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)
[source]
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
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 kind: {‘timestamp’, ‘period’}, optional Pass ‘timestamp’ to convert the resulting index to a 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. |
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Returns: |
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See also
groupby
See the user guide for more.
To learn more about the offset strings, please see this link.
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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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Series.resample.html