整理了 25 個 Python 文本處理案例，收藏！

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Python 處理文本是一項非常常見的功能，本文整理了多種文本提取及NLP相關的案例，還是非常用心的

文章很長，高低要忍一下，如果忍不了，那就收藏吧，總會用到的

• 提取 PDF 內(nèi)容

• 提取 Word 內(nèi)容

• 提取 Web 網(wǎng)頁內(nèi)容

• 讀取 Json 數(shù)據(jù)

• 讀取 CSV 數(shù)據(jù)

• 刪除字符串中的標點符號

• 使用 NLTK 刪除停用詞

• 使用 TextBlob 更正拼寫

• 使用 NLTK 和 TextBlob 的詞標記化

• 使用 NLTK 提取句子單詞或短語的詞干列表

• 使用 NLTK 進行句子或短語詞形還原

• 使用 NLTK 從文本文件中查找每個單詞的頻率

• 從語料庫中創(chuàng)建詞云

• NLTK 詞法散布圖

• 使用 countvectorizer 將文本轉(zhuǎn)換為數(shù)字

• 使用 TF-IDF 創(chuàng)建文檔術語矩陣

• 為給定句子生成 N-gram

• 使用帶有二元組的 sklearn CountVectorize 詞匯規(guī)范

• 使用 TextBlob 提取名詞短語

• 如何計算詞-詞共現(xiàn)矩陣

• 使用 TextBlob 進行情感分析

• 使用 Goslate 進行語言翻譯

• 使用 TextBlob 進行語言檢測和翻譯

• 使用 TextBlob 獲取定義和同義詞

• 使用 TextBlob 獲取反義詞列表

1提取 PDF 內(nèi)容

#?pip?install?PyPDF2??安裝?PyPDF2
import?PyPDF2
from?PyPDF2?import?PdfFileReader
?
#?Creating?a?pdf?file?object.
pdf?=?open("test.pdf",?"rb")
?
#?Creating?pdf?reader?object.
pdf_reader?=?PyPDF2.PdfFileReader(pdf)
?
#?Checking?total?number?of?pages?in?a?pdf?file.
print("Total?number?of?Pages:",?pdf_reader.numPages)
?
#?Creating?a?page?object.
page?=?pdf_reader.getPage(200)
?
#?Extract?data?from?a?specific?page?number.
print(page.extractText())
?
#?Closing?the?object.
pdf.close()

2提取 Word 內(nèi)容

#?pip?install?python-docx??安裝?python-docx


import?docx
?
?
def?main():
????try:
????????doc?=?docx.Document('test.docx')??#?Creating?word?reader?object.
????????data?=?""
????????fullText?=?[]
????????for?para?in?doc.paragraphs:
????????????fullText.append(para.text)
????????????data?=?'\n'.join(fullText)
?
????????print(data)
?
????except?IOError:
????????print('There?was?an?error?opening?the?file!')
????????return
?
?
if?__name__?==?'__main__':
????main()

3提取 Web 網(wǎng)頁內(nèi)容

#?pip?install?bs4??安裝?bs4

from?urllib.request?import?Request,?urlopen
from?bs4?import?BeautifulSoup
?
req?=?Request('http://www.cmegroup.com/trading/products/#sortField=oi&sortAsc=false&venues=3&page=1&cleared=1&group=1',
??????????????headers={'User-Agent':?'Mozilla/5.0'})
?
webpage?=?urlopen(req).read()
?
#?Parsing
soup?=?BeautifulSoup(webpage,?'html.parser')
?
#?Formating?the?parsed?html?file
strhtm?=?soup.prettify()
?
#?Print?first?500?lines
print(strhtm[:500])
?
#?Extract?meta?tag?value
print(soup.title.string)
print(soup.find('meta',?attrs={'property':'og:description'}))
?
#?Extract?anchor?tag?value
for?x?in?soup.find_all('a'):
????print(x.string)
?
#?Extract?Paragraph?tag?value????
for?x?in?soup.find_all('p'):
????print(x.text)

4讀取 Json 數(shù)據(jù)

import?requests
import?json

r?=?requests.get("https://support.oneskyapp.com/hc/en-us/article_attachments/202761727/example_2.json")
res?=?r.json()

#?Extract?specific?node?content.
print(res['quiz']['sport'])

#?Dump?data?as?string
data?=?json.dumps(res)
print(data)

