title: How to drop rows that are composed of a series of specifc values
body: <p>So I have the following df:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  
  data = [['John', 1.0,    2.0, 3.0],
          ['Mary', 0.0, np.nan,  ""],
          ['Chad', 0.0, np.nan, 1.0]]
  
  df = pd.DataFrame(data, columns=['Name', 'Value1', 'Value2', 'Value3'])
  df.set_index('Name', inplace=True)
  </code></pre>
  
  <p>I need to drop every column that contains only: 0.0 or NaN or "". In other words, on the example above I want to drop only Mary's row. Chad's row wouldn't be dropped since it has the 1.0 on the third column. But I got stuck. </p>
  
  <p>I have been trying to do it this way:</p>
  
  <pre><code>df = df.drop(df[(df[:] == 0) | (df[:] == '') | (df[:] == np.nan)])
  </code></pre>
  
  <p>But it returns this error:</p>
  
  <pre><code>FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
    result = method(y)
  </code></pre>
  
  <p>Any ideas on how to do this?</p>