爬取拉勾网并进行数据分析_Python

又到了一年一度的招聘热季，大量的工作向我们招手，今天我和大家一起看看拉勾网中各公司对于python人才的需求。

import jieba
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyecharts import Geo
from wordcloud import  WordCloud
import re
import matplotlib
from imageio import imread
url="https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false"
def data(page):
    return {
        "first": "true",
        "pn": f"{page}",
        "kd": "python",
        'sid': '4256fece2141497bb5a8e1bfa69bcee7'
    }
def get_cookies():
    headers={
        'origin': 'https://www.lagou.com',
        'referer': 'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput=',
        'authority': 'www.lagou.com',
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36',
    }
    response=requests.get('https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput=',headers=headers)
    return response.cookies.get_dict()
cookies=get_cookies()
headers={'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.61 Safari/537.36'
         ,'host':'www.lagou.com'
         ,'origin': 'https://www.lagou.com'
         ,'referer': 'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput='}
def get_data(data):
    response = requests.post(url=url, headers=headers, data=data, cookies=cookies)
    # json数据
    content = response.json()['content']['positionResult']['result']
    j = 1
    companyLabelstr=''
    for i in content:
        city = i['city']
        companyFullName = i['companyFullName']
        companySize = i['companySize']
        education = i['education']
        positionName = i['positionName']
        salary = i['salary']
        workYear = i['workYear']
        companyLabelList=i['companyLabelList']
        if len(companyLabelList)>0:
            companyLabelList=''.join(companyLabelList)
        else:
            companyLabelList=''
        '''
        companyLabelstr=companyLabelList+companyLabelstr
        print(workYear,companyLabelList)
        print(companyLabelstr)
        '''

        with open('python.csv', 'a+', encoding='utf-8')as f:
            f.write(f'{city},{companyFullName},{companySize},{education},{positionName},{salary},{workYear},{companyLabelList}\n')
    
        print(f'第{j}条数据成功')
        j += 1


if __name__ == '__main__':
    for i in range(1, 11):
        params = data(i)
        get_data(params)

文本信息如图所示：
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下面对爬取的文本进行分析

matplotlib.rcParams['font.family']='SimHei'
plt.rcParams['axes.labelsize']=16
plt.rcParams['xtick.labelsize']=14
plt.rcParams['ytick.labelsize']=14
plt.rcParams['legend.fontsize']=12
plt.rcParams['figure.figsize']=[15,9]
data=pd.read_excel(r'C:\Users\2020\Desktop\python2.xls',encoding='utf-8')

1.学历

data['学历'].value_counts().plot(kind='bar',rot=0)

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2.工作经验

data['年限'].value_counts().plot(kind='bar',rot=0,color='g')

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3.城市分析

plt.rcParams['figure.figsize']=[15,15]
data['城市'].value_counts().plot(kind='pie',autopct='%1.2f%%',explode=np.linspace(0,1.5,18))

在这里插入图片描述
4.公司待遇分析
（1）分词操作

a=len(data['公司福利'])
str=''
for i in range(a):
    b=data['公司福利'][i]
    if type(b)==float:
        b=''
    str=str+b
jieba.add_word('五险一金')
jieba.add_word('牛B')
jieba.add_word('年底双薪')
jieba.add_word('带薪年假')
jieba.add_word('股票期权')
jieba.add_word('定期体检')
jieba.add_word('节日礼物')
words = jieba.lcut(str)
counts = {}
for word in words:
    counts[word] = counts.get(word, 0) + 1
items = list(counts.items())
items.sort(key=lambda x: x[1], reverse=True)
with open('词频统计',mode='w',encoding='utf-8')as f:
    for i in range(20):
        word,count=items[i]
        f.writelines('{}\t{}\n'.format(word,count))

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（2）词云图展示

with open('词频统计',mode='r',encoding='utf-8')as f:
    text=f.read()
wc=WordCloud(font_path=r'C:\Users\2020\Desktop\simhei.ttf',background_color='white',width=1000,max_words=100,height=860,margin=2).generate(text)
plt.imshow(wc)
plt.axis('off')
plt.show()

在这里插入图片描述
5.全国工资水平分析

data2=list(map(lambda x:(data['城市'][x],eval(re.split('k|K',data['工资'][x])[0])*1000),range(len(data))))
data3=pd.DataFrame(data2,index)
data4=list(map(lambda x:(data3.groupby(0).mean()[1].index[x],data3.groupby(0).mean()[1].values[x]),range(len(data3.groupby(0)))))
geo=Geo('全国python工资布局','制作人：止疼',title_color='#fff',title_pos='left',width=1200,height=600,background_color='#404a59')
attr,value=geo.cast(data4)
geo.add('',attr,value,type='heatmap',is_visualmap=True,maptype='china',visual_range=[0,300],visual_text_color='#fff')
geo.render()

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本人是python小白，第一次写博客，不会创作，由于上了正心老师和挖掘机小王子老师的课程，自己整合的，如有侵权，请联系，本人立马删除。