Python 收款定制开发抓取数据并
前言
大家好，收款定制开发这次写作的目的是为了收款定制开发加深对数据可视化pyecharts的认识，收款定制开发也想和大家分享一下。收款定制开发如果下面文章中有错误收款定制开发的地方还请指正，哈哈哈！！！
收款定制开发本次主要用到的第三方库：
requests
pandas
pyecharts
收款定制开发之所以数据可视化选用pyecharts，收款定制开发是因为它含有丰富的精美图表，地图，收款定制开发也可轻松集成至 Flask，Django 等主流 Web 框架中，并且在html收款定制开发渲染网页时把图片保存下来（收款定制开发这里好像截屏就可以了，），任君挑选！！！
收款定制开发这次的数据采集是从招收款定制开发聘网址上抓取到的python收款定制开发招聘岗位信息，嗯……收款定制开发其实这抓取到的数据有点少（只有1200条左右，也没办法，收款定制开发岗位太少了…），所以在后面做图表的时候会导致不好看，骇。本来也考虑过用java（数据1万+）的数据来做测试的，但是想到写的是python，所以也就只能将就用这个数据了，当然如果有感兴趣的朋友，你们可以用java，前端这些岗位的数据来做测试，下面提供的数据抓取方法稍微改一下就可以抓取其它岗位了。
好了，废话不多说，直接开始吧！
演示使用的谷歌浏览器。
一、数据抓取篇
1.简单的构建反爬措施
这里为大家介绍一个很好用的网站，可以帮助我们在写爬虫时快速构建请求头、cookie这些。但是这个网站也不知为什么，反正在访问时也经常访问不了！额……，介绍下它的使用吧！首先，我们只需要根据下面图片上步骤一样。
完成之后，我们就复制好了请求头里面的内容了，然后打开网址https://curlconverter.com/ 进入后直接在输入框里Ctrl+v粘贴即可。然后就会在下面解析出内容，我们直接复制就完成了，快速，简单，哈哈哈。
2.解析数据
这里我们请求网址得到的数据它在script js元素标签里面，所以就不能用lxml，css选择器等这些来解析数据。这里我们用re正则来解析数据，得到的数据看到起来好像字典类型，但是它并不是，所以我们还需要用json来把它转化成字典类型的数据方便我们提取。
这里用json转化为字典类型的数据后，不好查看时，可以用pprint来打印查看。
3.完整代码
import requestsimport re, json, csv, time, random### url = '这里你们自己构建url吧，从上面的图片应该能看出，我写出来的话实在是不行，过不了审核，难受！！！'from base64 import b64decodeb64encode_url = 'aHR0cHM6Ly9zZWFyY2guNTFqb2IuY29tLw=='b64decode_url = b64decode(b64encode_url.encode("utf-8")).decode("utf-8")print("url：", b64decode_url)# 浏览器请求头，从列表中随机返回一个def chrome_ua(): fake_ua_list = ['Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.2 Safari/537.36', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1664.3 Safari/537.36', 'Mozilla/5.0 (X11; CrOS i686 3912.101.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.116 Safari/537.36', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.93 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.17 Safari/537.36'] return random.choice(fake_ua_list)def spider_data(query, start_page=1,end_page=1,write_mode="w+"): headers = { 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9', 'Accept-Language': 'zh-CN,zh;q=0.9', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive', # Requests sorts cookies= alphabetically # 'Cookie': 'guid=3e7fe47d0c9f737c7199a4f351e8df3d; nsearch=jobarea%3D%26%7C%26ord_field%3D%26%7C%26recentSearch0%3D%26%7C%26recentSearch1%3D%26%7C%26recentSearch2%3D%26%7C%26recentSearch3%3D%26%7C%26recentSearch4%3D%26%7C%26collapse_expansion%3D; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%223e7fe47d0c9f737c7199a4f351e8df3d%22%2C%22first_id%22%3A%221835f92e9f44c9-04bbbbbbbbbbbbc-26021c51-1327104-1835f92e9f5a50%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTgzNWY5MmU5ZjQ0YzktMDRiYmJiYmJiYmJiYmJjLTI2MDIxYzUxLTEzMjcxMDQtMTgzNWY5MmU5ZjVhNTAiLCIkaWRlbnRpdHlfbG9naW5faWQiOiIzZTdmZTQ3ZDBjOWY3MzdjNzE5OWE0ZjM1MWU4ZGYzZCJ9%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%223e7fe47d0c9f737c7199a4f351e8df3d%22%7D%2C%22%24device_id%22%3A%221835f92e9f44c9-04bbbbbbbbbbbbc-26021c51-1327104-1835f92e9f5a50%22%7D; adv=ad_logid_url%3Dhttps%253