从熊猫切割分类箱

Question

使用pandas cut我可以通过提供边缘来定义bin,而pandas可以创建像bin这样的bin (a, b].

我的问题是如何对箱子进行分类(从最低到最高)？

import numpy as np
import pandas as pd

y = pd.Series(np.random.randn(100))

x1 = pd.Series(np.sign(np.random.randn(100)))
x2 = pd.cut(pd.Series(np.random.randn(100)), bins = [-3, -0.5, 0, 0.5, 3])

model = pd.concat([y, x1, x2], axis = 1, keys = ['Y', 'X1', 'X2'])

我有一个中间结果,其中保留了箱的顺序

int_output = model.groupby(['X1', 'X2']).mean().unstack()
int_output.columns = int_output.columns.get_level_values(1)

X2    (-3, -0.5]  (-0.5, 0]  (0, 0.5]  (0.5, 3]
X1                                             
-1.0    0.101475  -0.344419 -0.482992 -0.015179
 1.0    0.249961   0.484757 -0.066383 -0.249414

但后来我做了其他任意改变垃圾箱顺序的操作:

output = pd.concat(int_output.to_dict('series'), axis = 1)

      (-0.5, 0]  (-3, -0.5]  (0, 0.5]  (0.5, 3]
X1                                             
-1.0  -0.344419    0.101475 -0.482992 -0.015179
 1.0   0.484757    0.249961 -0.066383 -0.249414

现在我想在条形图中绘制数据,但我希望从最低(-3,-0.5)到最高(0.5,3)分类.

我想我可以通过操作字符串来实现这一点,使用","拆分然后清理括号,但我想知道是否有更好的方法.

Answer 1

主要问题是输ordered CategoricalIndex。

np.random.seed(12456)
y = pd.Series(np.random.randn(100))
x1 = pd.Series(np.sign(np.random.randn(100)))
x2 = pd.cut(pd.Series(np.random.randn(100)), bins = [-3, -0.5, 0, 0.5, 3])

model = pd.concat([y, x1, x2], axis = 1, keys = ['Y', 'X1', 'X2'])
int_output = model.groupby(['X1', 'X2']).mean().unstack()
int_output.columns = int_output.columns.get_level_values(1)

print (int_output)
X2    (-3, -0.5]  (-0.5, 0]  (0, 0.5]  (0.5, 3]
X1                                             
-1.0    0.230060  -0.079266 -0.079834 -0.064455
 1.0   -0.451351   0.268688  0.020091 -0.280218

print (int_output.columns)
CategoricalIndex(['(-3, -0.5]', '(-0.5, 0]', '(0, 0.5]', '(0.5, 3]'], 
                 categories=['(-3, -0.5]', '(-0.5, 0]', '(0, 0.5]', '(0.5, 3]'], 
                 ordered=True, name='X2', dtype='category')

output = pd.concat(int_output.to_dict('series'), axis = 1)
print (output)
      (-0.5, 0]  (-3, -0.5]  (0, 0.5]  (0.5, 3]
X1                                             
-1.0  -0.079266    0.230060 -0.079834 -0.064455
 1.0   0.268688   -0.451351  0.020091 -0.280218

print (output.columns)
Index(['(-0.5, 0]', '(-3, -0.5]', '(0, 0.5]', '(0.5, 3]'], dtype='object')

一个可能的解决方案是extract第一个数字output.columns，创建助手系列并对其进行排序。最后的reindex原始专栏：

cat = output.columns.str.extract('\((.*),', expand=False).astype(float)
a = pd.Series(cat, index=output.columns).sort_values()
print (a)
(-3, -0.5]   -3.0
(-0.5, 0]    -0.5
(0, 0.5]      0.0
(0.5, 3]      0.5
dtype: float64

output = output.reindex(columns=a.index)
print (output)
      (-3, -0.5]  (-0.5, 0]  (0, 0.5]  (0.5, 3]
X1                                             
-1.0    0.230060  -0.079266 -0.079834 -0.064455
 1.0   -0.451351   0.268688  0.020091 -0.280218