爱站程序员基地用python爬取腾讯招聘网岗位信息保存到表格，并做成可视化。
代码运行展示
开发环境
Windows 10
python3.6
开发工具
pycharm
库
numpy、matplotlib、time、xlutils.copy、os、xlwt, xlrd, random
开发思路
1.打开腾讯招聘的网址右击检查进行抓包，进入网址的时候发现有异步渲染，我们要的数据为异步加载
2.构造起始地址：
start_url = ‘https://careers.tencent.com/tencentcareer/api/post/Query’
参数在headers的最下面
timestamp: 1625641250509
countryId:
cityId:
bgIds:
productId:
categoryId:
parentCategoryId:
attrId:
keyword:
pageIndex: 1
pageSize: 10
language: zh-cn
area: cn
3.发送请求，获取响应
self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'# 构造请求参数params = {# 捕捉当前时间戳'timestamp': str(int(time.time() * 1000)),'countryId': '','cityId': '','bgIds': '','productId': '','categoryId': '','parentCategoryId': '','attrId': '','keyword': '','pageIndex': str(self.start_page),'pageSize': '10','language': 'zh-cn','area': 'cn'}headers = {'user-agent': random.choice(USER_AGENT_LIST)}response = session.get(url=self.start_url, headers=headers, params=params).json()
4.提取数据，获取岗位信息大列表，提取相应的数据
# 获取岗位信息大列表json_data = response['Data']['Posts']# 判断结果是否有数据if json_data is None:# 没有数据，设置循环条件为Falseself.is_running = False# 反之，开始提取数据else:# 循环遍历，取出列表中的每一个岗位字典# 通过key取value值的方法进行采集数据for data in json_data:# 工作地点LocationName = data['LocationName']# 往地址大列表中添加数据self.addr_list.append(LocationName)# 工作属性CategoryName = data['CategoryName']# 往工作属性大列表中添加数据self.category_list.append(CategoryName)# 岗位名称RecruitPostName = data['RecruitPostName']# 岗位职责Responsibility = data['Responsibility']# 发布时间LastUpdateTime = data['LastUpdateTime']# 岗位地址PostURL = data['PostURL']
5.数据生成折线图、饼图、散点图、柱状图
# 第一张图：根据岗位地址和岗位属性二者数量生成折线图# 146，147两行代码解决图中中文显示问题plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False# 由于二者数据数量不统一，在此进行切片操作x_axis_data = [i for i in addr_dict.values()][:5]y_axis_data = [i for i in cate_dict.values()][:5]# print(x_axis_data, y_axis_data)# plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='数量')# 显示标签，如果不加这句，即使在plot中加了label='一些数字'的参数，最终还是不会显示标签plt.legend(loc="upper right")plt.xlabel('地点数量')plt.ylabel('工作属性数量')plt.savefig('根据岗位地址和岗位属性二者数量生成折线图.png')plt.show()
# 第二张图：根据岗位地址数量生成饼图"""工作地址饼图"""addr_dict_key = [k for k in addr_dict.keys()]addr_dict_value = [v for v in addr_dict.values()]plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = Falseplt.pie(addr_dict_value, labels=addr_dict_key, autopct='%1.1f%%')plt.title(f'岗位地址和岗位属性百分比分布')plt.savefig(f'岗位地址和岗位属性百分比分布-饼图')plt.show()
# 第三张图：根据岗位地址和岗位属性二者数量生成散点图# 这两行代码解决 plt 中文显示的问题plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False# 输入岗位地址和岗位属性数据production = [i for i in data.keys()]tem = [i for i in data.values()]colors = np.random.rand(len(tem)) # 颜色数组plt.scatter(tem, production, s=200, c=colors) # 画散点图，大小为 200plt.xlabel('数量') # 横坐标轴标题plt.ylabel('名称') # 纵坐标轴标题plt.savefig(f'岗位地址和岗位属性散点图')plt.show()
# 第四张图：根据岗位地址和岗位属性二者数量生成柱状图import matplotlib;matplotlib.use('TkAgg')plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = Falsezhfont1 = matplotlib.font_manager.FontProperties(fname='C:\\Windows\\Fonts\\simsun.ttc')name_list = [name for name in data.keys()]num_list = [value for value in data.values()]width = 0.5 # 柱子的宽度index = np.arange(len(name_list))plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='岗位数量')plt.legend(['分解能耗', '真实能耗'], prop=zhfont1, labelspacing=1)for a, b in zip(index, num_list): # 柱子上的数字显示plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)plt.xticks(rotation=270)plt.title('岗位数量和岗位属性数量柱状图')plt.ylabel('次')plt.legend()plt.savefig(f'岗位数量和岗位属性数量柱状图-柱状图', bbox_inches='tight')plt.show()
源码展示：