A%252F%252Ftrace.51job.com%252Ftrace.php%253Fpartner%253Dsem_pcbaidu5_153287%2526ajp%253DaHR0cHM6Ly9ta3QuNTFqb2IuY29tL3RnL3NlbS9MUF8yMDIwXzEuaHRtbD9mcm9tPWJhaWR1YWQ%253D%2526k%253Dd946ba049bfb67b64f408966cbda3ee9%2526bd_vid%253D11472256346800030279%26%7C%26; partner=51jobhtml5; acw_tc=ac11000116673048509656210e00de5ff8daa4dc09143457b9d79eae3d2f69; acw_sc__v3=63610d9543621eaca0736d477fb79ad95c463726; search=jobarea%7E%60000000%7C%21ord_field%7E%600%7C%21recentSearch0%7E%60000000%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch1%7E%60090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch2%7E%60090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA01%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch3%7E%60090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA01%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FAJava%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch4%7E%60000000%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA01%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FAJava%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21collapse_expansion%7E%601%7C%21; ssxmod_itna=QqmxgDnDy7K7wbxl4x+rF10xDKi=q3tCaTKHaDlagoxA5D8D6DQeGTbRYDB0or0hG5tMWm8BDQGCm8vqeLliGi3PC7RopN4bDCPGnDBFxoMmDYYkDt4DTD34DYDigQGIDieDFGkVtCDbxi7DiKDpx0kho5DWa5DED5DFaYDvPYCMDv8DYHGDD5Qsx07DQ5CyZcIP33Dn=01aWxCD75EDlp59RxCzzkUCjy1XKLwEA9h5boDjaYCDmW7+4GducvwVDPxYY2FIg23qW2to02DNo2DPBhx1jidqgx4K00l706ZCDDWM7KHaYD==; ssxmod_itna2=QqmxgDnDy7K7wbxl4x+rF10xDKi=q3tCaTKaD6hFxGq4DsheDLA=y4u=wQyZF1w0GzaKeMKiNKzEk7PtQXhQSiN3zrdLwADoCaHFtiMzwaW8alC/9yOnKG7oGFBglfl3llCkLC4SCG6HldiPeWU4fhXx57YqdOidIg2MGkRPrnGOnebl9uUxsf5Q=eWg9qAQ7CK1SeAFoDKlIO9gAOFYcxjKs2xFCuy=g0Y0Ur6FXxIHPW5CPo3VI2jIr1LILYc6DbttXS95cIFWX8YgAlYUHnDrevwSGA6I4ZyhmPhZnnYg490Mebf/CNF6wxZN17N=10xED1=qcB58AwMGGsoNYt05KvxfN4er5KEDO2bG2Y2wyW0+Z0xldGAXlYp5Sp=KfpPTN3LqQiVg2MlLO1vr+YE=Ysbw5IK8OnYpnN3v3b0T2WOOAAoLRxeOPAOEXL9nZQdLbdjtY6FXaEiSidu3W0+4DQFPtxgnhxR4dBmCwxifLkUMtlDDFqD+2xxD', 'Pragma': 'no-cache', 'Sec-Fetch-Dest': 'document', 'Sec-Fetch-Mode': 'navigate', 'Sec-Fetch-Site': 'same-origin', 'Sec-Fetch-User': '?1', 'Upgrade-Insecure-Requests': '1', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36', 'sec-ch-ua': '"Google Chrome";v="107", "Chromium";v="107", "Not=A?Brand";v="24"', 'sec-ch-ua-mobile': '?0', 'sec-ch-ua-platform': '"Windows"', } save_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()).replace(' ', '_').replace(':','_') file_path = f'./全国{query}岗位数据-{save_time}.csv' f_csv = open(file_path, mode=write_mode, encoding='utf-8', newline='') fieldnames = ['公司名字', '职位名字', '薪资', '工作地点', '招聘要求', '公司待遇','招聘更新时间', '招聘发布时间', '公司人数', '公司类型', 'companyind_text', 'job_href', 'company_href'] dict_write = csv.DictWriter(f_csv, fieldnames=fieldnames) dict_write.writeheader() # 写入表头 error_time = 0 #在判断 职位名字中是否没有关键字的次数，这里定义出现200次时，while循环结束 # （因为在搜索岗位名字时（如：搜索python）,会在网站20多页时就没有关于python的岗位了，但是仍然有其它的岗位出现，所以这里就需要if判断，使其while循环结束） for page in range(start_page,end_page+1): print(f'第{page}页抓取中……') headers['User-Agent'] = chrome_ua() url = b64decode_url + f'list/000000,000000,0000,00,9,99,{query},2,{page}.html' url = url + '?lang=c&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&ord_field=0&dibiaoid=0&line=&welfare=' headers['Referer'] = url try: response = requests.get(url=url, headers=headers, cookies=cookies) parse_data = re.findall('"engine_jds":(.*?),"jobid_count"',response.text) parse_data_dict = json.loads(parse_data[0]) except: f_csv.close() print('\033[31m可更换cookies后再尝试！！！\033[0m') raise Exception(f"第{page}页——请求异常！") for i in parse_data_dict: ###在这里要处理下异常，因为在爬取多页时，可能是网站某些原因会导致这里的结构变化 try: companyind_text = i['companyind_text'] except Exception as e: # print(f'\033[31m异常：{e}\033[0m') companyind_text = None dic = { '公司名字': i['company_name'], '职位名字': i['job_name'], '薪资': i['providesalary_text'], '工作地点': i['workarea_text'], '招聘要求': ' '.join(i['attribute_text']), '公司待遇': i['jobwelf'], '招聘更新时间': i['updatedate'], '招聘发布时间': i['issuedate'], '公司人数': i['companysize_text'], '公司类型': i['companytype_text'], 'companyind_text': companyind_text, 'job_href': i['job_href'], 'company_href': i['company_href'], } if 'Python' in dic['职位名字'] or 'python' in dic['职位名字']: dict_write.writerow(dic) # 写入数据 print(dic['职位名字'], '——保存完毕！') else: error_time += 1 if error_time == 200: break if error_time >= 200: break time.sleep((random.random() + 0.5) * 3) #这里随机休眠一下(0-3秒)，简单反爬处理，反正我们用的是单线程爬取，也不差那一点时间是吧 print('抓取完成！') f_csv.close()###这里还是要添加cookies的好，我们要伪装好不是？防止反爬，如果你用之前提到的方法，也就很快的构建出cookies。cookies = { 'guid': '3e7fe47d0c9f737c7199a4f351e8df3d', 'nsearch': 'jobarea%3D%26%7C%26ord_field%3D%26%7C%26recentSearch0%3D%26%7C%26recentSearch1%3D%26%7C%26recentSearch2%3D%26%7C%26recentSearch3%3D%26%7C%26recentSearch4%3D%26%7C%26collapse_expansion%3D', 'sensorsdata2015jssdkcross': '%7B%22distinct_id%22%3A%223e7fe47d0c9f737c7199a4f351e8df3d%22%2C%22first_id%22%3A%221835f92e9f44c9-04bbbbbbbbbbbbc-26021c51-1327104-1835f92e9f5a50%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22identities%22%3A%22eyIkaWRlbnRpdHlfY29va2llX2lkIjoiMTgzNWY5MmU5ZjQ0YzktMDRiYmJiYmJiYmJiYmJjLTI2MDIxYzUxLTEzMjcxMDQtMTgzNWY5MmU5ZjVhNTAiLCIkaWRlbnRpdHlfbG9naW5faWQiOiIzZTdmZTQ3ZDBjOWY3MzdjNzE5OWE0ZjM1MWU4ZGYzZCJ9%22%2C%22history_login_id%22%3A%7B%22name%22%3A%22%24identity_login_id%22%2C%22value%22%3A%223e7fe47d0c9f737c7199a4f351e8df3d%22%7D%2C%22%24device_id%22%3A%221835f92e9f44c9-04bbbbbbbbbbbbc-26021c51-1327104-1835f92e9f5a50%22%7D', 'adv': 'ad_logid_url%3Dhttps%253A%252F%252Ftrace.51job.com%252Ftrace.php%253Fpartner%253Dsem_pcbaidu5_153287%2526ajp%253DaHR0cHM6Ly9ta3QuNTFqb2IuY29tL3RnL3NlbS9MUF8yMDIwXzEuaHRtbD9mcm9tPWJhaWR1YWQ%253D%2526k%253Dd946ba049bfb67b64f408966cbda3ee9%2526bd_vid%253D11472256346800030279%26%7C%26', 'partner': '51jobhtml5', 'acw_tc': 