pd.read_excel('DataX.xlsx')

时间: 2024-02-09 22:29:58 浏览: 27
回答: pd.read_excel('DataX.xlsx')是一个使用pandas库中的read_excel函数来读取名为DataX.xlsx的Excel文件，并将其存储为一个DataFrame对象的代码。\[3\]这个函数可以读取Excel文件中的数据，并将其转换为一个可操作的数据结构，方便进行数据分析和处理。 #### 引用[.reference_title] - *1* *2* [data文件数据转换为csv文件](https://blog.csdn.net/Smilng/article/details/115351317)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item] - *3* [EXCEL文件（xlsx）的读取与data的写入](https://blog.csdn.net/Poisson_SHAN/article/details/82959561)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

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df=pd.read_csv(r"D:/商丘师范学院/课程/数据可视化/数据集/vote_result.csv")datax=df['Areas_of_interest'] datay=df['Votes'] plt.pie(datay,labels=datax,radius=1,autopct='%1.2f%%',shadow=True) plt.show()

首先使用pandas库读取csv文件，并将领域和对应的得票数分别存储在datax和datay两个变量中。然后使用matplotlib库的pie函数绘制饼图，其中labels参数指定每个扇形的标签，datay参数指定每个扇形的大小，autopct参数...

下面的这段python代码，哪里有错误，修改一下：import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def __init__(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).__init__() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values # 定义数据处理函数 def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - ...

df = pd.read_csv(r"D:/商丘师范学院/课程/数据可视化/数据集/vote_result.csv") datax=df['Areas_of_interest'] datay=df['Votes'].astype(float) plt.pie(datay,[0.2,0.2,0,0,0,0,0,0,0],datax,radius=2,autopct='%1.1f%%',shadow=True)什么意思

其中，程序首先通过 Pandas 读取 csv 文件中的数据，将 "Areas_of_interest" 列的数据赋值给变量 datax，将 "Votes" 列的数据转换为浮点数类型并赋值给变量 datay。接着，程序使用 Matplotlib 中的 plt.pie() 方法画...

环形图 Out[3]: df = pd.read_csv(r"D:/商丘师范学院/课程/数据可视化/数据集/vote_result.csv") datax=df['Areas_of_interest'] datay=df['Votes'] pie=Pie("用户感兴趣领域",title_pos='center') pie.add("",datax,datay,legend_pos='left',legend_orient='vertical',is_label_show=True) pie

其中，程序首先通过 Pandas 读取 csv 文件中的数据，将 "Areas_of_interest" 列的数据赋值给变量 datax，将 "Votes" 列的数据赋值给变量 datay。接着，程序使用 pyecharts 中的 Pie() 方法画出一个环形图，其中 ...

请指出下列python代码的错误并改正。from sklearn.metrics import confusion_matrix from sklearn.model_selection import KFold from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression kf = KFold(n_splits=3) accuracy_rate=[] cm=[] for train_index, test_index in kf.split(data): dataX_train = x[train_index] dataX_test=x[test_index] dataY_train = y[train_index] dataY_test=y[test_index] model = LogisticRegression() model.fit(dataX_train, dataY_train.astype('int')) # print(model.predict(dataX_test)) # print(dataY_test.tolist()) accuracy_rate.append(sum(model.predict(dataX_test)==dataY_test.tolist())/len(dataY_test)) cm.append(confusion_matrix(y_true=dataY_test, y_pred=model.predict(dataX_test)).T)

这段代码缺少了要导入的库和相关数据。需要根据具体情况添加和修改代码。另外，在定义 model 时需要加上缩进。 修改后的代码如下： ... model.fit(dataX_train, dataY_train.astype('int'))

将冒号后面的代码改写成一个nn.module类：data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test,...

