# pandas.Series.resample

`Series.``resample`(rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)

  Convenience method for frequency conversion and resampling of time series. Object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or pass datetime-like values to the on or level keyword.

  Parameters:

  rule : string

  > the offset string or object representing target conversion

  axis : int, optional, default 0

  closed : {‘right’, ‘left’}

  > Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

  label : {‘right’, ‘left’}

  > Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

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

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

  loffset : timedelta

  > Adjust the resampled time labels

  base : int, default 0

  > For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for ‘5min’ frequency, base could range from 0 through 4. Defaults to 0

  on : string, optional

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

  > New in version 0.19.0.

  level : string or int, optional

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

  > New in version 0.19.0.

  Notes

  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='T')
      >>> 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: T, dtype: int64

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

      >>> series.resample('3T').sum()
      2000-01-01 00:00:00     3
      2000-01-01 00:03:00    12
      2000-01-01 00:06:00    21
      Freq: 3T, 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). To include this value close the right side of the bin interval as illustrated in the example below this one.

      >>> series.resample('3T', 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: 3T, dtype: int64

  Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

      >>> series.resample('3T', 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: 3T, 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 `pad` method.

      >>> series.resample('30S').pad()[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(array_like):
      ...     return np.sum(array_like)+5

      >>> series.resample('3T').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: 3T, dtype: int64

  For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

      >>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
                                                      freq='A',
                                                      periods=2))
      >>> s
      2012    1
      2013    2
      Freq: A-DEC, dtype: int64

  Resample by month using ‘start’ convention. Values are assigned to the first month of the period.

      >>> s.resample('M', convention='start').asfreq().head()
      2012-01    1.0
      2012-02    NaN
      2012-03    NaN
      2012-04    NaN
      2012-05    NaN
      Freq: M, dtype: float64

  Resample by month using ‘end’ convention. Values are assigned to the last month of the period.

      >>> s.resample('M', convention='end').asfreq()
      2012-12    1.0
      2013-01    NaN
      2013-02    NaN
      2013-03    NaN
      2013-04    NaN
      2013-05    NaN
      2013-06    NaN
      2013-07    NaN
      2013-08    NaN
      2013-09    NaN
      2013-10    NaN
      2013-11    NaN
      2013-12    2.0
      Freq: M, dtype: float64

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

      >>> df = pd.DataFrame(data=9*[range(4)], columns=['a', 'b', 'c', 'd'])
      >>> df['time'] = pd.date_range('1/1/2000', periods=9, freq='T')
      >>> df.resample('3T', on='time').sum()
                           a  b  c  d
      time
      2000-01-01 00:00:00  0  3  6  9
      2000-01-01 00:03:00  0  3  6  9
      2000-01-01 00:06:00  0  3  6  9

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

      >>> time = pd.date_range('1/1/2000', periods=5, freq='T')
      >>> df2 = pd.DataFrame(data=10*[range(4)],
                             columns=['a', 'b', 'c', 'd'],
                             index=pd.MultiIndex.from_product([time, [1, 2]])
                             )
      >>> df2.resample('3T', level=0).sum()
                           a  b   c   d
      2000-01-01 00:00:00  0  6  12  18
      2000-01-01 00:03:00  0  4   8  12

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