title: working with dates in pandas - remove unseen characters in datetime and convert to string
body: <p>I am using pandas to import data    <code>dfST = read_csv( ... , parse_dates={'timestamp':[date]})</code>
  In my csv, date is in the format YYY/MM/DD, which is all I need - there is no time.  I have several data sets that I need to compare for membership.  When I convert theses 'timestamp' to a string, sometimes I get something like this:</p>
  
  <pre><code>'1977-07-31T00:00:00.000000000Z'
  </code></pre>
  
  <p>which I understand is a datetime including milliseconds and a timezone.  Is there any way to suppress the addition of the extraneous time on import?  If not, I need to exclude it somehow.</p>
  
  <pre><code>dfST.timestamp[1]
  Out[138]: Timestamp('1977-07-31 00:00:00')
  </code></pre>
  
  <p>I have tried formatting it, which seemed to work until I called the formatted values:</p>
  
  <pre><code>dfSTdate=pd.to_datetime(dfST.timestamp, format="%Y-%m-%d")  
  dfSTdate.head()
  Out[123]: 
  0   1977-07-31
  1   1977-07-31
  Name: timestamp, dtype: datetime64[ns]
  </code></pre>
  
  <p>But no... when I test the value of this I also get the time:</p>
  
  <pre><code>dfSTdate[1]
  Out[124]: Timestamp('1977-07-31 00:00:00')
  </code></pre>
  
  <p>When I convert this to an array, the time is included with the millisecond and the timezone, which really messes my comparisons up.</p>
  
  <pre><code>test97=np.array(dfSTdate)
  test97[1]
  Out[136]: numpy.datetime64('1977-07-30T20:00:00.000000000-0400')
  </code></pre>
  
  <p>How can I get rid of the time?!?
  Ultimately I wish to compare membership among data sets using <code>numpy.in1d</code> with date as a string ('YYYY-MM-DD') as one part of the comparison</p>