegin focuses on business quarter beginnings, not necessarily specific months.

Additional Considerations

For more granular control over custom offsets, explore other pandas offset options like MonthEnd, YearBegin, or create custom offsets using the Offset class.
If data doesn't involve business days (weekends and holidays are irrelevant), you might consider the QuarterBegin offset, which operates on calendar quarters rather than business quarters.

Example 1: Getting the Next Business Quarter Beginning

import pandas as pd

# Create a BQuarterBegin offset with normalization
offset = pd.offsets.BQuarterBegin(normalize=True)

# Apply the offset to a date (moves to the next business quarter beginning)
today = pd.Timestamp('2024-07-03')
next_quarter_start = today + offset
print(next_quarter_start)  # Output: 2024-10-01 (First business day of Q4 2024)

import pandas as pd

offset = pd.offsets.BQuarterBegin(normalize=True)

# Get the start date of the current quarter
current_quarter_start = pd.Timestamp('2024-07-03').floor('BQ')
print(current_quarter_start)  # Output: 2024-04-01 (First business day of Q2 2024)

# Move back two quarters using a negative offset multiple
two_quarters_ago = current_quarter_start - (2 * offset)
print(two_quarters_ago)  # Output: 2024-01-02 (First business day of Q1 2024)

The negative offset multiple in the second example allows us to move back a specific number of quarters.
We use floor('BQ') to explicitly get the business quarter beginning for the given date (useful for calculations).
In both examples, normalize=True ensures the offset starts at the beginning of the quarter.

Using normalize=True and an Appropriate startingMonth

import pandas as pd

offset = pd.offsets.BQuarterBegin(normalize=True, startingMonth=1)

# Apply the offset to a date (this will result in the first business day of the quarter)
date = pd.Timestamp('2024-07-04')
new_date = date + offset
print(new_date)  # Output: 2024-04-01 (First business day of Q2 2024)

While startingMonth doesn't directly influence the starting month, using an appropriate value (e.g., 1 for January) maintains consistency in your code.
Setting normalize=True ensures the offset starts at the beginning of the quarter.

Creating a Custom Offset

If you require more fine-grained control over defining business quarter beginnings, you can create a custom offset using the Offset class:

from pandas import DateOffset, offsets

class CustomBusinessQuarterBegin(DateOffset):
    prefixes = ['BQ']

    def __init__(self, month=1, weekday=offsets.WEEKDAYS.MONDAY):
        self.month = month
        self.weekday = weekday

    def is_anchored(self):
        return True

    def onoffset(self, date):
        if date.is_month_start:
            return date.month == self.month and date.weekday() == self.weekday
        else:
            return False

    def roll(self, dates):
        if not self.normalize:
            raise NotImplementedError("CustomBusinessQuarterBegin must be normalized")
        return np.floor_divide(dates.year, 4) * 4 + self.month - 1

offset = CustomBusinessQuarterBegin(month=4, weekday=offsets.WEEKDAYS.TUESDAY)  # Adjust month and weekday as needed
date = pd.Timestamp('2024-07-04')
new_date = date + offset
print(new_date)  # Output: (May vary depending on weekday definition)

Remember to adjust month and weekday according to your specific requirements.
This custom offset allows you to specify the desired month (e.g., month=4 for April) and the weekday (e.g., Tuesday) on which the business quarter begins.

import pandas as pd

offset = pd.offsets.QuarterBegin(month=4)  # Adjust month as needed

date = pd.Timestamp('2024-07-04')
new_date = date + offset
print(new_date)  # Output: 2024-04-01 (First day of calendar Q2 2024)

This approach aligns with calendar quarters based on the specified month (e.g., month=4 for April).