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#!/usr/bin/env python
#! encoding=utf-8
# Author : [email protected]
# Blog : http://kesalin.github.io
# Date : 2016/07/13
# Description : 抓取豆瓣上指定标签的书籍并导出为 Markdown 文件，多线程版本.
# Version : 1.0.0.0
# Python Version: Python 2.7.3
# Python Queue : https://docs.python.org/2/library/queue.html
# Beautiful Soup: http://beautifulsoup.readthedocs.io/zh_CN/v4.4.0/#
import os
import time
import timeit
import datetime
import re
import string
import urllib2
import math
from threading import Thread
from Queue import Queue
from bs4 import BeautifulSoup
# 获取 url 内容
gUseCookie = True
gHeaders = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36',
'Cookie': 'Put your cookie here'
}
def getHtml(url):
try:
if gUseCookie:
opener = urllib2.build_opener()
for k, v in gHeaders.items():
opener.addheaders.append((k, v))
response = opener.open(url)
data = response.read().decode('utf-8')
else:
request = urllib2.Request(url, None, gHeaders)
response = urllib2.urlopen(request)
data = response.read().decode('utf-8')
except urllib2.URLError as e:
if hasattr(e, "code"):
print("The server couldn't fulfill the request: " + url)
print("Error code: %s" % e.code)
elif hasattr(e, "reason"):
print("We failed to reach a server. Please check your url: " +
url + ", and read the Reason.")
print("Reason: %s" % e.reason)
return data
# 书籍信息类
class BookInfo:
def __init__(self, name, url, icon, num, people, comment):
self.name = name
self.url = url
self.icon = icon
self.ratingNum = num
self.ratingPeople = people
self.comment = comment
self.compositeRating = num
def __hash__(self):
return hash(self.url)
def __eq__(self, other):
if self.url == other.url:
return True
return False
def __sortByCompositeRating(self, other):
val = self.compositeRating - other.compositeRating
if val < 0:
return 1
elif val > 0:
return -1
else:
val = self.ratingPeople - other.ratingPeople
if val < 0:
return 1
elif val > 0:
return -1
else:
return 0
def __cmp__(self, other):
return self.__sortByCompositeRating(other)
# 导出为 Markdown 格式文件
def exportToMarkdown(tag, books, total):
path = "{0}.md".format(tag)
if(os.path.isfile(path)):
os.remove(path)
today = datetime.datetime.now()
todayStr = today.strftime('%Y-%m-%d %H:%M:%S %z')
file = open(path, 'a')
file.write('## 说明\n\n')
file.write(' > 本页面是由 Python 爬虫根据图书推荐算法抓取豆瓣图书信息自动生成，列出特定主题排名靠前的一百本图书。 \n\n')
file.write(' > 我使用的推荐算法似乎要比豆瓣默认的算法要可靠些，因为我喜欢书，尤其是对非虚构类图书有一定了解，'
'所以我可以根据特定主题对推荐算法进行调整。大家可以访问 '
'[豆瓣图书爬虫](https://github.com/luozhaohui/PythonSnippet/blob/master/exportTopBooksFromDouban.py) 查看推荐算法。'
'希望能得到大家的反馈与建议，改善算法，提供更精准的图书排名。 \n\n')
file.write(' > 联系方式： \n')
file.write(' + 微博：[飘飘白云](http://weibo.com/kesalin) \n')
file.write('\n## {0} Top {1} 图书\n\n'.format(tag, len(books)))
file.write('### 总共分析了 {0} 本图书，更新时间：{1}\n'.format(total, todayStr))
i = 0
for book in books:
file.write('\n### No.{0:d} {1}\n'.format(i + 1, book.name))
file.write(' > **图书名称**： [{0}]({1}) \n'.format(book.name, book.url))
file.write(' > **豆瓣链接**： [{0}]({1}) \n'.format(book.url, book.url))
file.write(' > **豆瓣评分**： {0} \n'.format(book.ratingNum))
file.write(' > **评分人数**： {0} 人 \n'.format(book.ratingPeople))
file.write(' > **内容简介**： {0} \n'.format(book.comment))
i = i + 1
