title: In pandas (>1.0), how to convert dtype of a float series with missing values to integer?
body: <p>Converting <code>Series</code> dtypes is easy:</p>
  
  <p><code>s = pd.Series([1.1, 2.2]).astype(int)</code></p>
  
  <p>However, if the <code>Series contains a</code>pd.NA` (N/A value), the conversion breaks</p>
  
  <p><code>s = pd.Series([1.1, 2.2, pd.NA]).astype(int)</code></p>
  
  <p>Results in <code>TypeError: int() argument must be a string, a bytes-like object or a number, not 'NAType'</code></p>
  
  <p>Despite the fact, that we can store missing values in <code>Series</code> of ints</p>
  
  <p><code>s = pd.Series([1, 2, pd.NA]).astype(pd.Int64Dtype())</code></p>