5讀取 CSV 數(shù)據(jù)

import?csv

with?open('test.csv','r')?as?csv_file:
????reader?=csv.reader(csv_file)
????next(reader)?#?Skip?first?row
????for?row?in?reader:
????????print(row)

6刪除字符串中的標點符號

import?re
import?string
?
data?=?"Stuning?even?for?the?non-gamer:?This?sound?track?was?beautiful!\
It?paints?the?senery?in?your?mind?so?well?I?would?recomend\
it?even?to?people?who?hate?vid.?game?music!?I?have?played?the?game?Chrono?\
Cross?but?out?of?all?of?the?games?I?have?ever?played?it?has?the?best?music!?\
It?backs?away?from?crude?keyboarding?and?takes?a?fresher?step?with?grate\
guitars?and?soulful?orchestras.\
It?would?impress?anyone?who?cares?to?listen!"
?
#?Methood?1?:?Regex
#?Remove?the?special?charaters?from?the?read?string.
no_specials_string?=?re.sub('[!#?,.:";]',?'',?data)
print(no_specials_string)
?
?
#?Methood?2?:?translate()
#?Rake?translator?object
translator?=?str.maketrans('',?'',?string.punctuation)
data?=?data.translate(translator)
print(data)

7使用 NLTK 刪除停用詞

from?nltk.corpus?import?stopwords
?
?
data?=?['Stuning?even?for?the?non-gamer:?This?sound?track?was?beautiful!\
It?paints?the?senery?in?your?mind?so?well?I?would?recomend\
it?even?to?people?who?hate?vid.?game?music!?I?have?played?the?game?Chrono?\
Cross?but?out?of?all?of?the?games?I?have?ever?played?it?has?the?best?music!?\
It?backs?away?from?crude?keyboarding?and?takes?a?fresher?step?with?grate\
guitars?and?soulful?orchestras.\
It?would?impress?anyone?who?cares?to?listen!']
?
#?Remove?stop?words
stopwords?=?set(stopwords.words('english'))
?
output?=?[]
for?sentence?in?data:
????temp_list?=?[]
????for?word?in?sentence.split():
????????if?word.lower()?not?in?stopwords:
????????????temp_list.append(word)
????output.append('?'.join(temp_list))
?
?
print(output)

8使用 TextBlob 更正拼寫

from?textblob?import?TextBlob

data?=?"Natural?language?is?a?cantral?part?of?our?day?to?day?life,?and?it's?so?antresting?to?work?on?any?problem?related?to?langages."

output?=?TextBlob(data).correct()
print(output)

9使用 NLTK 和 TextBlob 的詞標記化

import?nltk
from?textblob?import?TextBlob


data?=?"Natural?language?is?a?central?part?of?our?day?to?day?life,?and?it's?so?interesting?to?work?on?any?problem?related?to?languages."

nltk_output?=?nltk.word_tokenize(data)
textblob_output?=?TextBlob(data).words

print(nltk_output)
print(textblob_output)

Output:

['Natural', 'language', 'is', 'a', 'central', 'part', 'of', 'our', 'day', 'to', 'day', 'life', ',', 'and', 'it', "'s", 'so', 'interesting', 'to', 'work', 'on', 'any', 'problem', 'related', 'to', 'languages', '.']
['Natural', 'language', 'is', 'a', 'central', 'part', 'of', 'our', 'day', 'to', 'day', 'life', 'and', 'it', "'s", 'so', 'interesting', 'to', 'work', 'on', 'any', 'problem', 'related', 'to', 'languages']

10使用 NLTK 提取句子單詞或短語的詞干列表

from?nltk.stem?import?PorterStemmer
?
st?=?PorterStemmer()
text?=?['Where?did?he?learn?to?dance?like?that?',
????????'His?eyes?were?dancing?with?humor.',
????????'She?shook?her?head?and?danced?away',
????????'Alex?was?an?excellent?dancer.']
?
output?=?[]
for?sentence?in?text:
????output.append("?".join([st.stem(i)?for?i?in?sentence.split()]))
?
for?item?in?output:
????print(item)
?
print("-"?*?50)
print(st.stem('jumping'),?st.stem('jumps'),?st.stem('jumped'))

Output:

where did he learn to danc like that?
hi eye were danc with humor.
she shook her head and danc away
alex wa an excel dancer.
--------------------------------------------------
jump jump jump

