title: Pandas - adding new date column based on parsing other date column
body: <p>I am trying to take a Pandas dataframe, parse a column that represents dates and add a new column to the dataframe with a simple mm/dd/yyyy format.</p>
  <p>Here is the data and libraries:</p>
  <pre><code>import pandas as pd
  import datetime
  from dateutil.parser import parse
  
  df = pd.DataFrame([['row1', 'Tue Jun 16 19:05:44 UTC 2020', 'record1'], ['row2', 'Tue Jun 16 17:10:02 UTC 2020', 'record2'], ['row3', 'Fri Jun 12 17:52:37 UTC 2020', 'record3']], columns=[&quot;row&quot;, &quot;checkin&quot;, &quot;record&quot;])
  </code></pre>
  <p>From picking bits and pieces from around here I crafted this line to parse and add the new column of data:</p>
  <pre><code>df['NewDate'] = df.apply(lambda row: datetime.date.strftime(parse(df['checkin']), &quot;%m/%d/%Y&quot;), axis = 1)
  </code></pre>
  <p>But I end up with this error when run, can anyone suggest a fix or easier way to do this, seems like it should be simpler and more pythonic than I am finding</p>
  <p><strong>TypeError: ('Parser must be a string or character stream, not Series', 'occurred at index 0')</strong></p>
  <p>Thanks for any help you can offer.</p>