两个数据帧的简单线性回归

Question

我有两个带有日期时间索引的数据帧。第一个数据帧可能包含nan值，而第二个数据帧可能不包含nan值

data1['A']
2019-06-01 00:00:00          NaN
2019-06-01 01:00:00          NaN
2019-06-01 02:00:00          NaN
2019-06-01 03:00:00          NaN
2019-06-01 04:00:00          NaN
                         ...    
2019-06-30 19:00:00    14.086600
2019-06-30 20:00:00    14.101033
2019-06-30 21:00:00    14.160733
2019-06-30 22:00:00    13.940633
2019-06-30 23:00:00    13.989567
Freq: H, Name: A, Length: 720, dtype: float64

data2['B']
2019-06-01 00:00:00    243.168989
2019-06-01 01:00:00    243.104673
2019-06-01 02:00:00    242.571222
2019-06-01 03:00:00    240.685214
2019-06-01 04:00:00    242.652392
                          ...    
2019-06-30 19:00:00    243.611821
2019-06-30 20:00:00    243.338931
2019-06-30 21:00:00    243.296361
2019-06-30 22:00:00    243.676107
2019-06-30 23:00:00    243.507886
Name: B, Length: 720, dtype: float64

当这两个数据帧在某个日期时间都有值（没有nan）时，我如何对这两个数据帧进行简单的线性回归模型。谢谢你的帮助

Answer 1

您可以尝试这样的方法，您没有指定要对什么进行回归，因此在下面的示例中，我将x设为“A”，y设为“B”：

from sklearn import linear_model
import pandas as pd
import numpy as np

data1 = pd.DataFrame({'A':[np.NaN,np.NaN,np.NaN,np.NaN,14.086600,14.101033,14.160733,13.940633,13.989567]})
data2 = pd.DataFrame ({ 'B':[243.168989,243.104673,242.571222,240.685214,242.652392,
                             243.611821,243.338931,243.296361,243.676107,243.507886]})
is_finite = np.isfinite(data1['A']) & np.isfinite(data2['B'])
mdl = linear_model.LinearRegression()
mdl.fit(data1.loc[is_finite][['A']],data2.loc[is_finite]['B'])
mdl.coef_