pd_data=data[data['country'].isin(('China','United States','Russia','United Kingdom','France'))]

这行代码的作用是从名为`data`的DataFrame对象中选取`country`列包含以下五个国家名称中任意一个的行，并将选取的结果存储到名为`pd_data`的新的DataFrame对象中：China、United States、Russia、United Kingdom和France。

在这行代码中，`data['country']`选取了`data`中的`country`列，生成一个Series对象；`.isin(('China','United States','Russia','United Kingdom','France'))`方法判断Series对象中的每个元素是否属于给定的五个国家名称中的一个，返回一个布尔类型的Series对象；最后，将这个布尔类型的Series对象传递给`data`对象，实现了行的筛选。

相关问题

请仿照这一段代码，if self.type == "csv": try: self.pd_data = pd.read_csv(self.file_path) except: raise ValueError('csv路径错误') elif self.type == "txt": self.pd_data = pd.DataFrame( pd.read_table(self.file_path, sep=' ', header=None, engine='python', names=self.names)) elif self.type == "pmlb": try: self.pd_data = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=False) self.x, self.y = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=True) except: raise ValueError('pmlb数据集名称错误') elif self.type == "numpy": try: self.pd_data = pd.DataFrame(self.file_path) self.x, self.y = pd.DataFrame(self.file_path[:, :-1]), pd.DataFrame(self.file_path[:, -1]) except: raise ValueError('numpy数据集设置失败')

这是一个根据文件类型读取数据的代码示例。以下是根据您提供的代码进行修改后的示例：

import pandas as pd

class DataReader:
    def __init__(self, file_path, file_type, names=None):
        self.file_path = file_path
        self.file_type = file_type
        self.names = names
        self.pd_data = None
        self.x = None
        self.y = None

    def read_data(self):
        if self.file_type == "csv":
            try:
                self.pd_data = pd.read_csv(self.file_path)
            except:
                raise ValueError('csv路径错误')
        elif self.file_type == "txt":
            self.pd_data = pd.DataFrame(
                pd.read_table(self.file_path, sep='\t', header=None, engine='python', names=self.names))
        elif self.file_type == "pmlb":
            try:
                self.pd_data = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=False)
                self.x, self.y = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=True)
            except:
                raise ValueError('pmlb数据集名称错误')
        elif self.file_type == "numpy":
            try:
                self.pd_data = pd.DataFrame(self.file_path)
                self.x, self.y = pd.DataFrame(self.file_path[:, :-1]), pd.DataFrame(self.file_path[:, -1])
            except:
                raise ValueError('numpy数据集设置失败')

在上面的示例中，我们创建了一个名为DataReader的类，它接受文件路径和文件类型作为参数，并在read_data方法中根据文件类型读取数据。根据不同的文件类型，我们使用相应的pandas函数来读取数据。请注意，对于txt文件，我们使用了制表符('\t')作为分隔符。对于pmlb和numpy文件，我们根据需要设置了self.x和self.y变量。如果读取过程中出现错误，将引发ValueError异常。

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

修改后的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score

data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')

train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)

train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)

start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0

train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)

请注意，我做了以下修改：

1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。

请根据你的实际需求进一步调整代码。