How do I select and store columns greater than a number in pandas?

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Sample DF:

In [79]: df = pd.DataFrame(np.random.randint(5, 15, (10, 3)), columns=list('abc'))

In [80]: df
Out[80]:
    a   b   c
0   6  11  11
1  14   7   8
2  13   5  11
3  13   7  11
4  13   5   9
5   5  11   9
6   9   8   6
7   5  11  10
8   8  10  14
9   7  14  13

present only those rows where b > 10

In [81]: df[df.b > 10]
Out[81]:
   a   b   c
0  6  11  11
5  5  11   9
7  5  11  10
9  7  14  13

Minimums (for all columns) for the rows satisfying b > 10 condition

In [82]: df[df.b > 10].min()
Out[82]:
a     5
b    11
c     9
dtype: int32

Minimum (for the b column) for the rows satisfying b > 10 condition

In [84]: df.loc[df.b > 10, 'b'].min()
Out[84]: 11

UPDATE: starting from Pandas 0.20.1 the .ix indexer is deprecated, in favor of the more strict .iloc and .loc indexers.

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