要画“2025 中超联赛各队排名折线图”，我需要每轮排名的数据。你有每轮的球队名与排名吗？如果有，给我一个 CSV 或数据源链接，我直接帮你生成图。没有数据也行，我可以写脚本，等你把 CSV 放进去就能出图。

下面给一份通用脚本，读取每轮排名并输出折线图（名次从上到下 1→N），支持筛选球队或只画最终前 N 名。

使用说明

• CSV格式要求（列名不区分大小写，中文也可）:
  • 轮次: round / 轮次 / r
  • 球队: team / 球队 / club
  • 名次: rank / 名次 / position
• 示例列头: round,team,rank
• 运行:
  • 安装: pip install pandas matplotlib
  • 执行:
    • 画全部球队: python plot_csl_rankings.py --input standings_2025.csv
    • 只画前 6 名: python plot_csl_rankings.py --input standings_2025.csv --top 6
    • 指定球队: python plot_csl_rankings.py --input standings_2025.csv --teams 上海海港,山东泰山
    • 指定输出: --output csl_2025.png

代码（保存为 plot_csl_rankings.py）

#!/usr/bin/env python3
import argparse
import sys
from pathlib import Path

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cycler

# Try to show Chinese labels if fonts available
plt.rcParams["font.sans-serif"] = ["SimHei", "Noto Sans CJK SC", "Microsoft YaHei", "Arial"]
plt.rcParams["axes.unicode_minus"] = False
plt.style.use("seaborn-v0_8-whitegrid")

def normalize_columns(df: pd.DataFrame) -> pd.DataFrame:
    mapping = {}
    for c in df.columns:
        lc = str(c).strip().lower()
        if lc in ["round", "轮次", "r", "round_no", "matchday"]:
            mapping[c] = "round"
        elif lc in ["team", "球队", "club", "俱乐部"]:
            mapping[c] = "team"
        elif lc in ["rank", "名次", "position", "pos"]:
            mapping[c] = "rank"
        elif lc in ["season", "赛季", "year"]:
            mapping[c] = "season"
        else:
            mapping[c] = lc  # keep as-is for potential future use
    df = df.rename(columns=mapping)
    required = {"round", "team", "rank"}
    if not required.issubset(df.columns):
        missing = required - set(df.columns)
        raise ValueError(f"Missing required columns: {missing}. "
                         f"Have columns: {list(df.columns)}")
    return df

def pick_teams(df: pd.DataFrame, teams_arg: str | None, top: int | None) -> list[str]:
    all_teams = sorted(df["team"].unique())
    if teams_arg:
        wanted = [t.strip() for t in teams_arg.split(",") if t.strip()]
        # keep order as provided, validate existence
        missing = [t for t in wanted if t not in all_teams]
        if missing:
            raise ValueError(f"Teams not found in data: {missing}. Available: {all_teams}")
        return wanted
    if top:
        # determine final round, then pick top N by the final round rank
        last_round = int(df["round"].max())
        last_table = df[df["round"] == last_round].sort_values("rank")
        return last_table["team"].head(top).tolist()
    return all_teams

def main():
    ap = argparse.ArgumentParser(description="Plot CSL 2025 ranking trajectory by round.")
    ap.add_argument("--input", required=True, help="CSV with columns: round, team, rank")
    ap.add_argument("--output", default="csl_rankings_2025.png", help="Output image path (png/svg)")
    ap.add_argument("--teams", default=None, help="Comma-separated team names to include")
    ap.add_argument("--top", type=int, default=None, help="Only plot top N teams (by final round)")
    ap.add_argument("--title", default="2025中超联赛排名走势", help="Chart title")
    ap.add_argument("--width", type=float, default=12.0, help="Figure width inches")
    ap.add_argument("--height", type=float, default=7.0, help="Figure height inches")
    args = ap.parse_args()

    csv_path = Path(args.input)
    if not csv_path.exists():
        print(f"Input not found: {csv_path}", file=sys.stderr)
        sys.exit(1)

    df = pd.read_csv(csv_path)
    df = normalize_columns(df)

    # Coerce types
    df["round"] = pd.to_numeric(df["round"], errors="coerce")
    df["rank"] = pd.to_numeric(df["rank"], errors="coerce")
    df = df.dropna(subset=["round", "team", "rank"])
    df["round"] = df["round"].astype(int)
    df["rank"] = df["rank"].astype(int)

    # Sort and basic sanity
    df = df.sort_values(["round", "rank", "team"])
    rounds = sorted(df["round"].unique())
    if len(rounds) == 0:
        raise SystemExit("No rounds found in data.")
    max_round = rounds[-1]
    max_rank = int(df["rank"].max())

    teams = pick_teams(df, args.teams, args.top)

    # Pivot to have rounds as index, teams as columns
    pivot = df.pivot_table(index="round", columns="team", values="rank", aggfunc="min")
    # Ensure all rounds exist as index
    idx = pd.Index(range(int(min(rounds)), int(max_round) + 1), name="round")
    pivot = pivot.reindex(idx)

    # Figure
    fig, ax = plt.subplots(figsize=(args.width, args.height))

    # Color cycle
    colors = plt.cm.tab20.colors + plt.cm.tab20b.colors + plt.cm.tab20c.colors
    ax.set_prop_cycle(cycler(color=colors))

    # Plot each selected team
    plotted = []
    for team in teams:
        if team not in pivot.columns:
            continue
        y = pivot[team]
        # If there are missing ranks for some rounds, leave gaps
        ax.plot(pivot.index, y, marker="o", linewidth=2, markersize=3, label=team)
        plotted.append(team)

    if not plotted:
        raise SystemExit("No selected teams were found in the data to plot.")

    # Rank axis: 1 at top
    ax.invert_yaxis()
    ax.set_ylim(max_rank + 0.5, 0.5)
    ax.set_yticks(range(1, max_rank + 1))
    ax.set_xticks(pivot.index)
    ax.set_xlabel("轮次")
    ax.set_ylabel("排名")
    ax.set_title(args.title)

    # Move team labels to the right of last round
    x_right = max_round + 0.5
    for line, team in zip(ax.get_lines(), plotted):
        y_last = line.get_ydata()[-1]
        if pd.isna(y_last):
            # find last valid
            valid = pd.Series(line.get_ydata()).dropna()
            if not valid.empty:
                y_last = valid.iloc[-1]
            else:
                continue
        ax.text(x_right, y_last, team, va="center", fontsize=9)

    # Expand x limit to make space for labels
    ax.set_xlim(pivot.index.min() - 0.2, max_round + 2.0)

    # Tidy
    ax.grid(True, axis="both", linestyle="--", alpha=0.3)
    for spine in ["top", "right"]:
        ax.spines[spine].set_visible(False)

    # Legend optional (can be crowded). Comment out if using right-side labels.
    # ax.legend(ncol=2, fontsize=8)

    fig.tight_layout()
    out_path = Path(args.output)
    fig.savefig(out_path, dpi=200)
    print(f"Saved: {out_path.resolve()}")

if __name__ == "__main__":
    main()

如果你把一个示例 CSV（含每轮排名）发给我，我可以直接跑脚本并把成品图发给你。或者如果你没有数据，我可以尝试自动抓取 2025 赛季每轮积分榜并生成图，要不要我来抓取并出图？