共计 3911 个字符，预计需要花费 10 分钟才能阅读完成。
又到了一年一度的招聘热季，大量的工作向咱们招手，明天我和大家一起看看拉勾网中各公司对于 python 人才的需要。
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} 条数据胜利 ’)
j += 1
if __name__ == ‘__main__’:
for i in range(1, 11):
params = data(i)
get_data(params)
上面对爬取的文本进行剖析
XM 返佣 https://www.kaifx.cn/broker/x…
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)
2. 工作教训
data[‘ 年限 ’].value_counts().plot(kind=’bar’,rot=0,color=’g’)
3. 城市剖析
plt.rcParams[‘figure.figsize’]=[15,15]
data[‘ 城市 ’].value_counts().plot(kind=’pie’,autopct=’%1.2f%%’,explode=np.linspace(0,1.5,18))
4. 公司待遇剖析
(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))
(2)词云图展现
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()
5. 全国工资水平剖析
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()
