title: Exception handling when changing Pandas dataframe type
body: <p>I have a Pandas dataframe with a single column of strings. I want to convert the column data to float. Some of the values cannot be converted to float due to their format. I want to omit these "illegal strings" from the result and only extract values that can be legally re-cast as floats. The starting data:</p>
  
  <pre><code>test=pd.DataFrame()
  test.loc[0,'Value']='&lt;3'
  test.loc[1,'Value']='10'
  test.loc[2,'Value']='Detected'
  test.loc[3,'Value']=''
  </code></pre>
  
  <p>The desired output contains only strings that could be re-cast as floats (in this case, 10):</p>
  
  <pre><code>cleanDF=test['Value'].astype(float)
  cleanDF
  0    10
  Name: Value, dtype: float64
  </code></pre>
  
  <p>Of course, this throws an error as expected on the illegal string for float conversion:</p>
  
  <pre><code>ValueError: could not convert string to float: &lt;3
  </code></pre>
  
  <p>Is there a simple way to solve this if the dataframe is large and contains many illegal strings in 'Value'?</p>
  
  <p>Thanks.</p>