Python爬取招聘网站数据并做数据可视化

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

 

通过开发者工具可以看到，获取返回数据后，数据是在window_search_result_里面，可以使用正则匹配数据。如下所示：

https://jobs.51job.com/beijing/120995776.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 h1::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 = '
'.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:pythondemo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:pythondemo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')

工作经验：

content = pd.read_csv(r'D:pythondemo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')