cols = ['date1', 'date2', 'created_at']
df[cols] = df[cols].apply(pd.to_datetime, errors='coerce')

# 找出所有 object 类型且非空的列，尝试转 datetime
date_cols = df.select_dtypes(include=['object']).columns.tolist()
for col in date_cols:
    # 粗略判断：前 10 个非空值是否像日期（可选，跳过也行）
    sample_vals = df[col].dropna().head(10).astype(str)
    if sample_vals.str.match(r'^\d{4}-\d{1,2}-\d{1,2}').any():
        df[col] = pd.to_datetime(df[col], errors='coerce')

def safe_to_datetime(df, columns, errors='coerce'):
    failed_conversions = {}
    for col in columns:
        original_count = df[col].notna().sum()
        converted = pd.to_datetime(df[col], errors=errors)
        na_count = converted.isna().sum()
        if na_count > 0:
            failed_conversions[col] = na_count
        df[col] = converted
    if failed_conversions:
        print("⚠️ 以下列存在无法解析的日期值（已转为 NaT）：")
        for col, n in failed_conversions.items():
            print(f"  - {col}: {n} 个值")
    return df
使用
df = safe_to_datetime(df, ['order_date', 'ship_date', 'expire_time'])

df['log_time'] = pd.to_datetime(
    df['log_time'], 
    format='%Y-%m-%d %H:%M:%S', 
    errors='coerce',
    utc=True
)