如何在python中生成词频矩阵

因为你用 machine-learning ,我建议您使用 sklearn.CountVectorizer :

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer

corpus = ['He is a good person',
          'He is bad student',
          'He is hardworking']
df = pd.DataFrame(data=corpus, columns=['sentences'])

vectorizer = CountVectorizer(vocabulary=['he', 'is', 'a', 'good', 'person', 'bad', 'student', 'hardworking'], min_df=0,
                             stop_words=frozenset(), token_pattern=r"(?u)\b\w+\b")
X = vectorizer.fit_transform(df['sentences'].values)
result = pd.DataFrame(data=X.toarray(), columns=vectorizer.get_feature_names())
print(result)

产量

   he  is  a  good  person  bad  student  hardworking
0   1   1  1     1       1    0        0            0
1   1   1  0     0       0    1        1            0
2   1   1  0     0       0    0        0            1

这是 get_dummies 问题,为了达到你所需要的,你只需要跟随 reindex

s='He,is,a,good,person,bad,student,hardworking'.split(',')
df.W1.str.get_dummies(sep=' ').reindex(columns=s)
Out[914]: 
   He  is  a  good  person  bad  student  hardworking
0   1   1  1     1       1    0        0            0
1   1   1  0     0       0    1        1            0
2   1   1  0     0       0    0        0            1

数据输入

                    W1               W2
0  He is a good person     Positive Sense
1  He is bad student       Negative Sense
2  He is hardworking       Positive Sense