How to resample time series data in pandas

Event-level tables often contain several transactions inside one reporting period, while summaries and charts need a single row for each interval. pandas can collapse those irregular timestamps into calendar-aligned buckets without moving the timestamp column into the index permanently.

The DataFrame.resample() method works like a time-based groupby. The on=“sold_at” argument selects a datetime column for bucketing, and named aggregations produce separate daily revenue and order-count columns.

Daily bins use the left edge for labels and membership by default, but weekly and period-end offsets can use the right edge. Explicit label=“left” and closed=“left” values keep each displayed date aligned with the transactions from that calendar day.

1. Save the timestamped sales rows and datetime conversion in resample_sales.py.

   resample_sales.py

   import pandas as pd
    
   sales = pd.DataFrame(
       {
           "sold_at": [
               "2026-06-17 08:15",
               "2026-06-17 11:40",
               "2026-06-18 09:05",
               "2026-06-18 13:10",
               "2026-06-18 18:30",
               "2026-06-19 10:20",
           ],
           "revenue": [120, 95, 180, 245, 400, 160],
       }
   )
   sales["sold_at"] = pd.to_datetime(sales["sold_at"])

2. Add the daily resampling block after the timestamp conversion.

   daily = sales.resample(
       "D", on="sold_at", label="left", closed="left"
   ).agg(
       revenue=("revenue", "sum"),
       orders=("revenue", "size"),
   )
   daily.index.name = "sales_day"

   The “D” rule creates one calendar-day bucket. on=“sold_at” leaves the original timestamp column available in sales instead of making it the permanent row index.

3. Append the assertions and result output after the daily aggregation.

   assert daily.index.freqstr == "D"
   assert int(daily.loc["2026-06-18", "revenue"]) == 825
   assert int(daily.loc["2026-06-18", "orders"]) == 3
   assert int(daily["revenue"].sum()) == int(sales["revenue"].sum())
    
   print(daily.to_string())
   print()
   print("2026-06-18 revenue:", int(daily.loc["2026-06-18", "revenue"]))
   print("2026-06-18 orders:", int(daily.loc["2026-06-18", "orders"]))
   print("source revenue:", int(sales["revenue"].sum()))
   print("resampled revenue:", int(daily["revenue"].sum()))

   The known June 18 values check bucket membership, while the final assertion checks that resampling preserved the complete revenue total.

4. Run resample_sales.py to display the asserted daily buckets.

   $ python3 resample_sales.py
               revenue  orders
   sales_day                  
   2026-06-17      215       2
   2026-06-18      825       3
   2026-06-19      160       1
    
   2026-06-18 revenue: 825
   2026-06-18 orders: 3
   source revenue: 1200
   resampled revenue: 1200