Time series and windows Updated November 2026

resample

df.resample("ME").sum() groups rows on a DatetimeIndex into calendar months and sums each one; any frequency alias works ("D", "W", "QE", "YE"). Use on="date_col" when the dates are in a column, or Grouper inside groupby to combine it with other keys.

Syntax

df.resample(rule, on=None, closed=None, label=None).agg_method()

Part	What it is
`rule`	Frequency alias: "D" day, "W" week, "ME" month end, "MS" month start, "QE" quarter end, "YE" year end, "h" hour
`on`	Name of a datetime column to use instead of the index

Examples

import pandas as pd

df = pd.DataFrame({"date": pd.to_datetime(["2024-01-05", "2024-01-20", "2024-02-03", "2024-02-28"]),
                   "sales": [100, 50, 80, 20]})
df.resample("ME", on="date")["sales"].sum()

Result: date 2024-01-31 150 2024-02-29 100 Freq: ME, Name: sales, dtype: int64

df.set_index("date")["sales"].resample("MS").agg(["sum", "count"])

Result: sum count date 2024-01-01 150 2 2024-02-01 100 2

MS labels each bucket with the first day of the month.

s = pd.Series([1, 3], index=pd.to_datetime(["2024-01-01", "2024-01-03"]))
s.resample("D").ffill()

Result: 2024-01-01 1 2024-01-02 1 2024-01-03 3 Freq: D, dtype: int64

Upsampling to daily and forward filling the gap.

Common mistakes

TypeError: Only valid with DatetimeIndex: set the date column as the index, or pass on="date".
FutureWarning: 'M' is deprecated: pandas 2.2 renamed the aliases to "ME", "QE", "YE" and lowercase "h", "min".
Empty months show NaN (for mean) or 0 (for sum) because resample fills in every period; filter them out if unwanted.

Practice resample

These drills check your answer on live data, free, no account needed.

Monthly totals with resampleTotal `amount` per calendar month with `resample`, labeling each month by its first day (rule "MS"). Months with no transactions must appear with a total of 0.

Questions

resample or groupby with dt.month?

resample creates a proper time axis including empty periods and labels them with dates; groupby(df.date.dt.to_period("M")) is fine for simple monthly totals.

How do I resample per customer?

df.groupby(["customer", pd.Grouper(key="date", freq="ME")])["sales"].sum().

How do I resample to the start of the week?

Use "W-MON" with label="left", closed="left" so each bucket is labeled by its Monday.

pd.to_datetimeHow to parse dates in pandas with pd.to_datetime: formats, day-first dates, coercing bad values, and extracting year, month and weekday with the dt accessor.
rollingHow to compute a moving average in pandas with rolling: window size, min_periods to avoid leading NaN, centered windows, rolling sum and std, time windows.
groupbyHow groupby works in pandas: group by one or more columns, apply sum, mean, count or size, get a flat table with as_index=False, and iterate over groups.
shift and diffHow to use shift and diff in pandas to get the previous or next row, compute the change and percent change between periods, and lag within groups.