file.close()
# 解析图书信息
def parseItemInfo(tag, minNum, maxNum, k, page, bookInfos):
soup = BeautifulSoup(page, 'html.parser')
items = soup.find_all("li", "subject-item")
for item in items:
# print(item.prettify().encode('utf-8'))
# get book name
bookName = ''
content = item.find("h2")
if content:
href = content.find("a")
if href:
bookName = href['title'].strip().encode('utf-8')
span = href.find("span")
if span and span.string:
subTitle = span.string.strip().encode('utf-8')
bookName = '{0}{1}'.format(bookName, subTitle)
#print(" > name: {0}".format(bookName))
# get description
description = ''
content = item.find("p")
if content:
description = content.string.strip().encode('utf-8')
#print(" > description: {0}".format(description))
# get book url and image
bookUrl = ''
bookImage = ''
content = item.find("div", "pic")
if content:
tag = content.find('a')
if tag:
bookUrl = tag['href'].encode('utf-8')
tag = content.find('img')
if tag:
bookImage = tag['src'].encode('utf-8')
#print(" > url: {0}, image: {1}".format(bookUrl, bookImage))
# get rating
ratingNum = 0.0
ratingPeople = 0
content = item.find("span", "rating_nums")
if content:
ratingStr = content.string.strip().encode('utf-8')
if len(ratingStr) > 0:
ratingNum = float(ratingStr)
content = item.find("span", "pl")
if content:
ratingStr = content.string.strip().encode('utf-8')
pattern = re.compile(r'(\()([0-9]*)(.*)(\))')
match = pattern.search(ratingStr)
if match:
ratingStr = match.group(2).strip()
if len(ratingStr) > 0:
ratingPeople = int(ratingStr)
#print(" > ratingNum: {0}, ratingPeople: {1}".format(ratingNum, ratingPeople))
# add book info to list
bookInfo = BookInfo(bookName, bookUrl, bookImage,
ratingNum, ratingPeople, description)
bookInfo.compositeRating = computeCompositeRating(
tag, minNum, maxNum, k, ratingNum, ratingPeople)
bookInfos.append(bookInfo)
#=============================================================================
# 生产者-消费者模型
#=============================================================================
class Producer(Thread):
url = ''
def __init__(self, t_name, url, queue):
Thread.__init__(self, name=t_name)
self.url = url
self.queue = queue
def run(self):
page = getHtml(self.url)
if page:
# block util a free slot available
self.queue.put(page, True)
class Consumer(Thread):
running = True
tag = ''
books = []
queue = None
minNum = 5
maxNum = 5000
k = 0.25
def __init__(self, t_name, tag, minNum, maxNum, k, queue, books):
Thread.__init__(self, name=t_name)
self.queue = queue
self.books = books
self.tag = tag
self.minNum = max(10, min(200, minNum))
self.maxNum = max(1000, min(maxNum, 20000))
self.k = max(0.01, min(1.0, k))
def stop(self):
self.running = False
def run(self):
while True:
if not self.running and self.queue.empty():
break
page = self.queue.get()
if page:
parseItemInfo(self.tag, self.minNum, self.maxNum,
self.k, page, self.books)
self.queue.task_done()
def spider(tag, minNum, maxNum, k):
print(' 抓取 [{0}] 图书 ...'.format(tag))
start = timeit.default_timer()
# all producers
queue = Queue(20)
bookInfos = []
producers = []
# get first page of doulist
url = "https://book.douban.com/tag/{0}".format(tag)
page = getHtml(url)
if not page:
print(' > invalid url {0}'.format(url))
else:
