title: How to replace row with float values with in a nested numpy array with a row of `NaN`s?
body: <p>Say i have a numpy array:</p>
  
  <pre><code>a=np.array([[7,2,4],[1.2,7.4,3],[1.5,3.6,3.4]])
  </code></pre>
  
  <p>And my goal is to replace rows that which contain floats with a row of <code>NaN</code>s, and so far this is my attempt:</p>
  
  <pre><code>a[a.dtype==float]=np.nan
  </code></pre>
  
  <p>Which works, but only the first row that should be <code>NaN</code>, there's an second row that should be <code>NaN</code> that's left alone.</p>
  
  <p>So my desired output would look like:</p>
  
  <pre><code>[[ 7.   2.   4.]
   [ nan  nan  nan]
   [ nan  nan  nan]]
  </code></pre>