Python 处理文本是一项非常常见的功能，本文整理了多种文本提取及NLP相关的案例，还是非常用心的。文章很长，要忍一下，如果忍不了，那就收藏吧，总会用到的！
目录
1提取PDF内容
2提取Word内容
3提取Web网页内容
4读取json数据
5读取CSV数据
6删除字符串中的标点符号
7使用NLTK删除停用词
8使用TextBlob更正拼写
9使用NLTK和TextBlob的词标记化
10使用NLTK提取句子单词或短语的词干列表
11使用NLTK进行句子或短语词形还原
12使用NLTK从文本文件中查找每个单词的频率
13从语料库中创建词云
14NLTK词法散布图
15使用countvectorizer将文本转换为数字
16使用TF-IDF创建文档术语矩阵
17为给定句子生成N-gram
18使用带有二元组的sklearnCountVectorize词汇规范
19使用TextBlob提取名词短语
20如何计算词-词共现矩阵
21使用TextBlob进行情感分析
22使用Goslate进行语言翻译
23使用TextBlob进行语言检测和翻译
24使用TextBlob获取定义和同义词
25使用TextBlob获取反义词列表
1提取 PDF 内容
# pip install PyPDF2 安装 PyPDF2import PyPDF2from 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 内容
# pip install python-docx 安装 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 网页内容
# pip install bs4 安装 bs4 from urllib.request import Request, urlopenfrom 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() # Parsingsoup = BeautifulSoup(webpage, 'html.parser') # Formating the parsed html filestrhtm = soup.prettify() # Print first 500 linesprint(strhtm[:500]) # Extract meta tag valueprint(soup.title.string)print(soup.find('meta', attrs={'property':'og:description'})) # Extract anchor tag valuefor x in soup.find_all('a'): print(x.string) # Extract Paragraph tag valuefor x in soup.find_all('p'): print(x.text)4读取 Json 数据
import requestsimport 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 stringdata = json.dumps(res)print(data)5读取 CSV 数据
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删除字符串中的标点符号
import reimport 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 objecttranslator = str.maketrans('', '', string.punctuation)data = data.translate(translator)print(data)7使用 NLTK 删除停用词
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 wordsstopwords = 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 更正拼写
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 的词标记化
import nltkfrom 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 提取句子单词或短语的词干列表
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 进行句子或短语词形还原
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 从文本文件中查找每个单词的频率
import nltkfrom nltk.corpus import webtextfrom 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从语料库中创建词云
import nltkfrom nltk.corpus import webtextfrom nltk.probability import FreqDistfrom wordcloud import WordCloudimport matplotlib.pyplot as plt nltk.download('webtext')wt_words = webtext.words('testing.txt') # Sample datadata_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 wordcloudplt.imshow(wcloud, interpolation="bilinear") plt.axis("off")(-0.5, 399.5, 199.5, -0.5)plt.show()14NLTK 词法散布图
import nltkfrom nltk.corpus import webtextfrom nltk.probability import FreqDistfrom wordcloud import WordCloudimport 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 将文本转换为数字
import pandas as pdfrom sklearn.feature_extraction.text import CountVectorizer # Sample data for analysisdata1 = "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]}) # Initializevectorizer = CountVectorizer()doc_vec = vectorizer.fit_transform(df1.iloc[0]) # Create dataFramedf2 = pd.DataFrame(doc_vec.toarray().transpose(), index=vectorizer.get_feature_names()) # Change column headersdf2.columns = df1.columnsprint(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 创建文档术语矩阵
import pandas as pdfrom sklearn.feature_extraction.text import TfidfVectorizer # Sample data for analysisdata1 = "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]}) # Initializevectorizer = TfidfVectorizer()doc_vec = vectorizer.fit_transform(df1.iloc[0]) # Create dataFramedf2 = pd.DataFrame(doc_vec.toarray().transpose(), index=vectorizer.get_feature_names()) # Change column headersdf2.columns = df1.columnsprint(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为给定句子生成 N-gram
自然语言工具包：NLTK
import nltkfrom 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))文本处理工具：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使用带有二元组的 sklearn CountVectorize 词汇规范
import pandas as pdfrom sklearn.feature_extraction.text import CountVectorizer # Sample data for analysisdata1 = "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]}) # Initializevectorizer = CountVectorizer(ngram_range=(2, 2))doc_vec = vectorizer.fit_transform(df1.iloc[0]) # Create dataFramedf2 = pd.DataFrame(doc_vec.toarray().transpose(), index=vectorizer.get_feature_names()) # Change column headersdf2.columns = df1.columnsprint(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 提取名词短语
from textblob import TextBlob #Extract nounblob = 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如何计算词-词共现矩阵
import numpy as npimport nltkfrom nltk import bigramsimport itertoolsimport 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 listsdata = 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 进行情感分析
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 进行语言翻译
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 进行语言检测和翻译
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?
你好吗？
24使用 TextBlob 获取定义和同义词
from textblob import TextBlobfrom 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 获取反义词列表
from textblob import TextBlobfrom 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'}
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