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 修改0 is not in range

这段代码出现了错误，可能是由于数据的长度不足以进行指定窗口大小的滚动计算，导致出现了“修改0 is not in range”的错误提示。

你可以添加一些判断，确保数据的长度足够进行计算，例如：

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

这样就可以避免出现“修改0 is not in range”的错误提示了。

相关问题

df = pd.read_csv('stock_data.csv') if len(df) >= 4: df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 if len(df) >= 3: df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1增加如果窗口大小小于或等于零则不操作

可以在代码中增加对窗口大小的判断，如果窗口大小小于等于 0，就不进行滑动窗口计算。修改后的代码如下：

df = pd.read_csv('stock_data.csv') 
if len(df) >= 4 and window_size > 0: 
    df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 
if len(df) >= 3 and window_size > 0: 
    df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1

其中，window_size 为窗口大小，可以在代码中对其进行设置。如果窗口大小小于等于 0，就不会进行滑动窗口计算。

df = pd.read_csv('stock_data.csv') if len(df) >= 4 and window_size > 0: df['four_days_increase'] = df['close'].rolling(window=4).apply(lambda x: all(x[i] < x[i+1] for i in range(3))) * 1 if len(df) >= 3 and window_size > 0: df['three_days_decrease'] = df['close'].rolling(window=3).apply(lambda x: all(x[i] > x[i+1] for i in range(2))) * 1增加定义window_size

可以在代码中定义 window_size 变量，如下：

window_size = 4  # 定义窗口大小
df = pd.read_csv('stock_data.csv') 
if len(df) >= window_size and window_size > 0: 
    df['four_days_increase'] = df['close'].rolling(window=window_size).apply(lambda x: all(x[i] < x[i+1] for i in range(window_size-1))) * 1 
if len(df) >= window_size-1 and window_size > 0: 
    df['three_days_decrease'] = df['close'].rolling(window=window_size-1).apply(lambda x: all(x[i] > x[i+1] for i in range(window_size-2))) * 1

其中，window_size 的值可以根据需要进行调整。如果窗口大小小于等于 0，就不会进行滑动窗口计算。