Python爬取招聘網站數據並做數據可視化

基本開發環境

· Python 3.6

· Pycharm

 

相關模塊使用

爬蟲模塊

import requests
import re
import parsel
import csv

詞雲模塊

import jieba

import wordcloud

 

 

目標網頁分析

 

 

 

 

 

通過開發者工具可以看到，獲取返回數據后，數據是在window_search_result_里面，可以使用正則匹配數據。如下所示：

 

 

 

https://jobs.51job.com/beijing/120995776.html?s=01&t=0

 

每一個招聘信息的詳情頁都是有對應的ID，只需要正則匹配提取ID值，通過拼接URL，然后再去招聘詳情頁提取招聘數據即可。

 

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)

 

 

 

 

雖然網站是靜態網頁，但是網頁編碼是亂碼，在爬取的過程中需要轉碼。

 

f = open('招聘.csv', mode='a', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=['標題', '地區', '工作經驗', '學歷', '薪資', '福利', '招聘人數', '發布日期'])
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()      # 標題
salary = selector.css('div.cn strong::text').get()       # 薪資
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]         # 地區
work_experience = data_info[1]      # 工作經驗
educational_background = data_info[2]       # 學歷
number_of_people = data_info[3]     # 招聘人數
release_date = data_info[-1].replace('發布', '')     # 發布日期
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 = {
    '標題': title,
    '地區': area,
    '工作經驗': work_experience,
    '學歷': educational_background,
    '薪資': salary,
    '福利': welfare_info,
    '招聘人數': number_of_people,
    '發布日期': release_date,
}
csv_writer.writerow(dit)
with open('招聘信息.txt', mode='a', encoding='utf-8') as f:
    f.write(all_info)

 

以上步驟即可完成關於招聘的相關數據爬取

 

 

簡單粗略的數據清洗

薪資待遇：

 

content = pd.read_csv(r'D:\python\demo\數據分析\招聘\招聘.csv', encoding='utf-8')
salary = content['薪資']
salary_1 = salary[salary.notnull()]
salary_count = pd.value_counts(salary_1)

 

 

 

 

學歷要求：

content = pd.read_csv(r'D:\python\demo\數據分析\招聘\招聘.csv', encoding='utf-8')
educational_background = content['學歷']
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("學歷", educational_background_count.values.tolist())
bar.render('bar.html')

 

 

 

工作經驗：

content = pd.read_csv(r'D:\python\demo\數據分析\招聘\招聘.csv', encoding='utf-8')
work_experience = content['工作經驗']
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("經驗要求", work_experience_count.values.tolist())
bar.render('bar.html')

 

 

詞雲分析，技術點要求

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 分詞 分割詞匯
txt_list = jieba.lcut(txt)
string = ' '.join(txt_list)
# 詞雲圖設置
wc = wordcloud.WordCloud(
        width=1000,         # 圖片的寬
        height=700,         # 圖片的高
        background_color='white',   # 圖片背景顏色
        font_path='msyh.ttc',    # 詞雲字體
        mask=py,     # 所使用的詞雲圖片
        scale=15,
        stopwords={' '},
        # contour_width=5,
        # contour_color='red'  # 輪廓顏色
)
# 給詞雲輸入文字
wc.generate(string)
# 詞雲圖保存圖片地址
wc.to_file(r'python招聘信息.png')