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# pandas.Series.resample

Series.resample(rule, axis=<no_default>, closed=None, label=None, convention=<no_default>, kind=<no_default>, on=None, level=None, origin='start_day', offset=None, group_keys=False)

  Resample time-series data.

  Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the `on`/`level` keyword parameter.

  Parameters:
    ruleDateOffset, Timedelta or str

      The offset string or object representing target conversion.

    axis{0 or ‘index’, 1 or ‘columns’}, default 0

      Which axis to use for up- or down-sampling. For Series this parameter is unused and defaults to 0. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.

      Deprecated since version 2.0.0: Use frame.T.resample(…) instead.

    closed{‘right’, ‘left’}, default None

      Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

    label{‘right’, ‘left’}, default None

      Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ which all have a default of ‘right’.

    convention{‘start’, ‘end’, ‘s’, ‘e’}, default ‘start’

      For PeriodIndex only, controls whether to use the start or end of rule.

      Deprecated since version 2.2.0: Convert PeriodIndex to DatetimeIndex before resampling instead.

    kind{‘timestamp’, ‘period’}, optional, default None

      Pass ‘timestamp’ to convert the resulting index to a DateTimeIndex or ‘period’ to convert it to a PeriodIndex. By default the input representation is retained.

      Deprecated since version 2.2.0: Convert index to desired type explicitly instead.

    onstr, optional

      For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

    levelstr or int, optional

      For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.

    originTimestamp or str, default ‘start_day’

      The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following:

      - ‘epoch’: origin is 1970-01-01

      - ‘start’: origin is the first value of the timeseries

      - ‘start_day’: origin is the first day at midnight of the timeseries

      - ‘end’: origin is the last value of the timeseries

      - ‘end_day’: origin is the ceiling midnight of the last day

      Added in version 1.3.0.

      Note

      Only takes effect for Tick-frequencies (i.e. fixed frequencies like days, hours, and minutes, rather than months or quarters).

    offsetTimedelta or str, default is None

      An offset timedelta added to the origin.

    group_keysbool, default False

      Whether to include the group keys in the result index when using `.apply()` on the resampled object.

      Added in version 1.5.0: Not specifying `group_keys` will retain values-dependent behavior from pandas 1.4 and earlier (see pandas 1.5.0 Release notes for examples).

      Changed in version 2.0.0: `group_keys` now defaults to `False`.

  Returns:
    pandas.api.typing.Resampler

      `Resampler` object.

  See also

  `Series.resample`

    Resample a Series.

  `DataFrame.resample`

    Resample a DataFrame.

  `groupby`

    Group Series/DataFrame by mapping, function, label, or list of labels.

  `asfreq`

    Reindex a Series/DataFrame with the given frequency without grouping.

  Notes

  See the user guide for more.

  To learn more about the offset strings, please see this link.

  Examples

  Start by creating a series with 9 one minute timestamps.

      >>> index = pd.date_range('1/1/2000', periods=9, freq='min')
      >>> series = pd.Series(range(9), index=index)
      >>> series
      2000-01-01 00:00:00    0
      2000-01-01 00:01:00    1
      2000-01-01 00:02:00    2
      2000-01-01 00:03:00    3
      2000-01-01 00:04:00    4
      2000-01-01 00:05:00    5
      2000-01-01 00:06:00    6
      2000-01-01 00:07:00    7
      2000-01-01 00:08:00    8
      Freq: min, dtype: int64

  Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

      >>> series.resample('3min').sum()
      2000-01-01 00:00:00     3
      2000-01-01 00:03:00    12
      2000-01-01 00:06:00    21
      Freq: 3min, dtype: int64

  Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket `2000-01-01 00:03:00` contains the value 3, but the summed value in the resampled bucket with the label `2000-01-01 00:03:00` does not include 3 (if it did, the summed value would be 6, not 3).

      >>> series.resample('3min', label='right').sum()
      2000-01-01 00:03:00     3
      2000-01-01 00:06:00    12
      2000-01-01 00:09:00    21
      Freq: 3min, dtype: int64

  To include this value close the right side of the bin interval, as shown below.

      >>> series.resample('3min', label='right', closed='right').sum()
      2000-01-01 00:00:00     0
      2000-01-01 00:03:00     6
      2000-01-01 00:06:00    15
      2000-01-01 00:09:00    15
      Freq: 3min, dtype: int64

  Upsample the series into 30 second bins.

      >>> series.resample('30s').asfreq()[0:5]   # Select first 5 rows
      2000-01-01 00:00:00   0.0
      2000-01-01 00:00:30   NaN
      2000-01-01 00:01:00   1.0
      2000-01-01 00:01:30   NaN
      2000-01-01 00:02:00   2.0
      Freq: 30s, dtype: float64

  Upsample the series into 30 second bins and fill the `NaN` values using the `ffill` method.

