title: How to filter a pandas's DataFrame without referencing columns?
body: <p>In a pandas's Dataframe, I look for a way to remove all rows that contain a False value.</p>
  
  <p>I need to do it for any DataFrame, that means I don't know the name of columns and I cannot reference them.</p>
  
  <p>For example:</p>
  
  <pre><code>df = pd.DataFrame( { 'a': np.random.random_integers(0, 10, 10), 'b': np.random.random_integers(0, 10, 10) } )
  
  # filter without referencing columns:
  print( df[ df % 2 == 0] )
  
  # filter with column referencing :
  print( df[ (df.a % 2 == 0) &amp; (df.b % 2 == 0)] )
  </code></pre>
  
  <p>..produces the result:</p>
  
  <pre><code>      a     b
  0   NaN   NaN
  1   NaN   6.0
  2   4.0   NaN
  3   8.0  10.0
  4  10.0   NaN
  5   4.0   NaN
  6   NaN   2.0
  7   NaN   NaN
  8   6.0   NaN
  9   0.0   NaN
  
     a   b
  3  8  10
  </code></pre>
  
  <p>The goal is to have the result filtered (like the second output), but without referencing columns, in order to enable a filter that does not depend on a specific DataFrame.</p>
  
  <p>With the same code: </p>
  
  <pre><code>df = pd.DataFrame( { 'Nantes': np.random.random_integers(0, 10, 10), 'Paris': np.random.random_integers(0, 10, 10) } )
  </code></pre>
  
  <p>would produce</p>
  
  <pre><code>   Nantes   Paris
  3  8        10
  </code></pre>