title: Python Pandas join dataframes on index
body: <p>I am trying to join to dataframe on the same column "Date", the code is as follow:</p>
  
  <pre><code>import pandas as pd
  from datetime import datetime
  df_train_csv = pd.read_csv('./train.csv',parse_dates=['Date'],index_col='Date')
  
  start = datetime(2010, 2, 5)
  end = datetime(2012, 10, 26)
  
  df_train_fly = pd.date_range(start, end, freq="W-FRI")
  df_train_fly = pd.DataFrame(pd.Series(df_train_fly), columns=['Date'])
  
  merged = df_train_csv.join(df_train_fly.set_index(['Date']), on = ['Date'], how = 'right', lsuffix='_x')
  </code></pre>
  
  <p>It complains dataframe df_train_csv has no column named "Date". I'd like to set "Date" in both dataframe as index and I am wondering what is the best way to join dataframe with date as the index?</p>
  
  <h1>UPDATE:</h1>
  
  <p>That is the sample data</p>
  
  <pre><code>Date,Weekly_Sales
  2010-02-05,24924.5
  2010-02-12,46039.49
  2010-02-19,41595.55
  2010-02-26,19403.54
  2010-03-05,21827.9
  2010-03-12,21043.39
  2010-03-19,22136.64
  2010-03-26,26229.21
  2010-04-02,57258.43
  2010-04-09,42960.91
  2010-04-16,17596.96
  2010-04-23,16145.35
  2010-04-30,16555.11
  2010-05-07,17413.94
  2010-05-14,18926.74
  2010-05-21,14773.04
  2010-05-28,15580.43
  2010-06-04,17558.09
  2010-06-11,16637.62
  2010-06-18,16216.27
  2010-06-25,16328.72
  2010-07-02,16333.14
  2010-07-09,17688.76
  2010-07-16,17150.84
  2010-07-23,15360.45
  2010-07-30,15381.82
  2010-08-06,17508.41
  2010-08-13,15536.4
  2010-08-20,15740.13
  2010-08-27,15793.87
  2010-09-03,16241.78
  2010-09-10,18194.74
  2010-09-17,19354.23
  2010-09-24,18122.52
  2010-10-01,20094.19
  2010-10-08,23388.03
  2010-10-15,26978.34
  2010-10-22,25543.04
  2010-10-29,38640.93
  2010-11-05,34238.88
  2010-11-12,19549.39
  2010-11-19,19552.84
  2010-11-26,18820.29
  2010-12-03,22517.56
  2010-12-10,31497.65
  2010-12-17,44912.86
  2010-12-24,55931.23
  2010-12-31,19124.58
  2011-01-07,15984.24
  2011-01-14,17359.7
  2011-01-21,17341.47
  2011-01-28,18461.18
  2011-02-04,21665.76
  2011-02-11,37887.17
  2011-02-18,46845.87
  2011-02-25,19363.83
  2011-03-04,20327.61
  2011-03-11,21280.4
  2011-03-18,20334.23
  2011-03-25,20881.1
  2011-04-01,20398.09
  2011-04-08,23873.79
  2011-04-15,28762.37
  2011-04-22,50510.31
  2011-04-29,41512.39
  2011-05-06,20138.19
  2011-05-13,17235.15
  2011-05-20,15136.78
  2011-05-27,15741.6
  2011-06-03,16434.15
  2011-06-10,15883.52
  2011-06-17,14978.09
  2011-06-24,15682.81
  2011-07-01,15363.5
  2011-07-08,16148.87
  2011-07-15,15654.85
  2011-07-22,15766.6
  2011-07-29,15922.41
  2011-08-05,15295.55
  2011-08-12,14539.79
  2011-08-19,14689.24
  2011-08-26,14537.37
  2011-09-02,15277.27
  2011-09-09,17746.68
  2011-09-16,18535.48
  2011-09-23,17859.3
  2011-09-30,18337.68
  2011-10-07,20797.58
  2011-10-14,23077.55
  2011-10-21,23351.8
  2011-10-28,31579.9
  2011-11-04,39886.06
  2011-11-11,18689.54
  2011-11-18,19050.66
  2011-11-25,20911.25
  2011-12-02,25293.49
  2011-12-09,33305.92
  2011-12-16,45773.03
  2011-12-23,46788.75
  2011-12-30,23350.88
  2012-01-06,16567.69
  2012-01-13,16894.4
  2012-01-20,18365.1
  2012-01-27,18378.16
  2012-02-03,23510.49
  2012-02-10,36988.49
  2012-02-17,54060.1
  2012-02-24,20124.22
  2012-03-02,20113.03
  2012-03-09,21140.07
  2012-03-16,22366.88
  2012-03-23,22107.7
  2012-03-30,28952.86
  2012-04-06,57592.12
  2012-04-13,34684.21
  2012-04-20,16976.19
  2012-04-27,16347.6
  2012-05-04,17147.44
  2012-05-11,18164.2
  2012-05-18,18517.79
  2012-05-25,16963.55
  2012-06-01,16065.49
  2012-06-08,17666
  2012-06-15,17558.82
  2012-06-22,16633.41
  2012-06-29,15722.82
  2012-07-06,17823.37
  2012-07-13,16566.18
  2012-07-20,16348.06
  2012-07-27,15731.18
  2012-08-03,16628.31
  2012-08-10,16119.92
  2012-08-17,17330.7
  2012-08-24,16286.4
  2012-08-31,16680.24
  2012-09-07,18322.37
  2012-09-14,19616.22
  2012-09-21,19251.5
  2012-09-28,18947.81
  2012-10-05,21904.47
  2012-10-12,22764.01
  2012-10-19,24185.27
  2012-10-26,27390.81
  </code></pre>
  
  <p>I will read it from a csv file. But sometimes, some weeks may be missing. Therefore, I am trying to generate a date range like this:</p>
  
  <pre><code>df_train_fly = pd.date_range(start, end, freq="W-FRI")
  </code></pre>
  
  <p>This generated dataframe contains all weeks in the range so I need to merge those two dataframe into one.</p>
  
  <p>If I check df_train_csv['Date'] and df_train_fly['Date'] from the iPython console, they both showed as dtype: datetime64[ns]</p>