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。

相关问题

import pandas as pd 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修改此代码，避免出现0 is not in range

如果你仍然遇到 `0 is not in range` 的错误，可以尝试以下两种方法：

1. 检查数据中是否有缺失值。如果数据中有缺失值，可能会导致rolling函数中的窗口大小小于指定的窗口大小，从而导致错误。你可以使用 `df.isnull().sum()` 来检查数据中是否有缺失值。

2. 将lambda函数中的 `range(window_size-1)` 和 `range(window_size-2)` 改为 `range(window_size)` 和 `range(window_size-1)`，这样可以确保范围中包含数字0。

修改后的代码如下：

import pandas as pd 

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 

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-1))) * 1

注意，这里仅仅是对错误的解决方案，如果是数据问题，需要对数据进行处理。

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。