大熊猫变换时间序列为多列数据帧

Question

我有盘中日数据的时间序列看起来像下面

ts =pd.Series(np.random.randn(60),index=pd.date_range('1/1/2000',periods=60, freq='2h')) 

我希望将数据转换成数据帧，与列作为每个日期和行作为在日期的时间。

我尝试了这些，

key = lambda x:x.date() 
grouped = ts.groupby(key) 

但我怎么转换群体纳入日期数据帧过柱？或者有没有更好的办法？

Answer 1

import pandas as pd 
import numpy as np 

index = pd.date_range('1/1/2000', periods=60, freq='2h') 
ts = pd.Series(np.random.randn(60), index = index) 

key = lambda x: x.time() 
groups = ts.groupby(key) 

print pd.DataFrame({k:g for k,g in groups}).resample('D').T 

出来：

  2000-01-01 2000-01-02 2000-01-03 2000-01-04 2000-01-05 2000-01-06 \ 
00:00:00 0.109959 -0.124291 -0.137365 0.054729 -1.305821 -1.928468 
03:00:00 1.336467 0.874296 0.153490 -2.410259 0.906950 1.860385 
06:00:00 -1.172638 -0.410272 -0.800962 0.568965 -0.270307 -2.046119 
09:00:00 -0.707423 1.614732 0.779645 -0.571251 0.839890 0.435928 
12:00:00 0.865577 -0.076702 -0.966020 0.589074 0.326276 -2.265566 
15:00:00 1.845865 -1.421269 -0.141785 0.433011 -0.063286 0.129706 
18:00:00 -0.054569 0.277901 0.383375 -0.546495 -0.644141 -0.207479 
21:00:00 1.056536 0.031187 -1.667686 -0.270580 -0.678205 0.750386 

      2000-01-07 2000-01-08 
00:00:00 -0.657398 -0.630487 
03:00:00 2.205280 -0.371830 
06:00:00 -0.073235 0.208831 
09:00:00 1.720097 -0.312353 
12:00:00 -0.774391   NaN 
15:00:00 0.607250   NaN 
18:00:00 1.379823   NaN 
21:00:00 0.959811   NaN