python - 如何将 rdat/rdata xts 文件转换为 python pandas 原生时间序列文件？

Question

我有一个文件夹，其中包含 1000 多个股票数据的 rda 时间序列文件。下面是我在 rda 中保存我的时间序列(xts)文件所使用的示例代码。我使用 rda/rdata 而不是 csv，因为文件的保存和加载速度很快，而且与 csv 相比，rda 中的数据压缩也非常好。

library(quantmod)
AAPL <- getSymbols("AAPL",auto.assign=FALSE)
save(AAPL,file="/home/user/folder/AAPL.rda")

           AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
2007-01-03     86.29     86.58    81.90      83.80   309579900      10.96015
2007-01-04     84.05     85.95    83.82      85.66   211815100      11.20341
2007-01-05     85.77     86.20    84.40      85.05   208685400      11.12363
2007-01-08     85.96     86.53    85.28      85.47   199276700      11.17857
2007-01-09     86.45     92.98    85.15      92.57   837324600      12.10717
2007-01-10     94.75     97.80    93.45      97.00   738220000      12.68657

我将这些文件用于我在 R 中的许多数据分析实验。但现在我正在慢慢迁移到 python(使用 pandas)，因为它是一种通用语言。有没有办法将我当前的 rda xts 文件转换为 python pandas native 文件(h5 或 pickle，这是最好的格式)，而不是再次下载所有股票数据。我该怎么做？

编辑

这是我用python做的

import rpy2.robjects as robjects
import pandas.rpy.common as com
import pandas as pd

robj=robjects.r['load']("AAPL.rda")


for sets in robj:
    myRData = com.load_data(sets)
    # convert to DataFrame
    if not isinstance(myRData, pd.DataFrame):
        myRData = pd.DataFrame(myRData)

print(myRData)

输出是

     AAPL.Open  AAPL.High   AAPL.Low  AAPL.Close  AAPL.Volume  AAPL.Adjusted
1.0  86.289999  86.579999  81.899999   83.800002  309579900.0      10.960147
2.0  84.050001  85.949998  83.820003   85.659998  211815100.0      11.203415
3.0  85.770000  86.199997  84.400002   85.049997  208685400.0      11.123633
4.0  85.959998  86.529998  85.280003   85.470000  199276700.0      11.178565
5.0  86.450003  92.979999  85.150000   92.570003  837324600.0      12.107169

在python中将其转换为非时间序列数据集。我应该如何将其转换为时间序列？

编辑 2:

经过多次搜索和修改，我走到了这一步。我试图将我的 rda 文件中的 UTC 变量转换为本地时间

import rpy2.robjects as robjects
import pandas.rpy.common as com
import pandas as pd
import numpy as np

robj=robjects.r['load']("AAPL.rda")

myRData=None
for sets in robj:
   myRData = com.load_data(sets)
   # convert to DataFrame
   if not isinstance(myRData, pd.DataFrame):
       myRData = pd.DataFrame(myRData)

myRData.head(10)      
ts=np.array(robjects.r('attr(AAPL,"index")')).astype(int)

#changing index
myRData.index=pd.to_datetime(ts, utc=True, format='%Y-%m-%d')

myRData.tail(10)

现在的问题是转换后的本地时间索引格式不正确。尾部应包含最近日期的时间序列，而不是停留在 1970 年。

                                     AAPL.Close  AAPL.Volume  AAPL.Adjusted  
1970-01-01 00:00:01.476144+00:00     116.300003   64041000.0     116.300003  
1970-01-01 00:00:01.476230400+00:00  117.339996   37586800.0     117.339996  
1970-01-01 00:00:01.476316800+00:00  116.980003   35192400.0     116.980003  
1970-01-01 00:00:01.476403200+00:00  117.629997   35652200.0     117.629997  
1970-01-01 00:00:01.476662400+00:00  117.550003   23624900.0     117.550003  
1970-01-01 00:00:01.476748800+00:00  117.470001   24553500.0     117.470001  
1970-01-01 00:00:01.476835200+00:00  117.120003   20034600.0     117.120003  
1970-01-01 00:00:01.476921600+00:00  117.059998   24125800.0     117.059998  
1970-01-01 00:00:01.477008+00:00     116.599998   23192700.0     116.599998  
1970-01-01 00:00:01.477267200+00:00  117.650002   23311700.0     117.650002  

Answer 1

经过数小时的编辑和搜索。我用结果代码解决了我的问题。欢迎任何建议

import rpy2.robjects as robjects
import pandas.rpy.common as com
import pandas as pd
import numpy as np
from datetime import datetime

#loading external rda file
robj=robjects.r['load']("AAPL.rda")

myRData=None
for sets in robj:
   myRData = com.load_data(sets)
   # convert to DataFrame
   if not isinstance(myRData, pd.DataFrame):
       myRData = pd.DataFrame(myRData)

myRData.tail(10)    

#fetching UTC data from rda file  
ts=np.array(robjects.r('attr(AAPL,"index")')).astype(int)

#converting UTC to local time
d= np.array([])
for t in ts:
    s=datetime.utcfromtimestamp(t)
    d=np.append(s,d)

#sorting datetime 
d=np.sort(d, axis=0)

#changing index
myRData.index=pd.to_datetime(d)

myRData.tail(10)

结果

             AAPL.Open   AAPL.High    AAPL.Low  AAPL.Close  AAPL.Volume  \
2016-10-11  117.699997  118.690002  116.199997  116.300003   64041000.0   
2016-10-12  117.349998  117.980003  116.750000  117.339996   37586800.0   
2016-10-13  116.790001  117.440002  115.720001  116.980003   35192400.0   
2016-10-14  117.879997  118.169998  117.129997  117.629997   35652200.0   
2016-10-17  117.330002  117.839996  116.779999  117.550003   23624900.0   
2016-10-18  118.180000  118.209999  117.449997  117.470001   24553500.0   
2016-10-19  117.250000  117.760002  113.800003  117.120003   20034600.0   
2016-10-20  116.860001  117.379997  116.330002  117.059998   24125800.0   
2016-10-21  116.809998  116.910004  116.279999  116.599998   23192700.0   
2016-10-24  117.099998  117.739998  117.000000  117.650002   23311700.0