title: pandas: function equivalent to SQL's datediff()?
body: <p>Is there an equivalent to SQL's datediff function in Python's pandas?
  The answer to this question:
  <a href="https://stackoverflow.com/questions/22132525/add-column-with-number-of-days-between-dates-in-dataframe-pandas">Add column with number of days between dates in DataFrame pandas</a>
  explains how to calculate the difference in days. For example:</p>
  
  <pre><code>&gt;&gt;&gt; (pd.to_datetime('15-10-2010') - pd.to_datetime('15-07-2010')) / pd.offsets.Day(1)
  92.0
  </code></pre>
  
  <p>However, I have two questions:</p>
  
  <ol>
  <li>Is  there a way to calculate the difference in months? I can approximate dividing the result above by 30, by 31, but I was wondering if there is some built-in function that does this automatically.</li>
  <li>what is the syntax of pd.offsets? I tried dividing by pd.offsets.Month(1) and it doesn't work. I looked up the documentation here (which is atrocious, like all of Python's documentation!): <a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects" rel="nofollow noreferrer">http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects</a> but Day() is not there, so I'm confused</li>
  </ol>