?一、数据采集（selenium）

from selenium import webdriver
import time
import re
import pandas as pd
import os

?在爬取的过程中可能会有登陆弹窗，要先定义一个处理弹窗的函数

def close_windows():
    #如果有登录弹窗，就关闭
    try:
        time.sleep(0.5)
        if dr.find_element_by_class_name("jconfirm").find_element_by_class_name("closeIcon"):
            dr.find_element_by_class_name("jconfirm").find_element_by_class_name("closeIcon").click()
    except BaseException as e:
        print('close_windows,没有弹窗',e)

?爬取部分，这里爬取维度为11列，基本上包含了职位的大部分信息

def get_current_region_job(k_index):
    flag = 0
    # page_num_set=0#每区获取多少条数据，对30取整

    df_empty = pd.DataFrame(columns=['岗位', '地点', '薪资', '工作经验', '学历', '公司名称', '技能','工作福利','工作类型','融资情况','公司规模'])
    while (flag == 0):
        # while (page_num_set<151)&(flag == 0):#每次只能获取150条信息
        time.sleep(0.5)
        close_windows()
        job_list = dr.find_elements_by_class_name("job-primary")
        for job in job_list:#获取当前页的职位30条
            job_name = job.find_element_by_class_name("job-name").text
            # print(job_name)
            job_area = job.find_element_by_class_name("job-area").text
            # salary = job.find_element_by_class_name("red").get_attribute("textContent")  # 获取薪资
            salary_raw = job.find_element_by_class_name("red").get_attribute("textContent")  # 获取薪资
            salary_split = salary_raw.split('·')  # 根据·分割
            salary = salary_split[0]  # 只取薪资，去掉多少薪

            # if re.search(r'天', salary):
            #     continue

            experience_education = job.find_element_by_class_name("job-limit").find_element_by_tag_name(
                "p").get_attribute("innerHTML")

            # experience_education_raw = '1-3年<em class="vline"></em>本科'
            experience_education_raw = experience_education
            split_str = re.search(r'[a-zA-Z =<>/"]{23}', experience_education_raw)  # 搜索分割字符串<em class="vline"></em>
            # print(split_str)

            experience_education_replace = re.sub(r'[a-zA-Z =<>/"]{23}', ",", experience_education_raw)  # 分割字符串替换为逗号
            # print(experience_education_replace)

            experience_education_list = experience_education_replace.split(',')  # 根据逗号分割
            # print('experience_education_list:',experience_education_list)

            if len(experience_education_list)!=2:
                print('experience_education_list不是2个，跳过该数据',experience_education_list)
                break
            experience = experience_education_list[0]
            education = experience_education_list[1]
            # print(experience)
            # print(education)

            company_type = job.find_element_by_class_name("company-text").find_element_by_tag_name(
                "p").get_attribute("innerHTML")
            company_type_size_row=company_type
            split_str_2=re.search(r'[a-zA-Z =<>/"]{23}', company_type_size_row)
            # print(split_str_2)
            # print("split2------------------------------------------------------")
            company_size_replace= re.sub(r'[a-zA-Z =<>/"]{23}', ",", company_type_size_row)
            # print(company_size_replace)

            company_size_list=company_size_replace.split(',')
            # print(company_size_list)

            if len(company_size_list) != 3:
                print('company_size_list不是3个，跳过该数据', company_size_list)
                break
            company_direct_info = company_size_list[0].split(">")[1]
            company_salary_info = company_size_list[1].split(">")[1]
            company_size_info=company_size_list[2]



            company = job.find_element_by_class_name("company-text").find_element_by_class_name("name").text

            skill_list = job.find_element_by_class_name("tags").find_elements_by_class_name("tag-item")
            skill = []
            job_advantage=job.find_element_by_class_name("info-desc").text
            for skill_i in skill_list:
                skill_i_text = skill_i.text
                if len(skill_i_text) == 0:
                    continue
                skill.append(skill_i_text)
            # print(job_name)
            # print(skill)

            df_empty.loc[k_index, :] = [job_name, job_area, salary, experience, education, company, skill,job_advantage,company_direct_info,company_salary_info,company_size_info]
            print(df_empty.loc[k_index, :])
            k_index = k_index + 1
            # page_num_set=page_num_set+1
            print("已经读取数据{}条".format(k_index))

        close_windows()
        try:#点击下一页
            cur_page_num=dr.find_element_by_class_name("page").find_element_by_class_name("cur").text
            # print('cur_page_num',cur_page_num)