11使用 NLTK 進行句子或短語詞形還原

from?nltk.stem?import?WordNetLemmatizer

wnl?=?WordNetLemmatizer()
text?=?['She?gripped?the?armrest?as?he?passed?two?cars?at?a?time.',
????????'Her?car?was?in?full?view.',
????????'A?number?of?cars?carried?out?of?state?license?plates.']

output?=?[]
for?sentence?in?text:
????output.append("?".join([wnl.lemmatize(i)?for?i?in?sentence.split()]))

for?item?in?output:
????print(item)

print("*"?*?10)
print(wnl.lemmatize('jumps',?'n'))
print(wnl.lemmatize('jumping',?'v'))
print(wnl.lemmatize('jumped',?'v'))

print("*"?*?10)
print(wnl.lemmatize('saddest',?'a'))
print(wnl.lemmatize('happiest',?'a'))
print(wnl.lemmatize('easiest',?'a'))

Output:

She gripped the armrest a he passed two car at a time.
Her car wa in full view.
A number of car carried out of state license plates.
**********
jump
jump
jump
**********
sad
happy
easy

12使用 NLTK 從文本文件中查找每個單詞的頻率

import?nltk
from?nltk.corpus?import?webtext
from?nltk.probability?import?FreqDist
?
nltk.download('webtext')
wt_words?=?webtext.words('testing.txt')
data_analysis?=?nltk.FreqDist(wt_words)
?
#?Let's?take?the?specific?words?only?if?their?frequency?is?greater?than?3.
filter_words?=?dict([(m,?n)?for?m,?n?in?data_analysis.items()?if?len(m)?>?3])
?
for?key?in?sorted(filter_words):
????print("%s:?%s"?%?(key,?filter_words[key]))
?
data_analysis?=?nltk.FreqDist(filter_words)
?
data_analysis.plot(25,?cumulative=False)

Output:

[nltk_data] Downloading package webtext to
[nltk_data]     C:\Users\amit\AppData\Roaming\nltk_data...
[nltk_data]   Unzipping corpora\webtext.zip.
1989: 1
Accessing: 1
Analysis: 1
Anyone: 1
Chapter: 1
Coding: 1
Data: 1
...

13從語料庫中創(chuàng)建詞云

import?nltk
from?nltk.corpus?import?webtext
from?nltk.probability?import?FreqDist
from?wordcloud?import?WordCloud
import?matplotlib.pyplot?as?plt
?
nltk.download('webtext')
wt_words?=?webtext.words('testing.txt')??#?Sample?data
data_analysis?=?nltk.FreqDist(wt_words)
?
filter_words?=?dict([(m,?n)?for?m,?n?in?data_analysis.items()?if?len(m)?>?3])
?
wcloud?=?WordCloud().generate_from_frequencies(filter_words)
?
#?Plotting?the?wordcloud
plt.imshow(wcloud,?interpolation="bilinear")
?
plt.axis("off")
(-0.5,?399.5,?199.5,?-0.5)
plt.show()

14NLTK 詞法散布圖

import?nltk
from?nltk.corpus?import?webtext
from?nltk.probability?import?FreqDist
from?wordcloud?import?WordCloud
import?matplotlib.pyplot?as?plt
?
words?=?['data',?'science',?'dataset']
?
nltk.download('webtext')
wt_words?=?webtext.words('testing.txt')??#?Sample?data
?
points?=?[(x,?y)?for?x?in?range(len(wt_words))
??????????for?y?in?range(len(words))?if?wt_words[x]?==?words[y]]
?
if?points:
????x,?y?=?zip(*points)
else:
????x?=?y?=?()
?
plt.plot(x,?y,?"rx",?scalex=.1)
plt.yticks(range(len(words)),?words,?color="b")
plt.ylim(-1,?len(words))
plt.title("Lexical?Dispersion?Plot")
plt.xlabel("Word?Offset")
plt.show()

