title: Pandas and python datetime timestamps from CSV
body: <p>I Have a csv file of tidal data with a gap in it where the data was not collected. It looks like this:</p>
  
  <pre><code>    datetime,tidal_water_level
      2014-05-13 00:00:00,0.008
      2014-05-13 01:00:00,-0.283
      2014-05-13 02:00:00,-0.491
      2014-05-13 03:00:00,-0.557
      ...
  </code></pre>
  
  <p>etc</p>
  
  <p>My opening gambit for loading it into a data frame is this:</p>
  
  <pre><code>    import matplotlib.pyplot as plt
      import datetime as dt
  
      headers = ['timestamp', 'tide_height']
      dtypes = [dt.datetime, float] 
      df = pd.read_csv('tides_clean.csv', names=headers, dtype=dtypes, header=1, parse_dates=True)
  </code></pre>
  
  <p>which I have put together from various other examples. It complains that dt.datetime is not understood.</p>
  
  <p>If I remove the dtype arguments then it looks like this:</p>
  
  <pre><code>    df
      Out[103]: 
                      timestamp  tide_height
      0     2014-05-13 01:00:00       -0.283
      1     2014-05-13 02:00:00       -0.491
      2     2014-05-13 03:00:00       -0.557
      ...
  </code></pre>
  
  <p>How can I work with the time series data itself as a datetime type, rather than the index, please? e.g. plot time v. tide, not index v. tide</p>
  
  <p>I try:
          plt.scatter(x=df.timestamp, y=df.tide_height)
  and get an empty 1.0 x 1.0 square plot plus a an error thus:
          ValueError: First argument must be a sequence</p>
  
  <p>My ultimate goal is to resample the data onto a different sampling frequency but this seems to be my first hurdle.</p>