Optional Pandas integration

[jorenham]

Most lmo.l_* functions are now available as pandas.Series and pandas.DataFrame (accessor) methods.

Whereas the lmo.l_* functions return numpy arrays (or scalars), the new methods return properly-indexed Series or DataFrame instances.
Also, the univariate DataFrame methods accept pandas-style axis parameters, e.g. axis='index or axis='columns , as alias for axis=0 or axis=1, respectively.

Examples

Univariate summary statistics:

>>> df = pd.DataFrame({'a': [1, 2, 2, 3, 4], 'b': [3, 4, 4, 4, 4]})
>>> df.l_stats()
        a    b
r
1  2.400000  3.8
2  0.700000  0.2
3  0.142857 -1.0
4  0.285714  1.0
>>> df.aggregate(['mean', 'std', 'skew', 'kurt'])
            a         b
mean  2.400000  3.800000
std   1.140175  0.447214
skew  0.404796 -2.236068
kurt -0.177515  5.000000

Comparison of L-correlation, and Pearson correlation matrices:

>>> df = pd.DataFrame({'dogs': [.2, .0, .5, .4], 'cats': [.3, .2, .0, .1]})
>>> df.l_corr()
    dogs      cats
dogs   1.0 -0.764706
cats  -0.8  1.000000
>>> df.corr()
        dogs      cats
dogs  1.000000 -0.756889
cats -0.756889  1.000000