data.index = range(1960, 2020, 10)

As an AI language model, I cannot determine the context of this code snippet. However, based on the syntax, it appears to be assigning a new index to a data object that spans 1960 to 2010 with a step size of 10.

相关问题

约瑟夫环改错class Node: def __init__(self,data): self.data=data self.next=Noneclass linklist: def __init__(self): self.head=None self.data=None def isEmpty(self): if self.head: return False else: return True def length(self): if self.isEmpty(): return 0 else: t = self.head n = 1 while t.next: if t.next == self.head: break t = t.next n = n + 1 return n def addhead(self,data): node = Node(data) if self.isEmpty(): self.head = node self.tail = self.head else: node.next = self.head self.head = node self.tail.next = self.head def addtail(self,data): node=Node(data) if self.isEmpty(): self.addhead(data) else: t=self.head n=1 l=self.length() while n<l: n=n+1 t=t.next t.next=node node.next=self.head self.tail=node def delete(self,index): if self.isEmpty(): print("The linked list is empty") else: t = self.head l = self.length() if index == 0: self.head = t.next self.tail.next = self.head elif index == l - 1: n = 1 while n < l - 1: t = t.next n = n + 1 t.next = self.head self.tail = t elif index > l - 1: print("Out of range") elif index < 0: print("Wrong operation") else: n = 1 while n < index - 1: t = t.next n = n + 1 a = t.next.next t.next = a def insert(self,data,index): l = self.length() if index == 0 or self.isEmpty(): self.addhead(data) elif index >= l: self.addtail(data) else: node = Node(data) t = self.head n = 1 while n < index - 1: t = t.next n = n + 1 a = t.next t.next = node node.next = a def search(self,a): t=self.head for i in range(a): t=t.next return t.data def form(self,datalist): self.addhead(datalist[0]) for i in range(1,len(datalist)): self.addtail(datalist[i]) t = self.head while t.next != self.head: t = t.nextn,p=map(int,input().split(' '))data=[]p=p-1for i in range(1,n+1): data.append(i)print(data)datalist=[]for i in range(len(data)): datalist.append(int(data[i]))link=linklist()link.form(datalist)a=pb=[]while link.length()>0: b.append(link.search(a)) link.delete(a) a=a+p while a>=link.length(): a=a-link.length()print(b)

改错如下：

class Node:
    def __init__(self, data):
        self.data = data
        self.next = None

class LinkList:
    def __init__(self):
        self.head = None
        self.tail = None
    
    def isEmpty(self):
        if self.head:
            return False
        else:
            return True
    
    def length(self):
        if self.isEmpty():
            return 0
        else:
            t = self.head
            n = 1
            while t.next:
                if t.next == self.head:
                    break
                t = t.next
                n += 1
            return n
    
    def addhead(self, data):
        node = Node(data)
        if self.isEmpty():
            self.head = node
            self.tail = self.head
        else:
            node.next = self.head
            self.head = node
            self.tail.next = self.head
    
    def addtail(self, data):
        node = Node(data)
        if self.isEmpty():
            self.addhead(data)
        else:
            t = self.head
            n = 1
            l = self.length()
            while n < l:
                n += 1
                t = t.next
            t.next = node
            node.next = self.head
            self.tail = node
    
    def delete(self, index):
        if self.isEmpty():
            print("The linked list is empty")
        else:
            t = self.head
            l = self.length()
            if index == 0:
                self.head = t.next
                self.tail.next = self.head
            elif index == l - 1:
                n = 1
                while n < l - 1:
                    t = t.next
                    n += 1
                t.next = self.head
                self.tail = t
            elif index > l - 1:
                print("Out of range")
            elif index < 0:
                print("Wrong operation")
            else:
                n = 1
                while n < index:
                    t = t.next
                    n += 1
                a = t.next.next
                t.next = a
    
    def insert(self, data, index):
        l = self.length()
        if index == 0 or self.isEmpty():
            self.addhead(data)
        elif index >= l:
            self.addtail(data)
        else:
            node = Node(data)
            t = self.head
            n = 1
            while n < index:
                t = t.next
                n += 1
            a = t.next
            t.next = node
            node.next = a
    
