pandas.Series.resample   Series.resample(rule, axis=0, closed=None, label=None, convention='start', kind=None, loffset=None, base=None, on=None, level=None, origin='start_day', offset=None)[source]
 
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 
 
rule:DateOffset, 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 will default to 0, i.e. along the rows. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.  
closed:{‘right’, ‘left’}, default None


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’}, default None


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’}, default ‘start’


For PeriodIndex only, controls whether to use the start or end of rule.  
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.  
loffset:timedelta, default None


Adjust the resampled time labels.  Deprecated since version 1.1.0: You should add the loffset to the df.index after the resample. See below.   
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.  Deprecated since version 1.1.0: The new arguments that you should use are ‘offset’ or ‘origin’.   
on:str, optional


For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.  
level:str or int, optional


For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.  
origin:Timestamp 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   New in version 1.1.0.   ‘end’: origin is the last value of the timeseries ‘end_day’: origin is the ceiling midnight of the last day   New in version 1.3.0.   
offset:Timedelta or str, default is None


An offset timedelta added to the origin.  New in version 1.1.0.     Returns 
 pandas.core.Resampler

Resampler object.      See also  Series.resample

Resample a Series.  DataFrame.resample

Resample a DataFrame.  groupby

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

Reindex a Series 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='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(arraylike):
...     return np.sum(arraylike) + 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. Resample a year by quarter using ‘start’ convention. Values are assigned to the first quarter of the period. 
>>> 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
>>> s.resample('Q', convention='start').asfreq()
2012Q1    1.0
2012Q2    NaN
2012Q3    NaN
2012Q4    NaN
2013Q1    2.0
2013Q2    NaN
2013Q3    NaN
2013Q4    NaN
Freq: Q-DEC, dtype: float64
  Resample quarters by month using ‘end’ convention. Values are assigned to the last month of the period. 
>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
...                                                   freq='Q',
...                                                   periods=4))
>>> q
2018Q1    1
2018Q2    2
2018Q3    3
2018Q4    4
Freq: Q-DEC, dtype: int64
>>> q.resample('M', convention='end').asfreq()
2018-03    1.0
2018-04    NaN
2018-05    NaN
2018-06    2.0
2018-07    NaN
2018-08    NaN
2018-09    3.0
2018-10    NaN
2018-11    NaN
2018-12    4.0
Freq: M, dtype: float64
  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('M', 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: 7T, 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: 17T, 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: 17T, 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: 17T, 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: 17T, 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: 17T, 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: 17T, 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: 17T, dtype: int64
  To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2: 
>>> ts.resample('17min', offset='2min').sum()
2000-10-01 23:16:00     0
2000-10-01 23:33:00     9
2000-10-01 23:50:00    36
2000-10-02 00:07:00    39
2000-10-02 00:24:00    24
Freq: 17T, dtype: int64
  To replace the use of the deprecated loffset argument: 
>>> from pandas.tseries.frequencies import to_offset
>>> loffset = '19min'
>>> ts_out = ts.resample('17min').sum()
>>> ts_out.index = ts_out.index + to_offset(loffset)
>>> ts_out
2000-10-01 23:33:00     0
2000-10-01 23:50:00     9
2000-10-02 00:07:00    21
2000-10-02 00:24:00    54
2000-10-02 00:41:00    24
Freq: 17T, dtype: int64