'ac11000116673048509656210e00de5ff8daa4dc09143457b9d79eae3d2f69', 'acw_sc__v3': '63610d9543621eaca0736d477fb79ad95c463726', 'search': 'jobarea%7E%60000000%7C%21ord_field%7E%600%7C%21recentSearch0%7E%60000000%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch1%7E%60090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch2%7E%60090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA01%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FApython%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch3%7E%60090200%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA01%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FAJava%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21recentSearch4%7E%60000000%A1%FB%A1%FA000000%A1%FB%A1%FA0000%A1%FB%A1%FA00%A1%FB%A1%FA01%A1%FB%A1%FA%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA99%A1%FB%A1%FA9%A1%FB%A1%FA99%A1%FB%A1%FA%A1%FB%A1%FA0%A1%FB%A1%FAJava%A1%FB%A1%FA2%A1%FB%A1%FA1%7C%21collapse_expansion%7E%601%7C%21', 'ssxmod_itna': 'Yqjx0Dg7qQqWqD5iQDXDnWpe4BKIoRRR0Yk0hb4GNoWDZDiqAPGhDC+b94RG872hxYCWPmr35QYA44xdjCQ7G3tKF8iaXxWDCPGnDBKw6iADYYkDt4DTD34DYDi2QGIDieDFzHy/kDbxi7DiKDpx0kGP5DWr5DElKDFrYDvxBkilF8DY5GDD5Qzx07DQyk9gpIeK3Dn2G1bY2kD7yADlpqc80k18dUKAv1U4kwfmIhKtoDjrBkDme7h4GdR2Hw/lnDFihFpgDdQYxeYQ+b8u07tG05F0x3xgGPY0x4RvR99kDDioro/m+DD=', 'ssxmod_itna2': 'Yqjx0Dg7qQqWqD5iQDXDnWpe4BKIoRRR0Yk0xA=WmxqD/CQYDF2+xOF4qR179o55HF/PnPwOCRrAH=wex3rYb88CrTEnLufRmKewbQmqEFjdy4Fd0f4Okld8++sIkl8RArfFAXjZvr=IIMXImAQIUT4wUMthKTiHKLiD82vAeRRmiTu9ChRPpYnQZr+bsQvbTQawD=NLnmXNd1nNq=naETLkOjA11RfPdqcisrQpKwDK704kvhRgBrT3uRhOs9nFo4n8hKMYxPodon965BWkZ2ba6aE2kZ0v5AkSD6LN2hkBx4Wns5ng0eixKigANZDjP0qbqD93KBUjEPA=K3hq71wvaoKAtOroKjYf2ExBUAY0Tm2FaGv=Qkr03ohlEmucRvEKoQd2O2Qox/pPxFX32iFSrg2o2rt+S3oY=/=5LPbXor/2b2QIr/pPBjMlro6bnDR8EtQg4i+YR/QRotN66YtAXPKgOF3zb6QHf3NC=egOi/=DP3UYRxWDG2iGtI4mKwPlMQQjPIR+GG+Ojq7DDFqD+ODxD===',}if __name__ == '__main__': # key_word = 'java' ##这里不能输入中文，网址做了url字体加密，简单的方法就是直接从网页url里面复制下来用（如：前端） # key_word = '%25E5%2589%258D%25E7%25AB%25AF' #前端 query = 'python' ''' 这里将测试爬取1到3页的数据 网站的页数，可根据自己的需求更改页数 ''' spider_data(query,start_page=1,end_page=3)- 1
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二、数据可视化篇
1.数据可视化库选用
本次数据可视化选用的是pyecharts第三方库，它制作图表是多么的强大与精美！！！想要对它进行一些简单地了解话可以前往这篇博文：
安装： pip install pyecharts
2.案例实战
本次要对薪资、工作地点、招聘要求里面的经验与学历进行数据处理并可视化。
(1).柱状图Bar
按住鼠标中间滑轮或鼠标左键可进行调控。
import pandas as pdfrom pyecharts import options as optspython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')python_data['工作地点'] = [i.split('-')[0] for i in python_data['工作地点']]city = python_data['工作地点'].value_counts()###柱状图from pyecharts.charts import Barc = ( Bar() .add_xaxis(city.index.tolist()) #城市列表数据项 .add_yaxis("Python", city.values.tolist())#城市对应的岗位数量列表数据项 .set_global_opts( title_opts=opts.TitleOpts(title="Python招聘岗位所在城市分布情况"), datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], xaxis_opts=opts.AxisOpts(name='城市'), # 设置x轴名字属性 yaxis_opts=opts.AxisOpts(name='岗位数量'), # 设置y轴名字属性 ) .render("bar_datazoom_both.html"))- 1