15使用 countvectorizer 將文本轉(zhuǎn)換為數(shù)字

import?pandas?as?pd
from?sklearn.feature_extraction.text?import?CountVectorizer
?
#?Sample?data?for?analysis
data1?=?"Java?is?a?language?for?programming?that?develops?a?software?for?several?platforms.?A?compiled?code?or?bytecode?on?Java?application?can?run?on?most?of?the?operating?systems?including?Linux,?Mac?operating?system,?and?Linux.?Most?of?the?syntax?of?Java?is?derived?from?the?C++?and?C?languages."
data2?=?"Python?supports?multiple?programming?paradigms?and?comes?up?with?a?large?standard?library,?paradigms?included?are?object-oriented,?imperative,?functional?and?procedural."
data3?=?"Go?is?typed?statically?compiled?language.?It?was?created?by?Robert?Griesemer,?Ken?Thompson,?and?Rob?Pike?in?2009.?This?language?offers?garbage?collection,?concurrency?of?CSP-style,?memory?safety,?and?structural?typing."
?
df1?=?pd.DataFrame({'Java':?[data1],?'Python':?[data2],?'Go':?[data2]})
?
#?Initialize
vectorizer?=?CountVectorizer()
doc_vec?=?vectorizer.fit_transform(df1.iloc[0])
?
#?Create?dataFrame
df2?=?pd.DataFrame(doc_vec.toarray().transpose(),
???????????????????index=vectorizer.get_feature_names())
?
#?Change?column?headers
df2.columns?=?df1.columns
print(df2)

Output:

             Go  Java  Python
and           2     2       2
application   0     1       0
are           1     0       1
bytecode      0     1       0
can           0     1       0
code          0     1       0
comes         1     0       1
compiled      0     1       0
derived       0     1       0
develops      0     1       0
for           0     2       0
from          0     1       0
functional    1     0       1
imperative    1     0       1
...

16使用 TF-IDF 創(chuàng)建文檔術語矩陣

import?pandas?as?pd
from?sklearn.feature_extraction.text?import?TfidfVectorizer

#?Sample?data?for?analysis
data1?=?"Java?is?a?language?for?programming?that?develops?a?software?for?several?platforms.?A?compiled?code?or?bytecode?on?Java?application?can?run?on?most?of?the?operating?systems?including?Linux,?Mac?operating?system,?and?Linux.?Most?of?the?syntax?of?Java?is?derived?from?the?C++?and?C?languages."
data2?=?"Python?supports?multiple?programming?paradigms?and?comes?up?with?a?large?standard?library,?paradigms?included?are?object-oriented,?imperative,?functional?and?procedural."
data3?=?"Go?is?typed?statically?compiled?language.?It?was?created?by?Robert?Griesemer,?Ken?Thompson,?and?Rob?Pike?in?2009.?This?language?offers?garbage?collection,?concurrency?of?CSP-style,?memory?safety,?and?structural?typing."

df1?=?pd.DataFrame({'Java':?[data1],?'Python':?[data2],?'Go':?[data2]})

#?Initialize
vectorizer?=?TfidfVectorizer()
doc_vec?=?vectorizer.fit_transform(df1.iloc[0])

#?Create?dataFrame
df2?=?pd.DataFrame(doc_vec.toarray().transpose(),
???????????????????index=vectorizer.get_feature_names())

#?Change?column?headers
df2.columns?=?df1.columns
print(df2)

Output:

                   Go      Java    Python
and          0.323751  0.137553  0.323751
application  0.000000  0.116449  0.000000
are          0.208444  0.000000  0.208444
bytecode     0.000000  0.116449  0.000000
can          0.000000  0.116449  0.000000
code         0.000000  0.116449  0.000000
comes        0.208444  0.000000  0.208444
compiled     0.000000  0.116449  0.000000
derived      0.000000  0.116449  0.000000
develops     0.000000  0.116449  0.000000
for          0.000000  0.232898  0.000000
...

17為給定句子生成 N-gram

NLTK

import?nltk
from?nltk.util?import?ngrams

#?Function?to?generate?n-grams?from?sentences.
def?extract_ngrams(data,?num):
????n_grams?=?ngrams(nltk.word_tokenize(data),?num)
????return?[?'?'.join(grams)?for?grams?in?n_grams]

data?=?'A?class?is?a?blueprint?for?the?object.'

print("1-gram:?",?extract_ngrams(data,?1))
print("2-gram:?",?extract_ngrams(data,?2))
print("3-gram:?",?extract_ngrams(data,?3))
print("4-gram:?",?extract_ngrams(data,?4))

TextBlob

from?textblob?import?TextBlob
?
#?Function?to?generate?n-grams?from?sentences.
def?extract_ngrams(data,?num):
????n_grams?=?TextBlob(data).ngrams(num)
????return?[?'?'.join(grams)?for?grams?in?n_grams]
?
data?=?'A?class?is?a?blueprint?for?the?object.'
?
print("1-gram:?",?extract_ngrams(data,?1))
print("2-gram:?",?extract_ngrams(data,?2))
print("3-gram:?",?extract_ngrams(data,?3))
print("4-gram:?",?extract_ngrams(data,?4))

