College entrance examination score line climbing

import json

import numpy as np
import pandas as pd
import requests
import os
import time
import random
class School:
    school_id:""
    type:""
    name:""
    province_name:""
    city_name:""
    f211:""
    f985:""
    dual_class_name:""

# Data of colleges and universities over the years 
# First cycle to get the colleges in each page id, Then from colleges and Universities id Data acquisition over the years for preparation 
def get_one_page(page_num):
    heads={'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36'}# Request header , This is still understandable 
    url='https://api.eol.cn/gkcx/api/?access_token=&admissions=&central=&department=&dual_class=&f211=&f985=&is_doublehigh=&is_dual_class=&keyword=&nature=&page=%s&province_id=&ranktype=&request_type=1&school_type=&signsafe=&size=20&sort=view_total&top_school_id=[766,707]&type=&uri=apidata/api/gk/school/lists'
    list=[]
    df_all = pd.DataFrame()
    for i in range(1, page_num):  #  You don't have to say that , use  i  To replace the page number , from 1 To 143
        response = requests.get(url % (i), headers=heads)  #  Needless to say , Get a data 
        print(response.text)
        json_data = json.loads(response.text)  #  get json data , Don't know Baidu , I also copy the great God 
        #print(json_data)
        # try:  This usage Baidu , Prevent the program from running down 
        my_json = json_data['data']['item']  #  get josn  The root directory of the data 

        for my in my_json:  #  Make a cycle , Pick up the school id  And the name of the school 
            ss=School()
            ss.school_id=my['school_id']
            ss.name = my['name']
            ss.province_name = my['province_name']
            ss.city_name = my['city_name']
            if my['f211']==1:
                ss.f211=1
            else: ss.f211=0
            if my['f985']==1:
                ss.f985=1
            else: ss.f985=0
            if my['dual_class_name']==" Double top ":
                ss.dual_class_name=1
            else: ss.dual_class_name=0
            ss.type=my['type_name']
            li = {my['school_id']: my['name']}  #  Every school id  Form a dictionary key value pair with the school \


            #2021
            urlkzx = 'https://static-data.eol.cn/www/2.0/schoolprovinceindex/2021/%s/35/2/1.json'  #  Construction is really a website ,
            res = requests.get(urlkzx % (my['school_id']), headers=heads)  #  Get data ,school_id From the last cycle of crawling the school name 
            print(my['school_id'])
            json_data=json.loads(res.text)
            print(json_data)
            if json_data!='':
                data=json_data['data']['item'][0]
                df_one = pd.DataFrame({
                    ' School id':my['school_id'],
                    ' School name ': ss.name,
                    ' Province ': ss.province_name,
                    ' The city name ': ss.city_name,
                    ' Double top ': ss.dual_class_name,
                    'f985': ss.f985,
                    'f211': ss.f211,
                    ' School type ': ss.type,
                    ' year ': data['year'],
                    ' batch ': data['local_batch_name'],
                    ' type ': data['zslx_name'],
                    ' Lowest score ': data['min'],
                    ' Lowest ranking ': data['min_section'],
                    ' Batch fraction ': data['proscore'],
                }, index=[0])
                print(df_one)
                df_all = df_all.append(df_one, ignore_index=True)

             #2020
            urlkzx = 'https://static-data.eol.cn/www/2.0/schoolprovinceindex/2020/%s/35/2/1.json'  #  Construction is really a website ,
            res = requests.get(urlkzx % (my['school_id']), headers=heads)  #  Get data ,school_id From the last cycle of crawling the school name 
            print(my['school_id'])
            json_data = json.loads(res.text)
            if json_data != '':
                data = json_data['data']['item'][0]

                df_one = pd.DataFrame({
                    ' School id': my['school_id'],
                    ' School name ': ss.name,
                    ' Province ': ss.province_name,
                    ' The city name ': ss.city_name,
                    ' Double top ': ss.dual_class_name,
                    'f985': ss.f985,
                    'f211': ss.f211,
                    ' School type ': ss.type,
                    ' year ': data['year'],
                    ' batch ': data['local_batch_name'],
                    ' type ': data['zslx_name'],
                    ' Lowest score ': data['min'],
                    ' Lowest ranking ': data['min_section'],
                    ' Batch fraction ': data['proscore'],
                }, index=[0])
                print(df_one)
                df_all = df_all.append(df_one, ignore_index=True)

            #2019
            urlkzx = 'https://static-data.eol.cn/www/2.0/schoolprovinceindex/2019/%s/35/2/1.json'  #  Construction is really a website ,
            res = requests.get(urlkzx % (my['school_id']), headers=heads)  #  Get data ,school_id From the last cycle of crawling the school name 
            print(my['school_id'])
            json_data = json.loads(res.text)
            if json_data != '':
                data = json_data['data']['item'][0]
                df_one = pd.DataFrame({
                    ' School id': my['school_id'],
                    ' School name ': ss.name,
                    ' Province ': ss.province_name,
                    ' The city name ': ss.city_name,
                    ' Double top ': ss.dual_class_name,
                    'f985': ss.f985,
                    'f211': ss.f211,
                    ' School type ': ss.type,
                    ' year ': data['year'],
                    ' batch ': data['local_batch_name'],
                    ' type ': data['zslx_name'],
                    ' Lowest score ': data['min'],
                    ' Lowest ranking ': data['min_section'],
                    ' Batch fraction ': data['proscore'],
                }, index=[0])
                print(df_one)
                df_all = df_all.append(df_one, ignore_index=True)


