文字編碼入門

Feature Engineering for Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

文字標準化

自由文字範例：

Fellow-Citizens of the Senate and of the House of Representatives: AMONG the vicissitudes incident to life no event could have filled me with greater anxieties than that of which the notification was transmitted by your order, and received on the th day of the present month.

資料集

print(speech_df.head())

                  Name           Inaugural Address    \ 
0    George Washington     First Inaugural Address
1    George Washington    Second Inaugural Address
2    John Adams                  Inaugural Address    
3    Thomas Jefferson      First Inaugural Address    
4    Thomas Jefferson     Second Inaugural Address

                        Date                               text
0    Thursday, April 30, 1789    Fellow-Citizens of the Sena...
1       Monday, March 4, 1793    Fellow Citizens: I AM again...
2     Saturday, March 4, 1797    WHEN it was first perceived...
3    Wednesday, March 4, 1801    Friends and Fellow-Citizens...
4       Monday, March 4, 1805    PROCEEDING, fellow-citizens...

移除不需要的字元

[a-zA-Z]：所有字母字元
[^a-zA-Z]：所有非字母字元

speech_df['text'] = speech_df['text']\
                   .str.replace('[^a-zA-Z]', ' ')

移除不需要的字元

處理前：

"Fellow-Citizens of the Senate and of the House of  
Representatives: AMONG the vicissitudes incident to   
life no event could have filled me with greater" ...

處理後：

"Fellow Citizens of the Senate and of the House of  
Representatives AMONG the vicissitudes incident to   
life no event could have filled me with greater" ...

統一大小寫

speech_df['text'] = speech_df['text'].str.lower()
print(speech_df['text'][0])

"fellow citizens of the senate and of the house of  
representatives among the vicissitudes incident to   
life no event could have filled me with greater"...

文字長度

speech_df['char_cnt'] = speech_df['text'].str.len()
print(speech_df['char_cnt'].head())

0    1889  
1     806  
2    2408  
3    1495  
4    2465
Name: char_cnt, dtype: int64

詞數

speech_df['word_cnt'] = 
    speech_df['text'].str.split()
speech_df['word_cnt'].head(1)

['fellow', 'citizens', 'of', 'the', 'senate', 'and',...

詞數

speech_df['word_counts'] = 
    speech_df['text'].str.split().str.len()
print(speech_df['word_splits'].head())

0    1432
1     135
2    2323
3    1736
4    2169
Name: word_cnt, dtype: int64

平均詞長

speech_df['avg_word_len'] = 
         speech_df['char_cnt'] / speech_df['word_cnt']

一起來練習吧！

Feature Engineering for Machine Learning in Python