pandasで日時オブジェクトシリーズが独立した値（x）のOLS回帰を実行する方法

Question

私は何か基本的なものを欠いていると思います。しかし、ここに私の質問です。私は25日間の時系列データを持っています、私は時系列であるY値とdatetimeインデックスであるX値でOLS回帰を実行したいと思います。

df['jDate'] = df.index.to_julian_date() 
ols(x=df.jDate, y=df.abc) 

コンプリート次のようになります：ここに私のコードが

import pandas as pd 
from pandas.stats.api import ols 

indx = [Timestamp('2015-06-01 00:00:00'), Timestamp('2015-06-02 00:00:00'), Timestamp('2015-06-03 00:00:00'), Timestamp('2015-06-04 00:00:00'), Timestamp('2015-06-05 00:00:00'), Timestamp('2015-06-06 00:00:00'), Timestamp('2015-06-07 00:00:00'), Timestamp('2015-06-08 00:00:00'), Timestamp('2015-06-09 00:00:00'), Timestamp('2015-06-10 00:00:00'), Timestamp('2015-06-11 00:00:00'), Timestamp('2015-06-12 00:00:00'), Timestamp('2015-06-13 00:00:00'), Timestamp('2015-06-14 00:00:00'), Timestamp('2015-06-15 00:00:00'), Timestamp('2015-06-16 00:00:00'), Timestamp('2015-06-17 00:00:00'), Timestamp('2015-06-18 00:00:00'), Timestamp('2015-06-19 00:00:00'), Timestamp('2015-06-20 00:00:00'), Timestamp('2015-06-21 00:00:00'), Timestamp('2015-06-22 00:00:00'), Timestamp('2015-06-23 00:00:00'), Timestamp('2015-06-24 00:00:00'), Timestamp('2015-06-25 00:00:00')] 
col = [51.219999999999999, 51.189999999999998, 51.210000000000001, 51.229999999999997, 51.219999999999999, 51.219999999999999, 51.219999999999999, 51.229999999999997, 51.240000000000002, 51.219999999999999, 51.200000000000003, 51.200000000000003, 51.200000000000003, 51.219999999999999, 51.219999999999999, 51.219999999999999, 51.219999999999999, 51.270000000000003, 51.280000000000001, 51.280000000000001, 51.299999999999997, 51.299999999999997, 51.280000000000001, 51.280000000000001, 51.270000000000003] 
df = pd.DataFrame(col,index=indx,columns=['abc']) 
sumstat = ols(y=df['abc'],x=df.index) 

あるインデックスはdatetime型のオブジェクト

Exception: Invalid RHS type: <class 'pandas.tseries.index.DatetimeIndex'> 

Answer 1

はあなたのコードにこれを追加しているので、私は以下のエラーを取得

import pandas as pd 
from pandas.stats.api import ols 

indx = [Timestamp('2015-06-01 00:00:00'), Timestamp('2015-06-02 00:00:00'), Timestamp('2015-06-03 00:00:00'), Timestamp('2015-06-04 00:00:00'), Timestamp('2015-06-05 00:00:00'), Timestamp('2015-06-06 00:00:00'), Timestamp('2015-06-07 00:00:00'), Timestamp('2015-06-08 00:00:00'), Timestamp('2015-06-09 00:00:00'), Timestamp('2015-06-10 00:00:00'), Timestamp('2015-06-11 00:00:00'), Timestamp('2015-06-12 00:00:00'), Timestamp('2015-06-13 00:00:00'), Timestamp('2015-06-14 00:00:00'), Timestamp('2015-06-15 00:00:00'), Timestamp('2015-06-16 00:00:00'), Timestamp('2015-06-17 00:00:00'), Timestamp('2015-06-18 00:00:00'), Timestamp('2015-06-19 00:00:00'), Timestamp('2015-06-20 00:00:00'), Timestamp('2015-06-21 00:00:00'), Timestamp('2015-06-22 00:00:00'), Timestamp('2015-06-23 00:00:00'), Timestamp('2015-06-24 00:00:00'), Timestamp('2015-06-25 00:00:00')] 
col = [51.219999999999999, 51.189999999999998, 51.210000000000001, 51.229999999999997, 51.219999999999999, 51.219999999999999, 51.219999999999999, 51.229999999999997, 51.240000000000002, 51.219999999999999, 51.200000000000003, 51.200000000000003, 51.200000000000003, 51.219999999999999, 51.219999999999999, 51.219999999999999, 51.219999999999999, 51.270000000000003, 51.280000000000001, 51.280000000000001, 51.299999999999997, 51.299999999999997, 51.280000000000001, 51.280000000000001, 51.270000000000003] 
df = pd.DataFrame(col,index=indx,columns=['abc']) 
df['jDate'] = df.index.to_julian_date() 
sumstat = ols(x=df.jDate, y=df.abc)