Úvod do kódování textu

Feature Engineering for Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

Standardizace textu

Příklad volného textu:

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.

Datová sada

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...

Odstranění nežádoucích znaků

[a-zA-Z]: Všechny písmenkové znaky
[^a-zA-Z]: Všechny nepísmenkové znaky

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

Odstranění nežádoucích znaků

Před:

"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" ...

Po:

"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" ...

Sjednocení velikosti písmen

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"...

Délka textu

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

Počet slov

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

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

Počet slov

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

Průměrná délka slova

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

Pojďme si procvičit!

Feature Engineering for Machine Learning in Python