title: Unexpected results of min() and max() methods of Pandas series made of Timestamp objects
body: <p>I encountered this behaviour when doing basic data munging, like in this example:</p>
  
  <pre><code>In [55]: import pandas as pd
  In [56]: import numpy as np
  In [57]: rng = pd.date_range('1/1/2000', periods=10, freq='4h')
  In [58]: lvls = ['A','A','A','B','B','B','C','C','C','C']
  In [59]: df = pd.DataFrame({'TS': rng, 'V' : np.random.randn(len(rng)), 'L' : lvls})
  
  In [60]: df
  Out[60]: 
     L                  TS         V
  0  A 2000-01-01 00:00:00 -1.152371
  1  A 2000-01-01 04:00:00 -2.035737
  2  A 2000-01-01 08:00:00 -0.493008
  3  B 2000-01-01 12:00:00 -0.279055
  4  B 2000-01-01 16:00:00 -0.132386
  5  B 2000-01-01 20:00:00  0.584091
  6  C 2000-01-02 00:00:00 -0.297270
  7  C 2000-01-02 04:00:00 -0.949525
  8  C 2000-01-02 08:00:00  0.517305
  9  C 2000-01-02 12:00:00 -1.142195
  </code></pre>
  
  <p>the problem:</p>
  
  <pre><code>In [61]: df['TS'].min()
  Out[61]: 31969-04-01 00:00:00
  
  In [62]: df['TS'].max()
  Out[62]: 31973-05-10 00:00:00
  </code></pre>
  
  <p>while this looks ok:</p>
  
  <pre><code>In [63]: df['V'].max()
  Out[63]: 0.58409076701429163
  
  In [64]: min(df['TS'])
  Out[64]: &lt;Timestamp: 2000-01-01 00:00:00&gt;
  </code></pre>
  
  <p>when aggregating after groupby:</p>
  
  <pre><code>In [65]: df.groupby('L').min()
  Out[65]: 
               TS         V
  L                        
  A  9.466848e+17 -2.035737
  B  9.467280e+17 -0.279055
  C  9.467712e+17 -1.142195
  
  In [81]: val = df.groupby('L').agg('min')['TS']['A']
  In [82]: type(val)
  Out[82]: numpy.float64
  </code></pre>
  
  <p>Apparently in this particular case it has something to do with using frequency datetime index as argument of pd.Series function:</p>
  
  <pre><code>In [76]: rng.min()
  Out[76]: &lt;Timestamp: 2000-01-01 00:00:00&gt;
  
  In [77]: ts = pd.Series(rng)
  In [78]: ts.min()
  Out[78]: 31969-04-01 00:00:00
  
  In [79]: type(ts.min())
  Out[79]: numpy.datetime64
  </code></pre>
  
  <p>However, my initial problem was with min/max of Timestamp series parsed from strings via pd.read_csv()</p>
  
  <p>What am I doing wrong?</p>