共计 15668 个字符，预计需要花费 40 分钟才能阅读完成。
Python 解决文本是一项十分常见的性能，本文整顿了多种文本提取及 NLP 相干的案例，还是十分用心的
文章很长，高下要忍一下，如果忍不了，那就珍藏吧，总会用到的
萝卜哥也贴心的做成了 PDF，在文末获取！
[TOC]
提取 PDF 内容
# pip install PyPDF2 装置 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()提取 Word 内容
# pip install python-docx 装置 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()提取 Web 网页内容
# pip install bs4 装置 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)读取 Json 数据
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)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)删除字符串中的标点符号
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)应用 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 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)应用 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)应用 NLTK 和 TextBlob 的词标记化
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']应用 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应用 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应用 NLTK 从文本文件中查找每个单词的频率
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
...从语料库中创立词云
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()NLTK 词法分布图
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()应用 countvectorizer 将文本转换为数字
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
...应用 TF-IDF 创立文档术语矩阵
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
...为给定句子生成 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']应用带有二元组的 sklearn CountVectorize 词汇标准
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
...应用 TextBlob 提取名词短语
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如何计算词 - 词共现矩阵
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]应用 TextBlob 进行情感剖析
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应用 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)应用 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？应用 TextBlob 获取定义和同义词
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'}应用 TextBlob 获取反义词列表
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'}本文由 mdnice 多平台公布
正文完