title: Pandas concat: ValueError: Shape of passed values is blah, indices imply blah2
body: <p>I'm trying to merge a (Pandas 14.1) dataframe and a series. The series should form a new column, with some NAs (since the index values of the series are a subset of the index values of the dataframe).</p>
  
  <p>This works for a toy example, but not with my data (detailed below).</p>
  
  <p>Example:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  
  df1 = pd.DataFrame(np.random.randn(6, 4), columns=['A', 'B', 'C', 'D'], index=pd.date_range('1/1/2011', periods=6, freq='D'))
  df1
  
  A   B   C   D
  2011-01-01  -0.487926   0.439190    0.194810    0.333896
  2011-01-02  1.708024    0.237587    -0.958100   1.418285
  2011-01-03  -1.228805   1.266068    -1.755050   -1.476395
  2011-01-04  -0.554705   1.342504    0.245934    0.955521
  2011-01-05  -0.351260   -0.798270   0.820535    -0.597322
  2011-01-06  0.132924    0.501027    -1.139487   1.107873
  
  s1 = pd.Series(np.random.randn(3), name='foo', index=pd.date_range('1/1/2011', periods=3, freq='2D'))
  s1
  
  2011-01-01   -1.660578
  2011-01-03   -0.209688
  2011-01-05    0.546146
  Freq: 2D, Name: foo, dtype: float64
  
  pd.concat([df1, s1],axis=1)
  
  A   B   C   D   foo
  2011-01-01  -0.487926   0.439190    0.194810    0.333896    -1.660578
  2011-01-02  1.708024    0.237587    -0.958100   1.418285    NaN
  2011-01-03  -1.228805   1.266068    -1.755050   -1.476395   -0.209688
  2011-01-04  -0.554705   1.342504    0.245934    0.955521    NaN
  2011-01-05  -0.351260   -0.798270   0.820535    -0.597322   0.546146
  2011-01-06  0.132924    0.501027    -1.139487   1.107873    NaN
  </code></pre>
  
  <p>The situation with the data (see below) seems basically identical -  concatting a series with a DatetimeIndex whose values are a subset of the dataframe's. But it gives the ValueError in the title (blah1 = (5, 286) blah2 = (5, 276) ). Why doesn't it work?:</p>
  
  <pre><code>In[187]: df.head()
  Out[188]:
  high    low loc_h   loc_l
  time                
  2014-01-01 17:00:00 1.376235    1.375945    1.376235    1.375945
  2014-01-01 17:01:00 1.376005    1.375775    NaN NaN
  2014-01-01 17:02:00 1.375795    1.375445    NaN 1.375445
  2014-01-01 17:03:00 1.375625    1.375515    NaN NaN
  2014-01-01 17:04:00 1.375585    1.375585    NaN NaN
  In [186]: df.index
  Out[186]:
  &lt;class 'pandas.tseries.index.DatetimeIndex'&gt;
  [2014-01-01 17:00:00, ..., 2014-01-01 21:30:00]
  Length: 271, Freq: None, Timezone: None
  
  In [189]: hl.head()
  Out[189]:
  2014-01-01 17:00:00    1.376090
  2014-01-01 17:02:00    1.375445
  2014-01-01 17:05:00    1.376195
  2014-01-01 17:10:00    1.375385
  2014-01-01 17:12:00    1.376115
  dtype: float64
  
  In [187]:hl.index
  Out[187]:
  &lt;class 'pandas.tseries.index.DatetimeIndex'&gt;
  [2014-01-01 17:00:00, ..., 2014-01-01 21:30:00]
  Length: 89, Freq: None, Timezone: None
  
  In: pd.concat([df, hl], axis=1)
  Out: [stack trace] ValueError: Shape of passed values is (5, 286), indices imply (5, 276)
  </code></pre>