简单的用Python采集猎聘招聘数据内容，并做可视化分析！

selenium  # pip install selenium==3.141.0 自动化测试 操作浏览器
csv  # 保存数据 保存csv文件

driver = webdriver.Chrome()
# 访问网站
# 完整代码+视频讲解+软件工具领取
# 直接加这个抠裙：592539176
driver.get(
    'https://www.liepin.com/zhaopin/?inputFrom=www_index&workYearCode=0&key=python&scene=input&ckId=rn8762mqhk78fi3d9fiqzzoobk7f66u9&dq=')
"""
找到我们需要的数据内容
    前提 -> 你要让网页元素<数据>加载出来
    通过元素定位, 获取数据内容 <通过标签提取数据> 
"""
# 设置延时, 让网页元素加载完整
driver.implicitly_wait(10)  # 隐式等待, 只要网页加载出来就运行下面的代码
time.sleep(1)  # 死等, 一定要等够
# 获取所有招聘信息对应div标签
divs = driver.find_elements_by_css_selector('.job-list-box div .job-card-left-box')
# print(driver) # webdriver.Chrome() 返回selenium对象
# print(divs)  # 返回列表, 列表里面元素是selenium对象
for div in divs:
    """
    提取具体数据内容, 提取每个div标签里面所包含的数据内容
        .job-title-box div.ellipsis-1
    """
    # 职位
    title = div.find_element_by_css_selector('.job-title-box div.ellipsis-1').text
    # 城市
    city = div.find_element_by_css_selector('.job-title-box span.ellipsis-1').text
    salary = div.find_element_by_css_selector('.job-salary').text
    # 列表推导式
    info_list = [i.text for i in div.find_elements_by_css_selector('.job-labels-box .labels-tag')]
    print(info_list)
    exp = info_list[0]
    edu = info_list[1]
    labels = ','.join(info_list[2:])
    company = div.find_element_by_css_selector('.company-name').text
    company_type = div.find_element_by_css_selector('.company-tags-box span').text
    company_num = div.find_element_by_css_selector('.company-tags-box span:last-of-type').text
    href = div.find_element_by_css_selector('.job-detail-box a').get_attribute('href')
    dit = {
        '职位': title,
        '城市': city,
        '薪资': salary,
        '经验': exp,
        '学历': edu,
        '技术点': labels,
        '公司': company,
        '公司领域': company_type,
        '公司规模': company_num,
        '详情页': href,
    }
    csv_writer.writerow(dit)
    print(dit)

f = open('python.csv', mode='w', encoding='utf-8', newline='')
csv_writer = csv.DictWriter(f, fieldnames=[
    '职位',
    '城市',
    '薪资',
    '经验',
    '学历',
    '技术点',
    '公司',
    '公司领域',
    '公司规模',
    '详情页',
])
csv_writer.writeheader()

import pandas as pd

df = pd.read_csv('data.csv')
df.head()

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.globals import CurrentConfig, NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB
c = (
    Pie()
    .add(
        "",
        [
            list(z)
            for z in zip(
                edu_type,
                edu_num,
            )
        ],
        center=["40%", "50%"],
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="python职位学历需求分布"),
        legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    
)
c.load_javascript()

edu_num = df['经验'].value_counts().to_list()
edu_type = df['经验'].value_counts().index.to_list()
c = (
    Pie()
    .add(
        "",
        [
            list(z)
            for z in zip(
                edu_type,
                edu_num,
            )
        ],
        center=["40%", "50%"],
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="python职位经验需求分布"),
        legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    
)
c.render_notebook()

edu_num = df['城市'].str[:2].value_counts().to_list()
edu_type = df['城市'].str[:2].value_counts().index.to_list()
c = (
    Pie()
    .add(
        "",
        [
            list(z)
            for z in zip(
                edu_type,
                edu_num,
            )
        ],
        center=["40%", "50%"],
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="python职位城市分布"),
        legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    
)
c.render_notebook()

edu_num = df['公司领域'].value_counts().to_list()
edu_type = df['公司领域'].value_counts().index.to_list()
c = (
    Pie()
    .add(
        "",
        [
            list(z)
            for z in zip(
                edu_type,
                edu_num,
            )
        ],
        center=["40%", "50%"],
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="python公司领域分布"),
        legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    
)
c.render_notebook()