这是一段代码，我不明白为什么在最后一列 rm-5 上，前 4 项得到 NaN。
我知道对于 rm 列，前 4 项未填充，因为没有可用数据，但如果我移动列计算，应该进行，不是吗？
同样，我不明白为什么 rm-5 列中有 5 个而不是 4 个项目是 NaN
import pandas as pd
import numpy as np
index = pd.date_range('2000-1-1', periods=100, freq='D')
df = pd.DataFrame(data=np.random.randn(100), index=index, columns=['A'])
df['rm']=pd.rolling_mean(df['A'],5)
df['rm-5']=pd.rolling_mean(df['A'].shift(-5),5)
print df.head(n=8)
print df.tail(n=8)
A rm rm-5
2000-01-01 0.109161 NaN NaN
2000-01-02 -0.360286 NaN NaN
2000-01-03 -0.092439 NaN NaN
2000-01-04 0.169439 NaN NaN
2000-01-05 0.185829 0.002341 0.091736
2000-01-06 0.432599 0.067028 0.295949
2000-01-07 -0.374317 0.064222 0.055903
2000-01-08 1.258054 0.334321 -0.132972
A rm rm-5
2000-04-02 0.499860 -0.422931 -0.140111
2000-04-03 -0.868718 -0.458962 -0.182373
2000-04-04 0.081059 -0.443494 -0.040646
2000-04-05 0.500275 -0.093048 NaN
2000-04-06 -0.253915 -0.008288 NaN
2000-04-07 -0.159256 -0.140111 NaN
2000-04-08 -1.080027 -0.182373 NaN
2000-04-09 0.789690 -0.040646 NaN
您可以更改操作顺序。现在你首先进行移位，然后取平均值。由于您的第一次转变，您在最后创建了 NaN。
index = pd.date_range('2000-1-1', periods=100, freq='D')
df = pd.DataFrame(data=np.random.randn(100), index=index, columns=['A'])
df['rm']=pd.rolling_mean(df['A'],5)
df['shift'] = df['A'].shift(-5)
df['rm-5-shift_first']=pd.rolling_mean(df['A'].shift(-5),5)
df['rm-5-mean_first']=pd.rolling_mean(df['A'],5).shift(-5)
print( df.head(n=8))
print( df.tail(n=8))
A rm shift rm-5-shift_first rm-5-mean_first
2000-01-01 -0.120808 NaN 0.830231 NaN 0.184197
2000-01-02 0.029547 NaN 0.047451 NaN 0.187778
2000-01-03 0.002652 NaN 1.040963 NaN 0.395440
2000-01-04 -1.078656 NaN -1.118723 NaN 0.387426
2000-01-05 1.137210 -0.006011 0.469557 0.253896 0.253896
2000-01-06 0.830231 0.184197 -0.390506 0.009748 0.009748
2000-01-07 0.047451 0.187778 -1.624492 -0.324640 -0.324640
2000-01-08 1.040963 0.395440 -1.259306 -0.784694 -0.784694
A rm shift rm-5-shift_first rm-5-mean_first
2000-04-02 -1.283123 -0.270381 0.226257 0.760370 0.760370
2000-04-03 1.369342 0.288072 2.367048 0.959912 0.959912
2000-04-04 0.003363 0.299997 1.143513 1.187941 1.187941
2000-04-05 0.694026 0.400442 NaN NaN NaN
2000-04-06 1.508863 0.458494 NaN NaN NaN
2000-04-07 0.226257 0.760370 NaN NaN NaN
2000-04-08 2.367048 0.959912 NaN NaN NaN
2000-04-09 1.143513 1.187941 NaN NaN NaN
更多请参见：
http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments
http://pandas.pydata.org/pandas-docs/dev/ generated/pandas.DataFrame.shift.html http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.shift.html
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