title: python-pandas: dealing with NaT type values in a date columns of pandas dataframe
body: <p>I have a dataframe with mixed datatype column, and I applied <code>pd.to_datetime(df['DATE'],coerce=True)</code> and got the below dataframe</p>
  <pre><code>CUSTOMER_name     DATE
   abc                 NaT
   def                 NaT
   abc               2010-04-15 19:09:08
   def               2011-01-25 15:29:37
   abc               2010-04-10 12:29:02
  </code></pre>
  <p>Now I want to apply some agg function (here i want to groupby mailid and take min() of Date to find that mailid's date of first transaction).</p>
  <pre><code>df['DATE'] = [x.date() for x in df['DATE']]
  #Here the value goes to 
   CUSTOMER_name     DATE
   abc               0001-255-255 ####how??
   def               0001-255-255  ###How??
   abc               2010-04-15
   def               2011-01-25
   abc               2010-04-10
  #Then when i do a groupby and applying min on DATE
  df.groupby('CUSTOMER_name')['DATE'].min()
  #CUSTOMER_name     DATE
   abc               0001-255-255 ####i want 2010-04-10
   def               0001-255-255  ### i want 2011-01-25
  </code></pre>
  <p>SO can anyone please suggest , how to deal with this NaT while converting to date() and while doing groupby and min(), how to exclude NaT for calculation.</p>
  <h2>if for any customer_name only NaT will be there in DATE field, then on groupby and min(), I am okay with nan or Null values.</h2>