抓取了招聘信息并告诉你哪种Python 程序员最赚钱

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本文以Python爬虫、数据分析、后端、数据挖掘、全栈开发、运维开发、高级开发工程师、大数据、机器学习、架构师这10个岗位，从拉勾网上爬取了相应的职位信息和任职要求，并通过数据分析可视化，直观地展示了这10个职位的平均薪资和学历、工作经验要求。

这是之前写的两篇文章的整合版（Python职位分析上与Python职位分析下），由csdn排版，这几天这个文章又活起来了(不过的确是挺好的，当时写花了好几天时间)，所以特地发一遍，让新读者也看看，文章很长，耐心观看。

爬虫准备

1、先获取薪资和学历、工作经验要求

由于拉勾网数据加载是动态加载的，需要我们分析。分析方法如下：

F12分析页面数据存储位置

我们发现网页内容是通过post请求得到的，返回数据是json格式，那我们直接拿到json数据即可。

我们只需要薪资和学历、工作经验还有单个招聘信息，返回json数据字典中对应的英文为：positionId,salary, education, workYear（positionId为单个招聘信息详情页面编号）。相关操作代码如下：

文件存储：

def file_do(list_info):
    # 获取文件大小
    file_size = os.path.getsize(r'G:\lagou_anv.csv')
    if file_size == 0:
        # 表头
        name = ['ID','薪资', '学历要求', '工作经验']
        # 建立DataFrame对象
        file_test = pd.DataFrame(columns=name, data=list_info)
        # 数据写入
        file_test.to_csv(r'G:\lagou_anv.csv', encoding='gbk', index=False)
    else:
        with open(r'G:\lagou_anv.csv', 'a+', newline='') as file_test:
            # 追加到文件后面
            writer = csv.writer(file_test)
            # 写入文件
            writer.writerows(list_info)

 

基本数据获取：

# 1. post 请求 url
req_url = 'https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false'
# 2.请求头 headers
headers = {
    'Accept': 'application/json,text/javascript,*/*;q=0.01',
    'Connection': 'keep-alive',
    'Cookie': '你的Cookie值，必须加上去',
    'Host': 'www.lagou.com',
    'Referer': 'https://www.lagou.com/jobs/list_Python?labelWords=&fromSearch=true&suginput=',
    'User-Agent':  str(UserAgent().random),
}

def get_info(headers):
    # 3.for 循环请求（一共30页）
    for i in range(1, 31):
        # 翻页
        data = {
            'first': 'true',
            'kd': 'Python爬虫',
            'pn': i
        }
        # 3.1 requests 发送请求
        req_result = requests.post(req_url, data=data, headers=headers)
        req_result.encoding = 'utf-8'
        print("第%d页："%i+str(req_result.status_code))
        # 3.2 获取数据
        req_info = req_result.json()
        # 定位到我们所需数据位置
        req_info = req_info['content']['positionResult']['result']
        print(len(req_info))
        list_info = []
        # 3.3 取出具体数据
        for j in range(0, len(req_info)):
            salary = req_info[j]['salary']
            education = req_info[j]['education']
            workYear = req_info[j]['workYear']
            positionId = req_info[j]['positionId']
            list_one = [positionId,salary, education, workYear]
            list_info.append(list_one)
        print(list_info)
        # 存储文件
        file_do(list_info)
        time.sleep(1.5)

 

运行结果：

2、根据获取到的positionId来访问招聘信息详细页面

根据positionId还原访问链接：

position_url = []
def read_csv():
    # 读取文件内容
    with open(r'G:\lagou_anv.csv', 'r', newline='') as file_test:
        # 读文件
        reader = csv.reader(file_test)
        i = 0
        for row in reader:
            if i != 0 :
                # 根据positionID补全链接
                url_single = "https://www.lagou.com/jobs/%s.html"%row[0]
                position_url.append(url_single)
            i = i + 1
        print('一共有：'+str(i-1)+'个')
        print(position_url)

 

访问招聘信息详情页面，获取职位描述（岗位职责和岗位要求）并清理数据：

def get_info():
    for position_url in position_urls:
        work_duty = ''
        work_requirement = ''
        response00 = get_response(position_url,headers = headers)
        time.sleep(1)
        content = response00.xpath('//*[@id="job_detail"]/dd[2]/div/p/text()')
        # 数据清理
        j = 0
        for i in range(len(content)):
            content[i] = content[i].replace('\xa0',' ')
            if content[i][0].isdigit():
                if j == 0:
                    content[i] = content[i][2:].replace('、',' ')
                    content[i] = re.sub('[；;.0-9。]','', content[i])
                    work_duty = work_duty+content[i]+ '/'
                    j = j + 1
                elif content[i][0] == '1' and not content[i][1].isdigit():
                    break
                else:
                    content[i] = content[i][2:].replace('、', ' ')
                    content[i] = re.sub('[、；;.0-9。]','',content[i])
                    work_duty = work_duty + content[i]+ '/'
        m = i
        # 岗位职责
        write_file(work_duty)
        print(work_duty)
        # 数据清理
        j = 0
        for i in range(m,len(content)):
            content[i] = content[i].replace('\xa0',' ')
            if content[i][0].isdigit():
                if j == 0:
                    content[i] = content[i][2:].replace('、', ' ')
                    content[i] = re.sub('[、；;.0-9。]', '', content[i])
                    work_requirement = work_requirement + content[i] + '/'
                    j = j + 1
                elif content[i][0] == '1' and not content[i][1].isdigit():
                    # 控制范围
                    break
                else:
                    content[i] = content[i][2:].replace('、', ' ')
                    content[i] = re.sub('[、；;.0-9。]', '', content[i])
                    work_requirement = work_requirement + content[i] + '/'
        # 岗位要求
        write_file2(work_requirement)
        print(work_requirement)
        print("-----------------------------")