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(2).地图Map
省份
这里对所在省份进行可视化。
import pandas as pdimport copyfrom pyecharts import options as optspython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')python_data_deepcopy = copy.deepcopy(python_data) #深复制一份数据python_data['工作地点'] = [i.split('-')[0] for i in python_data['工作地点']]city = python_data['工作地点'].value_counts()city_list = [list(ct) for ct in city.items()]def province_city(): '''这是从接口里爬取的数据（不太准，但是误差也可以忽略不计！）''' area_data = {} with open('./中国省份_城市.txt', mode='r', encoding='utf-8') as f: for line in f: line = line.strip().split('_') area_data[line[0]] = line[1].split(',') province_data = [] for ct in city_list: for k, v in area_data.items(): for i in v: if ct[0] in i: ct[0] = k province_data.append(ct) area_data_deepcopy = copy.deepcopy(area_data) for k in area_data_deepcopy.keys(): area_data_deepcopy[k] = 0 for i in province_data: if i[0] in area_data_deepcopy.keys(): area_data_deepcopy[i[0]] = area_data_deepcopy[i[0]] +i[1] province_data = [[k,v]for k,v in area_data_deepcopy.items()] best = max(area_data_deepcopy.values()) return province_data,bestprovince_data,best = province_city()#地图_中国地图（带省份）Map-VisualMap（连续型）c2 = ( Map() .add( "Python",province_data, "china") .set_global_opts( title_opts=opts.TitleOpts(title="Python招聘岗位——全国分布情况"), visualmap_opts=opts.VisualMapOpts(max_=int(best / 2)), ) .render("map_china.html"))- 1
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这是 中国省份_城市.txt 里面的内容，通过[接口]抓取到的中国地区信息。
爬取中国省份_城市.txt的源码：
import requestsimport jsonheader = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36",}response = requests.get('https://j.i8tq.com/weather2020/search/city.js',headers=header)result = json.loads(response.text[len('var city_data ='):])print(result)each_province_data = {}f = open('./中国省份_城市.txt',mode='w',encoding='utf-8')for k,v in result.items(): province = k if k in ['上海', '北京', '天津', '重庆']: city = '，'.join(list(v[k].keys())) else: city = '，'.join(list(v.keys())) f.write(f'{province}_{city}') each_province_data[province] = cityf.close()print(each_province_data)- 1
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城市
这里对所在城市进行可视化。
import pandas as pdimport copyfrom pyecharts import options as optspython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')python_data_deepcopy = copy.deepcopy(python_data) #深复制一份数据python_data['工作地点'] = [i.split('-')[0] for i in python_data['工作地点']]city = python_data['工作地点'].value_counts()city_list = [list(ct) for ct in city.items()]###地图_中国地图（带城市）——Map-VisualMap（分段型）from pyecharts.charts import Mapc1 = ( Map(init_opts=opts.InitOpts(width="1244px", height="700px",page_title='Map-中国地图（带城市）', bg_color="#f4f4f4")) .add( "Python", city_list, "china-cities", #地图 label_opts=opts.LabelOpts(is_show=False), ) .set_global_opts( title_opts=opts.TitleOpts(title="Python招聘岗位——全国分布情况"), visualmap_opts=opts.VisualMapOpts(max_=city_list[0][1],is_piecewise=True), ) .render("map_china_cities.html"))- 1
