【7月最新实现】使用Python获取全网招聘数据，实现可视化分析
哈喽兄弟们，今天来实现采集一下最新的qcwu招聘数据。因为网站嘛，大家都爬来爬去的，人家就会经常更新，所以代码对应的也要经常重新去写。
对于会的人来说，当然无所谓，任他更新也拦不住，但是对于不会的小伙伴来说，网站一更新，当场自闭。
所以这期是出给不会的小伙伴的，我还录制了视频进行详细讲解，跟源码一起打包好了，代码里有领取方式
软件工具
先来看看需要准备啥
环境使用
Python 3.8
Pycharm
模块使用
# 第三方模块 需要安装的
requests>>> pip install requests
csv
实现爬虫基本流程
一、数据来源分析: 思路固定
[*]明确需求:
- 明确采集网站以及数据内容
网址: 51job
内容: 招聘信息
[*]通过开发者工具, 进行抓包分析, 分析具体数据来源
I. 打开开发者工具: F12 / 右键点击检查选择network
II. 刷新网页, 让数据内容重新加载一遍
III. 通过搜索去找数据具体位置
招聘信息数据包: https://we.***.com/api/job/search-pc?api_key=51job×tamp=1688645783&keyword=python&searchType=2&function=&industry=&jobArea=010000%2C020000%2C030200%2C040000%2C090200&jobArea2=&landmark=&metro=&salary=&workYear=°ree=&companyType=&companySize=&jobType=&issueDate=&sortType=0&pageNum=1&requestId=&pageSize=20&source=1&accountId=&pageCode=sou%7Csou%7Csoulb
二、代码实现步骤: 步骤固定
[*]发送请求, 模拟浏览器对于url地址发送请求
请求链接: 招聘信息数据包url
[*]获取数据, 获取服务器返回响应数据
开发者工具: response
[*]解析数据, 提取我们想要的数据内容
招聘基本信息
[*]保存数据, 把信息数据保存表格文件里面
代码解析
模块
# 导入数据请求模块
import requests
# 导入格式化输出模块
# Python学习交流扣裙708525271
# 代码和视频在裙里拿
from pprint import pprint
# 导入csv
import csv
发送请求, 模拟浏览器对于url地址发送请求
headers = {
'Cookie': 'guid=54b7a6c4c43a33111912f2b5ac6699e2; sajssdk_2015_cross_new_user=1; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2254b7a6c4c43a33111912f2b5ac6699e2%22%2C%22first_id%22%3A%221892b08f9d11c8-09728ce3464dad8-26031d51-3686400-1892b08f9d211e7%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTg5MmIwOGY5ZDExYzgtMDk3MjhjZTM0NjRkYWQ4LTI2MDMxZDUxLTM2ODY0MDAtMTg5MmIwOGY5ZDIxMWU3IiwiJGlkZW50aXR5X2xvZ2luX2lkIjoiNTRiN2E2YzRjNDNhMzMxMTE5MTJmMmI1YWM2Njk5ZTIifQ%3D%3D%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%2254b7a6c4c43a33111912f2b5ac6699e2%22%7D%2C%22%24device_id%22%3A%221892b08f9d11c8-09728ce3464dad8-26031d51-3686400-1892b08f9d211e7%22%7D; nsearch=jobarea%3D%26%7C%26ord_field%3D%26%7C%26recentSearch0%3D%26%7C%26recentSearch1%3D%26%7C%26recentSearch2%3D%26%7C%26recentSearch3%3D%26%7C%26recentSearch4%3D%26%7C%26collapse_expansion%3D; search=jobarea%7E%60010000%2C020000%2C030200%2C040000%2C090200%7C%21recentSearch0%7E%60010000%2C020000%2C030200%2C040000%2C090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21; privacy=1688644161; Hm_lvt_1370a11171bd6f2d9b1fe98951541941=1688644162; Hm_lpvt_1370a11171bd6f2d9b1fe98951541941=1688644162; JSESSIONID=BA027715BD408799648B89C132AE93BF; acw_tc=ac11000116886495592254609e00df047e220754059e92f8a06d43bc419f21; ssxmod_itna=Qqmx0Q0=K7qeqD5itDXDnBAtKeRjbDce3=e8i=Ax0vTYPGzDAxn40iDtrrkxhziBemeLtE3Yqq6j7rEwPeoiG23pAjix0aDbqGkPA0G4GG0xBYDQxAYDGDDPDocPD1D3qDkD7h6CMy1qGWDm4kDWPDYxDrjOKDRxi7DDvQkx07DQ5kQQGxjpBF=FHpu=i+tBDkD7ypDlaYj9Om6/fxMp7Ev3B3Ix0kl40Oya5s1aoDUlFsBoYPe723tT2NiirY6QiebnnDsAhWC5xyVBDxi74qTZbKAjtDirGn8YD===; ssxmod_itna2=Qqmx0Q0=K7qeqD5itDXDnBAtKeRjbDce3=e8i=DnIfwqxDstKhDL0iWMKV3Ekpun3DwODKGcDYIxxD==; acw_sc__v2=64a6bf58f0b7feda5038718459a3b1e625849fa8',
'Referer': 'https://we.51job.com/pc/search?jobArea=010000,020000,030200,040000,090200&keyword=python&searchType=2&sortType=0&metro=',
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
}
# 请求链接
url = 'https://we.***.com/api/job/search-pc'
# 请求参数
data = {
'api_key': '51job',