            #2018
            urlkzx = 'https://static-data.eol.cn/www/2.0/schoolprovinceindex/2018/%s/35/2/1.json'  #  Construction is really a website ,
            res = requests.get(urlkzx % (my['school_id']), headers=heads)  #  Get data ,school_id From the last cycle of crawling the school name 
            print(my['school_id'])
            json_data = json.loads(res.text)
            if json_data != '':
                data = json_data['data']['item'][0]
                df_one = pd.DataFrame({
                    ' School id': my['school_id'],
                    ' School name ': ss.name,
                    ' Province ': ss.province_name,
                    ' The city name ': ss.city_name,
                    ' Double top ': ss.dual_class_name,
                    'f985': ss.f985,
                    'f211': ss.f211,
                    ' School type ': ss.type,
                    ' year ': data['year'],
                    ' batch ': data['local_batch_name'],
                    ' type ': data['zslx_name'],
                    ' Lowest score ': data['min'],
                    ' Lowest ranking ': data['min_section'],
                    ' Batch fraction ': data['proscore'],
                }, index=[0])
                print(df_one)
                df_all = df_all.append(df_one, ignore_index=True)
    return df_all

# College information data 
def detail(page_num):
    heads = {
        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36'}  #  Request header , This is still understandable 
    url = 'https://api.eol.cn/gkcx/api/?access_token=&admissions=&central=&department=&dual_class=&f211=&f985=&is_doublehigh=&is_dual_class=&keyword=&nature=&page=%s&province_id=&ranktype=&request_type=1&school_type=&signsafe=&size=20&sort=view_total&top_school_id=[766,707]&type=&uri=apidata/api/gk/school/lists'
    list = []
    d2 = pd.DataFrame()
    for i in range(1, page_num):  #  use  i  To replace the page number 
        response = requests.get(url % (i), headers=heads)  #  Get a data 
        print(response.text)
        json_data = json.loads(response.text)  #  get json data 
        my_json = json_data['data']['item']  #  get josn  The root directory of the data 

        for my in my_json:  #  Make a cycle , Pick up the school id  And the name of the school 
            ss = School()
            ss.school_id = my['school_id']
            ss.name = my['name']
            ss.province_name = my['province_name']
            ss.city_name = my['city_name']
            if my['f211'] == 1:
                ss.f211 = 1
            else:
                ss.f211 = 0
            if my['f985'] == 1:
                ss.f985 = 1
            else:
                ss.f985 = 0
            if my['dual_class_name'] == " Double top ":
                ss.dual_class_name = 1
            else:
                ss.dual_class_name = 0
            ss.type = my['type_name']
            li = {my['school_id']: my['name']}  #  Every school id  Form a dictionary key value pair with the school 

            # 2021
            urlkzx = 'https://static-data.eol.cn/www/2.0/schoolprovinceindex/2020/%s/35/2/1.json'  #  Construction is really a website ,
            res = requests.get(urlkzx % (my['school_id']), headers=heads)  #  Get data ,school_id From the last cycle of crawling the school name 
            print(my['school_id'])
            json_data = json.loads(res.text)
            print(json_data)
            if json_data != '':
                data = json_data['data']['item'][0]

                df2 = pd.DataFrame({
                    ' School id': my['school_id'],
                    ' School name ': ss.name,
                    ' Province ': ss.province_name,
                    ' The city name ': ss.city_name,
                    ' Double top ': ss.dual_class_name,
                    'f985': ss.f985,
                    'f211': ss.f211,
                    ' School type ': ss.type,
                }, index=[0])
                print(df2)
                d2 = d2.append(df2, ignore_index=True)
    return d2


def get_all_page(all_page_num):
    print(all_page_num)
    #  Storage table 
    df_all1 = pd.DataFrame()
    #  Call function 
    df_all = get_one_page(page_num=all_page_num)
    #  Additional 
    df_all1 = df_all1.append(df_all, ignore_index=True)
    time.sleep(5)
    return df_all1

def getdetail(all_page_num):
    print(all_page_num)
    #  Storage table 
    d1 = pd.DataFrame()
    #  Call function 
    d2 = detail(page_num=all_page_num)
    #  Additional 
    d2 = d2.append(d1, ignore_index=True)
    time.sleep(5)
    return d2

df_school = get_all_page(100)
dd=getdetail(100)
df_school.to_excel('data.xlsx', index=False)
dd.to_excel('dd.xlsx', index=False)