pandas resample 重采样

Posted 2020-12-03 jinqier

下方是pandas中resample方法的定义，帮助文档http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling中有更加详细的解释。

 

    def resample(self, 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):
        """
        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.（数据重采样和频率转换，数据必须有时间类型的索引列）

        Parameters
        ----------
        rule : string
            the offset string or object representing target conversion（代表目标转换的偏移量）
        axis : int, optional, default 0（操作的轴信息）
        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‘.（哪一个方向的间隔是关闭的，）
        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‘.（区间的哪一个方向的边界标签保留）
        convention : {‘start‘, ‘end‘, ‘s‘, ‘e‘}
            For PeriodIndex only, controls whether to use the start or end of
            `rule`
        kind: {‘timestamp‘, ‘period‘}, optional
            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.
        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.

            .. versionadded:: 0.19.0

        level : string or int, optional
            For a MultiIndex, level (name or number) to use for
            resampling.  Level must be datetime-like.

            .. versionadded:: 0.19.0

        Returns
        -------
        Resampler object

        Notes
        -----
        See the `user guide
        <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#resampling>`_
        for more.

        To learn more about the offset strings, please see `this link
        <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.

        Examples
        --------

        Start by creating a series with 9 one minute timestamps.（新建频率为1min的时间序列）

        >>> 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.（下采样为三分钟）

        >>> 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()（保留间隔的右侧标签，上一个结果是左侧标签）
        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