bigframes.pandas.Series.resample#

Series.resample(rule: str, *, closed: Literal['right', 'left'] | None = None, label: Literal['right', 'left'] | None = None, level: str | int | Sequence[str | int] | None = None, origin: Timestamp | datetime | datetime64 | int | float | str | Literal['epoch', 'start', 'start_day', 'end', 'end_day'] = 'start_day') → SeriesGroupBy[source]#

Resample time-series data.

Examples:

>>> import bigframes.pandas as bpd
>>> data = {
...     "timestamp_col": pd.date_range(
...         start="2021-01-01 13:00:00", periods=30, freq="1s"
...     ),
...     "int64_col": range(30),
... }
>>> s = bpd.DataFrame(data).set_index("timestamp_col")
>>> s.resample(rule="7s", origin="epoch").min()
                    int64_col
2021-01-01 12:59:56          0
2021-01-01 13:00:03          3
2021-01-01 13:00:10         10
2021-01-01 13:00:17         17
2021-01-01 13:00:24         24

[5 rows x 1 columns]
Parameters:

  • rule (str) – The offset string representing target conversion. Offsets ‘ME’, ‘YE’, ‘QE’, ‘BME’, ‘BA’, ‘BQE’, and ‘W’ are not supported.

  • closed (Literal['left'] | None) – Which side of bin interval is closed. The default is ‘left’ for all supported frequency offsets.

  • label (Literal['right'] | Literal['left'] | None) – Which bin edge label to label bucket with. The default is ‘left’ for all supported frequency offsets.

  • on (str, default None) – For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

  • level (str or int, default None) – For a MultiIndex, level (name or number) to use for resampling. level must be datetime-like.

  • origin (str, default 'start_day') – The timestamp on which to adjust the grouping. 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 Origin values ‘end’ and ‘end_day’ are not supported.

Returns:

SeriesGroupBy object.

Return type:

SeriesGroupBy

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