python如何爬取招聘岗位数据

今天小编给大家分享一下python如何爬取招聘岗位数据的相关知识点，内容详细，逻辑清晰，相信大部分人都还太了解这方面的知识，所以分享这篇文章给大家参考一下，希望大家阅读完这篇文章后有所收获，下面我们一起来了解一下吧。

数据爬取的代码

#encoding='utf-8'from selenium import webdriverimport timeimport reimport pandas as pdimport osdef 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            # 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_indexdef 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.******.com/job_detail/?query=Python&city=100010000&industry=&position=")#全国    dr.get("https://www.******.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-8import collectionsimport wordcloudimport reimport pandas as pdimport numpy as npimport osimport matplotlib.pyplot as pltplt.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("查看是否有缺失值", data_df.isnull().sum())data_df_del_empty = data_df.dropna(subset=['岗位'], axis=0)# print("删除缺失值'岗位'的整行",data_df_del_empty)data_df_del_empty = data_df_del_empty.dropna(subset=['公司'], axis=0)# print("删除缺失值'公司'的整行",data_df_del_empty)print("查看是否有缺失值", data_df_del_empty.isnull().sum())print('去除缺失值后', 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 + resultprint('++++++++++++++++++++++++++++++++')# 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()

以上就是"python如何爬取招聘岗位数据"这篇文章的所有内容，感谢各位的阅读！相信大家阅读完这篇文章都有很大的收获，小编每天都会为大家更新不同的知识，如果还想学习更多的知识，请关注行业资讯频道。