Python 抓取数据并数据可视化

前言
一、数据抓取篇
- 1.简单的构建反爬措施
- 2.解析数据
- 3.完整代码
二、数据可视化篇
- 1.数据可视化库选用
- 2.案例实战
- - (1).柱状图Bar
  - (2).地图Map
  - - 省份
    - 城市
    - 地区
  - (3).饼图Pie
  - - Pie1
    - Pie2
  - (4).折线图Line
  - (5).组合图表

前言

大家好，这次写作的目的是为了加深对数据可视化pyecharts的认识，也想和大家分享一下。如果下面文章中有错误的地方还请指正，哈哈哈！！！
本次主要用到的第三方库：
requests
pandas
pyecharts

之所以数据可视化选用pyecharts，是因为它含有丰富的精美图表，地图，也可轻松集成至 Flask，Django 等主流 Web 框架中，并且在html渲染网页时把图片保存下来（这里好像截屏就可以了，），任君挑选！！！
这次的数据采集是从招聘网址上抓取到的python招聘岗位信息，嗯……其实这抓取到的数据有点少（只有1200条左右，也没办法，岗位太少了…），所以在后面做可视化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 requests
import 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 b64decode
b64encode_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&degreefrom=99&jobterm=99&companysize=99&ord_field=0&dibiaoid=0&line=&welfare='headers['Referer'] = urltry: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 = Nonedic = {'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 += 1if error_time == 200:breakif error_time >= 200:breaktime.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)

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：
https://blog.csdn.net/qq_59142194/article/details/124540409?spm=1001.2014.3001.5501
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 pd
from pyecharts import options as opts
python_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 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).render("bar_datazoom_both.html")
)

(2).地图Map

省份

这里对所在省份进行可视化。

import pandas as pd
import copy
from pyecharts import options as opts
python_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] = kprovince_data.append(ct)area_data_deepcopy = copy.deepcopy(area_data)for k in area_data_deepcopy.keys():area_data_deepcopy[k] = 0for 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,best
province_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")
)

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 requests
import json
header = {"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 = kif 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}\n')each_province_data[province] = city
f.close()
print(each_province_data)

城市

这里对所在城市进行可视化。

import pandas as pd
import copy
from pyecharts import options as opts
python_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 Map
c1 = (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")
)

地区

这里对上海地区可视化。

import pandas as pd
import copy
from pyecharts import options as opts
python_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.append
for 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")
)

(3).饼图Pie

Pie1

from pyecharts import options as opts
from pyecharts.charts import Pie
import pandas as pd
python_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')
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()]
print(require_list)
c = (Pie().add("",require_list,radius=["40%", "55%"],label_opts=opts.LabelOpts(position="outside",formatter="{a|{a}}{abg|}\n{hr|}\n {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")
)

Pie2

from pyecharts import options as opts
from pyecharts.charts import Pie
import pandas as pd
python_data = pd.read_csv('./testDataPython-2022-05-01_11_48_36.csv')
xueli_list = []
xl = xueli_list.append
for 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_list
xueli_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")
)

(4).折线图Line

这里对薪资情况进行可视化。

import pandas as pd
import re
python_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 opts
from pyecharts.charts import Line
x, 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")
)

(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 pd
from pyecharts.charts import Bar,Map,Pie,Line,Page
from 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 copyarea_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] = kprovince_data.append(ct)area_data_deepcopy = copy.deepcopy(area_data)for k in area_data_deepcopy.keys():area_data_deepcopy[k] = 0for 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.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()]c = (Pie().add("",require_list,radius=["40%", "55%"],label_opts=opts.LabelOpts(position="outside",formatter="{a|{a}}{abg|}\n{hr|}\n {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 resal = 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()

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 Page
Page.save_resize_html('./page_draggable_layout.html',cfg_file='chart_config.json')

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！！！

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