Create bool mask from filter results in Pandas : <p>I know how to create a mask to filter a dataframe when querying a single column:</p>

<pre><code>import pandas as pd
import datetime
index = pd.date_range('2013-1-1',periods=100,freq='30Min')
data = pd.DataFrame(data=list(range(100)), columns=['value'], index=index)
data['value2'] = 'A'
data['value2'].loc[0:10] = 'B'

data

    value   value2
2013-01-01 00:00:00 0   B
2013-01-01 00:30:00 1   B
2013-01-01 01:00:00 2   B
2013-01-01 01:30:00 3   B
2013-01-01 02:00:00 4   B
2013-01-01 02:30:00 5   B
2013-01-01 03:00:00 6   B
</code></pre>

<p>I use a simple mask here:</p>

<pre><code>mask = data['value'] &gt; 4
data[mask]
    value   value2
2013-01-01 02:30:00 5   B
2013-01-01 03:00:00 6   B
2013-01-01 03:30:00 7   B
2013-01-01 04:00:00 8   B
2013-01-01 04:30:00 9   B
2013-01-01 05:00:00 10  A
</code></pre>

<p>My question is how to create a mask with multiple columns?  So if I do this:</p>

<pre><code>data[data['value2'] == 'A' ][data['value'] &gt; 4]
</code></pre>

<p>This filters as I would expect but how do I create a bool mask from this as per my other example?  I have provided the test data for this but I often want to create a mask on other types of data so Im looking for any pointers please. </p>