"""ua大列表"""USER_AGENT_LIST = ['Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3451.0 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:57.0) Gecko/20100101 Firefox/57.0','Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.2999.0 Safari/537.36','Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.70 Safari/537.36','Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.2) Gecko/20100316 Firefox/3.6.2','Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.155 Safari/537.36 OPR/31.0.1889.174','Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.1.4322; MS-RTC LM 8; InfoPath.2; Tablet PC 2.0)','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61','Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.814.0 Safari/535.1','Mozilla/5.0 (Macintosh; U; PPC Mac OS X; ja-jp) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36','Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/6.0; Touch; MASMJS)','Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1041.0 Safari/535.21','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36','Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3451.0 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:57.0) Gecko/20100101 Firefox/57.0','Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/28.0.1500.71 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.2999.0 Safari/537.36','Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.70 Safari/537.36','Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4; en-US; rv:1.9.2.2) Gecko/20100316 Firefox/3.6.2','Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.155 Safari/537.36 OPR/31.0.1889.174','Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET CLR 1.1.4322; MS-RTC LM 8; InfoPath.2; Tablet PC 2.0)','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36 OPR/55.0.2994.61','Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML, like Gecko) Chrome/14.0.814.0 Safari/535.1','Mozilla/5.0 (Macintosh; U; PPC Mac OS X; ja-jp) AppleWebKit/418.9.1 (KHTML, like Gecko) Safari/419.3','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36','Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/6.0; Touch; MASMJS)','Mozilla/5.0 (X11; Linux i686) AppleWebKit/535.21 (KHTML, like Gecko) Chrome/19.0.1041.0 Safari/535.21','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36','Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4093.3 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko; compatible; Swurl) Chrome/77.0.3865.120 Safari/537.36','Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36','Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4086.0 Safari/537.36','Mozilla/5.0 (Windows NT 6.1; WOW64; rv:75.0) Gecko/20100101 Firefox/75.0','Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) coc_coc_browser/91.0.146 Chrome/85.0.4183.146 Safari/537.36','Mozilla/5.0 (Windows; U; Windows NT 5.2; en-US) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36 VivoBrowser/8.4.72.0 Chrome/62.0.3202.84','Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36 Edg/87.0.664.60','Mozilla/5.0 (Macintosh; Intel Mac OS X 10.16; rv:83.0) Gecko/20100101 Firefox/83.0','Mozilla/5.0 (X11; CrOS x86_64 13505.63.