Build a circular month window in pandas: wrap-around DataFrame slicing with modulo for a fixed six-row result

Learn how to implement a circular window in pandas: wrap-around DataFrame slicing by month using modulo to return six rows with the selected month reliably.

When you let users pick a month from a slider and need to display a fixed window of rows around that choice, a straightforward slice quickly breaks at the boundaries. The dataset is cyclical by nature, so the window must wrap cleanly from December to January and still return a constant number of rows. Below is a concise way to make pandas return exactly six rows, with the chosen month consistently positioned in the resulting view.

Example dataset that illustrates the issue

The sample data contains functions, services, and months. The goal is to select six consecutive entries in a circular fashion, ensuring the selected month is included regardless of whether it sits at the beginning, in the middle, or at the end of the DataFrame.

import pandas as pd

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

frame = pd.DataFrame(payload)
print(frame)

A naive attempt to map months to hard-coded slices fails, is incomplete, and doesn’t wrap at year boundaries.

def slice_window(choice: str, frame_obj):
    if choice == "January":
        frame_obj.iloc[0:6,]
    if choice == "February":
        frame_obj.iloc[1:6,]
    if choice == "March":
        frame_obj.iloc[2:6,]

What’s actually going on

Static slices can’t guarantee both constant size and inclusion of the target month as the position changes across the DataFrame. Even worse, they break at edges, for example when the selection sits near the beginning or end. The key is to compute positions dynamically from the selected month and use modular arithmetic to roll the index so the window “goes in circle.”

Solution: compute a circular window from the selected month

The idea is simple. Find the row index of the chosen month, build a range that spans six consecutive indices around it, and apply modulo by the DataFrame length to wrap around. The example below places the selected month as the third entry in the six-row window.

import pandas as pd

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

frame = pd.DataFrame(payload)

target_mon = "January"
hit_idx = frame[frame["mon"] == target_mon].index[0]
window_ids = [(i % len(frame)) for i in range(hit_idx - 2, hit_idx + 4)]
window_df = frame.loc[window_ids]  # add .reset_index(drop=True) if needed
print(window_df)

Output shows exactly six rows, with the selected month in the third position, and seamless wrap-around.

       fn svc       mon
10  test11   E  November
11  test12  YU  December
0    test1   A   January
1    test2   B  February
2    test3  AO     March
3    test4   M     April

If you want the selected month to appear in a different place inside the six-row window, adjust the bounds of the range. Putting it first uses range(hit_idx, hit_idx + 6). Making it last uses range(hit_idx - 5, hit_idx + 1). Changing these bounds also lets you alter the number of rows if needed.

Why this matters

Interactive analytics often depend on predictable, stable windows of context around a chosen point. Hard-coded slices are brittle and fail at boundaries; a circular window built from indices guarantees consistent size, correct inclusion of the selected month, and uniform behavior across the dataset. This keeps UI expectations stable and avoids edge-case surprises.

Takeaways

Derive the index from the user’s choice, build the window relative to that position, and wrap the index with modulo to keep the selection continuous across DataFrame edges. Adjust the range bounds to control where the selected month appears within the window or to tune the window size. Keep the logic in a single flow to avoid undefined-name mistakes and ensure the index is computed before it is used.