title: Python merging many dataframes by placing closely matching timestamps in same row
body: <p>I have three data frames. Data in each data frame occurs every 30 minutes and all have unique timestamps. 
  Some of them have missing data. </p>
  
  <pre><code>df1 = 
  index                     S1POA
  
  2019-01-09 13:20:17     742.961815
  2019-01-09 14:00:17     791.852790
  2019-01-09 14:40:17     794.034572
  2019-01-09 15:20:17     689.90
  
  df2 = 
  index                     S2POA
  2019-01-09 13:20:32     734.330740
  2019-01-09 14:00:32     803.439840
  2019-01-09 14:40:32     804.677047
  2019-01-09 15:40:17     567.90
  
  df3 = 
  index                     S3POA
  2019-01-09 13:20:47     725.699665
  2019-01-09 14:00:47     815.026890
  2019-01-09 14:40:47     815.319522 
  2019-01-09 15:40:47     457.90
  
  
  
  biglist3 = [df1,df2,df3] # actually up to df26
  </code></pre>
  
  <p>I would like to take df1 time as a reference, I want to place the data happened during that duration within the same row. For example, the first timestamp is 13:20:17. Data from remaining dfs that happened within 30 mins close to this timestamp should appear in the same row. 
  My present code is: </p>
  
  <pre><code>l = []
  s1 = []
  for i in range(0,len(biglist3),1):
      if i==0:
          s1 = biglist3[i]
          l1.append(s1)
      else:            
          l1.append(pd.merge_asof(s1,biglist3[i],left_index=True,right_index=True,direction='nearest'))
  combdf = pd.concat(l,axis=1) 
  </code></pre>
  
  <p>My present output is </p>
  
  <pre><code>index                     S1POA           S2POA          S3POA
  2019-01-09 13:20:17     742.961815     734.330740      725.699665
  2019-01-09 14:00:17     791.852790     803.439840      815.026890
  2019-01-09 14:40:17     794.034572     804.677047      815.319522
  2019-01-09 15:20:17     689.90         567.90          457.90
  </code></pre>
  
  <p>My expected output is </p>
  
  <pre><code>index                     S1POA           S2POA          S3POA
  2019-01-09 13:20:17     742.961815     734.330740      725.699665
  2019-01-09 14:00:17     791.852790     803.439840      815.026890
  2019-01-09 14:40:17     794.034572     804.677047      815.319522
  2019-01-09 15:20:17     689.90         nan             nan         # because df2 and df3 have no data around this time
  </code></pre>
  
  <p>How to rectify my code to get this desired output</p>