版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/brucewong0516/article/details/84768464
首先我们直接看官方的文档：
DataFrame.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)
- 聚合的时间参数rule
- 参数如下：
rule : 表示目标转换的偏移字符串或对象，一般是时间参数，比如“M”，“A”，“Q”，“BM”，“BA”，“BQ”和“W”；
axis : int, optional, default 0
closed : {‘right’, ‘left’}；间隔的哪一侧是关闭的，对于除“M”，“A”，“Q”，“BM”，“BA”，“BQ”和“W”之外的所有频率偏移，默认值为“左”，其默认值均为“右”
label : {‘right’, ‘left’}；用于标记bins，间隔的哪一侧是关闭的，对于除“M”，“A”，“Q”，“BM”，“BA”，“BQ”和“W”之外的所有频率偏移，默认值为“左”，其默认值均为“右”
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 : 调整重新采样的时间标签
on : 对于DataFrame，要使用的列而不是索引进行重新采样。列必须与日期时间相似的数据。
实例
1.1 创建时间索引，并查看按时间合并后进行求和计算
#首先创建一个包含9个一分钟时间戳的系列
index = pd.date_range('1/1/2000', periods=9, freq='T') # 生成频率为每分钟的数据
series = pd.Series(range(9), index=index)
series
Out[31]:
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
series.resample('3T')
# 3T就是rule，合并的方向默认是axis = 0；
Out[34]: DatetimeIndexResampler [freq=<3 * Minutes>, axis=0, closed=left, label=left, convention=start, base=0]
series.resample('3T').sum()
Out[35]:
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
1.2 close和left的左右
# 间隔的哪一侧是关闭的，对于除“M”，“A”，“Q”，“BM”，“BA”，“BQ”和“W”之外的所有频率偏移，默认值为“左”，其默认值均为“右”
series.resample('W')
Out[37]: DatetimeIndexResampler [freq=<Week: weekday=6>, axis=0, closed=right, label=right, convention=start, base=0]
series.resample('T')
Out[38]: DatetimeIndexResampler [freq=<Minute>, axis=0, closed=left, label=left, convention=start, base=0]
# 如果是T分钟频率，合并后的K线是从合并的时间开始计算，如果是2T，就从开始的第一根往后数2根；
series.resample('T').sum()
Out[40]:
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
# 对比可以看出是左侧关闭的
series.resample('2T').sum()
Out[42]:
2000-01-01 00:00:00 1
2000-01-01 00:02:00 5
2000-01-01 00:04:00 9
2000-01-01 00:06:00 13
2000-01-01 00:08:00 8
Freq: 2T, dtype: int64
# 如果是W周频率，那么整个合并的结果归集到最后一天
series = pd.Series(range(9), index=index)
series.resample('W').sum()
Out[53]:
2000-01-02 0
2000-01-09 3
2000-01-16 12
2000-01-23 13
2000-01-30 8
Freq: W-SUN, dtype: int64
series
Out[54]:
2000-01-01 0
2000-01-04 1
2000-01-07 2
2000-01-10 3
2000-01-13 4
2000-01-16 5
2000-01-19 6
2000-01-22 7
2000-01-25 8
Freq: 3D, dtype: int64
1.3 自定义label的方向,也就是数据归集的位置
index = pd.date_range('1/1/2000', periods=9, freq='T') # 生成频率为每分钟的数据
series = pd.Series(range(9), index=index)
series.resample('3T', label='right').sum()
Out[57]:
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
1.4 自定义close的方向
index = pd.date_range('1/1/2000', periods=9, freq='T') # 生成频率为每分钟的数据
series = pd.Series(range(9), index=index)
series
Out[63]:
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
# 被合并的数据的最右不显示，关闭箱间隔的右侧
series.resample('3T', closed='right').sum()
Out[64]:
1999-12-31 23:57:00 0
2000-01-01 00:00:00 6
2000-01-01 00:03:00 15
2000-01-01 00:06:00 15
Freq: 3T, dtype: int64
1.5 上采用成30s的数据，加上asfreq()
series.resample('30S').asfreq()
Out[66]:
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
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
2000-01-01 00:03:30 NaN
2000-01-01 00:04:00 4.0
2000-01-01 00:04:30 NaN
2000-01-01 00:05:00 5.0
2000-01-01 00:05:30 NaN
2000-01-01 00:06:00 6.0
2000-01-01 00:06:30 NaN
2000-01-01 00:07:00 7.0
2000-01-01 00:07:30 NaN
2000-01-01 00:08:00 8.0
Freq: 30S, dtype: float64
1.6 将系列上采样到30秒的箱中，并使用pad方法填充NaN值
series.resample('30S').pad()[0:5]
Out[67]:
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
1.7 将系列上上采样30秒的箱中，并使用bfill方法填充NaN值
series.resample('30S').bfill()[0:5]
Out[68]:
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
1.8 通过apply传递自定义功能
def custom_resampler(array_like):
return np.sum(array_like)+5
import numpy as np
series.resample('3T').apply(custom_resampler)
Out[74]:
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
1.9 对于具有PeriodIndex的Series，关键字约定可用于控制是否使用规则的开头或结尾。
s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
freq='A',
periods=2))
# 上采样需要asfreq( )
s.resample('M', convention='start').asfreq().head()
Out[76]:
2012-01 1.0
2012-02 NaN
2012-03 NaN
2012-04 NaN
2012-05 NaN
Freq: M, dtype: float64
s.resample('M', convention='end').asfreq()
Out[78]:
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
1.10 对于DataFrame对象，关键字on可用于指定列而不是重新取样的索引
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()
Out[81]:
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
1.11 how，未来不会用的功能
index = pd.date_range('1/1/2000', periods=9, freq='T') # 生成频率为每分钟的数据
series = pd.Series(range(9), index=index)
series.resample('2T',how='last')
Out[83]:
2000-01-01 00:00:00 1
2000-01-01 00:02:00 3
2000-01-01 00:04:00 5
2000-01-01 00:06:00 7
2000-01-01 00:08:00 8
Freq: 2T, dtype: int64
series.resample('2T',how='mean')
Out[84]:
2000-01-01 00:00:00 0.5
2000-01-01 00:02:00 2.5
2000-01-01 00:04:00 4.5
2000-01-01 00:06:00 6.5
2000-01-01 00:08:00 8.0
Freq: 2T, dtype: float64