在python中如何将字典转换为矩阵？

Question

我有一本这样的字典：

{设备1:（新闻1，新闻2，…），设备2:（新闻2，新闻4，…）}

如何在python中将它们转换成二维0-1矩阵？看起来像这样：

         news1 news2 news3 news4
device1    1     1     0      0
device2    0     1     0      1
device3    1     0     0      1

Answer 1

下面是将字典转换为矩阵的另一种选择：

# Load library
from sklearn.feature_extraction import DictVectorizer

# Our dictionary of data
data_dict = [{'Red': 2, 'Blue': 4},
             {'Red': 4, 'Blue': 3},
             {'Red': 1, 'Yellow': 2},
             {'Red': 2, 'Yellow': 2}]
# Create DictVectorizer object
dictvectorizer = DictVectorizer(sparse=False)

# Convert dictionary into feature matrix
features = dictvectorizer.fit_transform(data_dict)
print(features)
#output
'''
[[4. 2. 0.]
 [3. 4. 0.]
 [0. 1. 2.]
 [0. 2. 2.]]
'''
print(dictvectorizer.get_feature_names())
#output
'''
['Blue', 'Red', 'Yellow']
'''

Answer 2

下面是一些使用numpy包创建矩阵（或2D数组）的代码。注意，我们必须按顺序使用一个名称列表，因为字典不一定按输入的顺序存储键/值。

import numpy as np

dataDict = {'device1':(1,1,0,1), 'device2':(0,1,0,1), 'device3':(1,0,0,1)}
orderedNames = ['device1','device2','device3']

dataMatrix = np.array([dataDict[i] for i in orderedNames])

print dataMatrix

输出为：

[[1 1 0 1]
 [0 1 0 1]
 [1 0 0 1]]

Answer 3

再加上这一点，因为我认为之前的答案假设你的数据结构不同，不会直接解决你的问题。

假设我正确理解了您的数据结构，并且矩阵中索引的名称并不重要：

from sklearn.feature_extraction import DictVectorizer

dict = {'device1':['news1', 'news2'],
        'device2':['news2', 'news4'],
        'device3':['news1', 'news4']}

restructured = []

for key in dict:
    data_dict = {}
    for news in dict[key]:
        data_dict[news] = 1
    data_dict['news3'] = 0
    restructured.append(data_dict)

#restructured should now look like
'''
[{'news1':1, 'news2':1, 'news3':0},
 {'news2':1, 'news4':1, 'news3':0},
 {'news1':1, 'news4':1, 'news3':0}]
'''

dictvectorizer = DictVectorizer(sparse=False)
features = dictvectorizer.fit_transform(restructured)

print(features)

#output
'''
[[1, 1, 0, 0],
 [0, 1, 1, 0],
 [1, 0, 1, 0]]
'''
print(dictvectorizer.get_feature_names())
#output
'''
['news1', 'news2', 'news4', 'news3']
'''