Detailed Explanation of resample Method in Pandas

resample in Pandas, resampling, is a method to re-process the original sample, and it is a convenient method to re-sample and frequency convert the conventional time series data.

The format of the method is:

DataFrame.resample(rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start',kind=None, loffset=None, limit=None, base=0)

The detailed explanation of the parameters is:

参数	说明
freq	表示重采样频率，例如‘M'、‘5min'，Second(15)
how='mean'	用于产生聚合值的函数名或数组函数，例如‘mean'、‘ohlc'、np.max等，默认是‘mean'，其他常用的值由：‘first'、‘last'、‘median'、‘max'、‘min'
axis=0	默认是纵轴，横轴设置axis=1
fill_method = None	升采样时如何插值，比如‘ffill'、‘bfill'等
closed = ‘right'	在降采样时，各时间段的哪1段是闭合的，‘right'或‘left'，默认‘right'
label= ‘right'	在降采样时，如何设置聚合值的标签，例如，9：30-9：35会被标记成9：30还是9：35,默认9：35
loffset = None	面元标签的时间校正值，比如‘-1s'或Second(-1)用于将聚合标签调早1秒
limit=None	在向前或向后填充时，允许填充的最大时期数
kind = None	聚合到时期（‘period'）或时间戳（‘timestamp'），默认聚合到时间序列的索引类型
convention = None	当重采样时期时，将低频率转换到高频率所采用的约定（start或end）。默认‘end'

First, create an Series with a sampling frequency of 1 minute.

>>> 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

Reduce the sampling frequency to 3 minutes

>>> 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

Reduce the sampling frequency to 3 minutes, but use right instead of left for each tag. Note that values in bucket are used as labels.

>>> 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

Reduce the sampling frequency to 3 minutes, but close the right 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

Increase the sampling frequency to 30 seconds

>>> series.resample('30S').asfreq()[0:5] #select first 5 rows
2000-01-01 00:00:00   0
2000-01-01 00:00:30  NaN
2000-01-01 00:01:00   1
2000-01-01 00:01:30  NaN
2000-01-01 00:02:00   2
Freq: 30S, dtype: float64

Increase the sampling frequency to 30S and fill in the nan value 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

Increase the sampling frequency to 30S and fill in the nan value 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

Run 1 custom function through 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