如何在pandas.cut中获得合适的关卡表示？

Question

Is there an easy way to obtain the values of the levels produced by pandas.cut?

For example:

import pandas as pd
x = pd.cut(np.arange(0,20), 10)

x
Out[1]: 
 (-0.019, 1.9]
 (-0.019, 1.9]
    (1.9, 3.8]
    (1.9, 3.8]
    (3.8, 5.7]
    (3.8, 5.7]
    (5.7, 7.6]
    (5.7, 7.6]
    (7.6, 9.5]
    (7.6, 9.5]
   (9.5, 11.4]
   (9.5, 11.4]
  (11.4, 13.3]
  (11.4, 13.3]
  (13.3, 15.2]
  (13.3, 15.2]
  (15.2, 17.1]
  (15.2, 17.1]
    (17.1, 19]
    (17.1, 19]
Levels (10): Index(['(-0.019, 1.9]', '(1.9, 3.8]', '(3.8, 5.7]',
                    '(5.7, 7.6]', '(7.6, 9.5]', '(9.5, 11.4]',
                    '(11.4, 13.3]', '(13.3, 15.2]', '(15.2, 17.1]',
                    '(17.1, 19]'], dtype=object)

What I would like to get is something like:

x.magic_method
Out[2]:
[[-0.019, 1.9], [1.9, 3.8], [3.8, 5.7],
                        [5.7, 7.6], [7.6, 9.5], [9.5, 11.4],
                        [11.4, 13.3], [13.3, 15.2], (15.2, 17.1],
                        [17.1, 19]]

or some other representation more suitable to manipulation. Instead, we obtain the index by using x.levels, but this representation is a unicode object, so I have to use a couple of loops to get what I want.

UPDATE :

By the way, I need a solution that works with a sequence of values in the second argument: pd.cut(np.arange(0,20), arr)

Answer 1

You can convert from unicode list to an array by following code:

import pandas as pd
x = pd.cut(np.arange(0,20), 10)
np.array(map(lambda t:t[1:-1].split(","), x.levels), float)

Answer 2

You can do this, but prob better to explain what you are actually doing; eg you already have the Categorical variable.

In [27]: x, bins = pd.cut(np.arange(0,20), 10, retbins=True)

In [28]: [ [ round(l,3), round(r,3) ] for l, r in zip(bins[:-1],bins[1:]) ]
Out[28]: 
[[-0.019, 1.9],
 [1.9, 3.8],
 [3.8, 5.7],
 [5.7, 7.6],
 [7.6, 9.5],
 [9.5, 11.4],
 [11.4, 13.3],
 [13.3, 15.2],
 [15.2, 17.1],
 [17.1, 19.0]]