import pandas as pd data = pd.read_excel('C:\Users\home\Desktop\新建文件夹(1)\支撑材料\数据\111.xlsx','Sheet5',index_col=0) data.to_csv('data.csv',encoding='utf-8') import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt df = pd.read_csv(r"data.csv", encoding='utf-8', index_col=0).reset_index(drop=True) df from sklearn import preprocessing df = preprocessing.scale(df) df covX = np.around(np.corrcoef(df.T),decimals=3) covX featValue, featVec= np.linalg.eig(covX.T) featValue, featVec def meanX(dataX): return np.mean(dataX,axis=0) average = meanX(df) average m, n = np.shape(df) m,n data_adjust = [] avgs = np.tile(average, (m, 1)) avgs data_adjust = df - avgs data_adjust covX = np.cov(data_adjust.T) covX featValue, featVec= np.linalg.eig(covX) featValue, featVec tot = sum(featValue) var_exp = [(i / tot) for i in sorted(featValue, reverse=True)] cum_var_exp = np.cumsum(var_exp) plt.bar(range(1, 14), var_exp, alpha=0.5, align='center', label='individual explained variance') plt.step(range(1, 14), cum_var_exp, where='mid', label='cumulative explained variance') plt.ylabel('Explained variance ratio') plt.xlabel('Principal components') plt.legend(loc='best') plt.show() eigen_pairs = [(np.abs(featValue[i]), featVec[:, i]) for i in range(len(featValue))] eigen_pairs.sort(reverse=True) w = np.hstack((eigen_pairs[0][1][:, np.newaxis], eigen_pairs[1][1][:, np.newaxis])) X_train_pca = data_adjust.dot(w) colors = ['r', 'b', 'g'] markers = ['s', 'x', 'o'] for l, c, m in zip(np.unique(data_adjust), colors, markers): plt.scatter(data_adjust,data_adjust, c=c, label=l, marker=m) plt.xlabel('PC 1') plt.ylabel('PC 2') plt.legend(loc='lower left') plt.show()

详解pandas pd.read_excel参数：高效读取Excel文件与K60定时器应用

本篇文章详细介绍了如何在K60平台中使用Pandas库的pd.read_excel函数来读取Excel文件，以及相关的初始化和配置步骤。首先，文章提到了K60处理器的特性，如PIT（Peripherals Interrupt Timer）模块的50MHz时钟频率...

Pandas读取Excel参数详解：pd.read_excel操作与实践

"K60中文文档说明，pandas库pd.read_excel操作，读取excel文件参数，UART中断源" 在本文中，我们将深入探讨两个主要主题：一是如何使用pandas库中的pd.read_excel函数来读取Excel文件，二是了解K60微控制器中的UART...

import pandas as pd data = pd.read_excel(‪"C:\\Users\\LENOVO\\Desktop\\detail.csv")这个里面文件不存在

以下是回答：这段代码中，你尝试读取一个名为"detail.csv"的Excel文件，但是你使用的是pandas.read_excel()函数，应该使用pandas.read_csv()函数来...data = pd.read_csv(r"C:/Users/LENOVO/Desktop/detail.csv")

import pandas as pd data = pd.read_csv('C:\\Users\\15497\\Desktop\\detail.csv')

然后使用pandas库中的read_csv函数来读取名为detail.csv的CSV文件，该文件位于C:\\Users\\15497\\Desktop\\路径下。这行代码的作用是导入pandas库并使用其中的read_csv函数来读取CSV文件。pandas是一个强大的数据...

import pandas as pd df = pd.read_excel(r"C:\Users\asus\Desktop\用户账号情况统计表.xlsx") # 选择所属组织列和用户列 data = df[['用户账号', '姓名']] # 将内容转换为字典 organization_user_dict = data.set_index('用户账号')['姓名'].to_dict() print(organization_user_dict) df2 = pd.read_excel(r"C:\Users\asus\Desktop\人员分配企业岗位查询表.xlsx") print(df2)

接下来，又读取了第二个 Excel 文件，并将其内容保存在名为 df2 的 DataFrame 中，然后直接打印了 df2 的内容。需要注意的是，这段代码中的 Excel 文件路径需要根据实际情况进行修改，确保能够正确读取到文件...

