cudf.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]#

Convert the frequency of (“resample”) the given time series data.

Parameters:

rule: str: The offset string representing the frequency to use. Note that DateOffset objects are not yet supported.
closed: {“right”, “left”}, default None: Which side of bin interval is closed. The default is “left” for all frequency offsets except for “M” and “W”, which 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” and “W”, which have a default of “right”.
on: str, optional: For a DataFrame, column to use instead of the index for resampling. Column must be a datetime-like.
level: str or int, optional: For a MultiIndex, level to use instead of the index for resampling. The level must be a datetime-like.

Returns:

A Resampler object

Examples

First, we create a time series with 1 minute intervals:

>>> index = cudf.date_range(start="2001-01-01", periods=10, freq="1T")
>>> sr = cudf.Series(range(10), index=index)
>>> sr
2001-01-01 00:00:00    0
2001-01-01 00:01:00    1
2001-01-01 00:02:00    2
2001-01-01 00:03:00    3
2001-01-01 00:04:00    4
2001-01-01 00:05:00    5
2001-01-01 00:06:00    6
2001-01-01 00:07:00    7
2001-01-01 00:08:00    8
2001-01-01 00:09:00    9
dtype: int64

Downsampling to 3 minute intervals, followed by a “sum” aggregation:

>>> sr.resample("3T").sum()
2001-01-01 00:00:00     3
2001-01-01 00:03:00    12
2001-01-01 00:06:00    21
2001-01-01 00:09:00     9
dtype: int64

Use the right side of each interval to label the bins:

>>> sr.resample("3T", label="right").sum()
2001-01-01 00:03:00     3
2001-01-01 00:06:00    12
2001-01-01 00:09:00    21
2001-01-01 00:12:00     9
dtype: int64

Close the right side of the interval instead of the left:

>>> sr.resample("3T", closed="right").sum()
2000-12-31 23:57:00     0
2001-01-01 00:00:00     6
2001-01-01 00:03:00    15
2001-01-01 00:06:00    24
dtype: int64

Upsampling to 30 second intervals:

>>> sr.resample("30s").asfreq()[:5]  # show the first 5 rows
2001-01-01 00:00:00       0
2001-01-01 00:00:30    <NA>
2001-01-01 00:01:00       1
2001-01-01 00:01:30    <NA>
2001-01-01 00:02:00       2
dtype: int64

Upsample and fill nulls using the “bfill” method:

>>> sr.resample("30s").bfill()[:5]
2001-01-01 00:00:00    0
2001-01-01 00:00:30    1
2001-01-01 00:01:00    1
2001-01-01 00:01:30    2
2001-01-01 00:02:00    2
dtype: int64

Resampling by a specified column of a Dataframe:

>>> df = cudf.DataFrame({
...     "price": [10, 11, 9, 13, 14, 18, 17, 19],
...     "volume": [50, 60, 40, 100, 50, 100, 40, 50],
...     "week_starting": cudf.date_range(
...         "2018-01-01", periods=8, freq="7D"
...     )
... })
>>> df
price  volume week_starting
0     10      50    2018-01-01
1     11      60    2018-01-08
2      9      40    2018-01-15
3     13     100    2018-01-22
4     14      50    2018-01-29
5     18     100    2018-02-05
6     17      40    2018-02-12
7     19      50    2018-02-19
>>> df.resample("M", on="week_starting").mean()
               price     volume
week_starting
2018-01-31      11.4  60.000000
2018-02-28      18.0  63.333333

Pandas Compatibility Note

pandas.DataFrame.resample(), pandas.Series.resample()

Note that the dtype of the index (or the ‘on’ column if using ‘on=’) in the result will be of a frequency closest to the resampled frequency. For example, if resampling from nanoseconds to milliseconds, the index will be of dtype ‘datetime64[ms]’.