title: how to convert a datetime.time() object to datetime.datetime object pandas
body: <pre><code>import pandas as pd
  import datetime
  df = pd.DataFrame([{'st':datetime.datetime.strptime('21:00:00','%H:%M:%S').time(),'et':datetime.datetime.strptime('22:00:00','%H:%M:%S').time()}, {'st':datetime.datetime.strptime('1:00:00','%H:%M:%S').time(),'et':datetime.datetime.strptime('3:00:00','%H:%M:%S').time()}])
  
  
  Out[183]: df
           et        st
  0  22:00:00  21:00:00
  1  03:00:00  01:00:00
  </code></pre>
  
  <p>I would like to be able to convert the above dataframe with new fields having <code>datetime.datetime</code> objects with two other extra columns such as here having any dummy date in it and using the time from their respective rows: </p>
  
  <pre><code>      et        st      sdate_time                edate_time
  0  22:00:00  21:00:00   2018-01-01 21:00:00      2018-01-01 22:00:00  
  1  03:00:00  01:00:00   2018-01-01 1:00:00       2018-01-01 3:00:00
  </code></pre>
  
  <p>The approach I have tried is using apply method</p>
  
  <pre><code>df['et'].apply(lambda et: pd.datetime.combine(datetime.datetime.strptime('2018-01-01', '%Y-%m-%d').date(),et))
  </code></pre>
  
  <p>but turns out that the dataframe could be really huge and I would like to vectorize the above operation without the apply method . </p>