How to Flag Missing or Incomplete Years in Pandas Without Mistaking the Current Year

Learn a concise pandas approach to detect missing and partial years in asset timelines, set a months threshold, and exclude the current year in backfill.

When you analyze time-based operational data per asset, a recurring task is to detect years with no activity and distinguish them from years with incomplete activity. In a vehicle timeline, this often shows up as missing values before the first real record and partial months in the first recorded year. The goal is to systematically identify those “gaps” so downstream logic can fill them in, without falsely flagging the current, still-in-progress year.

Sample data and the problem it exposes

Consider a single vehicle whose yearly history is stored in a pandas DataFrame. Earlier years have no entries, the earliest recorded year may be partial, and the most recent year is ongoing and therefore also partial. We need to return the list of missing or incomplete years that should be filled, but never include the current year.

import pandas as pd
import numpy as np
records_df = pd.DataFrame({
  "Vehicle Type": ["van", "van", "van", "van", "van", "van", "van"],
  "Vehicle ID": ["ABC", "ABC", "ABC", "ABC", "ABC", "ABC", "ABC"],
  "Year": [1, 2, 3, 4, 5, 6, 7],
  "Earliest Fact": [pd.NaT, pd.NaT, "2018-04-18", "2019-01-02", "2020-01-02", "2021-01-01", "2022-01-01"],
  "Latest Fact": [pd.NaT, pd.NaT, "2019-01-01", "2020-01-01", "2020-12-31", "2021-12-31", "2023-01-01"],
  "Fact History": [np.nan, np.nan, 5, 11.7, 12, 12, 5.7],
  "Days Worked": [np.nan, np.nan, 100, 256, 273, 300, 94],
  "Days Available": [np.nan, np.nan, 130, 272, 290, 320, 141]
})

Here, the first two years are empty. The third year is the first year with data but it is not a full year. The seventh year is the current one and is also partial by its nature; it must not be treated as missing.

What exactly is missing and why

We want all years up to and including the first recorded year if that first year is partial, otherwise we want all years strictly before it. This stricter rule prevents accidentally including the current year. The simplest way to decide if a year was “full” is to look at the months of history: if the earliest recorded year has fewer than a chosen threshold of months, we consider that year incomplete and include it in the output along with all prior empty years. A practical threshold follows from the observed data with values like 11.7 in “Fact History”, so a cutoff of 11 months serves to separate “effectively full” from “partial”.

A concise way to get the missing years

The following snippet extracts the earliest worked year, checks if it is partial using a months threshold, computes a cutoff, and returns the target list of years. It avoids verbose joins and group-bys while preserving the intended logic.

# Because 11.7 appears in "Fact History", use this cutoff to decide if a year is considered full
FULL_YEAR_MIN_MONTHS = 11
active_years = records_df.query("not `Fact History`.isna()")
first_observed_row = active_years.query("`Year` == `Year`.min()").iloc[0]
first_year_is_partial = bool(first_observed_row["Fact History"] < FULL_YEAR_MIN_MONTHS)
first_observed_year = first_observed_row["Year"]
boundary_year = first_observed_year - (0 if first_year_is_partial else 1)
timeline_years = records_df["Year"].tolist()
missing_year_list = [y for y in timeline_years if y <= boundary_year]
print(missing_year_list)

This yields all prior empty years, and includes the first recorded year only if it was not full. The most recent year is not touched, because the boundary is calculated solely from the earliest recorded year.

If you prefer to keep the selection inside pandas instead of a Python list comprehension, you can filter with a query and then extract the values:

subset_df = records_df.query("`Year` <= @boundary_year")
year_values = subset_df["Year"].tolist()
print(year_values)

Why this distinction matters

Downstream filling logic often needs a clean, deterministic set of years to impute or backfill. Treating the earliest partial year as incomplete, while never labeling the current year as missing, keeps the data pipeline consistent. You avoid accidental leakage of “in-progress” signals into your gap detection, and you get a reproducible rule for historic backfill.

Practical takeaways

Start by isolating years with real data and find the earliest of them. Decide whether that year counts as full or partial using a months-worked threshold that aligns with the observed values. Derive a boundary and select all years up to that boundary. This targeted approach is compact, avoids fragile joining logic, and maps directly to how the data is recorded. With the resulting year list, you can safely feed your imputation or fill function and keep the current year out of the way until it is complete.