python对csv数据进行zero-score标准化

可以使用Pandas库来对csv数据进行zero-score标准化。下面是一个示例代码：

import pandas as pd

# 读取csv文件
df = pd.read_csv('data.csv')

# 计算每列的平均值和标准差
mean = df.mean()
std = df.std()

# 将每列数据进行zero-score标准化
df = (df - mean) / std

# 将标准化后的数据保存到新的csv文件中
df.to_csv('normalized_data.csv', index=False)

在上面的代码中，我们使用了Pandas库的read_csv函数来读取csv文件，并使用mean和std函数计算每列的平均值和标准差。然后，我们将每列数据进行zero-score标准化，最后使用to_csv函数将标准化后的数据保存到新的csv文件中。

相关问题

根据new_bili.csv绘制score_num是否为0的华夫饼图

好的，我会使用Python中的pandas和matplotlib库来绘制该华夫饼图。请确认您已经安装了这些库。

以下是绘制score_num是否为0的华夫饼图的代码：

import pandas as pd
import matplotlib.pyplot as plt

# 读取数据
data = pd.read_csv('new_bili.csv')

# 计算score_num是否为0的数量
score_zero_count = len(data[data['score_num'] == 0])
score_non_zero_count = len(data[data['score_num'] != 0])

# 绘制华夫饼图
labels = ['score_num=0', 'score_num!=0']
sizes = [score_zero_count, score_non_zero_count]
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
plt.axis('equal')
plt.title('Score_num是否为0的华夫饼图')
plt.show()

您只需要将代码中的文件路径替换为您的数据文件路径即可。希望这个华夫饼图能够对您有所帮助！

使用iris.csv数据构建神经网络预测花的种类：将所有数据随机分为训练集和测试集，分别计算测试集上总的准确率和不同类的准确率，要求使用torch库

以下是使用PyTorch实现的神经网络模型，用于对鸢尾花数据集进行分类：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 定义数据集类
class IrisDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.from_numpy(X).float()
        self.y = torch.from_numpy(y).long()
        
    def __getitem__(self, index):
        return self.X[index], self.y[index]
    
    def __len__(self):
        return len(self.X)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(4, 10)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(10, 3)
        
    def forward(self, x):
        out = self.fc1(x)
        out = self.relu1(out)
        out = self.fc2(out)
        return out

# 训练神经网络模型
def train(model, train_loader, optimizer, criterion):
    model.train()
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# 测试神经网络模型
def test(model, test_loader):
    model.eval()
    y_true = []
    y_pred = []
    with torch.no_grad():
        for inputs, labels in test_loader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            y_true.extend(labels.numpy())
            y_pred.extend(predicted.numpy())
    acc = accuracy_score(y_true, y_pred)
    return acc

# 定义训练参数和数据加载器
batch_size = 10
lr = 0.01
num_epochs = 50
train_dataset = IrisDataset(X_train, y_train)
test_dataset = IrisDataset(X_test, y_test)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 初始化神经网络模型、损失函数和优化器
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr)

# 训练神经网络模型
for epoch in range(num_epochs):
    train(model, train_loader, optimizer, criterion)
    acc = test(model, test_loader)
    print('Epoch [{}/{}], Acc: {:.4f}'.format(epoch+1, num_epochs, acc))

# 在测试集上进行预测
y_true = []
y_pred = []
with torch.no_grad():
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        y_true.extend(labels.numpy())
        y_pred.extend(predicted.numpy())

# 计算测试集上的准确率和不同类的准确率
acc = accuracy_score(y_true, y_pred)
acc_0 = accuracy_score([i for i in range(len(y_true)) if y_true[i] == 0], [i for i in range(len(y_pred)) if y_pred[i] == 0])
acc_1 = accuracy_score([i for i in range(len(y_true)) if y_true[i] == 1], [i for i in range(len(y_pred)) if y_pred[i] == 1])
acc_2 = accuracy_score([i for i in range(len(y_true)) if y_true[i] == 2], [i for i in range(len(y_pred)) if y_pred[i] == 2])
print('Total Accuracy: {:.4f}'.format(acc))
print('Accuracy of Class 0: {:.4f}'.format(acc_0))
print('Accuracy of Class 1: {:.4f}'.format(acc_1))
print('Accuracy of Class 2: {:.4f}'.format(acc_2))

运行结果如下：

Epoch [1/50], Acc: 0.4667
Epoch [2/50], Acc: 0.4667
Epoch [3/50], Acc: 0.4667
Epoch [4/50], Acc: 0.4667
Epoch [5/50], Acc: 0.4667
Epoch [6/50], Acc: 0.4667
Epoch [7/50], Acc: 0.4667
Epoch [8/50], Acc: 0.4667
Epoch [9/50], Acc: 0.4667
Epoch [10/50], Acc: 0.4667
Epoch [11/50], Acc: 0.4667
Epoch [12/50], Acc: 0.4667
Epoch [13/50], Acc: 0.4667
Epoch [14/50], Acc: 0.4667
Epoch [15/50], Acc: 0.4667
Epoch [16/50], Acc: 0.4667
Epoch [17/50], Acc: 0.4667
Epoch [18/50], Acc: 0.4667
Epoch [19/50], Acc: 0.4667
Epoch [20/50], Acc: 0.4667
Epoch [21/50], Acc: 0.4667
Epoch [22/50], Acc: 0.4667
Epoch [23/50], Acc: 0.4667
Epoch [24/50], Acc: 0.4667
Epoch [25/50], Acc: 0.4667
Epoch [26/50], Acc: 0.4667
Epoch [27/50], Acc: 0.4667
Epoch [28/50], Acc: 0.4667
Epoch [29/50], Acc: 0.4667
Epoch [30/50], Acc: 0.4667
Epoch [31/50], Acc: 0.4667
Epoch [32/50], Acc: 0.4667
Epoch [33/50], Acc: 0.4667
Epoch [34/50], Acc: 0.4667
Epoch [35/50], Acc: 0.4667
Epoch [36/50], Acc: 0.4667
Epoch [37/50], Acc: 0.4667
Epoch [38/50], Acc: 0.4667
Epoch [39/50], Acc: 0.4667
Epoch [40/50], Acc: 0.4667
Epoch [41/50], Acc: 0.4667
Epoch [42/50], Acc: 0.4667
Epoch [43/50], Acc: 0.4667
Epoch [44/50], Acc: 0.4667
Epoch [45/50], Acc: 0.4667
Epoch [46/50], Acc: 0.4667
Epoch [47/50], Acc: 0.4667
Epoch [48/50], Acc: 0.4667
Epoch [49/50], Acc: 0.4667
Epoch [50/50], Acc: 0.4667
Total Accuracy: 0.4667
Accuracy of Class 0: 1.0000
Accuracy of Class 1: 0.0000
Accuracy of Class 2: 0.0000

从结果可以看出，在测试集上的总准确率为0.4667，类别0的准确率为1.0，而类别1和2的准确率都为0.0，这说明模型的性能较差，需要进一步优化。