title: strange pandas behavior: masking missing datetime index values creates duplicate rows
body: <p>I've encountered a strange problem while trying to mask rows with missing datetime index values  using <a href="https://pandas.pydata.org/docs/reference/api/pandas.Index.dropna.html" rel="nofollow noreferrer">pandas.Index.dropna</a>. Unexpectedly, applying a mask to select only the non-missing datetime values has the consequence of duplicating identically-indexed rows.</p>
  <p>Here is a small example which reproduces the problem (pandas 1.4.2):</p>
  <pre><code>import pandas as pd
  dates = [&quot;01-May-2022&quot;, &quot;02-May-2022&quot;, &quot;02-May-2022&quot;, &quot;03-May-2022&quot;, &quot;04-May-2022&quot;]
  values = [1, 2, 3, 4, 5]
  series = pd.Series(values, index=dates)
  seriesB = series.loc[series.index.dropna()]
  
  In [28]: series
  Out[28]: 
  01-May-2022    1
  02-May-2022    2
  02-May-2022    3
  03-May-2022    4
  04-May-2022    5
  dtype: int64
  
  In [29]: seriesB
  Out[29]: 
  01-May-2022    1
  02-May-2022    2
  02-May-2022    3
  02-May-2022    2
  02-May-2022    3
  03-May-2022    4
  04-May-2022    5
  dtype: int64
  </code></pre>
  <p>Does anyone know why seriesB has the additional rows?</p>