暑假集训主要是在杭电oj上面刷题，白天与算法作斗争，晚上望干点自己喜欢的事情！
首先，确定要爬取哪些数据：
如上图所示，题目ID，名称，accepted，submissions，都很有用。
查看源代码知：
所有的数据都在一个script标签里面。
思路：用beautifulsoup找到这个标签，然后用正则表达式提取。
话不多说，上数据爬取的代码：
import requests
from bs4 import BeautifulSoup
import time
import random
import re
from requests.exceptions import RequestException
prbm_id = []
prbm_name = []
prbm_ac = []
prbm_sub = []
def get_html(url): # 获取html
try:
kv = {'user-agent': 'Mozilla/5.0'}
r = requests.get(url, timeout=5, headers=kv)
r.raise_for_status()
r.encoding = r.apparent_encoding
random_time = random.randint(1, 3)
time.sleep(random_time) # 应对反爬虫，随机休眠1至3秒
return r.text
except RequestException as e: # 异常输出
print(e)
return ""
def get_hdu():
count = 0
for i in range(1, 56):
url = "http://acm.hdu.edu.cn/listproblem.php?vol=" + str(i)
# print(url)
html = get_html(url)
# print(html)
soup = BeautifulSoup(html, "html.parser")
cnt = 1
for it in soup.find_all("script"):
if cnt == 5:
# print(it.get_text())
str1 = it.string
list_pro = re.split("p\(|\);", str1) # 去除 p(); 分割
# print(list_pro)
for its in list_pro:
if its != "":
# print(its)
temp = re.split(',', its)
len1 = len(temp)
prbm_id.append(temp[1])
prbm_name.append(temp[3])
prbm_ac.append(temp[len1-2])
prbm_sub.append(temp[len1-1])
cnt = cnt + 1
count = count + 1
print('\r当前进度：{:.2f}%'.format(count * 100 / 55, end='')) # 进度条
def main():
get_hdu()
root = "F://爬取的资源//hdu题目数据爬取2.txt"
len1 = len(prbm_id)
for i in range(0, len1):
with open(root, 'a', encoding='utf-8') as f: # 存储个人网址
f.write("hdu"+prbm_id[i] + "," + prbm_name[i] + "," + prbm_ac[i] + "," + prbm_sub[i] + '\n')
# print(prbm_id[i])
if __name__ == '__main__':
main()
爬取数据之后，想到用词云生成图片，来达到数据可视化。
本人能力有限，仅根据AC的数量进行分类，生成不同的词云图片。数据分析代码如下：
import re
import wordcloud
from scipy.misc import imread # 这是一个处理图像的函数
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
import matplotlib.pyplot as plt
import os
prbm_id = []
prbm_name = []
prbm_ac = []
prbm_sub = []
def read():
f = open(r"F://爬取的资源//hdu题目数据爬取2.txt", "r", encoding="utf-8")
list_str = f.readlines()
for it in list_str:
list_pre = re.split(",", it)
prbm_id.append(list_pre[0].strip('\n'))
prbm_name.append(list_pre[1].strip('\n'))
prbm_ac.append(list_pre[2].strip('\n'))
prbm_sub.append(list_pre[3].strip('\n'))
def data_Process():
for it in range(0, len(prbm_ac)):
# print(prbm_sub[it])
root = "F://爬取的资源//词语统计.txt"
num1 = int(prbm_ac[it])
# num2 = int(prbm_ac[it])*1.0/int(prbm_sub[it])
if 5000 <= num1 <= 10000: # 分类
with open(root, 'a', encoding='utf-8') as f: # 写入txt文件，用于wordcloud词云生成
for i in range(0, int(num1/100)): # num1/100,这里可根据num1，除数变化
f.write(prbm_id[it] + ' ')
def main():
read()
data_Process()
text = open(r"F://爬取的资源//词语统计.txt", "r", encoding='utf-8').read()
# 生成一个词云图像
back_color = imread('F://爬取的资源//acm.jpg') # 解析该图片
w = wordcloud.WordCloud(background_color='white', # 背景颜色
mask=back_color, # 以该参数值作图绘制词云，这个参数不为空时，width和height会被忽略
width=300,
height =100,
collocations=False # 去掉重复元素
)
w.generate(text)
plt.imshow(w)
plt.axis("off")
plt.show()
os.remove("F://爬取的资源//词语统计.txt")
w.to_file("F://爬取的资源//hdu热度词云5.png")
if __name__ == '__main__':
main()
生成的图片效果展示如下：
词云是根据每个分类里面，ac的数量生成的。
仅以此，向广大在杭电上刷题的苦逼acmer们，表达此刻心中的敬意。愿每位acmer都能勇往直前，披荆斩棘。