Pandas TimeSeries 重采样产生 NaN
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Pandas TimeSeries resample produces NaNs
提问by Peter Lenaers
I am resampling a Pandas TimeSeries. The timeseries consist of binary values (it is a categorical variable) with no missing values, but after resampling NaNs appear. How is this possible?
我正在重新采样 Pandas TimeSeries。时间序列由没有缺失值的二进制值（它是一个分类变量）组成，但在重新采样后出现 NaN。这怎么可能？
I can't post any example data here since it is sensitive info, but I create and resample the series as follows:
我无法在此处发布任何示例数据，因为它是敏感信息，但我创建并重新采样该系列如下：
series = pd.Series(data, ts)
series_rs = series.resample('60T', how='mean')
回答by jezrael
upsamplingconverts to a regular time interval, so if there are no samples you get NaN.
upsampling转换为固定的时间间隔，因此如果没有样本，您会得到NaN.
You can fill missing values backward by fill_method='bfill'or for forward - fill_method='ffill'or fill_method='pad'.
您可以通过fill_method='bfill'或 for forward -fill_method='ffill'或 向后填充缺失值fill_method='pad'。
import pandas as pd
ts = pd.date_range('1/1/2015', periods=10, freq='100T')
data = range(10)
series = pd.Series(data, ts)
print series
#2015-01-01 00:00:00 0
#2015-01-01 01:40:00 1
#2015-01-01 03:20:00 2
#2015-01-01 05:00:00 3
#2015-01-01 06:40:00 4
#2015-01-01 08:20:00 5
#2015-01-01 10:00:00 6
#2015-01-01 11:40:00 7
#2015-01-01 13:20:00 8
#2015-01-01 15:00:00 9
#Freq: 100T, dtype: int64
series_rs = series.resample('60T', how='mean')
print series_rs
#2015-01-01 00:00:00 0
#2015-01-01 01:00:00 1
#2015-01-01 02:00:00 NaN
#2015-01-01 03:00:00 2
#2015-01-01 04:00:00 NaN
#2015-01-01 05:00:00 3
#2015-01-01 06:00:00 4
#2015-01-01 07:00:00 NaN
#2015-01-01 08:00:00 5
#2015-01-01 09:00:00 NaN
#2015-01-01 10:00:00 6
#2015-01-01 11:00:00 7
#2015-01-01 12:00:00 NaN
#2015-01-01 13:00:00 8
#2015-01-01 14:00:00 NaN
#2015-01-01 15:00:00 9
#Freq: 60T, dtype: float64
series_rs = series.resample('60T', how='mean', fill_method='bfill')
print series_rs
#2015-01-01 00:00:00 0
#2015-01-01 01:00:00 1
#2015-01-01 02:00:00 2
#2015-01-01 03:00:00 2
#2015-01-01 04:00:00 3
#2015-01-01 05:00:00 3
#2015-01-01 06:00:00 4
#2015-01-01 07:00:00 5
#2015-01-01 08:00:00 5
#2015-01-01 09:00:00 6
#2015-01-01 10:00:00 6
#2015-01-01 11:00:00 7
#2015-01-01 12:00:00 8
#2015-01-01 13:00:00 8
#2015-01-01 14:00:00 9
#2015-01-01 15:00:00 9
#Freq: 60T, dtype: float64
回答by Bart Bisschops
Please note that fill_method has now been deprecated. resample()now returns a resampling object on which you can perform operations just like a groupby object.
请注意，fill_method 现在已被弃用。resample()现在返回一个重采样对象，您可以像 groupby 对象一样对其执行操作。
common downsampling operations:
常见的下采样操作：
.mean()
.sum()
.agg()
.apply()
upsampling operations:
上采样操作：
.ffill()
.bfill()
See the whats-new message in the documentation https://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0180-breaking-resample
请参阅文档中的最新消息 https://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0180-break-resample
so the example would become
所以这个例子会变成
series_rs = series.resample('60T').mean()
