将out.loc[:, '涨跌'] = out.apply(lambda row: "涨" if row[max_col[3]] > row[min_col[3]] else "跌" if row[max_col[3]] < row[min_col[3]] else "\\", axis=1)改用def函数写

可以将lambda函数改写为一个名为`get_zhang_die`的函数，具体代码如下：

def get_zhang_die(row, max_col, min_col):
    if row[max_col[3]] > row[min_col[3]]:
        return "涨"
    elif row[max_col[3]] < row[min_col[3]]:
        return "跌"
    else:
        return "\\"

out.loc[:, '涨跌'] = out.apply(get_zhang_die, args=(max_col, min_col), axis=1)

这里使用了`args`参数将`max_col`和`min_col`传递给`get_zhang_die`函数。

相关问题

解释一下每行代码 ori_data = pd.read_csv('F:/patient.csv') ori_data.loc[:, 'id'] = ori_data.loc[:, 'patient_id'].apply(lambda x: x[:7]) index = set(ori_data['id']) column = list(ori_data.columns) del column[0] del column[-1] average = pd.DataFrame(index=index, columns=column) for k in column: result = ori_data.groupby('id')[k].mean() average.loc[:, k] = result column = list(average.columns) data = average.loc[:, column[0]:column[-3]] # 自变量 target = average.loc[:, ['TIMEsurvival', 'EVENTdeath']]，

Sure, 这段代码的作用是对'F:/patient.csv'中的数据进行处理，提取出自变量和目标变量。下面分行解释每段代码的作用：

1. `ori_data = pd.read_csv('F:/patient.csv')`：从磁盘中读取'F:/patient.csv'文件中的数据，存储为一个名为ori_data的DataFrame。

2. `ori_data.loc[:, 'id'] = ori_data.loc[:, 'patient_id'].apply(lambda x: x[:7])`：在原始数据DataFrame中增加一列'id'，其中每个元素为'patient_id'的前7个字符。这一步目的是为了将所有ID转换为统一的格式，方便后面的计算。

3. `index = set(ori_data['id'])`：获取所有唯一的id值，存储为一个集合。

4. `column = list(ori_data.columns)`：获取所有列名，存储为一个列表。

5. `del column[0]`：删除第一列'patient_id'，因为已经用'id'列代替。

6. `del column[-1]`：删除最后一列，即'EVENTdeath'，因为这一列是目标变量。

7. `average = pd.DataFrame(index=index, columns=column)`：创建一个名为'average'的DataFrame，其中行是每个病人的ID，列是每个特征的名称。

8. `for k in column: result = ori_data.groupby('id')[k].mean() average.loc[:, k] = result`：对于每一列特征，计算每个病人的平均值，并将这些平均值存储在'average' DataFrame中。

9. `column = list(average.columns)`：获取'average' DataFrame中所有列的名称，存储为一个列表。

10. `data = average.loc[:, column[0]:column[-3]]`：从'average' DataFrame中提取自变量，即所有特征列，除了最后两列'TIMEsurvival'和'EVENTdeath'。

11. `target = average.loc[:, ['TIMEsurvival', 'EVENTdeath']]`：从'average' DataFrame中提取目标变量，即最后两列'TIMEsurvival'和'EVENTdeath'。

import pandas as pd df = pd.read_csv('stock_data.csv') df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1 capital = 1000000 max_stock_per_day = 10 max_stock_value = 100000 start_date = '2020-01-01' end_date = '2023-01-01' df = df[(df['date'] >= start_date) & (df['date'] < end_date)] df = df.reset_index(drop=True) hold_stock = [] for i, row in df.iterrows(): if len(hold_stock) > 0: sell_stock = [] for stock in hold_stock: if i - stock['buy_day'] >= 3: capital += stock['buy_price'] * stock['buy_qty'] * (1 - 0.002) sell_stock.append(stock) hold_stock = [stock for stock in hold_stock if stock not in sell_stock] df_today = df.loc[i:i+3] if i + 3 >= len(df): break if all(df_today['four_days_increase']) and all(df_today['three_days_decrease'].iloc[1:]): available_capital = capital available_stock = max_stock_per_day available_value = max_stock_value for j, stock_row in df_today.iterrows(): if available_capital > 0 and available_stock > 0 and available_value > 0: buy_qty = min(int(available_capital / (stock_row['close'] * 1.002)), available_stock, int(available_value / (stock_row['close'] * 1.002))) if buy_qty > 0: hold_stock.append({'buy_day': i, 'buy_price': stock_row['close'], 'buy_qty': buy_qty}) available_capital -= stock_row['close'] * buy_qty * 1.002 available_stock -= 1 available_value -= stock_row['close'] * buy_qty * 1.002 print('Final capital:', capital)让上述代码在jupyter里不报错

这段代码的错误可能是由于缺少依赖库或文件。请确保您已经安装了pandas库并且已经将stock_data.csv文件放在正确的位置。另外，您可能需要在Jupyter Notebook中使用`%matplotlib inline`来确保图表正确显示。以下是可能的修改：

import pandas as pd 
%matplotlib inline

df = pd.read_csv('stock_data.csv') 
df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 
df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1 

capital = 1000000 
max_stock_per_day = 10 
max_stock_value = 100000 
start_date = '2020-01-01' 
end_date = '2023-01-01' 

df = df[(df['date'] >= start_date) & (df['date'] < end_date)] 
df = df.reset_index(drop=True) 

hold_stock = [] 

for i, row in df.iterrows(): 
    if len(hold_stock) > 0: 
        sell_stock = [] 
        for stock in hold_stock: 
            if i - stock['buy_day'] >= 3: 
                capital += stock['buy_price'] * stock['buy_qty'] * (1 - 0.002) 
                sell_stock.append(stock) 
        hold_stock = [stock for stock in hold_stock if stock not in sell_stock] 

    df_today = df.loc[i:i+3] 

    if i + 3 >= len(df): 
        break 

    if all(df_today['four_days_increase']) and all(df_today['three_days_decrease'].iloc[1:]): 
        available_capital = capital 
        available_stock = max_stock_per_day 
        available_value = max_stock_value 
        for j, stock_row in df_today.iterrows(): 
            if available_capital > 0 and available_stock > 0 and available_value > 0: 
                buy_qty = min(int(available_capital / (stock_row['close'] * 1.002)), available_stock, int(available_value / (stock_row['close'] * 1.002))) 
                if buy_qty > 0: 
                    hold_stock.append({'buy_day': i, 'buy_price': stock_row['close'], 'buy_qty': buy_qty}) 
                    available_capital -= stock_row['close'] * buy_qty * 1.002 
                    available_stock -= 1 
                    available_value -= stock_row['close'] * buy_qty * 1.002 

print('Final capital:', capital)