优化以下代码使其提取csv文件中有小数点的行import numpy as np import pandas as pd data = pd.read_csv(r'C:\Users\Admin\Desktop\bisaifiles\population.csv',header=None) data decimal_data = df[df.dtypes == float] print(decimal_data.index.tolist())

data = pd.read_csv(r'C:\Users\Admin\Desktop\bisaifiles\population.csv', header=None) # 将数据类型转换为float data = data.apply(pd.to_numeric, errors='coerce') # 提取有小数点的行 decimal_data = data...

import pandas as pd path = r'data\\"C:\\Users\\31372\\Desktop\\regress_data2.csv"' data = pd.read_csv(path) data.head()运行错误

import pandas as pd path = r'data\\"C:\\Users\\31372\\Desktop\\regress_data2.csv"' data = pd.read_csv(path) data.head() 运行错误的原因可能是路径字符串中的引号使用不正确。在路径字符串中，引号应该...

import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn import linear_model from sklearn.metrics import r2_score path = 'C:/Users/asus/Desktop/台区电量样本.xlsx' data_B = pd.read_excel(path, header=None) data_B = data_B.iloc[1:284, 1:19] x = data_B.iloc[:, 1:19] y = data_B.iloc[:, 0:1] #对数据进行标准化处理 scaler=StandardScaler() scaledx=scaler.fit_transform(x) #线性回归模型 method=linear_model.LinearRegression() getmodel_1=method.fit(x,y) coef_,intercept_=getmodel_1.coef_,getmodel_1.intercept_ print('回归模型的系数为: {},截距为: {}'.format(coef_,intercept_)) #用R平方检验该模型的拟合度 predict_y=getmodel_1.predict(x) R_square=r2_score(y,predict_y) print('R_square is: ',R_square) #得到的值只有0.37，说明该模型不适合预估 #如果可行，就可以预估

你的数据文件路径是'C:/Users/asus/Desktop/台区电量样本.xlsx'，然后你读取了数据并进行了一些预处理操作。在这段代码中，你使用了StandardScaler对特征数据进行了标准化处理，然后使用LinearRegression建立了...

import sys import pandas as pd import numpy as np import matplotlib.pyplot as plt from PyQt5.QtWidgets import QApplication, QMainWindow from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas class MainWindow(QMainWindow): def init(self): super().init() self.setWindowTitle("Excel Data Plot") self.setGeometry(100, 100, 800, 600) self.fig = plt.Figure() self.canvas = FigureCanvas(self.fig) self.setCentralWidget(self.canvas) # 读取Excel文件 data = pd.read_excel("C:\\Users\\快乐DE骚年\\Desktop\\Python\\五指键盘\\五指键盘数据.xlsx") # 遍历每一列数据，生成一张子图 for i, column in enumerate(data.columns[1:6]): x_data = data[column].tolist() # 获取当前列数据 y_data = list(range(1, len(data)+1)) # 生成横坐标数据 ax = self.fig.add_subplot(2, 3, i+1) ax.plot(x_data, y_data) ax.set_xlabel(column) ax.set_ylabel("Column Number") self.fig.tight_layout() if name == "main": app = QApplication(sys.argv) window = MainWindow() window.show() sys.exit(app.exec_())解释一下代码

这段代码是一个简单的Python程序，使用PyQt5和Matplotlib库，读取Excel文件中的数据并在主窗口中绘制多张子图。首先，我们导入所需的库和模块，包括PyQt5、Matplotlib、Pandas和NumPy。接着，定义一个名为...

import numpy as np import pandas as pd file_one = pd.read_excel(r"C:\Users\86159\Desktop\0113013127hs.xls",skiprows=1) file_one

= file_one.dropna(how='all') # Remove rows with all missing values file_one = file_one.dropna(thresh=2) # Remove rows with less than 2 non-missing...one.head() # Show the first few rows of the data frame

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] x = data.drop(['y'], axis=1) print('输出新的特征集数据***') print(x.head()) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) def relu(x): output=np.maximum(0, x) return output def relu_back_propagation(derror_wrt_output,x): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,x): if activation_choose == 'relu': return relu(x) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def init(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output

data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt....

