04. 常用内置函数

# 教学示例数据：公司名称仅用于演示序列长度，不构成2018年排名依据
stock = ['中信证券', '国泰君安', '海通证券', '华泰证券', '广发证券',
         '招商证券', '申万宏源', '国信证券', '中信建投', '中国银河']

# len()函数返回列表的元素个数
num_companies = len(stock)
print(f'证券公司数量: {num_companies}')

# 检查数据是否完整
stock = ['中信证券', '国泰君安', '海通证券', '华泰证券', '广发证券',
         '招商证券', '申万宏源', '国信证券', '中信建投', '中国银河']

expected_count = 10  # 期望有10家公司

if len(stock) == expected_count:
    print('数据完整')
else:
    print(f'数据缺失, 期望{expected_count}家, 实际{len(stock)}家')

# 教学示例数据：无来源固定数值仅用于演示max，不构成2019Q1市场事实
stock = ['中信证券', '国泰君安', '海通证券', '华泰证券', '广发证券',
         '招商证券', '申万宏源', '国信证券', '中信建投', '中国银河']
return_Q1 = [0.547783, 0.315274, 0.594318, 0.383333, 0.275237,
             0.307463, 0.356265, 0.617682, 1.93341, 0.734604]

# 找出最大涨幅
return_max = max(return_Q1)
best_index = return_Q1.index(return_max)
best_company = stock[best_index]

print(f'最大涨幅: {return_max:.2%}')
print(f'表现最佳: {best_company}')

return_Q1 = [0.547783, 0.315274, 0.594318, 0.383333, 0.275237,
             0.307463, 0.356265, 0.617682, 1.93341, 0.734604]

# 找出最小涨幅
return_min = min(return_Q1)
print(f'最小涨幅: {return_min:.2%}')

# 风险预警: 检测是否存在亏损
if return_min < 0:
    print(f'警告: 存在亏损, 最大跌幅 {return_min:.2%}')
else:
    print('所有股票本季度均为正收益')

stocks_info = [
    {'name': '中信证券', 'profit': 98.76, 'pe': 15.2},
    {'name': '国泰君安', 'profit': 70.70, 'pe': 12.8},
    {'name': '海通证券', 'profit': 57.71, 'pe': 18.5}
]

# 按净利润找出最大
most_profitable = max(stocks_info, key=lambda x: x['profit'])
print(f'最盈利: {most_profitable["name"]}')

# 按PE比率找出最小(估值最低)
cheapest = min(stocks_info, key=lambda x: x['pe'])
print(f'最低PE: {cheapest["name"]}')

price = [24.78, 20.15, 14.03, 22.41, 16.17,
         17.52, 5.52, 13.54, 25.55, 11.83]

# 升序排序(默认)
price_sorted = sorted(price)
print(f'股价升序: {price_sorted}')

# 降序排序
price_desc = sorted(price, reverse=True)
print(f'股价降序: {price_desc}')

price = [24.78, 20.15, 14.03, 22.41, 16.17,
         17.52, 5.52, 13.54, 25.55, 11.83]

# 设置仅供语法演示的假设阈值
assumed_threshold = 18.0

# 列表推导式筛选
above_threshold = [p for p in price if p > assumed_threshold]
below_threshold = [p for p in price if p < assumed_threshold]

print(f'ABOVE_ASSUMED_THRESHOLD数量: {len(above_threshold)}, 数值: {sorted(above_threshold)}')
print(f'BELOW_ASSUMED_THRESHOLD数量: {len(below_threshold)}, 数值: {sorted(below_threshold)}')

# 从CSV读取的数据通常是字符串类型
price_str = '1850.50'
volume_str = '2500000'

# 转换为数值类型后才能进行计算
price = float(price_str)
volume = int(volume_str)

# 计算成交额
turnover = price * volume
print(f'成交额: {turnover:,.0f} 元')

# 包含缺失值的原始数据
missing_values = ['', 'N/A', 'None', '1850.50']

for val in missing_values:
    if val not in ['', 'N/A', 'None']:
        price = float(val)
        print(f'有效价格: {price}')
    else:
        print(f'缺失值: "{val}"')

import statistics

returns = [0.05, 0.03, -0.02, 0.04, 0.06, 0.02, -0.01, 0.07]

mean_val = statistics.mean(returns)
median_val = statistics.median(returns)
stdev_val = statistics.stdev(returns)

print(f'算术平均: {mean_val:.4f}')
print(f'中位数:   {median_val:.4f}')
print(f'标准差:   {stdev_val:.4f}')
print(f'方差:     {statistics.variance(returns):.4f}')

import statistics

returns = [0.05, 0.03, -0.02, 0.04, 0.06, 0.02, -0.01, 0.07]
risk_free_rate = 0.02  # 无风险利率

excess_return = statistics.mean(returns) - risk_free_rate
sharpe_ratio = excess_return / statistics.stdev(returns)
print(f'夏普比率: {sharpe_ratio:.4f}')

# 批量将字符串价格转换为浮点数
price_strings = ['24.78', '20.15', '14.03', '22.41', '16.17']
price_floats = list(map(float, price_strings))
print(f'转换结果: {price_floats}')

prices_old = [100, 105, 98, 102, 110]
prices_new = [105, 110, 95, 108, 115]

# 定义涨跌幅计算函数
calc_return = lambda old, new: (new - old) / old

# map 可接受多个可迭代对象
returns = list(map(calc_return, prices_old, prices_new))
print(f'涨跌幅: {[f"{r:.2%}" for r in returns]}')

all_returns = [0.05, -0.02, 0.03, -0.01, 0.04, -0.03, 0.06]

# 筛选正收益
positive_returns = list(filter(lambda x: x > 0, all_returns))
print(f'正收益: {[f"{r:.2%}" for r in positive_returns]}')

# 筛选高收益(>3%)
high_returns = list(filter(lambda x: x > 0.03, all_returns))
print(f'高收益: {[f"{r:.2%}" for r in high_returns]}')

stocks = [
    {'name': 'A', 'return': 0.05, 'risk': 0.10},
    {'name': 'B', 'return': 0.08, 'risk': 0.15},
    {'name': 'C', 'return': 0.06, 'risk': 0.08}
]

# 筛选条件: 收益 > 5% 且 风险 < 10%
filtered = list(filter(
    lambda s: s['return'] > 0.05 and s['risk'] < 0.10,
    stocks
))
print(f'符合条件的股票: {[s["name"] for s in filtered]}')

names = ['中信证券', '国泰君安', '海通证券']
prices = [24.78, 20.15, 14.03]
volumes = [2500000, 1800000, 3200000]

print('股票数据:')
for name, price, volume in zip(names, prices, volumes):
    turnover = price * volume
    print(f'  {name}: 价格={price:.2f}, 成交量={volume:,}, 成交额={turnover:,.0f}')

companies = ['公司甲', '公司乙', '公司丙', '公司丁', '公司戊']  # 使用中性名称避免把无来源数值冒充事实
profits = [98.76, 70.70, 57.71, 51.61, 46.32]
prices = [24.78, 20.15, 14.03, 22.41, 16.17]

# 基本统计
print('=' * 50)
print('2018年净利润排名前5位证券公司分析')
print('=' * 50)
print(f'  净利润总额: {sum(profits):.2f} 亿元')
print(f'  平均净利润: {sum(profits)/len(companies):.2f} 亿元')
print(f'  最高净利润: {max(profits):.2f} 亿元')
print(f'  最低净利润: {min(profits):.2f} 亿元')