      >>> series.resample('30s').ffill()[0:5]
      2000-01-01 00:00:00    0
      2000-01-01 00:00:30    0
      2000-01-01 00:01:00    1
      2000-01-01 00:01:30    1
      2000-01-01 00:02:00    2
      Freq: 30s, dtype: int64

  Upsample the series into 30 second bins and fill the `NaN` values using the `bfill` method.

      >>> series.resample('30s').bfill()[0:5]
      2000-01-01 00:00:00    0
      2000-01-01 00:00:30    1
      2000-01-01 00:01:00    1
      2000-01-01 00:01:30    2
      2000-01-01 00:02:00    2
      Freq: 30s, dtype: int64

  Pass a custom function via `apply`

      >>> def custom_resampler(arraylike):
      ...     return np.sum(arraylike) + 5
      ...
      >>> series.resample('3min').apply(custom_resampler)
      2000-01-01 00:00:00     8
      2000-01-01 00:03:00    17
      2000-01-01 00:06:00    26
      Freq: 3min, dtype: int64

  For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

      >>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
      ...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
      >>> df = pd.DataFrame(d)
      >>> df['week_starting'] = pd.date_range('01/01/2018',
      ...                                     periods=8,
      ...                                     freq='W')
      >>> df
         price  volume week_starting
      0     10      50    2018-01-07
      1     11      60    2018-01-14
      2      9      40    2018-01-21
      3     13     100    2018-01-28
      4     14      50    2018-02-04
      5     18     100    2018-02-11
      6     17      40    2018-02-18
      7     19      50    2018-02-25
      >>> df.resample('ME', on='week_starting').mean()
                     price  volume
      week_starting
      2018-01-31     10.75    62.5
      2018-02-28     17.00    60.0

  For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

      >>> days = pd.date_range('1/1/2000', periods=4, freq='D')
      >>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
      ...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
      >>> df2 = pd.DataFrame(
      ...     d2,
      ...     index=pd.MultiIndex.from_product(
      ...         [days, ['morning', 'afternoon']]
      ...     )
      ... )
      >>> df2
                            price  volume
      2000-01-01 morning       10      50
                 afternoon     11      60
      2000-01-02 morning        9      40
                 afternoon     13     100
      2000-01-03 morning       14      50
                 afternoon     18     100
      2000-01-04 morning       17      40
                 afternoon     19      50
      >>> df2.resample('D', level=0).sum()
                  price  volume
      2000-01-01     21     110
      2000-01-02     22     140
      2000-01-03     32     150
      2000-01-04     36      90

  If you want to adjust the start of the bins based on a fixed timestamp:

      >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
      >>> rng = pd.date_range(start, end, freq='7min')
      >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
      >>> ts
      2000-10-01 23:30:00     0
      2000-10-01 23:37:00     3
      2000-10-01 23:44:00     6
      2000-10-01 23:51:00     9
      2000-10-01 23:58:00    12
      2000-10-02 00:05:00    15
      2000-10-02 00:12:00    18
      2000-10-02 00:19:00    21
      2000-10-02 00:26:00    24
      Freq: 7min, dtype: int64

      >>> ts.resample('17min').sum()
      2000-10-01 23:14:00     0
      2000-10-01 23:31:00     9
      2000-10-01 23:48:00    21
      2000-10-02 00:05:00    54
      2000-10-02 00:22:00    24
      Freq: 17min, dtype: int64

      >>> ts.resample('17min', origin='epoch').sum()
      2000-10-01 23:18:00     0
      2000-10-01 23:35:00    18
      2000-10-01 23:52:00    27
      2000-10-02 00:09:00    39
      2000-10-02 00:26:00    24
      Freq: 17min, dtype: int64

      >>> ts.resample('17min', origin='2000-01-01').sum()
      2000-10-01 23:24:00     3
      2000-10-01 23:41:00    15
      2000-10-01 23:58:00    45
      2000-10-02 00:15:00    45
      Freq: 17min, dtype: int64

  If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

      >>> ts.resample('17min', origin='start').sum()
      2000-10-01 23:30:00     9
      2000-10-01 23:47:00    21
      2000-10-02 00:04:00    54
      2000-10-02 00:21:00    24
      Freq: 17min, dtype: int64

      >>> ts.resample('17min', offset='23h30min').sum()
      2000-10-01 23:30:00     9
      2000-10-01 23:47:00    21
      2000-10-02 00:04:00    54
      2000-10-02 00:21:00    24
      Freq: 17min, dtype: int64

  If you want to take the largest Timestamp as the end of the bins:

      >>> ts.resample('17min', origin='end').sum()
      2000-10-01 23:35:00     0
      2000-10-01 23:52:00    18
      2000-10-02 00:09:00    27
      2000-10-02 00:26:00    63
      Freq: 17min, dtype: int64

  In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

      >>> ts.resample('17min', origin='end_day').sum()
      2000-10-01 23:38:00     3
      2000-10-01 23:55:00    15
      2000-10-02 00:12:00    45
      2000-10-02 00:29:00    45
      Freq: 17min, dtype: int64