'timestamp': '*****',
'keyword': '****',
'searchType': '2',
'function': '',
'industry': '',
'jobArea': '010000,020000,030200,040000,090200',
'jobArea2': '',
'landmark': '',
'metro': '',
'salary': '',
'workYear': '',
'degree': '',
'companyType': '',
'companySize': '',
'jobType': '',
'issueDate': '',
'sortType': '0',
'pageNum': '1',
'requestId': '',
'pageSize': '20',
'source': '1',
'accountId': '',
'pageCode': 'sou|sou|soulb',
}
# 发送请求
response = requests.get(url=url, params=data, headers=headers)
获取数据
获取服务器返回响应数据
开发者工具: response
- response.json() 获取响应json数据
解析数据
提取我们想要的数据内容
for循环遍历
for index in response.json()['resultbody']['job']['items']:
# index 具体岗位信息 --> 字典
dit = {
'职位': index['jobName'],
'公司': index['fullCompanyName'],
'薪资': index['provideSalaryString'],
'城市': index['jobAreaString'],
'经验': index['workYearString'],
'学历': index['degreeString'],
'公司性质': index['companyTypeString'],
'公司规模': index['companySizeString'],
'职位详情页': index['jobHref'],
'公司详情页': index['companyHref'],
}
以字典方式进行数据保存
csv_writer.writerow(dit)
print(dit)
保存表格
f = open('python.csv', mode='w', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=[
'职位',
'公司',
'薪资',
'城市',
'经验',
'学历',
'公司性质',
'公司规模',
'职位详情页',
'公司详情页',
])
csv_writer.writeheader()
可视化部分
import pandas as pddf = pd.read_csv('data.csv')df.head()df['学历'] = df['学历'].fillna('不限学历')edu_type = df['学历'].value_counts().index.to_list()edu_num = df['学历'].value_counts().to_list()from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Fakerfrom pyecharts.globals import CurrentConfig, NotebookTypeCurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LABc = ( Pie() .add( "", [ list(z) for z in zip(edu_type,edu_num) ], center=["40%", "50%"], ) .set_global_opts( title_opts=opts.TitleOpts(title="Python学历要求"), legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"), ) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))c.load_javascript()c.render_notebook()df['城市'] = df['城市'].str.split('·').strcity_type = df['城市'].value_counts().index.to_list()city_num = df['城市'].value_counts().to_list()c = ( Pie() .add( "", [ list(z) for z in zip(city_type,city_num) ], center=["40%", "50%"], ) .set_global_opts( title_opts=opts.TitleOpts(title="Python招聘城市分布"), legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"), ) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))c.render_notebook()def LowMoney(i): if '万' in i: low = i.split('-') if '千' in low: low_num = low.replace('千', '') low_money = int(float(low_num) * 1000) else: low_money = int(float(low) * 10000) else: low = i.split('-') if '元/天' in low: low_num = low.replace('元/天', '') low_money = int(low_num) * 30 else: low_money = int(float(low) * 1000) return low_moneydf['最低薪资'] = df['薪资'].apply(LowMoney)def MaxMoney(j): Max = j.split('-')[-1].split('·') if '万' in Max and '万/年' not in Max: max_num = int(float(Max.replace('万', '')) * 10000) elif '千' in Max: max_num = int(float(Max.replace('千', '')) * 1000) elif '元/天' in Max: max_num = int(Max.replace('元/天', ''))* 30 else: max_num = int((int(Max.replace('万/年', ''))* 10000) / 12) return max_numdf['最高薪资'] = df['薪资'].apply(MaxMoney)def tranform_price(x): if x
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