Output:

1-gram:  ['A', 'class', 'is', 'a', 'blueprint', 'for', 'the', 'object']
2-gram:  ['A class', 'class is', 'is a', 'a blueprint', 'blueprint for', 'for the', 'the object']
3-gram:  ['A class is', 'class is a', 'is a blueprint', 'a blueprint for', 'blueprint for the', 'for the object']
4-gram:  ['A class is a', 'class is a blueprint', 'is a blueprint for', 'a blueprint for the', 'blueprint for the object']

18使用帶有二元組的 sklearn CountVectorize 詞匯規(guī)范

import?pandas?as?pd
from?sklearn.feature_extraction.text?import?CountVectorizer
?
#?Sample?data?for?analysis
data1?=?"Machine?language?is?a?low-level?programming?language.?It?is?easily?understood?by?computers?but?difficult?to?read?by?people.?This?is?why?people?use?higher?level?programming?languages.?Programs?written?in?high-level?languages?are?also?either?compiled?and/or?interpreted?into?machine?language?so?that?computers?can?execute?them."
data2?=?"Assembly?language?is?a?representation?of?machine?language.?In?other?words,?each?assembly?language?instruction?translates?to?a?machine?language?instruction.?Though?assembly?language?statements?are?readable,?the?statements?are?still?low-level.?A?disadvantage?of?assembly?language?is?that?it?is?not?portable,?because?each?platform?comes?with?a?particular?Assembly?Language"
?
df1?=?pd.DataFrame({'Machine':?[data1],?'Assembly':?[data2]})
?
#?Initialize
vectorizer?=?CountVectorizer(ngram_range=(2,?2))
doc_vec?=?vectorizer.fit_transform(df1.iloc[0])
?
#?Create?dataFrame
df2?=?pd.DataFrame(doc_vec.toarray().transpose(),
???????????????????index=vectorizer.get_feature_names())
?
#?Change?column?headers
df2.columns?=?df1.columns
print(df2)

Output:

                        Assembly  Machine
also either                    0        1
and or                         0        1
are also                       0        1
are readable                   1        0
are still                      1        0
assembly language              5        0
because each                   1        0
but difficult                  0        1
by computers                   0        1
by people                      0        1
can execute                    0        1
...

19使用 TextBlob 提取名詞短語

from?textblob?import?TextBlob

#Extract?noun
blob?=?TextBlob("Canada?is?a?country?in?the?northern?part?of?North?America.")

for?nouns?in?blob.noun_phrases:
????print(nouns)

Output:

canada
northern part
america

20如何計算詞-詞共現(xiàn)矩陣

import?numpy?as?np
import?nltk
from?nltk?import?bigrams
import?itertools
import?pandas?as?pd
?
?
def?generate_co_occurrence_matrix(corpus):
????vocab?=?set(corpus)
????vocab?=?list(vocab)
????vocab_index?=?{word:?i?for?i,?word?in?enumerate(vocab)}
?
????#?Create?bigrams?from?all?words?in?corpus
????bi_grams?=?list(bigrams(corpus))
?
????#?Frequency?distribution?of?bigrams?((word1,?word2),?num_occurrences)
????bigram_freq?=?nltk.FreqDist(bi_grams).most_common(len(bi_grams))
?
????#?Initialise?co-occurrence?matrix
????#?co_occurrence_matrix[current][previous]
????co_occurrence_matrix?=?np.zeros((len(vocab),?len(vocab)))
?
????#?Loop?through?the?bigrams?taking?the?current?and?previous?word,
????#?and?the?number?of?occurrences?of?the?bigram.
????for?bigram?in?bigram_freq:
????????current?=?bigram[0][1]
????????previous?=?bigram[0][0]
????????count?=?bigram[1]
????????pos_current?=?vocab_index[current]
????????pos_previous?=?vocab_index[previous]
????????co_occurrence_matrix[pos_current][pos_previous]?=?count
????co_occurrence_matrix?=?np.matrix(co_occurrence_matrix)
?
????#?return?the?matrix?and?the?index
????return?co_occurrence_matrix,?vocab_index
?
?
text_data?=?[['Where',?'Python',?'is',?'used'],
?????????????['What',?'is',?'Python'?'used',?'in'],
?????????????['Why',?'Python',?'is',?'best'],
?????????????['What',?'companies',?'use',?'Python']]
?
#?Create?one?list?using?many?lists
data?=?list(itertools.chain.from_iterable(text_data))
matrix,?vocab_index?=?generate_co_occurrence_matrix(data)
?
?
data_matrix?=?pd.DataFrame(matrix,?index=vocab_index,
?????????????????????????????columns=vocab_index)
print(data_matrix)

