2019浙大研究生复试机试刷题解题报告——A1055

本文介绍了一个算法,用于根据年龄范围筛选出《福布斯》富豪榜中财产最多的富豪。算法首先将富豪信息按财产、年龄和姓名排序,然后在指定年龄范围内找出财产最高的富豪。

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1055 The World’s Richest (25 分)
Forbes magazine publishes every year its list of billionaires based on the annual ranking of the world’s wealthiest people. Now you are supposed to simulate this job, but concentrate only on the people in a certain range of ages. That is, given the net worths of N people, you must find the M richest people in a given range of their ages.

Input Specification:
Each input file contains one test case. For each case, the first line contains 2 positive integers: N (≤10^5) - the total number of people, and K (≤10 ^3) - the number of queries. Then N lines follow, each contains the name (string of no more than 8 characters without space), age (integer in (0, 200]), and the net worth (integer in [−10^​6​​ ,10​^6​​ ]) of a person. Finally there are K lines of queries, each contains three positive integers: M (≤100) - the maximum number of outputs, and [Amin, Amax] which are the range of ages. All the numbers in a line are separated by a space.

Output Specification:
For each query, first print in a line Case #X: where X is the query number starting from 1. Then output the M richest people with their ages in the range [Amin, Amax]. Each person’s information occupies a line, in the format

“Name Age Net_Worth”

The outputs must be in non-increasing order of the net worths. In case there are equal worths, it must be in non-decreasing order of the ages. If both worths and ages are the same, then the output must be in non-decreasing alphabetical order of the names. It is guaranteed that there is no two persons share all the same of the three pieces of information. In case no one is found, output None.

Sample Input:
12 4
Zoe_Bill 35 2333
Bob_Volk 24 5888
Anny_Cin 95 999999
Williams 30 -22
Cindy 76 76000
Alice 18 88888
Joe_Mike 32 3222
Michael 5 300000
Rosemary 40 5888
Dobby 24 5888
Billy 24 5888
Nobody 5 0
4 15 45
4 30 35
4 5 95
1 45 50

Sample Output:
Case #1:
Alice 18 88888
Billy 24 5888
Bob_Volk 24 5888
Dobby 24 5888
Case #2:
Joe_Mike 32 3222
Zoe_Bill 35 2333
Williams 30 -22
Case #3:
Anny_Cin 95 999999
Michael 5 300000
Alice 18 88888
Cindy 76 76000
Case #4:
None

题目大意:

1.给出n个富豪的姓名、年龄、财产数
2.给出年龄范围,按规定格式输出该年龄范围内财产数最高的m个富豪的相关信息
3.如果没有符合要求的人,输出“None”
4.输出规则:按财产数由高到低输出,财产数相同的,按年龄非递减的顺序输出,年龄相同的,按姓名字典序输出。

思路:

  1. 查看排序规则,按要求容易写出排序规则的代码如下:
bool cmp(Rich a,Rich b)
{
     if(a.worths!=b.worths)
        return a.worths>b.worths;
     else if(a.age!=b.age)
        return a.age<b.age;
     else
        return strcmp(a.name,b.name)<0;
}
  1. 由于要保存每个人的信息,考虑使用结构体数组
typedef struct Rich
{
     char name[10];
     int age,worths;
}Rich;
  1. 按排序规则对数组进行排序
  2. 根据每组查询给出的年龄范围,枚举结构体数组,输出符合要求的富豪信息
  3. 枚举前要对数组进行预处理,否则在测试点2处会超时(方法:由题目中可注意到每组查询输出的人数m不超过一百,并且按财产从高到低的顺序排序的,所以如果遍历时发现某个年龄的人数超过100,则第100人之后的数据可以不用遍历,以提高查询效率)

代码

#include<iostream>
#include<cstdio>
#include<cstring>
#include<algorithm>

using namespace std;

typedef struct Richer
{
  char name[10];
  int age;
  int worths;
}Rich;

bool cmp(Rich a,Rich b)
{
  if(a.worths!=b.worths)
     return a.worths>b.worths;
  else if(a.age!=b.age)
     return a.age<b.age;
  else 
     return strcmp(a.name,b.name)<0;
}

int main()
{
  int n,k;
  scanf("%d%d",&n,&k);
  Rich init[n];
  int age[210]={0};
  for(int i=0;i<n;++i)
  {
    scanf("%s%d%d",init[i].name,&init[i].age,&init[i].worths);
  }
  sort(init,init+n,cmp);
  
  Rich valid[n];
  int validNum=0;
  for(int i=0;i<n;++i)
  {
    if(age[init[i].age]<100)
    {
      age[init[i].age]++;
      valid[validNum++]=init[i];
    }
  }
  
  for(int i=1;i<=k;++i)
  {
    printf("Case #%d:\n",i);
    int ageL,ageR,m,num=0;
    scanf("%d%d%d",&m,&ageL,&ageR);
    for(int j=0;j<validNum&&num<m;++j)
    {
      if(valid[j].age>=ageL&&valid[j].age<=ageR)
      {
        printf("%s %d %d\n",valid[j].name,valid[j].age,valid[j].worths);
        num++;
      }
      