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:68.0) Gecko/20100101 Firefox/68.0','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36','Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36 OPR/72.0.3815.400','Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.101 Safari/537.36',]from requests_html import HTMLSessionimport os, xlwt, xlrd, randomfrom xlutils.copy import copyimport numpy as npfrom matplotlib import pyplot as pltfrom matplotlib.font_manager import FontProperties # 字体库import timesession = HTMLSession()class TXSpider(object):def __init__(self):# 起始的请求地址self.start_url = 'https://careers.tencent.com/tencentcareer/api/post/Query'# 起始的翻页页码self.start_page = 1# 翻页条件self.is_running = True# 准备工作地点大列表self.addr_list = []# 准备岗位种类大列表self.category_list = []def parse_start_url(self):"""解析起始的url地址:return:"""# 条件循环模拟翻页while self.is_running:# 构造请求参数params = {# 捕捉当前时间戳'timestamp': str(int(time.time() * 1000)),'countryId': '','cityId': '','bgIds': '','productId': '','categoryId': '','parentCategoryId': '','attrId': '','keyword': '','pageIndex': str(self.start_page),'pageSize': '10','language': 'zh-cn','area': 'cn'}headers = {'user-agent': random.choice(USER_AGENT_LIST)}response = session.get(url=self.start_url, headers=headers, params=params).json()"""调用解析响应方法"""self.parse_response_json(response)"""翻页递增"""self.start_page += 1"""翻页终止条件"""if self.start_page == 20:self.is_running = False"""翻页完成，开始生成分析图"""self.crate_img_four_func()def crate_img_four_func(self):"""生成四张图方法:return:"""# 统计数量data = {} # 大字典addr_dict = {} # 工作地址字典cate_dict = {} # 工作属性字典for k_addr, v_cate in zip(self.addr_list, self.category_list):if k_addr in data:# 大字典统计工作地址数据data[k_addr] = data[k_addr] + 1# 地址字典统计数据addr_dict[k_addr] = addr_dict[k_addr] + 1else:data[k_addr] = 1addr_dict[k_addr] = 1if v_cate in data:# 大字典统计工作属性数据data[v_cate] = data[v_cate] + 1# 工作属性字典统计数据cate_dict[v_cate] = data[v_cate] + 1else:data[v_cate] = 1cate_dict[v_cate] = 1# 第一张图：根据岗位地址和岗位属性二者数量生成折线图# 146，147两行代码解决图中中文显示问题plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False# 由于二者数据数量不统一，在此进行切片操作x_axis_data = [i for i in addr_dict.values()][:5]y_axis_data = [i for i in cate_dict.values()][:5]# print(x_axis_data, y_axis_data)# plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签plt.plot(y_axis_data, x_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='数量')# 显示标签，如果不加这句，即使在plot中加了label='一些数字'的参数，最终还是不会显示标签plt.legend(loc="upper right")plt.xlabel('地点数量')plt.ylabel('工作属性数量')plt.savefig('根据岗位地址和岗位属性二者数量生成折线图.png')plt.show()# 第二张图：根据岗位地址数量生成饼图"""工作地址饼图"""addr_dict_key = [k for k in addr_dict.keys()]addr_dict_value = [v for v in addr_dict.values()]plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']plt.rcParams['axes.unicode_minus'] = Falseplt.pie(addr_dict_value, labels=addr_dict_key, autopct='%1.1f%%')plt.title(f'岗位地址和岗位属性百分比分布')plt.savefig(f'岗位地址和岗位属性百分比分布-饼图')plt.show()# 第三张图：根据岗位地址和岗位属性二者数量生成散点图# 这两行代码解决 plt 中文显示的问题plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = False# 输入岗位地址和岗位属性数据production = [i for i in data.keys()]tem = [i for