ies. DataFrame.resample : Resample a DataFrame. groupby : Group {klass} by mapping, function, label, or list of labels. asfreq : Reindex a {klass} 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 ``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('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 r