Python拉勾網資料採集與視覺化

全文簡介

本文是先採集拉勾網上面的資料，採集的是Python崗位的資料，然後用Python進行視覺化。主要涉及的是爬蟲&資料視覺化的知識。

爬蟲部分

先用Python來抓取拉勾網上面的資料，採用的是簡單好用的requests模組。主要註意的地方是，拉勾網屬於動態網頁，所以會用到瀏覽器的F12開發者工具進行抓包。抓包以後會發現，其實網頁是一個POST的形式，所以要提交資料，提交的資料如下圖：

真實網址是：

https://www.lagou.com/jobs/positionAjax.jsonneedAddtionalResult=false&isSchoolJob;=0

在上圖也可以輕鬆發現：kd是查詢關鍵詞，pn是頁數，可以實現翻頁。

程式碼實現

import requests # 網路請求
import re
import time
import random
# post的網址
url = 'https://www.lagou.com/jobs/positionAjax.json?needAddtionalResult=false&isSchoolJob;=0'
# 反爬措施
essay-header = {'Host': 'www.lagou.com',
'User-Agent':'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36',
'Accept': 'application/json, text/javascript, */*; q=0.01',
'Accept-Language': 'zh-CN,en-US;q=0.7,en;q=0.3',
'Accept-Encoding': 'gzip, deflate, br',
'Referer': 'https://www.lagou.com/jobs/list_Python?labelWords=&fromSearch;=true&suginput;=',
'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',
'X-Requested-With': 'XMLHttpRequest',
'X-Anit-Forge-Token': 'None',
'X-Anit-Forge-Code': '0',
'Content-Length': '26',
'Cookie': 'user_trace_token=20171103191801-9206e24f-9ca2-40ab-95a3-23947c0b972a; _ga=GA1.2.545192972.1509707889; LGUID=20171103191805-a9838dac-c088-11e7-9704-5254005c3644; JSESSIONID=ABAAABAACDBABJB2EE720304E451B2CEFA1723CE83F19CC; _gat=1; LGSID=20171228225143-9edb51dd-ebde-11e7-b670-525400f775ce; PRE_UTM=; PRE_HOST=www.baidu.com; PRE_SITE=https%3A%2F%2Fwww.baidu.com%2Flink%3Furl%3DKkJPgBHAnny1nUKaLpx2oDfUXv9ItIF3kBAWM2-fDNu%26ck%3D3065.1.126.376.140.374.139.129%26shh%3Dwww.baidu.com%26sht%3Dmonline_3_dg%26wd%3D%26eqid%3Db0ec59d100013c7f000000055a4504f6; PRE_LAND=https%3A%2F%2Fwww.lagou.com%2F; LGRID=20171228225224-b6cc7abd-ebde-11e7-9f67-5254005c3644; index_location_city=%E5%85%A8%E5%9B%BD; TG-TRACK-CODE=index_search; SEARCH_ID=3ec21cea985a4a5fa2ab279d868560c8',
'Connection': 'keep-alive',
'Pragma': 'no-cache',
'Cache-Control': 'no-cache'}
for n in range(30):
    # 要提交的資料
    form = {'first':'false',
            'kd':'Python',
            'pn':str(n)}
    time.sleep(random.randint(2,5))
    # 提交資料
    html = requests.post(url,data=form,essay-headers = essay-header)
    # 提取資料
    data = re.findall('{"companyId":.*?,"positionName":"(.*?)","workYear":"(.*?)","education":"(.*?)","jobNature":"(.*?)","financeStage":"(.*?)","companyLogo":".*?","industryField":".*?","city":"(.*?)","salary":"(.*?)","positionId":.*?,"positionAdvantage":"(.*?)","companyShortName":"(.*?)","district"',html.text)
    # 轉換成資料框
    data = pd.DataFrame(data)
    # 儲存在本地
    data.to_csv(r'D:\Windows 7 Documents\Desktop\My\LaGouDataMatlab.csv',essay-header = False, index = False, mode = 'a+')

import pandas as pd # 資料框操作
import numpy as np 
import matplotlib.pyplot as plt # 繪圖
import jieba # 分詞
from wordcloud import WordCloud # 詞雲視覺化
import matplotlib as mpl  # 配置字型
from pyecharts import Geo # 地理圖
mpl.rcParams["font.sans-serif"] = ["Microsoft YaHei"]
# 配置繪圖風格
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., 15.]

# 匯入資料
data = pd.read_csv('D:\\Windows 7 Documents\\Desktop\\My\\LaGouDataPython.csv',encoding='gbk')  # 匯入資料
data.head()

data.tail()

data['學歷要求'].value_counts().plot(kind='barh',rot=0)
plt.show()

data['工作經驗'].value_counts().plot(kind='bar',rot=0,color='b')
plt.show()

final = ''  
stopwords = ['PYTHON','python','Python','工程師','（','）','/'] # 停止詞
for n in range(data.shape[0]):
    seg_list = list(jieba.cut(data['崗位職稱'][n]))
    for seg in seg_list:  
        if seg not in stopwords:  
                final = final + seg + ' '
# final 得到的詞彙

data['工作地點'].value_counts().plot(kind='pie',autopct='%1.2f%%',explode = np.linspace(0,1.5,25))
plt.show()

# 提取資料框
data2 = list(map(lambda x:(data['工作地點'][x],eval(re.split('k|K',data['工資'][x])[0])*1000),range(len(data))))
# 提取價格資訊
data3 = pd.DataFrame(data2)
# 轉化成Geo需要的格式
data4 = list(map(lambda x:(data3.groupby(0).mean()[1].index[x],data3.groupby(0).mean()[1].values[x]),range(len(data3.groupby(0)))))
# 地理位置展示
geo = Geo("全國Python工資佈局", "製作人:挖掘機小王子", 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, visual_range=[0, 300], visual_text_color='#fff')
# 中國地圖Python工資，此分佈是最低薪資
geo