title: Pandas DataFrame concat vs append
body: <p>I have a list of 4 pandas dataframes containing a day of tick data that I want to merge into a single data frame. I cannot understand the behavior of concat on my timestamps. See details below:</p>
  
  <pre><code>data
  
  [&lt;class 'pandas.core.frame.DataFrame'&gt;
  DatetimeIndex: 35228 entries, 2013-03-28 00:00:07.089000+02:00 to 2013-03-28 18:59:20.357000+02:00
  Data columns:
  Price       4040  non-null values
  Volume      4040  non-null values
  BidQty      35228  non-null values
  BidPrice    35228  non-null values
  AskPrice    35228  non-null values
  AskQty      35228  non-null values
  dtypes: float64(6),
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  
  DatetimeIndex: 33088 entries, 2013-04-01 00:03:17.047000+02:00 to 2013-04-01 18:59:58.175000+02:00
  Data columns:
  Price       3969  non-null values
  Volume      3969  non-null values
  BidQty      33088  non-null values
  BidPrice    33088  non-null values
  AskPrice    33088  non-null values
  AskQty      33088  non-null values
  dtypes: float64(6),
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  
  DatetimeIndex: 50740 entries, 2013-04-02 00:03:27.470000+02:00 to 2013-04-02 18:59:58.172000+02:00
  Data columns:
  Price       7326  non-null values
  Volume      7326  non-null values
  BidQty      50740  non-null values
  BidPrice    50740  non-null values
  AskPrice    50740  non-null values
  AskQty      50740  non-null values
  dtypes: float64(6),
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  
  DatetimeIndex: 60799 entries, 2013-04-03 00:03:06.994000+02:00 to 2013-04-03 18:59:58.180000+02:00
  Data columns:
  Price       8258  non-null values
  Volume      8258  non-null values
  BidQty      60799  non-null values
  BidPrice    60799  non-null values
  AskPrice    60799  non-null values
  AskQty      60799  non-null values
  dtypes: float64(6)]
  </code></pre>
  
  <p>Using <code>append</code> I get:</p>
  
  <pre><code>pd.DataFrame().append(data)
  
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  DatetimeIndex: 179855 entries, 2013-03-28 00:00:07.089000+02:00 to 2013-04-03 18:59:58.180000+02:00
  Data columns:
  AskPrice    179855  non-null values
  AskQty      179855  non-null values
  BidPrice    179855  non-null values
  BidQty      179855  non-null values
  Price       23593  non-null values
  Volume      23593  non-null values
  dtypes: float64(6)
  </code></pre>
  
  <p>Using <code>concat</code> I get:</p>
  
  <pre><code>pd.concat(data)
  
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  DatetimeIndex: 179855 entries, 2013-03-27 22:00:07.089000+02:00 to 2013-04-03 16:59:58.180000+02:00
  Data columns:
  Price       23593  non-null values
  Volume      23593  non-null values
  BidQty      179855  non-null values
  BidPrice    179855  non-null values
  AskPrice    179855  non-null values
  AskQty      179855  non-null values
  dtypes: float64(6)
  </code></pre>
  
  <p>Notice how the index changes when using <code>concat</code>. Why is that happening and how would I go about using <code>concat</code> to reproduce the results obtained using <code>append</code>? (Since <code>concat</code> seems so much faster; 24.6 ms per loop vs 3.02 s per loop)</p>