title: Remove rows violating column datatype
body: <p>I have following dataframe from which I have to remove row violating the datatype of the column:</p>
  <pre><code>            ts       a         b         c      d
  0   1555338562    9.01      True   1648.37   1.01
  1   1555338563    9.01     1.022   1648.37   1.01
  2   1555338564    9.01     1.022   AVC       1.01
  3   1555338565    9.01     1.022   1648.37   1.01
  4   1555338566    9.01     1.022   1648.33   1.01
  5   1555338567    test     1.022   1648.33   1.01
  </code></pre>
  <p>Datatype of Columns:</p>
  <pre><code>data_types = { &quot;ts&quot;: &quot;int64&quot;, 
                 &quot;a&quot;: &quot;float64&quot;, 
                 &quot;b&quot;: &quot;float64&quot;, 
                 &quot;c&quot;: &quot;float64&quot;, 
                 &quot;d&quot;: &quot;float64&quot;
               }
  </code></pre>
  <p>In above example, rows 0, 2, and 5 will be removed as they violate datatype for columns b, c and a respectively. The expected output is:</p>
  <pre><code>            ts       a         b         c      d
  0   1555338563    9.01     1.022   1648.37   1.01
  1   1555338565    9.01     1.022   1648.37   1.01
  2   1555338566    9.01     1.022   1648.33   1.01
  </code></pre>
  <p>Any suggestion how can I achieve this using pandas?</p>
  <p>EDIT:
  We can have string or boolean column as well in future. For example,</p>
  <pre><code>            ts       a         b         c      d    e       f
  0   1555338562    9.01      True   1648.37   1.01 True  Test_1
  1   1555338563    9.01     1.022   1648.37   1.01 True  Test_2
  2   1555338564    9.01     1.022   AVC       1.01 True  Test_2
  3   1555338565    9.01     1.022   1648.37   1.01 True  Test_2
  4   1555338566    9.01     1.022   1648.33   1.01 True  Test_2
  5   1555338567    test     1.022   1648.33   1.01 False Test_2
  </code></pre>