当前位置:

首页 > 编程开发 > python爬虫 >

Python采集招聘数据信息(+详情页)并实现可视化

[环境使用]:

Python 3.8
Pycharm

[模块使用]:

requests >>> pip install requests
re
json
csv

如果安装python第三方模块:

win + R 输入 cmd 点击确定, 输入安装命令 pip install 模块名 (pip install requests) 回车
在pycharm中点击Terminal(终端) 输入安装命令

如何配置pycharm里面的python解释器?

选择file(文件) >>> setting(设置) >>> Project(项目) >>> python interpreter(python解释器)
点击齿轮, 选择add
添加python安装路径

pycharm如何安装插件?

选择file(文件) >>> setting(设置) >>> Plugins(插件)
点击 Marketplace 输入想要安装的插件名字比如:翻译插件输入 translation / 汉化插件输入 Chinese
选择相应的插件点击 install(安装) 即可
安装成功之后是会弹出重启pycharm的选项点击确定, 重启即可生效

基本流程思路: <可以通用>

一. 数据来源分析

网页开发者工具进行抓包分析....

F12打开开发者工具, 刷新网页
通过关键字进行搜索, 找到相应的数据, 查看response响应数据
确定数据之后, 查看headers确定请求url地址请求方式以及请求参数

二. 代码实现过程:

发送请求, 用python代码模拟浏览器对于url地址发送请求
获取数据, 获取服务器返回response响应数据
解析数据, 提取我们想要招聘信息数据
保存数据, 保存到表格文件里面

代码

导入模块

# 导入数据请求模块
import requests
# 导入正则表达式模块
import re
# 导入json模块
import json
# 导入格式化输出模块
import pprint
# 导入csv模块
import csv
# 导入时间模块
import time
# 导入随机模块
import random
# 有没有用utf-8保存表格数据,乱码的?
f = open('data多页_1.csv', mode='a', encoding='utf-8', newline='')  # 打开一个文件 data.csv
csv_writer = csv.DictWriter(f, fieldnames=[
    '职位',
    '城市',
    '经验',
    '学历',
    '薪资',
    '公司',
    '福利待遇',
    '公司领域',
    '公司规模',
    '公司类型',
    '发布日期',
    '职位详情页',
    '公司详情页',
])
csv_writer.writeheader()

1. 发送请求,

用python代码模拟浏览器对于url地址发送请求

不要企图一节课, 掌握所有内容, 要学习听懂思路, 每一步我们为什么这么做...
知道headers 1
不知道headers 2

headers 请求头, 作用伪装python代码, 伪装成浏览器
字典形式, 构建完整键值对

如果当你headers伪装不够的时候, 你可能会被服务器识别出来, 你是爬虫程序, 从而不给你相应的数据内容

for page in range(1, 15):
    print(f'正在采集第{page}页的数据内容')
    time.sleep(random.randint(1, 2))
    url = f'https://search.51job.com/list/010000%252C020000%252C030200%252C040000%252C090200,000000,0000,00,9,99,python,2,{page}.html'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36'
    }
    response = requests.get(url=url, headers=headers)
    print(response)  # <Response [200]> 响应对象

2. 获取数据

得到数据, 不是你想要数据内容, 你可能是被反爬了, 要多加一些伪装 <小伏笔>

# print(response.text)  字符串数据类型

3. 解析数据, 提取我们想要数据内容

re.findall() 就是从什么地方去找什么样数据内容

[0] 表示提取列表里面第一个元素 ---> list index out of range 所以你的列表是空列表

用正则表达式/css/xpath提取数据返回是空列表 ---> 1. 你语法写错 2. response.text 没有你想要数据

---> 是不是被反爬(验证码需要登陆) 是不是headers参数给少了是不是被封IP

    html_data = re.findall('window.__SEARCH_RESULT__ = (.*?)</script>', response.text)[0]
    # print(html_data)
    json_data = json.loads(html_data)
    # pprint.pprint(json_data)
    # 通过字典取值方法 把职位信息列表提取出来, 通过for循环遍历一个一个提取职位信息
    for index in json_data['engine_jds']:
        # 根据冒号左边的内容, 提取冒号右边的内容
        # pprint.pprint(index)
        try:
            dit = {
                '职位': index['job_title'],
                '城市': index['attribute_text'][0],
                '经验': index['attribute_text'][1],
                '学历': index['attribute_text'][2],
                '薪资': index['providesalary_text'],
                '公司': index['company_name'],
                '福利待遇': index['jobwelf'],
                '公司领域': index['companyind_text'],
                '公司规模': index['companysize_text'],
                '公司类型': index['companytype_text'],
                '发布日期': index['issuedate'],
                '职位详情页': index['job_href'],
                '公司详情页': index['company_href'],
            }
            csv_writer.writerow(dit)
            print(dit)
        except:
            pass

详情页数据

----> 爬虫基本思路是什么?