    def search(self, a):
        t = self.head
        for i in range(a):
            t = t.next
        return t.data
    
    def form(self, datalist):
        self.addhead(datalist[0])
        for i in range(1, len(datalist)):
            self.addtail(datalist[i])
        t = self.head
        while t.next != self.head:
            t = t.next

n, p = map(int, input().split(' '))
data = [i for i in range(1, n+1)]
print(data)

datalist = []
for i in range(len(data)):
    datalist.append(data[i])

link = LinkList()
link.form(datalist)

a = p-1
b = []
while link.length() > 0:
    b.append(link.search(a))
    link.delete(a)
    a += p-1
    while a >= link.length():
        a -= link.length()

print(b)

hbin = 10 for i in tqdm(data['WindNumber'].unique()): col = 'WindSpeed' cond = (data.WindNumber==i) & (data.label == 0) temp_df = data[cond] h_bins = pd.cut(temp_df.Power, np.arange(-1000, 3000, hbin)) temp_df['hbins'] = h_bins groups = [] for index,temp in temp_df.groupby("hbins"): if temp.shape[0]==0: continue iqr = temp[col].quantile(0.75) - temp[col].quantile(0.25) t1 = temp[col].quantile(0.25) - 1.5 * iqr t2 = temp[col].quantile(0.75) + 1.5 * iqr temp = temp[((temp[col]<t1) | (temp[col]>t2))] groups.append(temp) groups = pd.concat(groups).reset_index(drop = True) cond = (data.WindNumber==i) & (data.Time.isin(groups.Time)) data.loc[cond,'label'] = 3 cond = (data.WindNumber==1) vis_result_2D(data[cond],data[cond]['label'])详细解释

这段代码是对数据集中的每个风机进行处理，首先根据风机编号筛选数据，然后根据功率值将数据分为若干个区间，每个区间内的数据按照风速进行离群值检测，如果检测出离群值，则将对应的数据标记为异常（标签为3），最后将标记为异常的数据可视化展示。

具体解释如下：

1. `hbin = 10`：将功率值分为10个区间，每个区间的宽度为1000W。
2. `for i in tqdm(data['WindNumber'].unique()):`：循环遍历数据集中的所有风机。
3. `cond = (data.WindNumber==i) & (data.label == 0)`：根据风机编号和标签筛选数据，其中标签为0表示数据未被标记为异常。
4. `temp_df = data[cond]`：将筛选后的数据赋值给临时变量temp_df。
5. `h_bins = pd.cut(temp_df.Power, np.arange(-1000, 3000, hbin))`：根据功率值将数据分为若干个区间，并将区间标签赋值给 h_bins 变量。
6. `temp_df['hbins'] = h_bins`：将区间标签添加到数据集中。
7. `groups = []`：创建一个空列表，用于存储检测出的离群值数据。
8. `for index,temp in temp_df.groupby("hbins"):`：根据区间标签将数据分组，循环遍历每个区间。
9. `if temp.shape[0]==0:`：如果区间内没有数据，则跳过本次循环。
10. `iqr = temp[col].quantile(0.75) - temp[col].quantile(0.25)`：计算数据的四分位距。
11. `t1 = temp[col].quantile(0.25) - 1.5 * iqr`：计算下界阈值。
12. `t2 = temp[col].quantile(0.75) + 1.5 * iqr`：计算上界阈值。
13. `temp = temp[((temp[col]<t1) | (temp[col]>t2))]`：筛选出离群值数据，即风速小于下界阈值或大于上界阈值的数据。
14. `groups.append(temp)`：将检测出的离群值数据添加到 groups 列表中。
15. `groups = pd.concat(groups).reset_index(drop = True)`：将 groups 列表中的数据合并为一个数据集。
16. `cond = (data.WindNumber==i) & (data.Time.isin(groups.Time))`：根据风机编号和时间标记异常数据。
17. `data.loc[cond,'label'] = 3`：将标记为异常的数据标签设置为3。
18. `cond = (data.WindNumber==1)`：根据风机编号筛选数据。
19. `vis_result_2D(data[cond],data[cond]['label'])`：将标记后的数据可视化展示，其中 vis_result_2D 是一个自定义的函数。