title: groupby data with columns that have mixed data types
body: <p>Lets say I had this sample of a mixed dataset:</p>
  <pre><code>df:
  
  Property              Name      Date of entry   Old data      Updated data
  City                     Jim    1/7/2021      Jacksonville    Miami 
  State                    Jack   1/8/2021      TX              CA
  Zip                     Joe     2/2/2021     11111            22222
  Address                Harry    2/3/2021     123 lane         123 street
  Telephone              Lisa     3/1/2021     111-111-11111    333-333-3333
  Email                  Tammy    3/2/2021     tammy@yahoo.com  tammy@gmail.com
  Date Product Ordered    Lisa    3/3/2021     2/1/2021          2/10/2021
  Order count            Tammy    3/4/2021      2                3
  </code></pre>
  <p>I'd like to group by all this data starting with property and have it look like this:</p>
  <pre><code>grouped:
  
  Property    Name      Date of entry    Old data   Updated Data
  City        names1     date 1          data 1      data 2
              names2     date 2          data 1      data 2
              names3     date 3          data 1      data 2
  
  State       names1     date 1          data 1      data 2
              names2     date 2          data 1      data 2
              names3     date 3          data 1      data 2
  </code></pre>
  <pre><code>grouped = pd.DataFrame(df.groupby(['Property','Name','Date of entry','Old Data', 'updated data'])
               .size(),columns=['Count'])
  grouped
  </code></pre>
  <p><em>and I get a type error saying: '&lt;' not supported between instances of 'int' and 'datetime.datetime'</em></p>
  <p>Is there some sort of formatting that I need to do to the df['Old data'] &amp; df['Updated data'] columns to allow them to be added to the groupby?</p>
  <pre><code>added data types:
  
  Property: Object
  Name: Object
  Date of entry: datetime
  Old data: Object
  Updated data: Object
  </code></pre>