数据来源分析

请求响应请求那个网站呢? 网址是什么请求方式是什么请求参数要什么?

发送请求 ---> 获取数据 ---> 解析数据 ---> 保存数据

导入模块

import requests
import parsel
url = 'https://jobs.51job.com/shanghai-jdq/137393082.html?s=sou_sou_soulb&t=0_0'
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36',
}
response = requests.get(url=url, headers=headers)
response.encoding = response.apparent_encoding  # 自动识别编码
print(response.text)
selector = parsel.Selector(response.text)
content_1 = selector.css('.cn').get()
content_2 = selector.css('.tCompany_main').get()
content = content_1 + content_2
# 文件名 公司名字 + 职位名字
with open('python.html', mode='w', encoding='utf-8') as f:
    f.write(content)

可视化

代码

import pandas as pd
from pyecharts.charts import *
from pyecharts import options as opts
import re
from pyecharts.globals import ThemeType
from pyecharts.commons.utils import JsCode

df = pd.read_csv("招聘数据.csv")
df.head()

df.info()

df['薪资'].unique()
df['bottom']=df['薪资'].str.extract('^(\d+).*')
df['top']=df['薪资'].str.extract('^.*?-(\d+).*')
df['top'].fillna(df['bottom'],inplace=True)

df['commision_pct']=df['薪资'].str.extract('^.*?·(\d{2})薪')
df['commision_pct'].fillna(12,inplace=True)
df['commision_pct']=df['commision_pct'].astype('float64')
df['commision_pct']=df['commision_pct']/12

df.dropna(inplace=True)
源码、解答、教程可加Q裙：832157862免费领取
df['bottom'] = df['bottom'].astype('int64')
df['top'] = df['top'].astype('int64')
df['平均薪资'] = (df['bottom']+df['top'])/2*df['commision_pct']
df['平均薪资'] = df['平均薪资'].astype('int64')

df.head()

df['薪资'] = df['薪资'].apply(lambda x:re.sub('.*千/月', '0.3-0.7万/月', x))
df["薪资"].unique()

df['bottom'] = df['薪资'].str.extract('^(.*?)-.*?')
df['top'] = df['薪资'].str.extract('^.*?-(\d\.\d|\d)')
df.dropna(inplace=True)
df['bottom'] = df['bottom'].astype('float64')
df['top'] = df['top'].astype('float64')
df['平均薪资'] = (df['bottom']+df['top'])/2 * 10
df.head()

mean = df.groupby('学历')['平均薪资'].mean().sort_values()
x = mean.index.tolist()
y = mean.values.tolist()
c = (
    Bar()
    .add_xaxis(x)
    .add_yaxis(
        "学历",
        y
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="不同学历的平均薪资"),datazoom_opts=opts.DataZoomOpts())
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
c.render_notebook()

color_js = """new echarts.graphic.LinearGradient(0, 1, 0, 0,
    [{offset: 0, color: '#63e6be'}, {offset: 1, color: '#0b7285'}], false)"""

color_js1 = """new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                            offset: 0,
                            color: '#ed1941'
                        }, {
                            offset: 1,
                            color: '#009ad6'
                        }], false)"""

