Python 干 /从 pandas dataframe/

我创建 dataframe 有必须完成的建议。
我想使用 Snowballstemmer, 通过我的分类算法获得更高的准确性。 我怎样才能实现这一目标？


import pandas as pd

from nltk.stem.snowball import SnowballStemmer



# Use English stemmer.

stemmer = SnowballStemmer/"english"/



# Sentences to be stemmed.

data = ["programers program with programing languages", "my code is working so there must be a bug in the optimizer"] 

 

# Create the Pandas dataFrame.

df = pd.DataFrame/data, columns = ['unstemmed']/ 



# Split the sentences to lists of words.

df['unstemmed'] = df['unstemmed'].str.split//



# Make sure we see the full column.

pd.set_option/'display.max_colwidth', -1/



# Print dataframe.

df 



+----+---------------------------------------------------------------+

| | unstemmed |

|----+---------------------------------------------------------------|

| 0 | ['programmers', 'program', 'with', 'programming', 'languages']|

| 1 | ['my', 'code', 'is', 'working', 'so', 'there', 'must', | 

| | 'be', 'a', 'bug', 'in', 'the', 'interpreter'] |

+----+---------------------------------------------------------------+
2021-03-21 添加评论
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没有找到相关结果

    已邀请:

    八刀丁二

    赞同来自:

    您必须将生长应用于每个单词并将其保存在列中。 "stemmed".


    df['stemmed'] = df['unstemmed'].apply/lambda x: [stemmer.stem/y/ for y in x]/ # Stem every word.
    
df = df.drop/columns=['unstemmed']/ # Get rid of the unstemmed column.
    
df # Print dataframe.
    

    
+----+--------------------------------------------------------------+
    
| | stemmed |
    
|----+--------------------------------------------------------------|
    
| 0 | ['program', 'program', 'with', 'program', 'languag'] |
    
| 1 | ['my', 'code', 'is', 'work', 'so', 'there', 'must', | 
    
| | 'be', 'a', 'bug', 'in', 'the', 'interpret'] |
    
+----+--------------------------------------------------------------+
    2021-03-21 0 0
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