用Python爬取各大高校并可视化帮弟弟选大学,弟弟直呼牛X

Liana ·

更新时间:2024-09-20

· 274 次阅读

一、获取url

打开中国教育在线网，按 F12，顶部选择NetWork，选择XHR

刷新页面，观察url，通过对Reponse的分析找到真正的url为：https://api.eol.cn/gkcx/api/

数据存储在Json中。

再点击Headers，查看请求参数

请求方式为POST

二、发送请求

拿到url，我们就可以利用requests模拟浏览器发送请求，拿到返回的Json数据。代码如下：


# 导入包
import numpy as np
import pandas as pd
import requests
import json
from fake_useragent import UserAgent
import time
# 获取一页
def get_one_page(page_num):
    # 获取URL
    url = 'https://api.eol.cn/gkcx/api/'
    # 构造headers
    headers = {
        'User-Agent': UserAgent().random,
        'Origin': 'https://gkcx.eol.cn',
        'Referer': 'https://gkcx.eol.cn/school/search?province=&schoolflag=&recomschprop=',
    }
    # 构造data
    data = {
        'access_token': "",
        'admissions': "",
        'central': "",
        'department': "",
        'dual_class': "",
        'f211': "",
        'f985': "",
        'is_dual_class': "",
        'keyword': "",
        'page': page_num,
        'province_id': "",
        'request_type': 1,
        'school_type': "",
        'size': 20,
        'sort': "view_total",
        'type': "",
        'uri': "apigkcx/api/school/hotlists",
    }
    # 发起请求
    try:
        response = requests.post(url=url, data=data, headers=headers)
    except Exception as e:
        print(e) 
        time.sleep(3) 
        response = requests.post(url=url, data=data, headers=headers)

三、解析json数据

根据Response返回的Json格式，解析出我们想要的内容，代码如下：


# 解析获取数据
    school_data = json.loads(response.text)['data']['item']
    # 学校名
    school_name = [i.get('name') for i in school_data]
    # 隶属部门
    belong = [i.get('belong') for i in school_data]
    # 高校层次
    dual_class_name = [i.get('dual_class_name') for i in school_data]
    # 是否985
    f985 = [i.get('f985') for i in school_data]
    # 是否211
    f211 = [i.get('f211') for i in school_data]
    # 办学类型
    level_name = [i.get('level_name') for i in school_data]
    # 院校类型
    type_name = [i.get('type_name') for i in school_data]
    # 是否公办
    nature_name = [i.get('nature_name') for i in school_data]
    # 人气值
    view_total = [i.get('view_total') for i in school_data]
    # 省份
    province_name = [i.get('province_name') for i in school_data]
    # 城市
    city_name = [i.get('city_name') for i in school_data]
    # 区域
    county_name = [i.get('county_name') for i in school_data]
    # 保存数据
    df_one = pd.DataFrame({
        'school_name': school_name,
        'belong': belong,
        'dual_class_name': dual_class_name,
        'f985': f985,
        'f211': f211,
        'level_name': level_name,
        'type_name': type_name,
        'nature_name': nature_name,
        'view_total': view_total,
        'province_name': province_name,
        'city_name': city_name,
        'county_name': county_name,
    })
    return df_one

四、存入Excel

先将数据存入Pandas，用于做数据分析，再写入Excel存储。


# 获取多页
def get_all_page(all_page_num):
    # 存储表
    df_all = pd.DataFrame()
    # 循环页数
    for i in range(all_page_num):
        # 打印进度
        print(f'正在获取第{i + 1}页的高校信息')
        # 调用函数
        df_one = get_one_page(page_num=i+1)
        # 追加
        df_all = df_all.append(df_one, ignore_index=True)
        # 休眠
        time.sleep(np.random.uniform(2))
    return df_all
# 运行函数
df_school = get_all_page(all_page_num=143)
# 读出数据
df_school.to_excel('./data/全国高校数据.xlsx', index=False)

五、运行代码