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地区
这里对上海地区可视化。
import pandas as pdimport copyfrom pyecharts import options as optspython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')python_data_deepcopy = copy.deepcopy(python_data) #深复制一份数据shanghai_data = []sh = shanghai_data.appendfor i in python_data_deepcopy['工作地点']: if '上海' in i: if len(i.split('-')) > 1: sh(i.split('-')[1])shanghai_data = pd.Series(shanghai_data).value_counts()shanghai_data_list = [list(sh) for sh in shanghai_data.items()]#上海地图c3 = ( Map() .add("Python", shanghai_data_list, "上海") ###这个可以更改地区（如：成都）这里改了的话，上面的数据处理也要做相应的更改 .set_global_opts( title_opts=opts.TitleOpts(title="Map-上海地图"), visualmap_opts=opts.VisualMapOpts(max_=shanghai_data_list[0][1]) ) .render("map_shanghai.html"))- 1
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(3).饼图Pie
Pie1
from pyecharts import options as optsfrom pyecharts.charts import Pieimport pandas as pdpython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')require_list = []rl = require_list.appendfor i in python_data['招聘要求']: if '经验' in i: rl(i.split(' ')[1]) else: rl('未知')python_data['招聘要求'] = require_listrequire = python_data['招聘要求'].value_counts()require_list = [list(ct) for ct in require.items()]print(require_list)c = ( Pie() .add( "", require_list, radius=["40%", "55%"], label_opts=opts.LabelOpts( position="outside", formatter="{a|{a}}{abg|}{hr|} {b|{b}: }{c} {per|{d}%} ", background_color="#eee", border_color="#aaa", border_width=1, border_radius=4, rich={ "a": {"color": "#999", "lineHeight": 22, "align": "center"}, "abg": { "backgroundColor": "#e3e3e3", "width": "100%", "align": "right", "height": 22, "borderRadius": [4, 4, 0, 0], }, "hr": { "borderColor": "#aaa", "width": "100%", "borderWidth": 0.5, "height": 0, }, "b": {"fontSize": 16, "lineHeight": 33}, "per": { "color": "#eee", "backgroundColor": "#334455", "padding": [2, 4], "borderRadius": 2, }, }, ), ) .set_global_opts( title_opts=opts.TitleOpts(title="工作经验要求"), legend_opts=opts.LegendOpts(padding=20, pos_left=500), ) .render("pie_rich_label.html"))- 1
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Pie2
from pyecharts import options as optsfrom pyecharts.charts import Pieimport pandas as pdpython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')xueli_list = []xl = xueli_list.appendfor i in python_data['招聘要求']: if len(i.split(' ')) == 3: xl(i.split(' ')[2]) else: xl('未知')python_data['招聘要求'] = xueli_listxueli_require = python_data['招聘要求'].value_counts()xueli_require_list = [list(ct) for ct in xueli_require.items()]c = ( Pie() .add( "", xueli_require_list, radius=["30%", "55%"], rosetype="area", ) .set_global_opts(title_opts=opts.TitleOpts(title="学历要求")) .render("pie_rosetype.html"))- 1
