title: Should I convert my pandas datetime column to categorical?
body: <p>I just noticed in the documentation that you can convert a date to a category. I have never tried this and won't have a chance until Monday, but is anyone doing this? I don't think I have been taking advantage of the category dtype enough even though I deal with files that have a ton of repeating data.</p>
  
  <p>If I have a file with a few hundred thousand records all within 1 month (so the dates are repeating), will it be faster to convert to categories and then do .dt operations? I know I use .dt.strftime() often</p>
  
  <p>It also looks like the dtype is category but it still knows it is datetime64, does that mean timedeltas operations or offsets would still work?</p>
  
  <pre><code>In [129]: date_s = pd.Series(pd.date_range('1/1/2015', periods=5))
  
  In [130]: date_cat = date_s.astype('category')
  
  In [131]: date_cat
  Out[131]: 
  0   2015-01-01
  1   2015-01-02
  2   2015-01-03
  3   2015-01-04
  4   2015-01-05
  dtype: category
  Categories (5, datetime64[ns]): [2015-01-01, 2015-01-02, 2015-01-03, 2015-01-04, 2015-01-05]
  
  In [132]: date_cat.dt.day
  Out[132]: 
  0    1
  1    2
  2    3
  3    4
  4    5
  dtype: int64
  </code></pre>