读取SQL文件并使用Count Vectorizer获取单词出现

我想读取一个sql文件并使用CountVectorizer来获取单词出现.

到目前为止,我有以下代码：

import re
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer




df = pd.read_sql(q, dlconn)
print(df)

count_vect = CountVectorizer()
X_train_counts= count_vect.fit_transform(df)

print(X_train_counts.shape)
print(count_vect.vocabulary_)

这给出了’cat’：1,’dog’：0的输出

看来,它只是采用列动物的名称并从那里开始计数.

如何获取它以访问整列并获得一个图表,该图表显示该列中的每个单词及其频率？

解决方法:

根据the CountVectorizer docs,方法fit_transform()期望字符串可迭代.
它不能直接处理DataFrame.

但是遍历数据框将返回列的标签,而不是值.我建议您改用df.itertuples().

尝试这样的事情：

value_list = [
    row[0]
    for row in df.itertuples(index=False, name=None)]
print(value_list)
print(type(value_list))
print(type(value_list[0]))

X_train_counts = count_vect.fit_transform(value_list)

value_list中的每个值都应为str类型.
让我们知道是否有帮助.

这是一个小例子：

>>> import pandas as pd
>>> df = pd.DataFrame(['my big dog', 'my lazy cat'])
>>> df
             0
0   my big dog
1  my lazy cat

>>> value_list = [row[0] for row in df.itertuples(index=False, name=None)]
>>> value_list
['my big dog', 'my lazy cat']

>>> from sklearn.feature_extraction.text import CountVectorizer
>>> cv = CountVectorizer()
>>> x_train = cv.fit_transform(value_list)
>>> x_train
<2x5 sparse matrix of type '<class 'numpy.int64'>'
    with 6 stored elements in Compressed Sparse Row format>
>>> x_train.toarray()
array([[1, 0, 1, 0, 1],
       [0, 1, 0, 1, 1]], dtype=int64)
>>> cv.vocabulary_
{'my': 4, 'big': 0, 'dog': 2, 'lazy': 3, 'cat': 1}

现在,您可以显示每一行的单词计数(每个输入字符串分别)：

>>> for word, col in cv.vocabulary_.items():
...     for row in range(x_train.shape[0]):
...         print('word:{:10s} | row:{:2d} | count:{:2d}'.format(word, row, x_train[row,col]))
word:my         | row: 0 | count: 1
word:my         | row: 1 | count: 1
word:big        | row: 0 | count: 1
word:big        | row: 1 | count: 0
word:dog        | row: 0 | count: 1
word:dog        | row: 1 | count: 0
word:lazy       | row: 0 | count: 0
word:lazy       | row: 1 | count: 1
word:cat        | row: 0 | count: 0
word:cat        | row: 1 | count: 1

您还可以显示总字数(行总数)：

>>> x_train_sum = x_train.sum(axis=0)
>>> x_train_sum
    matrix([[1, 1, 1, 1, 2]], dtype=int64)
>>> for word, col in cv.vocabulary_.items():
...     print('word:{:10s} | count:{:2d}'.format(word, x_train_sum[0, col]))
word:my         | count: 2
word:big        | count: 1
word:dog        | count: 1
word:lazy       | count: 1
word:cat        | count: 1

>>> with open('my-file.csv', 'w') as f:
...     for word, col in cv.vocabulary_.items():
...         f.write('{};{}\n'.format(word, x_train_sum[0, col]))

这应该阐明如何使用现有工具.