def run(self): df = self.cleanData(self._df) title_length_average = int(df['标题'].str.len().mean()) print('新闻标题的评价长度为:{}' . format(title_length_average)) counts = df['发布地点'].value_counts() for index, count in counts.items(): print('发布地点:{},出现次数:{}' . format(index, count)) loc = counts.index.tolist() cnt = counts.tolist()
时间: 2024-03-30 11:39:42 浏览: 11
这段代码定义了一个 `run` 方法,用于执行数据处理的流程。该方法不接受任何参数。
该方法首先调用 `cleanData` 方法,对数据进行清洗,然后使用 `mean` 函数计算新闻标题的平均长度,并将结果打印出来。接着,对发布地点进行计数,并使用 `items` 函数获取地点和出现次数的列表,将其打印出来。最后,将地点和出现次数分别存入 `loc` 和 `cnt` 两个列表中。
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:
```python
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)
```
优化这段代码class dataset(Dataset.Dataset): def __init__(self, Data, transform=None): self.Data = Data self.transform = transform def __len__(self): return len(self.Data) def __getitem__(self, idx): data = self.Data[idx] img_transformed = self.transform(data) return img_transformed
A:这段代码的性能已经不错,但有一些小的优化可能会提高其效率:
1. 使用torchvision.transforms.Compose()将所有变换组合,以避免在每个样本中重复调用Transform;
2. 在获取项目时,将数据转换为Tensor,而不是在每个Transform中进行转换。这将减少在每个Transform上进行的操作;
3. 尝试缓存已转换的数据,以避免重复调用Transform。
修改后的代码示例:
```
from torchvision import transforms
class CustomDataset(Dataset):
def __init__(self, data, transform=None):
self.data = data
self.transform = transform
# 将所有变换组合为一个Compose Transforms
if transform is not None:
self.transform = transforms.Compose(transform)
else:
self.transform = transforms.Compose([transforms.ToTensor()])
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
# 缓存已转换的数据 (optional)
if idx in self.cache:
return self.cache[idx]
# 获取示例数据并转换为Tensor
img, label = self.data[idx]
img = transforms.ToTensor()(img)
# 应用变换
if self.transform is not None:
img = self.transform(img)
# 将转换后的数据缓存 (optional)
self.cache[idx] = img
return img, label
```
注意:这只是一些可能提高效率的建议,并不一定适用于所有数据集和变换。请根据您特定的应用程序进行适当的调整和测试。