title: Difference between different lenght timestamp-indexed DataFrames
body: <p>I have two dataframes, both are indexed with timestamp values like '2021-03-23 13:04:00.134000+00:00'.</p>
  <p>I would like to compute the difference between them on some columns, but the problem is that they are not time-aligned and have different number of rows.</p>
  <p>Is there a good way to makes the difference of all the elements that have a time delta less then a specyfied amount and put NaN in the other cases?</p>
  <p>Edit:</p>
  <p>Dataframe 1:</p>
  <pre><code>|index| val1 | val 2 |
  |--------------------------------| ---- | ---- |
  |2021-03-23 13:04:00.134000+00:00| 200  | 50   |
  |2021-03-23 13:34:00.134000+00:00| 100  | 10   |
  |2021-03-23 14:04:00.134000+00:00| 100  | 10   |
  </code></pre>
  <p>Dataframe 2:</p>
  <pre><code>|index| val1 | val 2 |
  |--------------------------------| ---- | ---- |
  |2021-03-23 13:24:00.134000+00:00| 200  | 50   |
  |2021-03-23 14:34:00.134000+00:00| 100  | 10   |
  </code></pre>
  <p>Expected output (difference between columns of Dataframe 1 and Dataframe 2) supposing that the time delta is 20 min:</p>
  <pre><code>|index| val1 | val 2 |
  |--------------------------------| ---- | ---- |
  |2021-03-23 13:04:00.134000+00:00| 0    | 0    |
  |2021-03-23 13:44:00.134000+00:00| NaN  | NaN  |
  |2021-03-23 15:04:00.134000+00:00| NaN  | NaN  |
  </code></pre>