Pandas Resampling

How it works

A daily sum can turn several drop-offs into one daily total. Use min_count when an empty period should remain missing instead of appearing as an observed zero. A resampling rule is not evidence that the source had complete coverage for every interval. Keep observation counts alongside a total when completeness affects interpretation, and name the timezone used for calendar boundaries.

Run the code

Select Try in playground to load the complete editable source. Nothing executes until you choose Run. The browser uses Pyodide 0.27.7, Pandas 2.2.3 and NumPy 2.0.2. Each ordinary execution starts a fresh isolated runtime. The tables are fictional and do not use personal files.

For local work, use a separate virtual environment and run python resampling.py. Download the complete source. The reviewed local environment uses Python 3.12.14, Pandas 3.0.6 and NumPy 2.5.4; the browser uses Pyodide 0.27.7 with Pandas 2.2.3 and NumPy 2.0.2. Compatible examples are checked in both. See the environment setup. No Apache or database server is required.

Example

import pandas as pd
import numpy as np

index = pd.to_datetime(["2026-10-01 09:00", "2026-10-01 15:00", "2026-10-03 10:00"])
series = pd.Series([12., 8., 6.], index=index)
daily = series.resample("D", closed="left", label="left").sum(min_count=1)
print(daily.dropna().tolist())
print(daily.isna().tolist())

Expected result

[20.0, 6.0]
[False, True, False]

The unobserved second day remains a visible gap rather than a fabricated zero collection. The first day combines two observations under the stated left-closed daily bin rule. A separate coverage policy is still needed before calling a daily total complete.