（盘点）25个值得收藏的Python文本处理案例

今天主要跟大家整理了25个值得收藏的Python文本处理案例。Python 处理文本是一项非常常见的功能，可以收藏起来，总会用到的，想要了解更多的关于python知识的，领取免费资源的，可以点击这个链接

1提取 PDF 内容

# 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()

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, 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)

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 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 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)

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 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 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从语料库中创建词云

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()

14NLTK 词法散布图

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()

15使用 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
...

16使用 TF-IDF 创建文档术语矩阵

import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer

# Sample data for analysis

data2 = "Python supports multiple programming paradigms and comes up with a large standard library, paradigms included are object-oriented, imperative, functional and procedural."

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为给定句子生成 N-gram

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']

18使用带有二元组的 sklearn CountVectorize 词汇规范

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

20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]

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 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 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'}