如何让Python爬取招聘网站数据并做数据可视化处理,相信很多没有经验的人对此束手无策,为此本文总结了问题出现的原因和解决方法,通过这篇文章希望你能解决这个问题。
基本开发环境
Python 3.6
Pycharm
相关模块使用
爬虫模块
import requests import re import parsel import csv
词云模块
import jieba import wordcloud
目标网页分析
'https://jobs.51job.com/beijing-ftq/127676506.html?s=01&t=0'
每一个招聘信息的详情页都是有对应的ID,只需要正则匹配提取ID值,通过拼接URL,然后再去招聘详情页提取招聘数据即可。
response = requests.get(url=url, headers=headers) lis = re.findall('"jobid":"(\d+)"', response.text) for li in lis: page_url = 'https://jobs.51job.com/beijing-hdq/{}.html?s=01&t=0'.format(li)
虽然网站是静态网页,但是网页编码是乱码,在爬取的过程中需要转码。
f = open('招聘.csv', mode='a', encoding='utf-8', newline='') csv_writer = csv.DictWriter(f, fieldnames=['标题', '地区', '工作经验', '学历', '薪资', '福利', '招聘人数', '发布日期']) csv_writer.writeheader() response = requests.get(url=page_url, headers=headers) response.encoding = response.apparent_encoding selector = parsel.Selector(response.text) title = selector.css('.cn h2::text').get() # 标题 salary = selector.css('div.cn strong::text').get() # 薪资 welfare = selector.css('.jtag div.t1 span::text').getall() # 福利 welfare_info = '|'.join(welfare) data_info = selector.css('.cn p.msg.ltype::attr(title)').get().split(' | ') area = data_info[0] # 地区 work_experience = data_info[1] # 工作经验 educational_background = data_info[2] # 学历 number_of_people = data_info[3] # 招聘人数 release_date = data_info[-1].replace('发布', '') # 发布日期 all_info_list = selector.css('div.tCompany_main > div:nth-child(1) > div p span::text').getall() all_info = '\n'.join(all_info_list) dit = { '标题': title, '地区': area, '工作经验': work_experience, '学历': educational_background, '薪资': salary, '福利': welfare_info, '招聘人数': number_of_people, '发布日期': release_date, } csv_writer.writerow(dit) with open('招聘信息.txt', mode='a', encoding='utf-8') as f: f.write(all_info)
以上步骤即可完成关于招聘的相关数据爬取。
简单粗略的数据清洗
薪资待遇
content = pd.read_csv(r'D:\python\demo\数据分析\招聘\招聘.csv', encoding='utf-8') salary = content['薪资'] salary_1 = salary[salary.notnull()] salary_count = pd.value_counts(salary_1)
学历要求
content = pd.read_csv(r'D:\python\demo\数据分析\招聘\招聘.csv', encoding='utf-8') educational_background = content['学历'] educational_background_1 = educational_background[educational_background.notnull()] educational_background_count = pd.value_counts(educational_background_1).head() print(educational_background_count) bar = Bar() bar.add_xaxis(educational_background_count.index.tolist()) bar.add_yaxis("学历", educational_background_count.values.tolist()) bar.render('bar.html')
显示招聘人数为无要求
工作经验
content = pd.read_csv(r'D:\python\demo\数据分析\招聘\招聘.csv', encoding='utf-8') work_experience = content['工作经验'] work_experience_count = pd.value_counts(work_experience) print(work_experience_count) bar = Bar() bar.add_xaxis(work_experience_count.index.tolist()) bar.add_yaxis("经验要求", work_experience_count.values.tolist()) bar.render('bar.html')
词云分析,技术点要求
py = imageio.imread("python.png") f = open('python招聘信息.txt', encoding='utf-8') re_txt = f.read() result = re.findall(r'[a-zA-Z]+', re_txt) txt = ' '.join(result) # jiabe 分词 分割词汇 txt_list = jieba.lcut(txt) string = ' '.join(txt_list) # 词云图设置 wc = wordcloud.WordCloud( width=1000, # 图片的宽 height=700, # 图片的高 background_color='white', # 图片背景颜色 font_path='msyh.ttc', # 词云字体 mask=py, # 所使用的词云图片 scale=15, stopwords={' '}, # contour_width=5, # contour_color='red' # 轮廓颜色 ) # 给词云输入文字 wc.generate(string) # 词云图保存图片地址 wc.to_file(r'python招聘信息.png')
总结:
数据分析是真的粗糙,属实辣眼睛。
看完上述内容,你们掌握如何让Python爬取招聘网站数据并做数据可视化处理的方法了吗?如果还想学到更多技能或想了解更多相关内容,欢迎关注亿速云行业资讯频道,感谢各位的阅读!
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