dq = df.groupby('城市')['职位'].count().to_frame('数量').sort_values(by='数量',ascending=False).reset_index()
x_data = dq['城市'].values.tolist()[:20]
y_data = dq['数量'].values.tolist()[:20]
b1 = (
        Bar(init_opts=opts.InitOpts(theme=ThemeType.DARK,bg_color=JsCode(color_js1),width='1000px',height='600px'))
        .add_xaxis(x_data)
        .add_yaxis('',
                   y_data ,
                   category_gap="50%",
                   label_opts=opts.LabelOpts(
                        font_size=12,
                        color='yellow',
                        font_weight='bold', 
                        font_family='monospace',
                        position='insideTop',  
                        formatter = '{b}\n{c}'  
                    ),
                  )
        .set_series_opts(
            itemstyle_opts={
                "normal": {
                    "color": JsCode(color_js),
                    "barBorderRadius": [15, 15, 0, 0],
                    "shadowColor": "rgb(0, 160, 221)",
                }
            }
        )
        .set_global_opts(
            title_opts=opts.TitleOpts(title='招 聘 数 量 前 20 的 城 市 区 域',
                                       title_textstyle_opts=opts.TextStyleOpts(color="yellow"),
                                       pos_top='7%',pos_left = 'center'
                                     ),
            legend_opts=opts.LegendOpts(is_show=False),
            xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
            yaxis_opts=opts.AxisOpts(name="",
                                     name_location='middle',
                                     name_gap=40,
                                     name_textstyle_opts=opts.TextStyleOpts(font_size=16)),
                         datazoom_opts=[opts.DataZoomOpts(range_start=1,range_end=50)]
                        )

    )
b1.render_notebook()

boss = df['学历'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
    Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c"))
    .add(
        series_name="学历需求占比",
        data_pair=data_pair,
        label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),
    )
    .set_series_opts(
        tooltip_opts=opts.TooltipOpts(
            trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
        ),
        label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="学历需求占比",
            pos_left="center",
            pos_top="20",
            title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
        ),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"])
)
c.render_notebook()

boss = df['经验'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
    Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c"))
    .add(
        series_name="经验需求占比",
        data_pair=data_pair,
        label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),
    )
    .set_series_opts(
        tooltip_opts=opts.TooltipOpts(
            trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
        ),
        label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="经验需求占比",
            pos_left="center",
            pos_top="20",
            title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
        ),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"])
)
c.render_notebook()

boss = df['公司领域'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
    Pie(init_opts=opts.InitOpts(width="1000px", height="600px", bg_color="#2c343c"))
    .add(
        series_name="公司领域占比",
        data_pair=data_pair,
        label_opts=opts.LabelOpts(is_show=False, position="center", color="rgba(255, 255, 255, 0.3)"),
    )
    .set_series_opts(
        tooltip_opts=opts.TooltipOpts(
            trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"
        ),
        label_opts=opts.LabelOpts(color="rgba(255, 255, 255, 0.3)"),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="公司领域占比",
            pos_left="center",
            pos_top="20",
            title_textstyle_opts=opts.TextStyleOpts(color="#fff"),
        ),
        legend_opts=opts.LegendOpts(is_show=False),
    )
    .set_colors(["#D53A35", "#334B5C", "#61A0A8", "#D48265", "#749F83"])
)
c.render_notebook()

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
boss = df['经验'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]
c = (
    Pie()
    .add("", data_pair)
    .set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"])
    .set_global_opts(title_opts=opts.TitleOpts(title="经验要求占比"))
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)
c.render_notebook()

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
boss = df['经验'].value_counts()
x = boss.index.tolist()
y = boss.values.tolist()
data_pair = [list(z) for z in zip(x, y)]

c = (
    Pie()
    .add(
        "",
        data_pair,
        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="Pie-富文本示例"))
    
)
c.render_notebook()

gsly = df['公司领域'].value_counts()[:10]
x1 = gsly.index.tolist()
y1 = gsly.values.tolist()
c = (
    Bar()
    .add_xaxis(x1)
    .add_yaxis(
        "公司领域",
        y1
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="公司领域"),datazoom_opts=opts.DataZoomOpts())
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
c.render_notebook()

gsgm = df['公司规模'].value_counts()[1:10]
x2 = gsgm.index.tolist()
y2 = gsgm.values.tolist()
c = (
    Bar()
    .add_xaxis(x2)
    .add_yaxis(
        "公司规模",
        y2
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="公司规模"),datazoom_opts=opts.DataZoomOpts())
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
c.render_notebook()

import stylecloud
from PIL import Image
welfares = df['福利'].dropna(how='all').values.tolist()
welfares_list = []
for welfare in welfares:
    welfares_list += welfare.split('，')
pic_name = '福利词云.png'
stylecloud.gen_stylecloud(
    text=' '.join(welfares_list),
    font_path='msyh.ttc',
    palette='cartocolors.qualitative.Bold_5',
    max_font_size=100,
    icon_name='fas fa-yen-sign',
    background_color='#212529',
    output_name=pic_name,
    )
Image.open(pic_name)