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Python抓取数据可视化

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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:

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!

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并没有在html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。

import pprint
pprint.pprint(parse_data_dict)

3.完整代码

import requests
import re
import json
import csv
import time
from random import random
from fake_useragent import UserAgent
def spider_python(key_word):
    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': UserAgent().Chrome,
        'sec-ch-ua': '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"',
        'sec-ch-ua-mobile': '?0',
        'sec-ch-ua-platform': '"Windows"',
    }
    params = {
        'lang': 'c',
        'postchannel': '0000',
        'workyear': '99',
        'cotype': '99',
        'degreefrom': '99',
        'jobterm': '99',
        'companysize': '99',
        'ord_field': '0',
        'dibiaoid': '0',
        'line': '',
        'welfare': '',
    }

    save_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()).replace(' ', '_').replace(':','_')
    file_path = f'./testDataPython-{save_time}.csv'
    f_csv =  open(file_path, mode='w', 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()
    page = 0       #页数
    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)
    flag = True
    while flag:
        page += 1
        print(f'第{page}抓取中……')
        try:
            time.sleep(random()*3) #这里随机休眠一下,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
            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!!!'
            ###这里还是要添加cookies的好,我们要伪装好不是?防止反爬,如果你用上面提供的方法,也就很快的构建出cookies。
            response = requests.get(url=url,params=params, headers=headers)
        except:
            print(f'\033[31m第{page}请求异常!033[0m')
            flag = False
        parse_data = re.findall('"engine_jds":(.*?),"jobid_count"',response.text)
        parse_data_dict = json.loads(parse_data[0])
        # import pprint
        # pprint.pprint(parse_data_dict)
        # exit()
        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)
                print(dic['职位名字'], '——保存完毕!')
            else:
                error_time += 1
            if error_time == 200:
                flag = False
                print('抓取完成!')
    f_csv.close()
if __name__ == '__main__':
    key_word = 'python'
    # 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
    spider_python(key_word)

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:

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] = 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()
#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。

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) #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 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()]

###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|}\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 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()

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!

总结

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