【PAT】A1055 The World‘s Richest

本博客介绍了一种算法挑战,即从全球富豪榜中筛选出特定年龄段内的最富有人群。通过解析输入数据,使用结构体存储姓名、年龄和财富信息,并实现定制的排序算法进行高效筛选。

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 Npeople, 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 [AminAmax] 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 [AminAmax]. 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

 解题思路:

这是一道排序中的常规题,读完题目后的解题程序和思考方式为:

  1. 创建结构体,包括名字数组(比题目给的8个空间多一位),年龄和财富参数
  2. 按题意创建比较数组cmp(),也比较常规。但是由于要比较字符数组,建议先用strcmp()函数赋值给一个变量再去判断变量与0的关系,会好很多,也是一个习惯;
  3. 输入参数,比较;
  4. 最后选择排序中,运用条件判断和计数器count,当count==num时(count初值为0),而则跳出当前轮查询,边查询边输出有利于不用再去定义新的空间来存放已有变量。
  5. 检验计数器count的值,当count=0时,也就意味着找不到区间中的人,所以输出None,进入下一轮查询。

代码:

#include <stdio.h>
#include <string.h>
#include <algorithm>
using namespace std;

struct per{
	char name[9];
	int age,weath;
	bool flat;
}p[100010];

bool cmp(per a,per b){
	int s=strcmp(a.name,b.name);
	if(a.weath!=b.weath) return a.weath>b.weath;
	else if(a.age!=b.age) return a.age<b.age;
	else if(s!=0) return s<0;
}
int main(){
	int n,k;
	scanf("%d%d",&n,&k);
	for(int i=0;i<n;i++){
		scanf("%s%d%d",p[i].name,&p[i].age,&p[i].weath);
	}
	sort(p,p+n,cmp);
	int num,Amin,Amax; 
	
	for(int i=0;i<k;i++){//查询轮数 
		scanf("%d%d%d",&num,&Amin,&Amax);
		int count=0;// 计数器每一轮清零 
		printf("Case #%d:\n",i+1);
		for(int j=0;j<n&&count<num;j++){//每一轮查找 
			if(p[j].age>=Amin&&p[j].age<=Amax){
				printf("%s %d %d\n",p[j].name,p[j].age,p[j].weaath);
				count++;
			}
		}
		if(count==0) printf("None\n"); 
	}
	return 0;
} 

 

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( 尝试修复(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 # 添加新列标题 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★", "碳强度年降幅★", "赫芬达尔指数★", "平台佣金率★", "社会议题投入占比★" ] 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. 碳强度年降幅 = (上年碳排放强度 - 本年碳排放强度)/上年碳排放强度" 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. 社会议题投入占比 = 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 - 各评级机构的转换分列 - 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**:自动转换各机构评级为分数,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
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

_之桐_

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值