mean = np.mean(data) std = np.std(data) threshold = 3 * std data = data[abs(data - mean) < threshold]如何导入excel数据

可以使用pandas库中的read_excel函数来导入Excel数据。

例如，假设我们有一个名为data.xlsx的Excel文件，包含一个名为Sheet1的工作表，其中包含我们要处理的数据。我们可以使用以下代码来导入数据：

import pandas as pd

# 读取Excel文件
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

# 将数据转换为numpy数组
data = df.to_numpy()

# 使用上述代码处理数据
mean = np.mean(data)
std = np.std(data)
threshold = 3 * std
data = data[abs(data - mean) < threshold]

在这个例子中，我们首先使用pandas的read_excel函数读取Excel文件，并将数据存储在一个名为df的DataFrame对象中。然后，我们使用to_numpy函数将DataFrame转换为numpy数组。最后，我们使用上述代码处理数据。

相关问题

mean = np.mean(data) std = np.std(data) threshold = 3 * std data = data[abs(data - mean) < threshold]

This code calculates the mean and standard deviation of a dataset (stored in the variable 'data') using the NumPy library. It then sets a threshold value as 3 times the standard deviation. The code next selects only the values in the dataset that are within this threshold range of the mean, effectively removing any outliers. The updated dataset is then stored back into the 'data' variable.

使用C++ eigen库翻译以下python代码import pandas as pd import numpy as np import time import random def main(): eigen_list = [] data = [[1,2,4,7,6,3],[3,20,1,2,5,4],[2,0,1,5,8,6],[5,3,3,6,3,2],[6,0,5,2,19,3],[5,2,4,9,6,3]] g_csi_corr = np.cov(data, rowvar=True) #print(g_csi_corr) eigenvalue, featurevector = np.linalg.eigh(g_csi_corr) print("eigenvalue:",eigenvalue) eigen_list.append(max(eigenvalue)) #以下代码验证求解csi阈值 eigen_list.append(1.22) eigen_list.append(-54.21) eigen_list.append(8.44) eigen_list.append(-27.83) eigen_list.append(33.12) #eigen_list.append(40.29) print(eigen_list) eigen_a1 = np.array(eigen_list) num1 = len(eigen_list) eigen_a2 = eigen_a1.reshape((-1, num1)) eigen_a3 = np.std(eigen_a2, axis=0) eigen_a4 = eigen_a3.tolist() k = (0.016 - 0.014) / (max(eigen_a4) - min(eigen_a4)) eigen_a5 = [0.014 + k * (i - min(eigen_a4)) for i in eigen_a4] tri_threshold = np.mean(eigen_a5)

#include <iostream>
#include <Eigen/Dense>

using namespace Eigen;

int main()
{
std::vector<double> eigen_list;
MatrixXd data(6, 6);
data << 1, 2, 4, 7, 6, 3,
3, 20, 1, 2, 5, 4,
2, 0, 1, 5, 8, 6,
5, 3, 3, 6, 3, 2,
6, 0, 5, 2, 19, 3,
5, 2, 4, 9, 6, 3;
MatrixXd g_csi_corr = data.transpose() * data / 6.0;
EigenSolver<MatrixXd> es(g_csi_corr);
VectorXd eigenvalue = es.eigenvalues().real();
std::cout << "eigenvalue: " << eigenvalue.transpose() << std::endl;
eigen_list.push_back(eigenvalue.maxCoeff());
eigen_list.push_back(1.22);
eigen_list.push_back(-54.21);
eigen_list.push_back(8.44);
eigen_list.push_back(-27.83);
eigen_list.push_back(33.12);
//eigen_list.push_back(40.29);
std::cout << "eigen_list: ";
for (std::vector<double>::iterator it = eigen_list.begin(); it != eigen_list.end(); ++it)
std::cout << *it << " ";
std::cout << std::endl;
int num1 = eigen_list.size();
MatrixXd eigen_a2 = Map<MatrixXd>(eigen_list.data(), num1, 1);
VectorXd eigen_a3 = eigen_a2.array().rowwise().mean().transpose();
VectorXd eigen_a4 = (eigen_a2 - eigen_a3.replicate(num1, 1)).array().abs().rowwise().mean().transpose();
double k = 0.002 / (eigen_a4.maxCoeff() - eigen_a4.minCoeff());
VectorXd eigen_a5 = 0.014 + k * (eigen_a4.array() - eigen_a4.minCoeff());
double tri_threshold = eigen_a5.mean();
std::cout << "tri_threshold: " << tri_threshold << std::endl;
return 0;
}