# get url of other pages in doulist
soup = BeautifulSoup(page, 'html.parser')
content = soup.find("div", "paginator")
# print(content.prettify().encode('utf-8'))
nextPageStart = 0
lastPageStart = 0
if content:
nextPageStart = 100000
for child in content.children:
if child.name == 'a':
pattern = re.compile(r'(start=)([0-9]*)(.*)(&type=)')
match = pattern.search(child['href'].encode('utf-8'))
if match:
index = int(match.group(2))
if nextPageStart > index:
nextPageStart = index
if lastPageStart < index:
lastPageStart = index
# process current page
#print(" > process page : {0}".format(url))
queue.put(page)
# create consumer
consumer = Consumer('Consumer', tag, minNum,
maxNum, k, queue, bookInfos)
consumer.start()
# create producers
producers = []
for pageStart in range(nextPageStart, lastPageStart + nextPageStart, nextPageStart):
pageUrl = "{0}?start={1:d}&type=T".format(url, pageStart)
producer = Producer('Producer_{0:d}'.format(
pageStart), pageUrl, queue)
producer.start()
producers.append(producer)
#print(" > process page : {0}".format(pageUrl))
time.sleep(0.3) # slow down a little
# wait for all producers
for producer in producers:
producer.join()
# wait for consumer
consumer.stop()
queue.put(None)
consumer.join()
# summrise
total = len(bookInfos)
elapsed = timeit.default_timer() - start
print(" 获取 %d 本 [%s] 图书信息，耗时 %.2f 秒" % (total, tag, elapsed))
return bookInfos
def process(tags, ignore):
tagList = tags[0].split(',')
backlist = tags[1]
minNum = tags[2]
maxNum = tags[3]
k = tags[4]
books = []
# spider
for tag in tagList:
tagBooks = spider(tag.strip(), minNum, maxNum, k)
books = list(set(books + tagBooks))
total = len(books)
print(" > 共获取 {0} 本 [{1}] 不重复图书信息".format(total, tags[0]))
if tags[0].find("文学") != -1 \
or tags[0].find("文化") \
or tags[0].find("绘本") \
or tags[0] == "小说" \
or tags[0] == "成长" \
or tags[0].find("哲学") \
or tags[0].find("政治"):
books = list(set(books) - set(ignore))
# sort
books = sorted(books)
# get top 100
topBooks = books[0:min(130, len(books))]
# ignore blacklist
if backlist:
delList = []
for book in topBooks:
for bl in backlist:
if book.name.find(bl) != -1:
delList.append(book.name)
break
topBooks = [book for book in topBooks if book.name not in delList]
topBooks = topBooks[0:min(100, len(topBooks))]
# export to markdown
exportToMarkdown(tagList[0], topBooks, total)
return books
#=============================================================================
# 排序算法
#=============================================================================
def computeCompositeRating(tag, minNum, maxNum, k, num, people):
people = max(1, min(maxNum, people))
if people <= minNum:
people = minNum / 3
peopleWeight = math.pow(people, k)
level4 = max(500, maxNum * 1 / 10)
level5 = max(1000, maxNum * 3 / 10)
if people < 50:
return (num * 40 + peopleWeight * 60) / 100.0
elif people < 100:
return (num * 50 + peopleWeight * 50) / 100.0
elif people < 200:
return (num * 60 + peopleWeight * 40) / 100.0
elif people < level4:
return (num * 70 + peopleWeight * 30) / 100.0
elif people < level5:
return (num * 80 + peopleWeight * 20) / 100.0
else:
return (num * 90 + peopleWeight * 10) / 100.0
#=============================================================================
