Demystifying pandas.Series.dt.is_year_end: A Guide to Finding Year-End Dates

Functionality

• It returns a new Series or DatetimeIndex (depending on the input) with boolean values (True for year-ends, False otherwise).
• It efficiently checks for each element in the Series whether it represents the last day of the corresponding year.
• pandas.Series.dt.is_year_end is an attribute used with Series objects containing datetime-like data (e.g., dates, timestamps).

Breakdown

1. pandas.Series
   A fundamental data structure in pandas used to store one-dimensional labeled arrays. Elements can be of various data types, including datetime-like data.

2. .dt Accessor
   When working with datetime-like Series, the .dt accessor provides convenient methods to extract or manipulate date/time components.

3. .is_year_end Attribute
   This attribute specifically focuses on identifying year-ends within the datetime elements.

Example

import pandas as pd

# Create a Series with sample dates
dates = pd.to_datetime(['2023-12-31', '2024-02-14', '2024-12-31'])
date_series = pd.Series(dates)

# Check for year-ends
year_ends = date_series.dt.is_year_end

print(year_ends)

Output

0     True
1    False
2     True
dtype: bool

Key Points

• For checking the beginning of a year, use pandas.Series.dt.is_year_start.
• It's particularly useful for tasks like filtering data based on year-ends, calculating year-end statistics, or flagging specific year-end entries.
• pandas.Series.dt.is_year_end operates efficiently on vectorized data, allowing for quick year-end identification across large datasets.

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Filtering Year-End Data

This example filters the original date_series to keep only the year-end dates:

import pandas as pd

dates = pd.to_datetime(['2023-12-31', '2024-02-14', '2024-12-31'])
date_series = pd.Series(dates)

year_end_dates = date_series[date_series.dt.is_year_end]

print(year_end_dates)

Output

0    2023-12-31
2    2024-12-31
dtype: datetime64[ns]

Conditional Operations Based on Year-End

This code assigns a specific value ("Year End") to the date_series elements on year-ends, and keeps the original values otherwise:

import pandas as pd

dates = pd.to_datetime(['2023-12-31', '2024-02-14', '2024-12-31'])
date_series = pd.Series(dates)

flags = ["Year End" if dt.is_year_end else dt for dt in date_series]
flagged_series = pd.Series(flags)

print(flagged_series)

Output

0      Year End
1        2024-02-14
2      Year End
dtype: object

Counting Year-End Occurrences

This code calculates the total number of year-ends in the date_series:

import pandas as pd

dates = pd.to_datetime(['2023-12-31', '2024-02-14', '2024-12-31'])
date_series = pd.Series(dates)

year_end_count = date_series.dt.is_year_end.sum()

print(year_end_count)

2

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Manual Comparison with Year-End Date

import pandas as pd

def is_year_end(date):
  """Checks if a date object is the last day of its year."""
  return date.month == 12 and date.day == 31

dates = pd.to_datetime(['2023-12-31', '2024-02-14', '2024-12-31'])
date_series = pd.Series(dates)

year_ends = date_series.apply(is_year_end)

print(year_ends)

• It then applies this function to each element of the date_series using .apply().
• This approach defines a custom function is_year_end that checks if the month is December and the day is 31.

Drawbacks

• More verbose and error-prone compared to the built-in method.
• Less efficient than dt.is_year_end for large datasets.

Vectorized Comparison with Masks

import pandas as pd

dates = pd.to_datetime(['2023-12-31', '2024-02-14', '2024-12-31'])
date_series = pd.Series(dates)

# Create a boolean mask for year-ends
year_end_mask = (date_series.dt.month == 12) & (date_series.dt.day == 31)

year_ends = date_series[year_end_mask]

print(year_ends)

• Filters the date_series using this mask to get the year-ends.
• Combines these masks using & (bitwise AND) to create a single year-end mask.
• Creates boolean masks using .dt.month == 12 and .dt.day == 31 to identify elements with December and day 31.

Drawbacks

• While more efficient than the manual approach, it can still be less performant than dt.is_year_end for very large datasets.

• For performance and conciseness, especially on large datasets, pandas.Series.dt.is_year_end remains the recommended choice.
• If you're dealing with small datasets or clarity is your primary concern, you might consider the manual comparison or vectorized mask approaches.