import pandas as pd from keras.models import Sequential from keras.layers import Dense import numpy as np from sklearn.metrics import accuracy_score file = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data（A题数据）.xlsx') # 第二阶段：转化定性变量为定量变量使用onehot函数同时读取新的excel x = pd.get_dummies(file, dtype=int) x.to_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') data = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') # 第三阶段：实现bp神经网络 train_data = data[:320] test_data = data[320:] train_features = train_data.drop('Class', axis=1).values train_labels = train_data['Class'].values test_features = test_data.drop('Class', axis=1).values test_labels = test_data['Class'].values model = Sequential() model.add(Dense(units=72, activation='relu', input_dim=train_features.shape[1])) model.add(Dense(units=72, activation='relu')) model.add(Dense(units=3, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(train_features, train_labels, epochs=10, batch_size=32, validation_data=(test_features, test_labels), verbose=1) predictions = model.predict(test_features) print(predictions)就是这段无法确定问题是什么

data = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') # 划分训练集和测试集 train_data = data[:320] test_data = data[320:] # 提取特征和标签 train_features = train_data.drop('...

import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/output.xlsx') # 提取需要用于聚类的特征列 feature_columns = ["X"] X = data[feature_columns] # 对特征数据进行标准化 scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 使用DBSCAN进行聚类 dbscan = DBSCAN(eps=0.5, min_samples=5) labels = dbscan.fit_predict(X_scaled) # 绘制散点图 plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=labels) plt.xlabel(feature_columns[0]) plt.ylabel(feature_columns[1]) plt.title('DBSCAN Clustering') plt.show()

然后，通过pd.read_excel读取Excel数据，并指定要用于聚类的特征列。在这个例子中，特征列只有一列，命名为"X"。接下来，使用StandardScaler对特征数据进行标准化，将数据转换为均值为0，方差为1的标准正态分布。 ...

import numpy as np from datetime import datetime, timedelta import pandas as pd test = pd.read_excel("C:\\Users\\1data\\Desktop\\DBM成本收入核算\\test.xlsx") start_date = pd.to_datetime(test['合同开始日期']) end_date = pd.to_datetime(test['合同截止日期']) test['合同周期月数'] = round((end_date - start_date) / np.timedelta64(1, 'M')) start_date_col = '合同开始日期' end_date_col = '合同截止日期' new_col = '日期' for index, row in test.iterrows(): start_date = pd.to_datetime(row[start_date_col]) end_date = pd.to_datetime(row[end_date_col]) date_list = [] if start_date.day <= 15: while start_date <= end_date: date_list.append(start_date) start_date = start_date + timedelta(days=30) else: start_date = start_date + timedelta(days=30) while start_date <= end_date + timedelta(days=30): date_list.append(start_date) start_date = start_date + timedelta(days=30) test.loc[index, new_col] = ','.join([str(date.date()) for date in date_list]) df_tmp=test['日期'].str.split(',',expand=True) df_tmp=df_tmp.stack() df_tmp = df_tmp.reset_index(level=1,drop=True) df_tmp.name='日期' df_new = test.drop(['日期'], axis=1).join(df_tmp).reset_index().drop(columns='index') print(df_new) df_new.to_excel('income_test.xlsx',index=False) 将这段代码导出的excel中合同开始日期字段不显示时分秒且新增日期字段的取年月的字段合同周期年月

test = pd.read_excel("C:\\Users\\1data\\Desktop\\DBM成本收入核算\\test.xlsx") start_date = pd.to_datetime(test['合同开始日期']) end_date = pd.to_datetime(test['合同截止日期']) test['合同周期月数'...

import pandas as pd df = pd.read_csv('C:\\Users\\王博艺\\Desktop\\Coursework_2(3).zip\\Coursework_2\\Data.csv') df请帮我检查这个读取有什么问题

df = pd.read_csv('C:/Users/王博艺/Desktop/Coursework_2(3).zip/Coursework_2/Data.csv', encoding='utf-8') # 或者使用双斜杠 df = pd.read_csv('C:\\Users\\王博艺\\Desktop\\Coursework_2(3).zip\\Coursework_...

