title: How to put a number of timedate data into a subset of dataframe while keeping the data type?
body: <p>I have a dataframe which has name as the index while a column of birth date e.g </p>
  
  <pre><code>&gt; df_birthdate
           date
  Paul     2009-03-07  
  Peter    2000-06-23  
  Pauline  2001-03-03  
  Paula    2002-02-17
  
  &gt; type(df_birthdate.date[0])
  pandas._libs.tslibs.timestamps.Timestamp
  
  &gt; df_huge = pd.DataFrame({'School': ['A','A','A','A','B','B','B','B']})
  &gt; df_huge['new_date'] = ''
  &gt; idx_t = df_huge.School == 'A'
  
  </code></pre>
  
  <p>And I have a huge dataframe called df_huge which I want to put the date into it.  I know that the order won't change.</p>
  
  <p><code>df_huge.loc[idx_t, "new_date"] = df_birthdate.values</code></p>
  
  <p>The above code works for me in the most cases, however, when the 'date' column is in datetime format, by applying .values, the data which I put into the df_huge dataframe are no longer in datetime format.  Any suggestion to put 'date' from df_birthdate into a specific location of the df_huge?  Many thanks.  </p>