Python 抓取数据并数据可视化
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
更新爬虫代码（2023.3.11）
逆向sign参数，逆向参考文章 https://blog.csdn.net/qq_59142194/article/details/129443287
注意：更新代码未写完，可根据自己需求进行修改！！！
import requests, urllib.parse, hmac, time, random
from hashlib import sha256
class QianChengWuYou:
def __init__(self, keyword:str='python 爬虫', city='全国', start_page=1, end_page=1, sortType=0):
'''
:param keyword: 搜索关键字
:param city: 城市
:param start_page: 从第几页开始爬取
:param end_page: 到第几页爬取结束
:param sortType: 排序类型（0：综合排序，1：最新优先，3：薪资优先）
其它更多参数 请自行去封装（如：salary：月薪范围，workYear：工作年限，degree：学历要求 等等）
'''
self.keyword = keyword
if city=='全国':
self.city_code = "000000"
else:
raise Exception("自定义城市需请自行去研究>请求携带的参数（jobArea）！！！")
self.start_page = start_page
self.end_page = end_page
self.sortType = sortType
self.headers = {
"Accept": "application/json, text/plain, */*",
"Accept-Language": "zh-CN,zh;q=0.9",
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"From-Domain": "51job_web",
"Origin": "https://we.51job.com",
"Pragma": "no-cache",
"Referer": "https://we.51job.com/",
"Sec-Fetch-Dest": "empty",
"Sec-Fetch-Mode": "cors",
"Sec-Fetch-Site": "same-site",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36",
"account-id": "",
"partner": "www_baidu_com",
"sec-ch-ua": "\"Chromium\";v=\"110\", \"Not A(Brand\";v=\"24\", \"Google Chrome\";v=\"110\"",
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": "\"Windows\"",
"sign": "",
"user-token": "",
"uuid": "01e694edebd8638b9402b5feb2436cb6"
}
self.session = requests.Session()
self.requestId = ""
def get_sign(self, msg):
key = 'abfc8f9dcf8c3f3d8aa294ac5f2cf2cc7767e5592590f39c3f503271dd68562b'.encode('utf-8')
return hmac.new(key=key, msg=msg.encode('utf-8'), digestmod=sha256).hexdigest()
def get_data(self, page):
time.sleep((random.random()+0.3)*2)
try:
params = {
"api_key": "51job",
"timestamp": str(int(time.time())),
"keyword": self.keyword,
"searchType": "2",
"function": "",
"industry": "",
"jobArea": str(self.city_code),
"jobArea2": "",
"landmark": "",
"metro": "",
"salary": "",
"workYear": "",
"degree": "",
"companyType": "",
"companySize": "",
"jobType": "",
"issueDate": "",
"sortType": str(self.sortType),
"pageNum": str(page),
"requestId": self.requestId,
"pageSize": "50",
"source": "1",
"accountId": "",
"pageCode": "sou|sou|soulb"
}
self.headers['sign'] = self.get_sign('/open/noauth/search-pc?' + urllib.parse.urlencode(params))
self.data = self.session.get("https://cupid.51job.com/open/noauth/search-pc", headers=self.headers,
params=params).json()
if not params['requestId']:
self.requestId = self.data['resultbody']['requestId']
except Exception as e:
print("异常（get_data）msg>>>", e)
raise Exception(f"请求第{page}页时出现异常！！！")
def parse_data(self):
print(self.data)
print(type(self.data))
def save_data(self):
pass
def main(self):
for page in range(self.start_page, self.end_page+1):
print(f"第{page}页，爬取中...")
try:
self.get_data(page)
self.parse_data()
self.save_data()
except Exception as e:
print("异常msg>>>", e)
break
self.session.close()
if __name__ == '__main__':
QianChengWuYou(keyword='python 爬虫', end_page=2).main()
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
from base64 import b64decode
b64encode_url = 'aHR0cHM6Ly9zZWFyY2guNTFqb2IuY29tLw=='
b64decode_url = b64decode(b64encode_url.encode("utf-8")).decode("utf-8")
print("url：", b64decode_url)
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',
'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()
error_time = 0
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:
try:
companyind_text = i['companyind_text']
except Exception as e:
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)
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)
print('抓取完成！')
f_csv.close()
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__':
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()
from pyecharts.charts import Bar
c = (
Bar()
.add_xaxis(city.index.tolist())
.add_yaxis("Python", city.values.tolist())
.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'),
yaxis_opts=opts.AxisOpts(name='岗位数量'),
)
.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)
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,best
province_data,best = province_city()
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 = 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}\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)
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()]
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",
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)
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()]
c3 = (
Map()
.add("Python", shanghai_data_list, "上海")
.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<s1.5:
xin_zi2.append(s)
elif 1.5<s2.5:
xin_zi3.append(s)
elif 2.5<s3.2:
xin_zi4.append(s)
elif 3.2<s4.0:
xin_zi5.append(s)
else:
xin_zi6.append(s)
xin_zi = [[',len(xin_zi1)],['10~15k',len(xin_zi2)],['15,len(xin_zi3)],
['25,len(xin_zi4)],['32,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])]
),
)
.set_global_opts(title_opts=opts.TitleOpts(title="薪资情况"),
xaxis_opts=opts.AxisOpts(name='薪资范围'),
yaxis_opts=opts.AxisOpts(name='数量'),
)
.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 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()
city_list = [list(ct) for ct in city.items()]
def bar_datazoom_slider() -> Bar:
c = (
Bar()
.add_xaxis(city.index.tolist())
.add_yaxis("Python", city.values.tolist())
.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'),
yaxis_opts=opts.AxisOpts(name='岗位数量'),
)
)
return c
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
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|}\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
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<s1.5:
xin_zi2.append(s)
elif 1.5<s2.5:
xin_zi3.append(s)
elif 2.5<s3.2:
xin_zi4.append(s)
elif 3.2<s4.0:
xin_zi5.append(s)
else:
xin_zi6.append(s)
xin_zi = [[',len(xin_zi1)],['10~15k',len(xin_zi2)],['15,len(xin_zi3)],
['25,len(xin_zi4)],['32,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'),
yaxis_opts=opts.AxisOpts(name='数量'),
)
)
return c
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！！！
Original: https://blog.csdn.net/qq_59142194/article/details/124532659
Author: 清＆轻
Title: Python 抓取数据并可视化
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