Output:

            best  use  What  Where  ...    in   is  Python  used
best         0.0  0.0   0.0    0.0  ...   0.0  0.0     0.0   1.0
use          0.0  0.0   0.0    0.0  ...   0.0  1.0     0.0   0.0
What         1.0  0.0   0.0    0.0  ...   0.0  0.0     0.0   0.0
Where        0.0  0.0   0.0    0.0  ...   0.0  0.0     0.0   0.0
Pythonused   0.0  0.0   0.0    0.0  ...   0.0  0.0     0.0   1.0
Why          0.0  0.0   0.0    0.0  ...   0.0  0.0     0.0   1.0
companies    0.0  1.0   0.0    1.0  ...   1.0  0.0     0.0   0.0
in           0.0  0.0   0.0    0.0  ...   0.0  0.0     1.0   0.0
is           0.0  0.0   1.0    0.0  ...   0.0  0.0     0.0   0.0
Python       0.0  0.0   0.0    0.0  ...   0.0  0.0     0.0   0.0
used         0.0  0.0   1.0    0.0  ...   0.0  0.0     0.0   0.0
 
[11 rows x 11 columns]

21使用 TextBlob 進行情感分析

from?textblob?import?TextBlob


def?sentiment(polarity):
????if?blob.sentiment.polarity?0:
????????print("Negative")
????elif?blob.sentiment.polarity?>?0:
????????print("Positive")
????else:
????????print("Neutral")


blob?=?TextBlob("The?movie?was?excellent!")
print(blob.sentiment)
sentiment(blob.sentiment.polarity)

blob?=?TextBlob("The?movie?was?not?bad.")
print(blob.sentiment)
sentiment(blob.sentiment.polarity)

blob?=?TextBlob("The?movie?was?ridiculous.")
print(blob.sentiment)
sentiment(blob.sentiment.polarity)

Output:

Sentiment(polarity=1.0, subjectivity=1.0)
Positive
Sentiment(polarity=0.3499999999999999, subjectivity=0.6666666666666666)
Positive
Sentiment(polarity=-0.3333333333333333, subjectivity=1.0)
Negative

22使用 Goslate 進行語言翻譯

import?goslate

text?=?"Comment?vas-tu?"

gs?=?goslate.Goslate()

translatedText?=?gs.translate(text,?'en')
print(translatedText)

translatedText?=?gs.translate(text,?'zh')
print(translatedText)

translatedText?=?gs.translate(text,?'de')
print(translatedText)

23使用 TextBlob 進行語言檢測和翻譯

from?textblob?import?TextBlob
?
blob?=?TextBlob("Comment?vas-tu?")
?
print(blob.detect_language())
?
print(blob.translate(to='es'))
print(blob.translate(to='en'))
print(blob.translate(to='zh'))

Output:

fr
?Como estas tu?
How are you?
你好嗎？

24使用 TextBlob 獲取定義和同義詞

from?textblob?import?TextBlob
from?textblob?import?Word
?
text_word?=?Word('safe')
?
print(text_word.definitions)
?
synonyms?=?set()
for?synset?in?text_word.synsets:
????for?lemma?in?synset.lemmas():
????????synonyms.add(lemma.name())
?????????
print(synonyms)

Output:

['strongbox where valuables can be safely kept', 'a ventilated or refrigerated cupboard for securing provisions from pests', 'contraceptive device consisting of a sheath of thin rubber or latex that is worn over the penis during intercourse', 'free from danger or the risk of harm', '(of an undertaking) secure from risk', 'having reached a base without being put out', 'financially sound']
{'secure', 'rubber', 'good', 'safety', 'safe', 'dependable', 'condom', 'prophylactic'}

25使用 TextBlob 獲取反義詞列表

from?textblob?import?TextBlob
from?textblob?import?Word

text_word?=?Word('safe')

antonyms?=?set()
for?synset?in?text_word.synsets:
????for?lemma?in?synset.lemmas():????????
????????if?lemma.antonyms():
????????????antonyms.add(lemma.antonyms()[0].name())????????

print(antonyms)

Output:

{'dangerous', 'out'}