title: Filter non-NaN values by column in Pandas
body: <p>I would like to filter out <code>NaN</code> values and keep remaining rows in <code>Label</code> column.</p>
  <p><code>df</code>:</p>
  <pre><code>        Timestamp               Label
  157505  2010-09-21 23:13:21.090 1
  321498  2010-09-22 00:44:14.890 1
  332687  2010-09-22 00:44:15.890 1
  330028  2010-09-22 00:44:17.890 NaN
  293410  2010-09-22 00:44:18.440 2
  23093   2010-09-22 00:44:19.890 2
  282054  2010-09-22 00:44:23.440 2
  158381  2010-09-22 01:04:33.440 NaN
  317397  2010-09-22 01:27:01.790 NaN
  170770  2010-09-22 02:18:52.850 NaN
  </code></pre>
  <p>reproducible example:</p>
  <pre><code>from pandas import *
  import numpy as np 
  import pandas as pd 
  
  df = pd.DataFrame({'Timestamp': {157505: Timestamp('2010-09-21 23:13:21.090000'),
    321498: Timestamp('2010-09-22 00:44:14.890000'),
    332687: Timestamp('2010-09-22 00:44:15.890000'),
    330028: Timestamp('2010-09-22 00:44:17.890000'),
    293410: Timestamp('2010-09-22 00:44:18.440000'),
    23093: Timestamp('2010-09-22 00:44:19.890000'),
    282054: Timestamp('2010-09-22 00:44:23.440000'),
    158381: Timestamp('2010-09-22 01:04:33.440000'),
    317397: Timestamp('2010-09-22 01:27:01.790000'),
    170770: Timestamp('2010-09-22 02:18:52.850000')},
   'Label': {157505: 1,
    321498: 1,
    332687: 1,
    330028: 'NaN',
    293410: 2,
    23093: 2,
    282054: 2,
    158381: 'NaN',
    317397: 'NaN',
    170770: 'NaN'}})
  df
  </code></pre>
  <p>I tried:</p>
  <pre><code>df[df.Label.notnull()]
  </code></pre>
  <p>and got exactly the same table:</p>
  <pre><code>
          Timestamp               Label
  157505  2010-09-21 23:13:21.090 1
  321498  2010-09-22 00:44:14.890 1
  332687  2010-09-22 00:44:15.890 1
  330028  2010-09-22 00:44:17.890 NaN
  293410  2010-09-22 00:44:18.440 2
  23093   2010-09-22 00:44:19.890 2
  282054  2010-09-22 00:44:23.440 2
  158381  2010-09-22 01:04:33.440 NaN
  317397  2010-09-22 01:27:01.790 NaN
  170770  2010-09-22 02:18:52.850 NaN
  </code></pre>
  <p>What's wrong and what's the best way to do it?</p>