title: find values of pandas columns that only has numbers
body: <p>I am transitioning from R to Python right now and have a sample pandas dataframe as follows:</p>
  
  <pre><code> df = pd.DataFrame({'x': pd.Series([1.0, 'Joe Young', '3M-Fit']), 'y': pd.Series(['1000', '1001', '1002'], dtype=int), 'z' : pd.Series(['Henry', 'Henry','Henry'])})
  
             x     y      z
  0          1  1000  Henry
  1  Joe Young  1001  Henry
  2     3M-Fit  1002  Henry
  </code></pre>
  
  <p>When I look at the datatype of each row for first column it is combination of str and float:</p>
  
  <pre><code>    df['x'].map(lambda x: type(x))
  
  0    &lt;type 'float'&gt;
  1      &lt;type 'str'&gt;
  2      &lt;type 'str'&gt;
  Name: x, dtype: object
  </code></pre>
  
  <p>What I would like to do is to print those rows of dataframe (x and y columns included) where type(x) is a float. So, in this case I would like sample output as: </p>
  
  <pre><code>               x     y      z
  0              1    1000   Henry
  </code></pre>
  
  <p>I looked <a href="https://stackoverflow.com/questions/25039626/find-numeric-columns-in-pandas-python">here</a> and <a href="https://stackoverflow.com/questions/21771133/finding-non-numeric-rows-in-dataframe-in-pandas">here</a>. But it either applies to complete dataframe or gives True and False values. I want to apply it to each row of a particular column of interest and want to get real values in all columns for those rows of interest. </p>