Python 使用 Pandas 将分类值转换为二进制值
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/32387266/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Converting categorical values to binary using pandas
提问by Rkz
I am trying to convert categorical values into binary values using pandas. The idea is to consider every unique categorical value as a feature (i.e. a column) and put 1 or 0 depending on whether a particular object (i.e. row) was assigned to this category. The following is the code:
我正在尝试使用 Pandas 将分类值转换为二进制值。这个想法是将每个唯一的分类值视为一个特征（即列），并根据是否将特定对象（即行）分配给该类别来放置 1 或 0。以下是代码：
data = pd.read_csv('somedata.csv')
converted_val = data.T.to_dict().values()
vectorizer = DV( sparse = False )
vec_x = vectorizer.fit_transform( converted_val )
numpy.savetxt('out.csv',vec_x,fmt='%10.0f',delimiter=',')
My question is, how to save this converted data with the column names?. In the above code, I am able to save the data using numpy.savetxtfunction, but this simply saves the array and the column names are lost. Alternatively, is there a much efficient way to perform the above operation?.
我的问题是，如何用列名保存这个转换后的数据？在上面的代码中，我可以使用numpy.savetxt函数保存数据，但这只是保存了数组并且列名丢失了。或者，有没有更有效的方法来执行上述操作？
采纳答案by YS-L
It seems that you are using scikit-learn's DictVectorizerto convert the categorical values to binary. In that case, to store the result along with the new column names, you can construct a new DataFrame with values from vec_xand columns from DV.get_feature_names(). Then, store the DataFrame to disk (e.g. with to_csv()) instead of the numpy array.
您似乎正在使用 scikit-learnDictVectorizer将分类值转换为二进制值。在这种情况下，要将结果与新列名称一起存储，您可以使用来自 的值vec_x和来自 的列构造一个新的 DataFrame DV.get_feature_names()。然后，将 DataFrame 存储到磁盘（例如使用to_csv()）而不是 numpy 数组。
Alternatively, it is also possible to use pandasto do the encoding directly with the get_dummiesfunction:
或者，也可以pandas直接使用get_dummies函数进行编码：
import pandas as pd
data = pd.DataFrame({'T': ['A', 'B', 'C', 'D', 'E']})
res = pd.get_dummies(data)
res.to_csv('output.csv')
print res
Output:
输出：
T_A T_B T_C T_D T_E
0 1 0 0 0 0
1 0 1 0 0 0
2 0 0 1 0 0
3 0 0 0 1 0
4 0 0 0 0 1
回答by YS-L
You mean "one-hot" encoding?
你的意思是“one-hot”编码？
Say you have the following dataset:
假设您有以下数据集：
import pandas as pd
df = pd.DataFrame([
['green', 1, 10.1, 0],
['red', 2, 13.5, 1],
['blue', 3, 15.3, 0]])
df.columns = ['color', 'size', 'prize', 'class label']
df
Now, you have multiple options ...
现在，您有多种选择...
A) The Tedious Approach
A) 乏味的方法
color_mapping = {
'green': (0,0,1),
'red': (0,1,0),
'blue': (1,0,0)}
df['color'] = df['color'].map(color_mapping)
df
import numpy as np
y = df['class label'].values
X = df.iloc[:, :-1].values
X = np.apply_along_axis(func1d= lambda x: np.array(list(x[0]) + list(x[1:])), axis=1, arr=X)
print('Class labels:', y)
print('\nFeatures:\n', X)
Yielding:
产量：
Class labels: [0 1 0]
Features:
[[ 0. 0. 1. 1. 10.1]
[ 0. 1. 0. 2. 13.5]
[ 1. 0. 0. 3. 15.3]]
B) Scikit-learn's DictVectorizer
B) Scikit-learn 的 DictVectorizer
from sklearn.feature_extraction import DictVectorizer
dvec = DictVectorizer(sparse=False)
X = dvec.fit_transform(df.transpose().to_dict().values())
X
Yielding:
产量：
array([[ 0. , 0. , 1. , 0. , 10.1, 1. ],
[ 1. , 0. , 0. , 1. , 13.5, 2. ],
[ 0. , 1. , 0. , 0. , 15.3, 3. ]])
C) Pandas' get_dummies
C) 熊猫 get_dummies
pd.get_dummies(df)
