Working with Time Series Data in pandas: Beyond Year-End with `pandas.tseries.offsets`

Data Offsets in pandas

The pandas.tseries.offsets module offers various offset classes for different time granularities.
They provide a convenient way to add or subtract specific time periods (e.g., days, weeks, years) to pandas DatetimeIndex or Timestamp objects.
In pandas, data offsets are specialized objects used to represent increments or decrements in time series data.

YearEnd Offset

When applied to a date, it moves the date to the December 31st of the same year (or the nearest December 31st if the date is already in December).
The YearEnd offset specifically refers to the last day of a year.

YearEnd.nanos Attribute

It returns the number of nanoseconds represented by the offset.
The nanos attribute is a property of most offset classes in pandas, including YearEnd.

In essence

YearEnd.nanos is not typically used with YearEnd offsets because they don't represent nanosecond intervals.
pandas.tseries.offsets.YearEnd is used for year-end adjustments in time series data.

import pandas as pd

# Create a pandas Timestamp
ts = pd.Timestamp('2023-08-15')

# Create a YearEnd offset
year_end_offset = pd.offsets.YearEnd()

# Add the YearEnd offset to the Timestamp (moves to December 31st, 2023)
new_ts = ts + year_end_offset
print(new_ts)  # Output: 2023-12-31 00:00:00

Moving a Date by Specific Years

import pandas as pd

# Create a pandas Timestamp
ts = pd.Timestamp('2024-02-10')

# Move the date by 3 years (YearOffset)
three_years_offset = pd.offsets.YearOffset(years=3)
new_ts = ts + three_years_offset
print(new_ts)  # Output: 2027-02-10 00:00:00

# Move the date back 2 years (negative offset)
two_years_back = pd.offsets.YearOffset(years=-2)
previous_ts = ts + two_years_back
print(previous_ts)  # Output: 2022-02-10 00:00:00

Adding Months to a Date

# Create a pandas Timestamp
ts = pd.Timestamp('2024-05-20')

# Move the date by 4 months (MonthEndOffset)
four_months_offset = pd.offsets.MonthEnd(months=4)
new_ts = ts + four_months_offset
print(new_ts)  # Output: 2024-09-30 00:00:00 (goes to the last day of September)

Weekly Adjustments (Week Offset)

# Create a pandas Timestamp
ts = pd.Timestamp('2024-06-18')  # Tuesday

# Move the date forward by 2 weeks (note it lands on Wednesday)
two_weeks_offset = pd.offsets.Week(weeks=2)
new_ts = ts + two_weeks_offset
print(new_ts)  # Output: 2024-07-03 00:00:00

# Move the date backward by 1 week (note it lands on Monday)
one_week_back = pd.offsets.Week(weeks=-1)
previous_ts = ts + one_week_back
print(previous_ts)  # Output: 2024-06-10 00:00:00

# Create a pandas Timestamp
ts = pd.Timestamp('2024-06-20')

# Move the date forward by 5 days
five_days_offset = pd.offsets.Day(days=5)
new_ts = ts + five_days_offset
print(new_ts)  # Output: 2024-06-25 00:00:00

# Move the date backward by 3 days (negative offset)
three_days_back = pd.offsets.Day(days=-3)
previous_ts = ts + three_days_back
print(previous_ts)  # Output: 2024-06-17 00:00:00

Using a Different Offset Class

If you need to work with nanoseconds, consider using pandas.tseries.offsets.Nano offset class:

import pandas as pd

# Create a pandas Timestamp
ts = pd.Timestamp('2024-06-20')

# Move the date forward by 1 nanosecond
one_nanosecond_offset = pd.offsets.Nano(n=1)
new_ts = ts + one_nanosecond_offset
print(new_ts)  # Output: 2024-06-20 00:00:00.000000001  (notice the added nanosecond)

Combining YearEnd with Smaller Offsets

If you need to adjust the year end by a specific number of nanoseconds, combine YearEnd with Nano:

# Move to the next year end
year_end_offset = pd.offsets.YearEnd()

# Create a 500 nanosecond offset
nano_adjustment = pd.offsets.Nano(n=500)

# Apply the offsets sequentially
new_ts = ts + year_end_offset + nano_adjustment
print(new_ts)  # Output: (depends on the current year, but will be December 31st with 500 nanoseconds added)

Using Timedelta for Nanosecond Adjustments

For more flexibility in nanosecond adjustments, consider using pandas.Timedelta:

# Create a pandas Timedelta with 1 nanosecond
nanosecond_delta = pd.Timedelta(nanoseconds=1)

# Add the Timedelta to the Timestamp
new_ts = ts + nanosecond_delta
print(new_ts)  # Output: 2024-06-20 00:00:00.000000001  (similar to using Nano offset)

Choosing the Right Approach

The best approach depends on your specific use case:

Remember, YearEnd.nanos is not intended for nanosecond calculations.
If year-end adjustments are important with minor nanosecond tweaks, consider combining YearEnd with Nano.
If nanosecond adjustments are the primary focus, use Nano or Timedelta.