 

运行结果：


3、四种图可视化数据+数据清理方式

矩形树图：

# 1.矩形树图可视化学历要求
from pyecharts import TreeMap
education_table = {}
for x in education:
    education_table[x] = education.count(x)
key = []
values = []
for k,v in education_table.items():
    key.append(k)
    values.append(v)

data = []
for i in range(len(key)) :
    dict_01 = {"value": 40, "name": "我是A"}
    dict_01["value"] = values[i]
    dict_01["name"] = key[i]
    data.append(dict_01)
tree_map = TreeMap("矩形树图", width=1200, height=600)
tree_map.add("学历要求",data, is_label_show=True, label_pos='inside')

 

玫瑰饼图：

# 2.玫瑰饼图可视化薪资
import re
import math
'''
# 薪水分类
parameter : str_01--字符串原格式:20k-30k
returned value : (a0+b0)/2 --- 解析后变成数字求中间值：25.0
'''
def assort_salary(str_01):
    reg_str01 = "(\d+)"
    res_01 = re.findall(reg_str01, str_01)
    if len(res_01) == 2:
        a0 = int(res_01[0])
        b0 = int(res_01[1])
    else :
        a0 = int(res_01[0])
        b0 = int(res_01[0])
    return (a0+b0)/2

from pyecharts import Pie
salary_table = {}
for x in salary:
    salary_table[x] = salary.count(x)

key = ['5k以下','5k-10k','10k-20k','20k-30k','30k-40k','40k以上']
a0,b0,c0,d0,e0,f0=[0,0,0,0,0,0]

for k,v in salary_table.items():
    ave_salary = math.ceil(assort_salary(k))
    print(ave_salary)
    if ave_salary < 5:
        a0 = a0 + v
    elif ave_salary in range(5,10):
        b0 = b0 +v
    elif ave_salary in range(10,20):
        c0 = c0 +v
    elif ave_salary in range(20,30):
        d0 = d0 +v
    elif ave_salary in range(30,40):
        e0 = e0 +v
    else :
        f0 = f0 + v
values = [a0,b0,c0,d0,e0,f0]

pie = Pie("薪资玫瑰图", title_pos='center', width=900)
pie.add("salary",key,values,center=[40, 50],is_random=True,radius=[30, 75],rosetype="area",is_legend_show=False,is_label_show=True)

 

普通柱状图：

# 3.工作经验要求柱状图可视化
from pyecharts import Bar
workYear_table = {}
for x in workYear:
    workYear_table[x] = workYear.count(x)
key = []
values = []
for k,v in workYear_table.items():
    key.append(k)
    values.append(v)
bar = Bar("柱状图")
bar.add("workYear", key, values, is_stack=True,center= (40,60))

 

词云图：

import jieba
from pyecharts import WordCloud
import pandas as pd
import re,numpy

stopwords_path = 'H:\PyCoding\Lagou_analysis\stopwords.txt'
def read_txt():
    with open("G:\lagou\Content\\ywkf_requirement.txt",encoding='gbk') as file:
        text = file.read()
        content = text
        # 去除所有评论里多余的字符
        content = re.sub('[,，。. \r\n]', '', content)
        segment = jieba.lcut(content)
        words_df = pd.DataFrame({'segment': segment})
        # quoting=3 表示stopwords.txt里的内容全部不引用
        stopwords = pd.read_csv(stopwords_path, index_col=False,quoting=3, sep="\t", names=['stopword'], encoding='utf-8')
        words_df = words_df[~words_df.segment.isin(stopwords.stopword)]
        words_stat = words_df.groupby(by=['segment'])['segment'].agg({"计数": numpy.size})
        words_stat = words_stat.reset_index().sort_values(by=["计数"], ascending=False)
        test = words_stat.head(200).values
        codes = [test[i][0] for i in range(0, len(test))]
        counts = [test[i][1] for i in range(0, len(test))]
        wordcloud = WordCloud(width=1300, height=620)
        wordcloud.add("必须技能", codes, counts, word_size_range=[20, 100])
        wordcloud.render("H:\PyCoding\Lagou_analysis\cloud_pit\ywkf_bxjn.html")

 

Python爬虫岗位

学历要求

工作月薪

工作经验要求


Python数据分析岗位

学历要求

工作月薪

工作经验要求