    }
    if(num==0)printf("None");
  }
  
  return 0;
  
}

以上代码已通过测试

Atalanta 2019.2.18

以下代码(1)报错:C:\Users\zh\AppData\Roaming\JetBrains\PyCharm2024.1\scratches\scratch_1.py:165: DeprecationWarning: Call to deprecated function create_named_range (Assign scoped named ranges directly to worksheets or global ones to the workbook. Deprecated in 3.1). wb.create_named_range( 尝修复(这个脚本会读取原始Excel文件,添加用于计算ESG得分的各列,并设置公式结构。最终结果将保存为3.xlsx,所有计算将在Excel中执行。) (1): ```python import pandas as pd import openpyxl from openpyxl.utils import get_column_letter from openpyxl.styles import PatternFill def add_esg_formulas(input_path, output_path): # 加载工作簿和工作表 wb = openpyxl.load_workbook(input_path) ws = wb.active # 添加新列标 new_columns = [ "基础ESG得分", "行业系数", "动态进步分", "供应链分", "垄断矫正分", "数据异常扣分", "总分", "MSCI转换分", "晨星转换分", "标普转换分", "华证转换分", "中证转换分", "Wind转换分", "Wind评级提升分", "减排技术研发投入率★", "Tier1供应商合规率★", "碳强度降幅★", "赫芬达尔指数★", "平台佣金率★", "社会议投入占比★" ] start_col = ws.max_column + 1 for i, col_name in enumerate(new_columns): col_letter = get_column_letter(start_col + i) ws[f"{col_letter}1"] = col_name # 为需要补充数据的列添加黄色背景 if "★" in col_name: for row in range(2, ws.max_row + 1): ws[f"{col_letter}{row}"].fill = PatternFill( start_color="FFFF00", end_color="FFFF00", fill_type="solid" ) # 设置公式 for row in range(3, ws.max_row + 1): # 从第3行开始(数据行) # 字母评级转换公式 letter_rating_formula = ( f'IF(ISBLANK(B{row}), "", ' f'IF(B{row}="AAA",9,' f'IF(B{row}="AA",8,' f'IF(B{row}="A",7,' f'IF(B{row}="BBB",6,' f'IF(B{row}="BB",5,' f'IF(B{row}="B",4,' f'IF(B{row}="CCC",3,3)))))))' ) # 晨星评分转换公式 morningstar_formula = ( f'IF(ISBLANK(E{row}), "", ' f'IF(E{row}>=40,9,' f'IF(E{row}>=30,7,' f'IF(E{row}>=20,6,' f'IF(E{row}>=10,5,3))))' ) # 基础ESG得分公式 base_esg_formula = ( f'=((IFERROR({get_column_letter(start_col+6)}{row},0)+' f'IFERROR({get_column_letter(start_col+7)}{row},0)+' f'IFERROR({get_column_letter(start_col+8)}{row},0)+' f'IFERROR({get_column_letter(start_col+9)}{row},0)+' f'IFERROR({get_column_letter(start_col+10)}{row},0)+' f'IFERROR({get_column_letter(start_col+11)}{row},0))/' f'MAX(1,COUNT({get_column_letter(start_col+6)}{row},' f'{get_column_letter(start_col+7)}{row},' f'{get_column_letter(start_col+8)}{row},' f'{get_column_letter(start_col+9)}{row},' f'{get_column_letter(start_col+10)}{row},' f'{get_column_letter(start_col+11)}{row})))*0.4' ) # 行业系数公式 industry_formula = ( f'=IF(OR(T{row}="能源类",T{row}="工业类"),' f'IF({get_column_letter(start_col+15)}{row}>=' f'IF(T{row}="能源类",0.08,0.05),1.2,0.9),' f'IF(OR(T{row}="科技类",T{row}="消费类"),' f'IF({get_column_letter(start_col+18)}{row}>=' f'IF(T{row}="科技类",0.025,0.018),1.1,1.0),1.0))' ) # Wind评级提升分公式 wind_improve_formula = ( f'=((IF(AND(NOT(ISBLANK(S{row})),NOT(ISBLANK(R{row}))),' f'MAX(VLOOKUP(R{row},RatingTable,2,0)-VLOOKUP(S{row},RatingTable,2,0),0),0)+' f'IF(AND(NOT(ISBLANK(R{row})),NOT(ISBLANK(Q{row}))),' f'MAX(VLOOKUP(Q{row},RatingTable,2,0)-VLOOKUP(R{row},RatingTable,2,0),0),0))/3)*10' ) # 动态进步分公式 progress_formula = ( f'={get_column_letter(start_col+12)}{row}+' f'MIN(10,({get_column_letter(start_col+13)}{row}/1.8)*10)+' f'MIN(10,({get_column_letter(start_col+14)}{row}/0.7)*10)' ) # 垄断矫正分公式 monopoly_formula = ( f'=IF(AND(OR(T{row}="金融类",T{row}="能源类"),U{row}="国企"),' f'-5*{get_column_letter(start_col+16)}{row},' f'IF(AND(OR(T{row}="科技类",T{row}="消费类"),' f'IF({get_column_letter(start_col+17)}{row}>0.1,-3*{get_column_letter(start_col+17)}{row},0),0))' ) # 数据异常扣分公式 penalty_formula = ( f'=IF(ABS(' f'AVERAGE({get_column_letter(start_col+6)}{row},{get_column_letter(start_col+8)}{row})' f'-AVERAGE({get_column_letter(start_col+9)}{row},{get_column_letter(start_col+10)}{row},{get_column_letter(start_col+11)}{row})' f')>=2,-3,0)' ) # 总分公式 total_formula = ( f'=({get_column_letter(start_col)}{row}*{get_column_letter(start_col+1)}{row})' f'+{get_column_letter(start_col+2)}{row}' f'+{get_column_letter(start_col+3)}{row}' f'+{get_column_letter(start_col+4)}{row}' f'+{get_column_letter(start_col+5)}{row}' ) # 写入公式 ws[f"{get_column_letter(start_col+6)}{row}"] = letter_rating_formula # MSCI转换分 ws[f"{get_column_letter(start_col+7)}{row}"] = morningstar_formula # 晨星转换分 for col_offset in [8, 9, 10, 11]: # 标普/华证/中证/Wind转换分 ws[f"{get_column_letter(start_col+col_offset)}{row}"] = letter_rating_formula.replace("B{row}", get_column_letter(2+col_offset-8)+str(row)) ws[f"{get_column_letter(start_col)}{row}"] = base_esg_formula # 基础ESG得分 ws[f"{get_column_letter(start_col+1)}{row}"] = industry_formula # 行业系数 ws[f"{get_column_letter(start_col+12)}{row}"] = wind_improve_formula # Wind评级提升分 ws[f"{get_column_letter(start_col+2)}{row}"] = progress_formula # 动态进步分 ws[f"{get_column_letter(start_col+4)}{row}"] = monopoly_formula # 垄断矫正分 ws[f"{get_column_letter(start_col+5)}{row}"] = penalty_formula # 数据异常扣分 ws[f"{get_column_letter(start_col+6)}{row}"] = total_formula # 总分 # 创建评级转换表 ws["A1000"] = "评级转换表" ratings = ["AAA", "AA", "A", "BBB", "BB", "B", "CCC"] scores = [9, 8, 7, 6, 5, 4, 3] for i, (rating, score) in enumerate(zip(ratings, scores), start=1001): ws[f"A{i}"] = rating ws[f"B{i}"] = score # 定义名称"RatingTable"引用这个区域 if "RatingTable" not in wb.defined_names: wb.create_named_range( "RatingTable", ws, f"$A$1001:$B${1000+len(ratings)}" ) # 添加说明文本 ws["A1050"] = "★需要手动补充的数据项:" ws["A1051"] = "1. 减排技术研发投入率 = (自主研发减碳技术投入/总营收)" ws["A1052"] = "2. Tier1供应商ESG合规率 = 接入区块链碳管理平台的供应商比例" ws["A1053"] = "3. 碳强度降幅 = (上碳排放强度 - 本碳排放强度)/上碳排放强度" ws["A1054"] = "4. 赫芬达尔指数(HHI) = Σ(企业市场份额)^2 (金融/能源类国企填写)" ws["A1055"] = "5. 平台商户佣金率 (科技/消费类填写)" ws["A1056"] = "6. 社会议投入占比 = 数据隐私/安全投入/总营收" # 保存工作簿 wb.save(output_path) # 执行函数 input_file = "D:/2.xlsx" output_file = "D:/3.xlsx" add_esg_formulas(input_file, output_file) ``` ### 功能说明: 1. **添加的列**: - 基础ESG得分、行业系数、动态进步分等核心计算列 - 各评级构的转换分列 - 带★号的外部数据补充列(标记为黄色背景) - 总分列 2. **核心公式实现**: - **基础ESG得分**:自动转换各构评级为分数,计算平均值后乘以40% - **行业系数**:根据行业类型和补充数据动态调整 - **动态进步分**:包含Wind评级提升、减排技术投入和供应链进步 - **垄断矫正**:针对金融/能源国企和科技/消费平台企业 - **数据异常扣分**:检测国内外评级差异 3. **特殊处理**: - 创建评级转换表(AAA→9分,AA→8分,...,CCC→3分) - 添加详细的数据补充说明(A1050-A1056) - 黄色背景标记需要手动补充的数据单元格 4. **使用说明**: - 在黄色标记的★列补充相应数据 - 总分列会自动计算最终ESG得分 - ≥75分表示高概率上榜福布斯ESG 50 此脚本保留了原始设计的所有核心逻辑,同时确保所有计算都在Excel中执行。用户只需在黄色单元格补充外部数据,即可自动生成最终ESG评分。
最新发布
07-05
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