Question

使用TextBlob库可以通过首先将它们定义为TextBlob对象然后使用org.gradle.jvmargs=-Xmx4g -XX:MaxPermSize=512m -XX:+HeapDumpOnOutOfMemoryError -Dfile.encoding=UTF-8方法来改进字符串的拼写。

示例：

correct

是否可以对Pandas DataFrame系列中的字符串执行此操作，例如：

from textblob import TextBlob
data = TextBlob('Two raods diverrged in a yullow waod and surry I culd not travl bouth')
print (data.correct())
Two roads diverged in a yellow wood and sorry I could not travel both

要归还：

data = [{'one': '3', 'two': 'two raods'}, 
         {'one': '7', 'two': 'diverrged in a yullow'}, 
        {'one': '8', 'two': 'waod and surry I'}, 
        {'one': '9', 'two': 'culd not travl bouth'}]
df = pd.DataFrame(data)
df

    one   two
0   3     Two raods
1   7     diverrged in a yullow
2   8     waod and surry I
3   9     culd not travl bouth

使用TextBlob或其他方法。

Answer 1

您可以执行以下操作：

for (j = 0; j < arr.length; j++) {
    map_helper[i][j] = new MapHelper(i, j, r2[j]);
}

使用pandas.Series.apply。

Answer 2

我仍在寻找更快的方法。但是，我认为python中有一个名为autocorrect的库，可以帮助进行拼写修正。我在演示数据上计算了两个库（autocorrect和testblob），这些是我得到的结果。

%%timeit
spell_correct_tb(['haave', 'naame'])
The slowest run took 4.36 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 505 µs per loop

%%timeit
spell_correct_autocorrect(['haave', 'naame'])
The slowest run took 4.80 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 303 µs per loop

这表明autocorrect工作得更快（或者我的假设是错误的？）。但是，我不太确定两个图书馆的准确度。

注意：您可以使用pip运行命令pip install autocorrect

来安装自动更正

如何纠正Pandas DataFrame中的拼写

2 个答案: