Python 预定义词的词频

Question

我在文本文件中有一组数据，我想根据预定义的词（drive、street、i、lives）构建一个频率 table。下面是示例

 ID |  Text
 ---|--------------------------------------------------------------------
 1  | i drive to work everyday in the morning and i drive back in the evening on main street
 2  | i drive back in a car and then drive to the gym on 5th street
 3  | Joe lives in Newyork on NY street
 4  | Tod lives in Jersey city on NJ street

这里是我想得到的输出

ID  |  drive |  street  |   i  |  lives
----|--------|----------|------|-------
1   |   2    |    1     |   2  |   0
2   |   2    |    1     |   1  |   0
3   |   0    |    1     |   0  |   1
4   |   0    |    1     |   0  |   1

这是我正在使用的代码，我可以找到字数，但这并不能解决我的需求，我想使用一组预定义的字来找到字数，如图所示以上

   from nltk.corpus import stopwords
   import string
   from collections import Counter
   import nltk
   from nltk.tag import pos_tag

   xy = open('C:\Python\data\file.txt').read().split()
   q = (w.lower() for w in xy)

   stopset = set(stopwords.words('english'))

   filtered_words = [word for word in xyz if not word  in stopset]
   filtered_words = []
   for word in xyz:
       if word not in stopset:
       filtered_words.append(word)
   print(Counter(filtered_words))
   print(len(filtered_words))

Answer 1

sklearn.feature_extraction.text.CountVectorizer 之类的内容似乎与您要查找的内容很接近。另外，collections.Counter 可能会有帮助。你打算如何使用这个数据结构？如果您碰巧尝试使用机器 learning/prediction，那么值得研究一下 sklearn.feature_extraction.text.

中的不同矢量化器

编辑：

text = ['i drive to work everyday in the morning and i drive back in the evening on main street',
        'i drive back in a car and then drive to the gym on 5th street',
        'Joe lives in Newyork on NY street',
        'Tod lives in Jersey city on NJ street']

from sklearn.feature_extraction.text import CountVectorizer

vocab = ['drive', 'street', 'i', 'lives']

vectorizer = CountVectorizer(vocabulary = vocab)

# turn the text above into a matrix of shape R X C
# where R is number of rows (elements in your text array)
# and C is the number of elements in the set of all words in your text array
X = vectorizer.fit_transform(text)

# sparse to dense matrix
X = X.toarray()

# get the feature names from the already-fitted vectorizer
vectorizer_feature_names = vectorizer.get_feature_names()

# prove that the vectorizer's feature names are identical to the vocab you specified above
assert vectorizer_feature_names == vocab

# make a table with word frequencies as values and vocab as columns
out_df = pd.DataFrame(data = X, columns = vectorizer_feature_names)

print(out_df)

并且，您的结果：

       drive  street  i  lives
    0      2       1  0      0
    1      2       1  0      0
    2      0       1  0      1
    3      0       1  0      1

Answer 2

只需询问您想要的词，而不是您不想要的停用词：

filtered_words = [word for word in xyz if word in ['drive', 'street', 'i', 'lives']]

Answer 3

如果你想找到列表中某个单词的数量，你可以使用 list.count(word) 来找到它，所以如果你有一个单词列表你想获得频率，你可以这样做像这样：

wanted_words = ["drive", "street", "i", "lives"]
frequencies = [xy.count(i) for i in wanted_words]

Answer 4

基于 Alex Halls 的想法进行预过滤 - 之后只需使用 defaultdict。用来数数真的很舒服

from collections import defaultdict
s = 'i drive to work everyday in the morning and i drive back in the evening on main street'
filtered_words = [word for word in s.split() 
                  if word in ['drive', 'street', 'i', 'lives']]
d = defaultdict(int)
for k in filtered_words: 
    d[k] += 1
print(d)

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