Select a six-month pandas DataFrame window by month without wraparound, preserving chronological order

Learn how to extract six consecutive rows around a target month in a pandas DataFrame without wraparound. Preserve order using clamped slices with iloc/loc.

Extracting a six-month window around a selected month in a pandas DataFrame sounds simple until the year boundary gets involved. A common approach with modulo arithmetic returns the right rows but breaks the chronological order, which is often the key requirement. Below is a minimal, reproducible pattern of the issue and a clean way to fix it.

Reproducing the problem

The goal is to select six consecutive rows such that the chosen month is present in the result. The following code uses wraparound indexing and produces an out-of-order result for boundary cases like January.

import pandas as pd

rows = {
    "function": ["test1","test2","test3","test4","test5","test6","test7","test8","test9","test10","test11","test12"],
    "service": ["A", "B", "AO", "M" ,"A", "PO", "MP", "YU", "Z", "R", "E", "YU"],
    "month": ["January","February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]
}

frame = pd.DataFrame(rows)

target_month = "January"
anchor_idx = frame[frame["month"] == target_month].index[0]
wrap_indices = [i % len(frame) for i in range(anchor_idx - 2, anchor_idx + 4)]
window_view = frame.loc[wrap_indices]  # add .reset_index(drop=True) if needed
print(window_view)

For January, this returns November and December first, then January–April. The selection is correct in terms of membership, but the order is not chronological.

Why the order breaks

The approach builds a list of positions around the match and uses modulo to keep them in range. When the selected month sits near the start of the DataFrame, negative offsets wrap to the end, so the first rows in the result come from the previous year tail. Since DataFrame.loc with a list preserves the list’s order, the output shows the tail first, then the beginning of the year, which violates the expected month progression.

Solution: a non-wrapping window anchored by the match

The fix is to avoid wraparound completely. Compute the index of the selected month, clamp the start of the window so it stays within the DataFrame bounds, and slice a six-row span in ascending index order. This preserves chronological order for all cases, including January and December.

import numpy as np

picked_idx = frame[frame["month"] == target_month].index[0]

start_idx = np.clip(picked_idx, 0, len(frame) - 6)

six_month_span = frame.loc[start_idx : start_idx + 5]
print(six_month_span)

Another variant uses a positional lookup and selects by position. If the value is guaranteed to exist, taking the first True with numpy and slicing by position is concise.

import numpy as np

first_hit = np.argmax(frame["month"] == target_month)

begin = np.clip(first_hit, 0, len(frame) - 6)

result_block = frame.iloc[begin : begin + 6]
print(result_block)

Both versions ensure the six rows are contiguous in the original order. If you prefer a clean 0-based index in the output, you can append .reset_index(drop=True).

Why this matters

When filtering time-like sequences, implicit wraparound quietly breaks chronological guarantees and leads to confusing downstream behavior, especially in reporting and visualization. A stable, non-wrapping slice ensures the selected month is included while preserving the natural progression. It also defines consistent edge behavior: January extends forward, December extends backward, and middle months anchor the window without surprises.

Takeaways

When you need a sliding window around a matched value, avoid modulo-based wraparound if output order matters. Derive the matching position, clamp the start to a valid range, and take a contiguous slice that yields exactly six rows. Rely on label-based selection if you have a default integer index, or positional selection when you prefer direct indexing. For presentation, optionally normalize indexing in the result to keep things tidy.