Python数据分析之Python和Selenium爬取BOSS直聘岗位

一、数据爬取的代码

#encoding='utf-8'
from selenium import webdriver
import time
import re
import pandas as pd
import os

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)


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', '技能'])
    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 = 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 = []
            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]
            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("数据.csv"):#存在追加，不存在创建
        df_empty.to_csv('数据.csv', mode='a', header=False, index=None, encoding='gb18030')
    else:
        df_empty.to_csv("数据.csv", index=False, encoding='gb18030')

    return k_index








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=Python&city=100010000&industry=&position=")#全国
    dr.get("https://www.zhipin.com/c101010100/?query=Python&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()



if __name__ == '__main__':
    main()

二、获取到的数据如图所示

三、数据分析的代码

# 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('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'], 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('Python|python')]
# 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('-------------------------------------------')


def draw_bar(row_lable, title):
    figsize_x = 10
    figsize_y = 6
    global list1_education, list2_education, df1, df2
    plt.figure(figsize=(figsize_x, figsize_y))
    list1_education = []
    list2_education = []
    for df1, df2 in data_df_python_keyword.groupby(row_lable):
        list1_education.append(df1)
        list2_education.append(len(df2))
    # print(list1_education)
    # print(list2_education)
    # 利用 * 解包方式 将 一个排序好的元组，通过元组生成器再转成list
    # print(*sorted(zip(list2_education,list1_education)))
    # print(sorted(zip(list2_education,list1_education)))
    # 排序，两个列表对应原始排序,按第几个列表排序，注意先后位置
    list2_education, list1_education = (list(t) for t in zip(*sorted(zip(list2_education, list1_education))))
    plt.bar(list1_education, list2_education)
    plt.title('{}'.format(title))
    plt.savefig('./image/{}分析.jpg'.format(title))
    # plt.show()
    plt.close()


# 学历
draw_bar('学历', '学历')
draw_bar('工作经验', '工作经验')
draw_bar('区间最小薪资(K)', '14个热门城市的薪资分布情况(K)')
# -----------------------------------------
# 根据城市地区求均值
list_group_city1 = []
list_group_city2 = []

for df1, df2 in data_df_python_keyword.groupby(data_df_python_keyword['城市地区']):
    # print(df1)
    # print(df2)
    list_group_city1.append(df1)
    salary_list_district = [int(i) for i in (df2['区间最小薪资(K)'].values.tolist())]
    district_salary_mean = round(np.mean(salary_list_district), 2)  # 每个区县的平均薪资 round(a, 2)保留2位小数
    list_group_city2.append(district_salary_mean)
    list_group_city2, list_group_city1 = (list(t) for t in
                                          zip(*sorted(zip(list_group_city2, list_group_city1), reverse=False)))
#
# print(list_group_city1)
# print(list_group_city2)

plt.figure(figsize=(10, 50))
plt.barh(list_group_city1, list_group_city2)
# 坐标轴上的文字说明
for ax, ay in zip(list_group_city1, list_group_city2):
    # 设置文字说明 第一、二个参数：坐标轴上的值； 第三个参数：说明文字；ha:垂直对齐方式；va：水平对齐方式
    plt.text(ay, ax, '%.2f' % ay, ha='center', va='bottom')
plt.title('14个热门城市的各区县招聘工资情况(K)')
plt.savefig('./image/14个热门城市的各区县招聘工资情况(K).jpg')
# plt.show()
plt.close()

# -----------------------------------------
# 根据城市分组排序，

list_group_city11 = []
list_group_city22 = []
list_group_city33 = []
list_group_city44 = []

for df_city1, df_city2 in data_df_python_keyword.groupby(data_df_python_keyword['城市']):
    # print(df_city1)#市
    # print(df_city2)
    list_group_district2 = []  # 区县列表
    district_mean_salary2 = []  # 工资均值列表
    for df_district1, df_district2 in df_city2.groupby(data_df_python_keyword['地区']):
        # print(df_district1)#区县
        # print(df_district2)#工作
        list_group_district2.append(df_district1)  # 记录区县
        salary_list_district2 = [int(i) for i in (df_district2['区间最小薪资(K)'].values.tolist())]  # 工资列表
        district_salary_mean2 = round(np.mean(salary_list_district2), 2)  # 每个区县的平均薪资 round(a, 2)保留2位小数
        district_mean_salary2.append(district_salary_mean2)  # 记录区县的平均工作的列表

    district_mean_salary2, list_group_district2 = (list(tt) for tt in zip(
        *sorted(zip(district_mean_salary2, list_group_district2), reverse=True)))
    plt.figure(figsize=(10, 6))
    plt.bar(list_group_district2, district_mean_salary2)

    # 坐标轴上的文字说明
    for ax, ay in zip(list_group_district2, district_mean_salary2):
        # 设置文字说明 第一、二个参数：坐标轴上的值； 第三个参数：说明文字；ha:垂直对齐方式；va：水平对齐方式
        plt.text(ax, ay, '%.2f' % ay, ha='center', va='bottom')

    plt.title('14个热门城市的各区县招聘工资情况_{}(K)'.format(df_city1))
    plt.savefig('./image/city/14个热门城市的各区县招聘工资情况_{}(K).jpg'.format(df_city1))
    # plt.show()
    plt.close()

# ----------------------------------------------------


skill_all = data_df_python_keyword['技能']
print(skill_all)

skill_list = []

for i in skill_all:
    # print(type(i))
    print(i)
    # print(i.split(", | ' | \[ | \]  |  \" | "))
    result = re.split(r'[,\' \[, \]  ]', i)
    print(result)
    # if type(i) == list:
    skill_list = skill_list + result
print('++++++++++++++++++++++++++++++++')
# print(skill_list)

list_new = skill_list

# 词频统计
word_counts = collections.Counter(list_new)  # 对分词做词频统计
word_counts_top10 = word_counts.most_common(30)  # 获取前10最高频的词
# print (word_counts_top10) # 输出检查
# print (word_counts_top10[0][0]) # 输出检查

# 生成柱状图
list_x = []
list_y = []
for i in word_counts_top10:
    list_x.append(i[0])
    list_y.append(i[1])
print('list_x', list_x[1:])
print('list_y', list_y[1:])
plt.figure(figsize=(30, 5))
plt.bar(list_x[1:], list_y[1:])
plt.savefig('./image/技能栈_词频_柱状图.png')
# plt.show()
plt.close()

list_new = " ".join(list_new)  # 列表转字符串，以空格间隔
# print(list_new)


wc = wordcloud.WordCloud(
    width=800,
    height=600,
    background_color="#ffffff",  # 设置背景颜色
    max_words=50,  # 词的最大数（默认为200）
    max_font_size=60,  # 最大字体尺寸
    min_font_size=10,  # 最小字体尺寸（默认为4）
    # colormap='bone',  # string or matplotlib colormap, default="viridis"
    colormap='hsv',  # string or matplotlib colormap, default="viridis"
    random_state=20,  # 设置有多少种随机生成状态，即有多少种配色方案
    # mask=plt.imread("mask2.gif"),  # 读取遮罩图片！！
    font_path='simhei.ttf'
)
my_wordcloud = wc.generate(list_new)

plt.imshow(my_wordcloud)
plt.axis("off")
# plt.show()
wc.to_file('./image/技能栈_词云.png')  # 保存图片文件
plt.close()

四、学历分析

五、工作经验分析

六、14个热门城市的各区县招聘薪资情况

七、各城市各区县的薪资情况

北京

上海

其余12个城市不再展示，生成代码都一样

八、技能栈

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