将冒号后面的代码改写成一个nn.module类：import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, LSTM data1 = pd.read_csv("终极1.csv", usecols=[17], encoding='gb18030') df = data1.fillna(method='ffill') data = df.values.reshape(-1, 1) scaler = MinMaxScaler(feature_range=(0, 1)) data = scaler.fit_transform(data) train_size = int(len(data) * 0.8) test_size = len(data) - train_size train, test = data[0:train_size, :], data[train_size:len(data), :] def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) model = Sequential() model.add(LSTM(50, input_shape=(1, look_back), return_sequences=True)) model.add(LSTM(50)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=6, batch_size=1, verbose=2) trainPredict = model.predict(trainX) testPredict = model.predict(testX) trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY])

dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 30 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset...

def temp(filename,x,dic): path='excelFile/'+filename df = pandas.read_excel(path,header=None) data = [] datax = df.iloc[0].tolist() datay=df.iloc[1].tolist() transInt(datax) transInt(datay) slope,intercept,r,p,std_err=stats.linregress(datax,datay) def myfunc(datax): return slope*datax+intercept mymodel=list(map(myfunc,datax)) plt.scatter(datax,datay) plt.plot(datax,mymodel) print(x) result=myfunc(int(x)) if r>=0.7 or r<=-0.7: count=2 while count<6 : mymodel_2=numpy.poly1d(numpy.polyfit(datax,datay,count)) myline=numpy.linspace(min(datax),max(datay),100) r2=r2_score(datay,mymodel_2(datax)) plt.scatter(datax,datay) plt.plot(myline,mymodel_2(myline)) result=mymodel_2(int(x)) if r2>=0.7 or r2<=-0.7: break plt.savefig('figs/result.png') dic['Y']=str(result)这段代码报错main thread is not in main loop

df = pandas.read_excel(path, header=None) data = [] datax = df.iloc[0].tolist() datay = df.iloc[1].tolist() transInt(datax) transInt(datay) slope, intercept, r, p, std_err = stats.linregress...

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()

首先读取一个 Excel 文件并将其转换为 CSV 格式，然后使用 sklearn 库中的 preprocessing 模块对数据进行标准化处理，接着计算数据集的协方差矩阵并求解其特征值和特征向量，用于评估数据集的主要特征。随后，进行...

import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码，并写出预测值和真实值的代码

dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y...

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1. 使用MySQL的增量抽取工具（如binlog）或ETL工具（如DataX、Kettle）定期抽取shtd_store库中sku_info表的增量数据，插入到Mysql的ods库中的sku_info表中。 2. 在ods库中的sku_info表中添加一个字段etl_date，类型...

import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn import pdb from torch.autograd import Variable import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) # 对训练集测试集划分,划分比例0.8 train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') class RNN(nn.Module): def __init__(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): # 补充forward函数 out, h = self.rnn(x) out = self.linear(out[0]) # print("output的形状", out.shape) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()画出预测值真实值图

抱歉，我作为一个语言模型，无法在这里画图。不过，你可以通过以下代码来画出预测值和真实值的图： ...data_predict = train_predict.data.numpy() dataY_plot = data_Y.reshape(-1) data_predict_plot = data_...

java.io.IOException: java.net.SocketException: Connection reset at com.starrocks.connector.datax.plugin.writer.starrockswriter.manager.StarRocksWriterManager.asyncFlush(StarRocksWriterManager.java:181) java.io.IOException: java.net.SocketException: Connection reset at com.starrocks.connector.datax.plugin.writer.starrockswriter.manager.StarRocksWriterManager.asyncFlush(StarRocksWriterManager.java:181) java.io.IOException: java.net.SocketException: Connection reset at com.starrocks.connector.datax.plugin.writer.starrockswriter.manager.StarRocksWriterManager.asyncFlush(StarRocksWriterManager.java:181) java.io.IOException: java.net.SocketException: Connection reset at com.starrocks.connector.datax.plugin.writer.starrockswriter.manager.StarRocksWriterManager.asyncFlush(StarRocksWriterManager.java:181) ... 1 more Caused by: java.net.SocketException: Connection reset datax connection reset

这个错误是由于网络连接中断导致的。"Connection reset" 表示与服务器的连接在数据传输过程中被重置了。通常情况下，这可能是由于网络中断、服务器端关闭连接或者网络配置问题等原因导致的。 要解决这个问题，你...

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