title: How do I remove time from Pandas df column when it isn't in the excel column?
body: <p>I have read in two worksheets separately into pandas dataframes. Both have date issued columns. </p>
  
  <p>In both worksheets the column is held in excel as dd/mm/yyyy. </p>
  
  <p>ws1 </p>
  
  <pre><code>13/02/2017
  01/02/2017
  08/11/2016
  05/08/2016
  16/03/2017
  
  53   2017-02-13
  51   2017-02-01
  22   2016-11-08
  0    2016-08-05
  63   2017-03-16
  Name: Date Issued, dtype: datetime64[ns]
  </code></pre>
  
  <p>But ws2</p>
  
  <pre><code>08/03/2017
  24/08/2016
  28/11/2016
  26/10/2016
  10/03/2017
  
  
  0    2017-03-08 00:00:00
  1    2016-08-24 00:00:00
  2    2016-11-28 00:00:00
  3    2016-10-26 00:00:00
  4    2017-03-10 00:00:00
  Name: Date Issued, dtype: object
  </code></pre>
  
  <p>Why is it that the dtypes are different and how can I apply remove the time?</p>
  
  <p>The code currently looks like nothing out the ordinary for dealing with pandas</p>
  
  <pre><code>df = pd.read_excel(file, 'ws2')
  df = df.loc[:, ['Date Issued', 'Person ID',
                            'First Name', 'Surname', 'Type',  'Amount']]
  df = df.sort_values(by=['Surname'])
  df['Date Issued'] = pd.to_datetime(df_loan['Date Issued'], dayfirst=True)
  </code></pre>
  
  <p>I have tried to use;</p>
  
  <pre><code>df['Date Issued'] = pd.to_datetime(df['Date Issued'], dayfirst=True)
  </code></pre>
  
  <p>But get the following error;</p>
  
  <pre><code>TypeError: invalid string coercion to datetime
  </code></pre>
  
  <p>Also; </p>
  
  <pre><code>df['Date Issued'] = df['Date Issued'].astype('datetime64[ns]')
  </code></pre>
  
  <p>But getting this error;</p>
  
  <pre><code>ValueError: Error parsing datetime string " " at position 1
  </code></pre>