            #点击下一页
            element = dr.find_element_by_class_name("page").find_element_by_class_name("next")
            dr.execute_script("arguments[0].click();", element)
            time.sleep(1)
            # print('点击下一页')

            new_page_num=dr.find_element_by_class_name("page").find_element_by_class_name("cur").text
            # print('new_page_num',new_page_num)

            if cur_page_num==new_page_num:
                flag = 1
                break

        except BaseException as e:
            print('点击下一页错误',e)
            break

    print(df_empty)
    if os.path.exists("ai数据.csv"):#存在追加，不存在创建
        df_empty.to_csv('ai数据.csv', mode='a', header=False, index=None, encoding='gb18030')
    else:
        df_empty.to_csv("ai数据.csv", index=False, encoding='gb18030')

    return k_index

?自动化爬取部分这里按照全国14个热门城市爬取若想爬取某个固定城市，需要把for循环去掉，去网站上找到对应城市编码，剪贴url即可

def main():
    # 打开浏览器
    # dr = webdriver.Firefox()
    global dr
    dr = webdriver.Chrome()
    # dr = webdriver.Ie()

    # # 后台打开浏览器
    # option=webdriver.ChromeOptions()
    # option.add_argument('headless')
    # dr = webdriver.Chrome(chrome_options=option)
    # print("打开浏览器")

    # 将浏览器最大化显示
    dr.maximize_window()

    # 转到目标网址
    dr.get("https://www.zhipin.com/job_detail/?query=人工智能&city=100010000&industry=&position=")#全国
    # dr.get("https://www.zhipin.com/c101010100/?query=人工智能&ka=sel-city-101010100")#北京
    print("打开网址")
    time.sleep(5)

    k_index = 0#数据条数、DataFrame索引

    flag_hot_city=0

    for i in range(3,17,1):
        # print('第',i-2,'页')

        # try:

        # 获取城市
        close_windows()
        hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
        close_windows()
        # hot_city_list[i].click()#防止弹窗，改为下面两句
        # element_hot_city_list_first = hot_city_list[i]
        dr.execute_script("arguments[0].click();", hot_city_list[i])

        # 输出城市名
        close_windows()
        hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
        print('城市：{}'.format(i-2),hot_city_list[i].text)
        time.sleep(0.5)


        # 获取区县
        for j in range(1,50,1):
            # print('第', j , '个区域')
            # try:

            # close_windows()
            # hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")

            # 在这个for循环点一下城市，不然识别不到当前页面已经更新了
            close_windows()
            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            close_windows()
            # hot_city_list[i].click()#防止弹窗，改为下面
            dr.execute_script("arguments[0].click();", hot_city_list[i])

            #输出区县名称
            close_windows()
            city_district = dr.find_element_by_class_name("condition-district").find_elements_by_tag_name("a")
            if len(city_district)==j:
                print('遍历完所有区县，没有不可点击的，跳转下一个城市')
                break
            print('区县：',j, city_district[j].text)
            # city_district_value=city_district[j].text#当前页面的区县值


            # 点击区县
            close_windows()
            city_district=  dr.find_element_by_class_name("condition-district").find_elements_by_tag_name("a")
            close_windows()
            # city_district[j].click()]#防止弹窗，改为下面两句
            # element_city_district = city_district[j]
            dr.execute_script("arguments[0].click();", city_district[j])



            #判断区县是不是点完了
            close_windows()
            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            print('点击后这里应该是区县', hot_city_list[1].text)#如果是不限，说明点完了，跳出

            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            print('如果点完了，这里应该是不限：',hot_city_list[1].text)

