Introduzione alla codifica del testo

Feature Engineering per il Machine Learning in Python

Robert O'Callaghan

Director of Data Science, Ordergroove

Standardizzare il testo

Esempio di testo libero:

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.

Insieme di dati

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...

Rimozione dei caratteri indesiderati

[a-zA-Z]: Tutte le lettere
[^a-zA-Z]: Tutti i caratteri non alfabetici

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

Rimozione dei caratteri indesiderati

Prima:

"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" ...

Dopo:

"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" ...

Uniformare le maiuscole/minuscole

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"...

Lunghezza del testo

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

Conteggio parole

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

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

Conteggio parole

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

Lunghezza media delle parole

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

Esercitiamoci!

Feature Engineering per il Machine Learning in Python