title: How to convert rdat/rdata xts files to python pandas native time series files?
body: <p>I have a folder with more than 1000 <code>rda</code> time series files of stock data. Below is the sample code using which I saved my time series(xts) files in rda. I used rda/rdata instead of csv because saving and loading of files are fast and data compression is also very good in rda compared to csv.</p>
  
  <pre><code>library(quantmod)
  AAPL &lt;- 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
  </code></pre>
  
  <p>I use these files for many of my data analysis experiments in R. But now I am slowly migrating to python(with pandas) because its a general purpose language. Instead of downloading all stock data once more, is there a way to convert my current rda xts files to python pandas native files(h5 or pickle which ever is the best format). How can I do it?</p>
  
  <p>EDIT</p>
  
  <p>This is what I did in python</p>
  
  <pre><code>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)
  </code></pre>
  
  <p>The output is</p>
  
  <pre><code>     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
  </code></pre>
  
  <p>it converted it into non-time series dataset in python. How should I convert it to time series?</p>
  
  <p>EDIT 2:</p>
  
  <p>After many searching and tinkering I have come this far. I tried to convert the UTC variable in my rda file to local time</p>
  
  <pre><code>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)
  </code></pre>
  
  <p>Now the problem is the converted local time index is not properly formatted. The tail part should contain time series of recent dates instead its stuck in 1970.</p>
  
  <pre><code>                                     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  
  </code></pre>