Question

我试图创建一个名为＆＃34; words_in_texts＆＃34;得到这样的结果

words_in_texts(['hello', 'bye', 'world'], 
               pd.Series(['hello', 'hello world hello'])

array([[1, 0, 0],
   [1, 0, 1]])

我相信这个函数的参数应该是一个包含所有单词和系列的列表。

def words_in_texts(words, texts):
'''
Args:
    words (list-like): words to find
    texts (Series): strings to search in

Returns:
    NumPy array of 0s and 1s with shape (n, p) where n is the
    number of texts and p is the number of words.
'''
indicator_array = texts.str.contains(words)

return indicator_array

我对如何创建二维数组结果感到困惑，有人可以帮我解决这个问题吗？提前谢谢！

Answer 1

使用sklearn.feature_extraction.text.CountVectorizer：

In [52]: from sklearn.feature_extraction.text import CountVectorizer

In [53]: vect = CountVectorizer(vocabulary=['hello', 'bye', 'world'], binary=True)

In [54]: X = vect.fit_transform(pd.Series(['hello', 'hello world hello']))

作为稀疏矩阵的结果：

In [55]: X
Out[55]:
<2x3 sparse matrix of type '<class 'numpy.int64'>'
        with 3 stored elements in Compressed Sparse Row format>

你可以把它转换成密集矩阵：

In [56]: X.A
Out[56]:
array([[1, 0, 0],
       [1, 0, 1]], dtype=int64)

功能（列名称）：

In [57]: vect.get_feature_names()
Out[57]: ['hello', 'bye', 'world']

如何在python

1 个答案: