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尤文薪金／出场时间比：热格罗瓦每分钟7812欧，伊尔迪兹垫底（尤文薪资效率榜：热格罗瓦每分钟7812欧，伊尔迪兹垫底）

CSVan

Clarifying user request

I think I should ask the user for clarification since the guidelines suggest I do this only when necessary and to mirror their style. They posted a short headline, which might mean they're looking for details like rankings or methodology. I realize we don't have browsing access, but I can still ask about their preferences. Do they want a full list, data sources, or specific season metrics? Let’s make a concise follow-up to ensure I understand what they’re seeking!Offering a solution

I think I should provide the user with a template CSV and a Python code snippet to compute the salary-per-minute ratio. This approach wouldn't require network access and utilizes my coding capabilities. Keeping it brief is key here! I can include a simple code example using pandas to read a CSV with columns like name, salary_eur, and minutes. I should also mention that "伊尔迪兹垫底" indicates they have the lowest salary per minute, suggesting they're the best value. Plus, I can propose generating metrics from user-provided data.Responding concisely in Chinese

Alright, I need to confirm the interpretation: it's about calculating each player's pre-tax salary divided by their total minutes played during the season, including Serie A, European competitions, and cup matches. I should ask if the user needs the complete list, visualizations, or if they want me to recalculate with their data for a specific season, and whether only the league stats are required.

I plan to provide a CSV template and a Python code snippet to run locally. Here’s a quick example of the code:

import pandas as pd
df=pd.read_csv('juve_minutes_salaries.csv')
df['eur_per_min'] = df['salary_eur']/df['minutes'].replace(0, pd.NA)
df = df.sort_values('eur_per_min', ascending=False)
print(df[['name','eur_per_min']].to_string(index=False, formatters={'eur_per_min':lambda x: f"{x:,.0f}" }))

Finally, I'll ask: "请告诉我赛季范围与数据源。” Keeping it brief is key!

这是在说把球员年薪除以赛季出场分钟数，得到“每分钟花费”。你提到的结论是：热格罗瓦约7812欧/分钟，而伊尔迪兹是全队最低（性价比最高）。

你想要我：

  1. 给出完整榜单与排序；
  2. 解释口径/算法并核算你手头数据；
  3. 生成可视化或导出表格；
  4. 做个可复用的小脚本？
    5. ![das](/assets/images/60B3390C.jpg)

如果你还没有整理数据，下面是口径与最小示例，发我数据我就能算。

  • 口径建议

    • 薪资：用税前固定年薪（不含奖金更可比）；若含奖金请说明
    • 出场：统一赛季范围（如意甲+欧战+杯赛，或仅联赛），统计实际上场分钟
    • 公式：每分钟花费 = 年薪(欧) / 出场分钟；分钟为0时标记为N/A

  • CSV 模板

    name,salary_eur,minutes
Kenan Yildiz,1000000,1800
Player A,4000000,350
Player B,8000000,2200

  • 快速计算脚本（Python/pandas）

    import pandas as pd

df = pd.read_csv("juve_minutes_salaries.csv")
# 避免除以0
df["minutes"] = df["minutes"].astype("Int64")
df["eur_per_min"] = df.apply(
    lambda r: (r["salary_eur"]/r["minutes"]) if pd.notna(r["minutes"]) and r["minutes"]>0 else pd.NA, axis=1
)
df["eur_per_90"] = df["eur_per_min"] * 90
df_sorted = df.sort_values(["eur_per_min"], ascending=False, na_position="last")

# 美化输出
def fmt(x):
  return "N/A" if pd.isna(x) else f"{x:,.0f}"

out = df_sorted.assign(
    eur_per_min_str=df_sorted["eur_per_min"].map(fmt),
    eur_per_90_str=df_sorted["eur_per_90"].map(fmt),
    salary_str=df_sorted["salary_eur"].map(lambda x: f"{x:,.0f}"),
)[["name","salary_str","minutes","eur_per_min_str","eur_per_90_str"]]

print(out.to_string(index=False))

把你的赛季范围（例如2024-25，是否只算联赛）和数据源发我，我就按同样方法核算并给出完整榜单与图表。