Series.
resample
Resample time-series data.
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.
The offset string or object representing target conversion.
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.
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’.
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’.
For PeriodIndex only, controls whether to use the start or end of rule.
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.
Adjust the resampled time labels.
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.
For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.
For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.
See also
groupby
Group by mapping, function, label, or list of labels.
Series.resample
Resample a Series.
DataFrame.resample
Resample a DataFrame.
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.
2000-01-01 00:03:00
>>> 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.
NaN
pad
>>> 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.
bfill
>>> 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
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.
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 = dict({'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 = dict({'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