FastF1でセッションのラップタイムを取得する方法【Python・コピペOK】

import fastf1
import os

# キャッシュフォルダを作成
os.makedirs('cache', exist_ok=True)

# キャッシュを有効化
fastf1.Cache.enable_cache('cache')

# セッション取得（年 / グランプリ / セッション）
session = fastf1.get_session(2025, 'Japan', 'FP2')

# データ読み込み
session.load()

import pandas as pd

# ラップ取得
laps = session.laps

# 有効ラップのみ
laps = laps[laps["IsAccurate"] & laps["LapTime"].notna()]

# 最速ラップ抽出
idx = laps.groupby("Driver")["LapTime"].idxmin()
best_laps = laps.loc[idx].copy()

# Vmax取得
def get_vmax(lap):
    try:
        return lap.get_car_data()["Speed"].max()
    except:
        return None

best_laps["Vmax"] = [get_vmax(lap) for _, lap in best_laps.iterrows()]

# 必要な列
df = best_laps[
    [
        "Driver",
        "Team",
        "LapTime",
        "Sector1Time",
        "Sector2Time",
        "Sector3Time",
        "Vmax",
        "Compound",
        "LapNumber",
    ]
].copy()

# ソート
df = df.sort_values("LapTime").reset_index(drop=True)
df.insert(0, "Pos", df.index + 1)

# ギャップ
best_time = df["LapTime"].iloc[0]
df["Gap"] = df["LapTime"] - best_time

# ラップ数
df["Laps"] = df["Driver"].map(session.laps.groupby("Driver").size())

# フォーマット関数
def format_laptime(t):
    if pd.isna(t):
        return ""
    total_sec = t.total_seconds()
    m = int(total_sec // 60)
    s = int(total_sec % 60)
    ms = int((total_sec - int(total_sec)) * 1000)
    return f"{m}:{s:02d}.{ms:03d}"

# 表示用
df["Time"] = df["LapTime"].map(format_laptime)
df["Gap"] = df["Gap"].map(lambda x: "" if x.total_seconds() == 0 else f"+{x.total_seconds():.3f}")
df["S1"] = df["Sector1Time"].map(format_laptime)
df["S2"] = df["Sector2Time"].map(format_laptime)
df["S3"] = df["Sector3Time"].map(format_laptime)
df["Vmax"] = df["Vmax"].round(1)

# カラム名変更
df = df.rename(columns={
    "Compound": "Tyre",
    "LapNumber": "Lap"
})

# 表示用テーブル
df = df[
    [
        "Pos",
        "Driver",
        "Team",
        "Time",
        "Gap",
        "S1",
        "S2",
        "S3",
        "Vmax",
        "Tyre",
        "Lap",
        "Laps",
    ]
]

# =========================
# トップの値をハイライト
# =========================

best_time = df["Time"].iloc[0]
best_s1 = df["S1"].min()
best_s2 = df["S2"].min()
best_s3 = df["S3"].min()
best_vmax = df["Vmax"].max()

def highlight(val, best):
    if val == best:
        return "background-color: #ffe599; font-weight: bold"
    return ""

styler = df.style

styler = styler.map(lambda v: highlight(v, best_time), subset=["Time"])
styler = styler.map(lambda v: highlight(v, best_s1), subset=["S1"])
styler = styler.map(lambda v: highlight(v, best_s2), subset=["S2"])
styler = styler.map(lambda v: highlight(v, best_s3), subset=["S3"])
styler = styler.map(lambda v: highlight(v, best_vmax), subset=["Vmax"])

styler