"不正当" pandas categorical.from_codes

时间:2017-12-17 18:08:04

标签: python pandas categorical-data python-iris

我必须为分类数据分配标签。让我们考虑一下虹膜的例子:

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris

iris = load_iris()

print "targets: ", np.unique(iris.target)
print "targets: ", iris.target.shape
print "target_names: ", np.unique(iris.target_names)
print "target_names: ", iris.target_names.shape

将打印出来:

  

目标:[0 1 2]目标:(150L,)target_names:[' setosa'   '云芝' ' virginica'] target_names:(3L,)

为了产生所需的标签我使用pandas.Categorical.from_codes:

print pd.Categorical.from_codes(iris.target, iris.target_names)
  

[setosa,setosa,setosa,setosa,setosa,...,virginica,virginica,   virginica,virginica,virginica]长度:150类别(3,对象):   [setosa,versicolor,virginica]

让我们尝试一个不同的例子:

# I define new targets
target = np.array([123,123,54,123,123,54,2,54,2])
target = np.array([1,1,3,1,1,3,2,3,2])
target_names = np.array(['paglia','gioele','papa'])
#---
print "targets: ", np.unique(target)
print "targets: ", target.shape
print "target_names: ", np.unique(target_names)
print "target_names: ", target_names.shape

如果我再次尝试转换标签中的分类值:

print pd.Categorical.from_codes(target, target_names) 

我收到错误消息:

  

C:\用户\ ianni \ Anaconda2 \ lib中\站点包\大熊猫\芯\ categorical.pyc   in from_codes(cls,代码,类别,有序)       459       460如果len(代码)和(codes.max()> = len(类别)或codes.min()< -1):    - > 461引发ValueError("代码需要介于-1和&#34之间;       462" len(类别)-1")       463

     

ValueError:代码需要介于-1和len(类别)-1之间

你知道为什么吗?

1 个答案:

答案 0 :(得分:1)

  

你知道为什么吗?

如果您将仔细查看错误追溯:

In [128]: pd.Categorical.from_codes(target, target_names)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-128-c2b4f6ac2369> in <module>()
----> 1 pd.Categorical.from_codes(target, target_names)

~\Anaconda3_5.0\envs\py36\lib\site-packages\pandas\core\categorical.py in from_codes(cls, codes, categories, ordered)
    619
    620         if len(codes) and (codes.max() >= len(categories) or codes.min() < -1):
--> 621             raise ValueError("codes need to be between -1 and "
    622                              "len(categories)-1")
    623

ValueError: codes need to be between -1 and len(categories)-1

您将看到满足以下条件:

codes.max() >= len(categories)

在你的情况下:

In [133]: target.max() >= len(target_names)
Out[133]: True

换句话说,pd.Categorical.from_codes()期望codes为从0len(categories) - 1

的连续数字

解决方法:

In [173]: target
Out[173]: array([123, 123,  54, 123, 123,  54,   2,  54,   2])

helper dicts:

In [174]: mapping = dict(zip(np.unique(target), np.arange(len(target_names))))

In [175]: mapping
Out[175]: {2: 0, 54: 1, 123: 2}

In [176]: reverse_mapping = {v:k for k,v in mapping.items()}

In [177]: reverse_mapping
Out[177]: {0: 2, 1: 54, 2: 123}

建立分类系列:

In [178]: ser = pd.Categorical.from_codes(pd.Series(target).map(mapping), target_names)

In [179]: ser
Out[179]:
[papa, papa, gioele, papa, papa, gioele, paglia, gioele, paglia]
Categories (3, object): [paglia, gioele, papa]

反向映射:

In [180]: pd.Series(ser.codes).map(reverse_mapping)
Out[180]:
0    123
1    123
2     54
3    123
4    123
5     54
6      2
7     54
8      2
dtype: int64