用python爬取杭电oj的数据

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。