            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            if hot_city_list[1].text == '不限':
                print('当前区县已经点完了，点击下一个城市')
                flag_hot_city=1
                break


            close_windows()
            k_index = get_current_region_job(k_index)#获取职位，爬取数据


            # 重新点回城市页面，再次获取区县。但此时多了区县，所以i+1
            close_windows()
            hot_city_list = dr.find_element_by_class_name("condition-city").find_elements_by_tag_name("a")
            close_windows()
            # hot_city_list[i+1].click()#防止弹窗，改为下面两句
            # element_hot_city_list_again = hot_city_list[i+1]
            dr.execute_script("arguments[0].click();", hot_city_list[i+1])



            # except BaseException as e:
            #     print('main的j循环-获取区县发生错误:', e)
            #     close_windows()

            time.sleep(0.5)



        # except BaseException as e:
        #     print('main的i循环发生错误:',e)
        #     close_windows()

        time.sleep(0.5)

    # 退出浏览器
    dr.quit()
    # p1.close()

最后调用main即可，爬取结果如下数据量共计一万（人工智能职位）

数据为两部分：分别为全国人工智能职位爬取? 热门城市人工职位数据爬取

二、数据预处理

? ?简单做一些缺失值和规范化的处理具体分析部分在Hive中

# coding=utf-8
import collections
import wordcloud
import re
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 设置正常显示符号


def create_dir_not_exist(path):  # 判断文件夹是否存在,不存在-新建
    if not os.path.exists(path):
        os.mkdir(path)


create_dir_not_exist(r'./image')
create_dir_not_exist(r'./image/city')

data = pd.read_csv('ai数据.csv', encoding='gb18030')
data_df = pd.DataFrame(data)
print("\n查看是否有缺失值\n", data_df.isnull().sum())

data_df_del_empty = data_df.dropna(subset=['岗位'], axis=0)
# print("\n删除缺失值‘岗位'的整行\n",data_df_del_empty)
data_df_del_empty = data_df_del_empty.dropna(subset=['公司名称'], axis=0)
# print("\n删除缺失值‘公司'的整行\n",data_df_del_empty)

print("\n查看是否有缺失值\n", data_df_del_empty.isnull().sum())
print('去除缺失值后\n', data_df_del_empty)

data_df_python_keyword = data_df_del_empty.loc[data_df_del_empty['岗位'].str.contains('人工智能|AI')]
# print(data_df_python_keyword)#筛选带有python的行

# 区间最小薪资
data_df_python_keyword_salary = data_df_python_keyword['薪资'].str.split('-', expand=True)[0]
print(data_df_python_keyword_salary)  # 区间最小薪资
# Dataframe新增一列  在第 列新增一列名为' ' 的一列 数据
data_df_python_keyword.insert(7, '区间最小薪资(K)', data_df_python_keyword_salary)
print(data_df_python_keyword)

# 城市地区
data_df_python_keyword_location_city = data_df_python_keyword['地点'].str.split('·', expand=True)[0]
print(data_df_python_keyword_location_city)  # 北京
data_df_python_keyword_location_district = data_df_python_keyword['地点'].str.split('·', expand=True)[1]
print(data_df_python_keyword_location_district)  # 海淀区

data_df_python_keyword_location_city_district = []
for city, district in zip(data_df_python_keyword_location_city, data_df_python_keyword_location_district):
    city_district = city + district
    data_df_python_keyword_location_city_district.append(city_district)
print(data_df_python_keyword_location_city_district)  # 北京海淀区
# Dataframe新增一列  在第 列新增一列名为' ' 的一列 数据
data_df_python_keyword.insert(8, '城市地区', data_df_python_keyword_location_city_district)
print(data_df_python_keyword)

data_df_python_keyword.insert(9, '城市', data_df_python_keyword_location_city)
data_df_python_keyword.insert(10, '地区', data_df_python_keyword_location_district)
data_df_python_keyword.to_csv("data_df_python_keyword.csv", index=False, encoding='gb18030')

print('-------------------------------------------')