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(4).折线图Line
这里对薪资情况进行可视化。
import pandas as pdimport repython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')sal = python_data['薪资']xin_zi1 = []xin_zi2 = []xin_zi3 = []xin_zi4 = []xin_zi5 = []xin_zi6 = []for s in sal: s = str(s) if '千' in s: xin_zi1.append(s) else: if re.findall('-(.*?)万',s): s = float(re.findall('-(.*?)万',s)[0]) if 1.0<s<=1.5: xin_zi2.append(s) elif 1.5<s<=2.5: xin_zi3.append(s) elif 2.5<s<=3.2: xin_zi4.append(s) elif 3.2<s<=4.0: xin_zi5.append(s) else: xin_zi6.append(s)xin_zi = [['<10k',len(xin_zi1)],['10~15k',len(xin_zi2)],['15<25k',len(xin_zi3)], ['25<32k',len(xin_zi4)],['32<40k',len(xin_zi5)],['>40k',len(xin_zi6),]]import pyecharts.options as optsfrom pyecharts.charts import Linex, y =[i[0] for i in xin_zi],[i[1] for i in xin_zi]c2 = ( Line() .add_xaxis(x) .add_yaxis( "Python", y, markpoint_opts=opts.MarkPointOpts( data=[opts.MarkPointItem(name="max", coord=[x[2], y[2]], value=y[2])] #name='自定义标记点' ), ) .set_global_opts(title_opts=opts.TitleOpts(title="薪资情况"), xaxis_opts=opts.AxisOpts(name='薪资范围'), # 设置x轴名字属性 yaxis_opts=opts.AxisOpts(name='数量'), # 设置y轴名字属性 ) .render("line_markpoint_custom.html"))- 1
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(5).组合图表
最后，将多个html上的图表进行合并成一个html图表。
首先，我们执行下面这串格式的代码（只写了四个图表，自己做相应添加即可）
import pandas as pdfrom pyecharts.charts import Bar,Map,Pie,Line,Pagefrom pyecharts import options as optspython_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')python_data['工作地点'] = [i.split('-')[0] for i in python_data['工作地点']]city = python_data['工作地点'].value_counts()city_list = [list(ct) for ct in city.items()]###柱状图def bar_datazoom_slider() -> Bar: c = ( Bar() .add_xaxis(city.index.tolist()) #城市列表数据项 .add_yaxis("Python", city.values.tolist())#城市对应的岗位数量列表数据项 .set_global_opts( title_opts=opts.TitleOpts(title="Python招聘岗位所在城市分布情况"), datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], xaxis_opts=opts.AxisOpts(name='城市'), # 设置x轴名字属性 yaxis_opts=opts.AxisOpts(name='岗位数量'), # 设置y轴名字属性 ) ) return c# 地图_中国地图（带省份）Map-VisualMap（连续型）def map_china() -> Map: import copy area_data = {} with open('./中国省份_城市.txt', mode='r', encoding='utf-8') as f: for line in f: line = line.strip().split('_') area_data[line[0]] = line[1].split(',') province_data = [] for ct in city_list: for k, v in area_data.items(): for i in v: if ct[0] in i: ct[0] = k province_data.append(ct) area_data_deepcopy = copy.deepcopy(area_data) for k in area_data_deepcopy.keys(): area_data_deepcopy[k] = 0 for i in province_data: if i[0] in area_data_deepcopy.keys(): area_data_deepcopy[i[0]] = area_data_deepcopy[i[0]] + i[1] province_data = [[k, v] for k, v in area_data_deepcopy.items()] best = max(area_data_deepcopy.values()) c = ( Map() .add("Python", province_data, "china") .set_global_opts( title_opts=opts.TitleOpts(title="Python招聘岗位——全国分布情况"), visualmap_opts=opts.VisualMapOpts(max_=int(best / 2)), ) ) return c#饼图def pie_rich_label() -> Pie: require_list = [] rl = require_list.append for i in python_data['招聘要求']: if '经验' in i: rl(i.split(' ')[1]) else: rl('未知') python_data['招聘要求'] = require_list require = python_data['招聘要求'].value_counts() require_list = [list(ct) for ct in require.items()] c = ( Pie() .add( "", require_list, radius=["40%", "55%"], label_opts=opts.LabelOpts( position="outside", formatter="{a|{a}}{abg|}{hr|} {b|{b}: }{c} {per|{d}%} ", background_color="#eee", border_color="#aaa", border_width=1, border_radius=4, rich={ "a": {"color": "#999", "lineHeight": 22, "align": "center"}, "abg": { "backgroundColor": "#e3e3e3", "width": "100%", "align": "right", "height": 22, "borderRadius": [4, 4, 0, 0], }, "hr": { "borderColor": "#aaa", "width": "100%", "borderWidth": 0.5, "height": 0, }, "b": {"fontSize": 16, "lineHeight": 33}, "per": { "color": "#eee", "backgroundColor": "#334455", "padding": [2, 4], "borderRadius": 2, }, }, ), ) .set_global_opts( title_opts=opts.TitleOpts(title="工作经验要求"), legend_opts=opts.LegendOpts(padding=20, pos_left=500), ) ) return c#折线图def line_markpoint_custom() -> Line: import re sal = python_data['薪资'] xin_zi1 = [] xin_zi2 = [] xin_zi3 = [] xin_zi4 = [] xin_zi5 = [] xin_zi6 = [] for s in sal: s = str(s) if '千' in s: xin_zi1.append(s) else: if re.findall('-(.*?)万',s): s = float(re.findall('-(.*?)万',s)[0]) if 1.0<s<=1.5: xin_zi2.append(s) elif 1.5<s<=2.5: xin_zi3.append(s) elif 2.5<s<=3.2: xin_zi4.append(s) elif 3.2<s<=4.0: xin_zi5.append(s) else: xin_zi6.append(s) xin_zi = [['<10k',len(xin_zi1)],['10~15k',len(xin_zi2)],['15<25k',len(xin_zi3)], ['25<32k',len(xin_zi4)],['32<40k',len(xin_zi5)],['>40k',len(xin_zi6),]] x, y =[i[0] for i in xin_zi],[i[1] for i in xin_zi] c = ( Line() .add_xaxis(x) .add_yaxis( "Python", y, markpoint_opts=opts.MarkPointOpts( data=[opts.MarkPointItem(name="MAX", coord=[x[2], y[2]], value=y[2])] ), ) .set_global_opts(title_opts=opts.TitleOpts(title="薪资情况"), xaxis_opts=opts.AxisOpts(name='薪资范围'), # 设置x轴名字属性 yaxis_opts=opts.AxisOpts(name='数量'), # 设置y轴名字属性 ) ) return c#合并def page_draggable_layout(): page = Page(layout=Page.DraggablePageLayout) page.add( bar_datazoom_slider(), map_china(), pie_rich_label(), line_markpoint_custom(), ) page.render("page_draggable_layout.html")if __name__ == "__main__": page_draggable_layout()- 1
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执行完后，会在当前目录下生成一个page_draggable_layout.html。
然后我们用浏览器打开，就会看到下面这样，我们可以随便拖动虚线框来进行组合，组合好后点击Save Config就会下载一个chart_config.json，然后在文件中找到它，剪切到py当前目录。
文件放置好后，可以新建一个py文件来执行以下代码，这样就会生成一个resize_render.html，也就完成了。
from pyecharts.charts import PagePage.save_resize_html('./page_draggable_layout.html',cfg_file='chart_config.json')- 1
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最后，点击打开resize_render.html，我们合并成功的图表就是这样啦！
对大家有用的话，记得点赞收藏一下！！！