i in data.values()]colors = np.random.rand(len(tem)) # 颜色数组plt.scatter(tem, production, s=200, c=colors) # 画散点图，大小为 200plt.xlabel('数量') # 横坐标轴标题plt.ylabel('名称') # 纵坐标轴标题plt.savefig(f'岗位地址和岗位属性散点图')plt.show()# 第四张图：根据岗位地址和岗位属性二者数量生成柱状图import matplotlib;matplotlib.use('TkAgg')plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = Falsezhfont1 = matplotlib.font_manager.FontProperties(fname='C:\\Windows\\Fonts\\simsun.ttc')name_list = [name for name in data.keys()]num_list = [value for value in data.values()]width = 0.5 # 柱子的宽度index = np.arange(len(name_list))plt.bar(index, num_list, width, color='steelblue', tick_label=name_list, label='岗位数量')plt.legend(['分解能耗', '真实能耗'], prop=zhfont1, labelspacing=1)for a, b in zip(index, num_list): # 柱子上的数字显示plt.text(a, b, '%.2f' % b, ha='center', va='bottom', fontsize=7)plt.xticks(rotation=270)plt.title('岗位数量和岗位属性数量柱状图')plt.ylabel('次')plt.legend()plt.savefig(f'岗位数量和岗位属性数量柱状图-柱状图', bbox_inches='tight')plt.show()def parse_response_json(self, response):"""解析响应:param response::return:"""# 获取岗位信息大列表json_data = response['Data']['Posts']# 判断结果是否有数据if json_data is None:# 没有数据，设置循环条件为Falseself.is_running = False# 反之，开始提取数据else:# 循环遍历，取出列表中的每一个岗位字典# 通过key取value值的方法进行采集数据for data in json_data:# 工作地点LocationName = data['LocationName']# 往地址大列表中添加数据self.addr_list.append(LocationName)# 工作属性CategoryName = data['CategoryName']# 往工作属性大列表中添加数据self.category_list.append(CategoryName)# 岗位名称RecruitPostName = data['RecruitPostName']# 岗位职责Responsibility = data['Responsibility']# 发布时间LastUpdateTime = data['LastUpdateTime']# 岗位地址PostURL = data['PostURL']# 构造保存excel所需要的格式字典data_dict = {# 该字典的key值与创建工作簿的sheet表的名称所关联'岗位详情': [RecruitPostName, LocationName, CategoryName, Responsibility, LastUpdateTime, PostURL]}"""调用保存excel表格方法，数据字典作为参数"""self.save_excel(data_dict)# 提示输出print(f"第{self.start_page}页--岗位{RecruitPostName}----采集完成----logging！！！")def save_excel(self, data_dict):"""保存excel:param data_dict: 数据字典:return:"""# 判断保存到当我文件目录的路径是否存在os_path_1 = os.getcwd() + '/数据/'if not os.path.exists(os_path_1):# 不存在，即创建这个目录，即创建”数据“这个文件夹os.mkdir(os_path_1)# 判断将数据保存到表格的这个表格是否存在，不存在，创建表格，写入表头os_path = os_path_1 + '腾讯招聘数据.xls'if not os.path.exists(os_path):# 创建新的workbook（其实就是创建新的excel）workbook = xlwt.Workbook(encoding='utf-8')# 创建新的sheet表worksheet1 = workbook.add_sheet("岗位详情", cell_overwrite_ok=True)excel_data_1 = ('岗位名称', '工作地点', '工作属性', '岗位职责', '发布时间', '岗位地址')for i in range(0, len(excel_data_1)):worksheet1.col(i).width = 2560 * 3# 行，列， 内容， 样式worksheet1.write(0, i, excel_data_1[i])workbook.save(os_path)# 判断工作表是否存在# 存在，开始往表格中添加数据(写入数据)if os.path.exists(os_path):# 打开工作薄workbook = xlrd.open_workbook(os_path)# 获取工作薄中所有表的个数sheets = workbook.sheet_names()for i in range(len(sheets)):for name in data_dict.keys():worksheet = workbook.sheet_by_name(sheets[i])# 获取工作薄中所有表中的表名与数据名对比if worksheet.name == name:# 获取表中已存在的行数rows_old = worksheet.nrows# 将xlrd对象拷贝转化为xlwt对象new_workbook = copy(workbook)# 获取转化后的工作薄中的第i张表new_worksheet = new_workbook.get_sheet(i)for num in range(0, len(data_dict[name])):new_worksheet.write(rows_old, num, data_dict[name][num])new_workbook.save(os_path)def run(self):"""启动运行:return:"""self.parse_start_url()if __name__ == '__main__':# 创建该类的对象t = TXSpider()# 通过实例方法，进行调用t.run()
