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本篇内容主要讲解“Python文本处理的案例有哪些”，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习“Python文本处理的案例有哪些”吧!
# pip install PyPDF2 安装 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()# 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()# pip install bs4 安装 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)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)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)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)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)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)
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']
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
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
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
...
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()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()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
...
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
...
自然语言工具包：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))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']
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
...
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
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]
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
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)
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？
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'}
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'}
到此，相信大家对“Python文本处理的案例有哪些”有了更深的了解，不妨来实际操作一番吧！这里是亿速云网站，更多相关内容可以进入相关频道进行查询，关注我们，继续学习！
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