import pandas as pd import os folder_path = 'C:\Users\cfmoto\Desktop\data_engine' excel_file = [os.path.join(folder_path,f) for f in os.listdir(folder_path) if f.endswith('.xlsx')] for file in excel_files: df = pd.read_excel(file)无法运行

import pandas as pd import os folder_path = 'C:/Users/cfmoto/Desktop/data_engine' excel_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith('.xlsx')] for file in ...

import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn import linear_model from sklearn.metrics import r2_score path = 'C:/Users/asus/Desktop/台区电量样本.xlsx' data_B = pd.read_excel(path, header=None) data_B = data_B.iloc x = data_B.iloc[:, 1:19] y = data_B.iloc[:, 0:1] #对数据进行标准化处理 scaler=StandardScaler() scaledx=scaler.fit_transform(x) #线性回归模型 method=linear_model.LinearRegression() getmodel_1=method.fit(x,y) coef_,intercept_=getmodel_1.coef_,getmodel_1.intercept_ print('回归模型的系数为: {},截距为: {}'.format(coef_,intercept_)) #用R平方检验该模型的拟合度 predict_y=getmodel_1.predict(x) R_square=r2_score(y,predict_y) print('R_square is: ',R_square) #得到的值只有0.37，说明该模型不适合预估 #如果可行，就可以预估把上面的pyton代码转为matlab代码

path = 'C:\Users\asus\Desktop\台区电量样本.xlsx'; data_B = xlsread(path); x = data_B(:, 2:19); y = data_B(:, 1); % 对数据进行标准化处理 scaledx = zscore(x); % 线性回归模型 method = fitlm(x, y); ...

import numpy as np import sklearn import pandas as pd data = pd.read_csv('C:/Users/86159/Desktop/TaxDetection_NoLable.csv') data.head(11) X=data.loc[:,:] y=data.loc[:,] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) from sklearn.neighbors import KNeighborsClassifier clf = KNeighborsClassifier(n_neighbors=3) clf.fit(X_train, y_train.astype('int')) print("Test set predictions: {}".format(clf.predict(X_test))) print("Training set score:{:.2f}".format(clf.score(X_train,y_train))) print("Test set accuracy: {:.2f}".format(clf.score(X_test, y_test)))

这段代码中有一个问题，即y的赋值语句中没有指定具体的列名或索引，因此会导致后面的代码无法正确地运行。你需要指定y的列名或索引...y = data.loc[:, "label"] 这样就可以将"label"列的数据提取出来作为y数据了。

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)

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_...

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详解pandas pd.read_excel参数：高效读取Excel文件与K60定时器应用

Pandas读取Excel参数详解：pd.read_excel操作与实践

import pandas as pd data = pd.read_excel(‪"C:\\Users\\LENOVO\\Desktop\\detail.csv")这个里面文件不存在

import pandas as pd data = pd.read_csv('C:\\Users\\15497\\Desktop\\detail.csv')

优化以下代码使其提取csv文件中有小数点的行import numpy as np import pandas as pd data = pd.read_csv(r'C:\Users\Admin\Desktop\bisaifiles\population.csv',header=None) data decimal_data = df[df.dtypes == float] print(decimal_data.index.tolist())

import pandas as pd path = r'data\\"C:\\Users\\31372\\Desktop\\regress_data2.csv"' data = pd.read_csv(path) data.head()运行错误

import numpy as np import pandas as pd file_one = pd.read_excel(r"C:\Users\86159\Desktop\0113013127hs.xls",skiprows=1) file_one

import pandas as pd df = pd.read_csv('C:\\Users\\王博艺\\Desktop\\Coursework_2(3).zip\\Coursework_2\\Data.csv') df请帮我检查这个读取有什么问题

import pandas as pd import os folder_path = 'C:\Users\cfmoto\Desktop\data_engine' excel_file = [os.path.join(folder_path,f) for f in os.listdir(folder_path) if f.endswith('.xlsx')] for file in excel_files: df = pd.read_excel(file)无法运行

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