# 程序入口：抓取指定标签的书籍
#=============================================================================
if __name__ == '__main__':
# blacklist
classicBL = ["新概念英语", "大明宫词", "费恩曼物理学讲义", "经济学原理"]
japanLibBL = ["苍井优", "知日", "杉浦康平", "设计中的设计", "我的造梦之路",
"版式设计原理", "家庭收纳1000例", "无缘社会", "未来ちゃん", "用洗脸盆吃羊肉饭"]
programBL = ["三双鞋", "触动人心", "破茧成蝶", "MFC", "李开复", "沸腾十五年"]
techBL = ["哲学家们都干了些什么", "你一定爱读的极简欧洲史", "一课经济学", "定本育儿百科",
"儿童百科", "宝石"]
politicsBL = ["剑桥中华人民共和国史", "我为什么要写作", "职场动物进化手册", "王小波",
"大明王朝", "观念的水位", "一课经济学", "雪", "毛泽东选集"]
lawBL = ["七号房的礼物", "走不出的风景", "美国常春藤上的中国蜗牛"]
philosophyBL = ["鲁迅全集", "人类简史", "孙子兵法", "上帝掷骰子吗", "中国历代政治得失",
"洞穴奇案", "规训与惩罚", "韦伯", "古拉格", "顾准", "王小波", "穷查理宝典", "思维的乐趣",
"毛泽东选集", "进化心理学", "一只特立独行的猪", "陈寅恪的最后20年", "生命之书", "自私的基因",
"金枝", "经济学的思维方式"]
anthropologyBL = ["考古学", "贫穷的本质", "基因组"]
pictureBookBL = ["花卉圣经", "窥视印度", "你今天心情不好吗", "猫国物语", "奈良美智横滨手稿",
"平如美棠", "一点巴黎", "一个人去跑步", "踮脚张望", "失乐园", "我的路"]
tags = [
# ["杂文", None, 50, 5000, 0.25],
# ["散文", None, 50, 8000, 0.25],
# ["诗歌", None, 50, 4000, 0.25],
# ["漫画,日本漫画", None, 50, 8000, 0.275],
# ["绘本", pictureBookBL, 20, 5000, 0.25],
# ["科幻,科幻小说", None, 50, 8000, 0.275],
# ["魔幻,魔幻小说,玄幻,玄幻小说", None, 50, 8000, 0.275],
# ["推理,推理小说", None, 50, 8000, 0.275],
# ["武侠", None, 50, 8000, 0.3],
# ["悬疑", None, 50, 8000, 0.3],
# ["言情", None, 50, 8000, 0.3],
# ["青春,青春文学", None, 50, 8000, 0.3],
# ["童话", None, 20, 8000, 0.275],
# ["考古", None, 50, 4000, 0.25],
# ["电影", None, 50, 8000, 0.275],
# ["小说", None, 50, 8000, 0.3],
# ["编程,程序,算法,互联网", programBL, 30, 3000, 0.25],
# ["宗教,佛教", None, 50, 4000, 0.25],
# ["心理,心理学", None, 30, 3000, 0.25],
# ["社会,社会学", None, 30, 5000, 0.275],
# ["政治,政治学,自由主义", politicsBL, 30, 4000, 0.22],
# ["经济,经济学,金融", None, 30, 5000, 0.275],
# ["商业,投资,管理,创业", None, 30, 8000, 0.275],
# ["哲学,西方哲学,自由主义,思想", philosophyBL, 30, 3000, 0.25],
# ["法律,法学,民法,刑法,国际法", lawBL, 50, 4000, 0.25],
# ["文化,人文,思想,国学", None, 30, 8000, 0.275],
# ["历史,中国历史,近代史", None, 30, 8000, 0.3],
# ["人类学", anthropologyBL, 50, 4000, 0.25],
# ["数学", None, 50, 4000, 0.25],
# ["化学", None, 50, 4000, 0.25],
# ["地理,地理学", None, 50, 4000, 0.25],
# ["物理,物理学", None, 50, 4000, 0.25],
# ["生物,生物学", None, 50, 4000, 0.25],
# ["医学,临床医学", None, 50, 4000, 0.25],
# ["科技,科普,科学,神经网络", techBL, 30, 5000, 0.24],
# ["设计,用户体验,交互,交互设计,UCD,UE", None, 30, 3000, 0.25],
# ["成长,教育", None, 50, 5000, 0.25],
# ["名著,外国名著,经典,古典文学", classicBL, 50, 8000, 0.275],
# ["文学,经典,名著,外国名著,外国文学,中国文学,日本文学,当代文学", None, 50, 8000, 0.3],
# ["外国文学,外国名著", None, 50, 8000, 0.3],
# ["日本文学,日本", japanLibBL, 50, 8000, 0.3],
# ["中国文学", None, 50, 8000, 0.3],
["逻辑", None, 30, 3000, 0.3],
["教育", None, 30, 3000, 0.3],
["音乐", None, 30, 3000, 0.3],
]
start = timeit.default_timer()
ignore = []
for tag in tags:
tagName = tag[0]
books = process(tag, ignore)
if tagName.find("绘本") != -1 \
or tagName.find("漫画") != -1 \
or tagName.find("童话") != -1 \
or tagName.find("青春") != -1 \
or tagName.find("言情") != -1 \
or tagName.find("武侠") != -1 \
or tagName.find("悬疑") != -1 \
or tagName.find("推理") != -1 \
or tagName.find("科幻") != -1 \
or tagName.find("魔幻") != -1 \
or tagName.find("编程") != -1 \
or tagName.find("宗教") != -1:
ignore = list(set(books + ignore))
elapsed = timeit.default_timer() - start
print("== 总耗时 %.2f 秒 ==" % (elapsed))