Introduction / 引言 大学期间用来打发无聊时间学的Python没想到竟然在写毕业论文的时候用处这么大,整个硕士论文所做研究,从前期的数据整理、数据分析,到最后的数据可视化我基本上都使用Python来完成,这篇博客就来分享下我毕业论文课题中所做数据分析相关的Python代码。 本博文所有相关的代码都上传在GitHub仓库:Data-Analys...
fromkeras.modelsimportSequentialfromkeras.layersimportLSTM,Denseimportnumpyasnp# 准备数据data=df['Traffic_Volume'].values data=data.reshape(-1,1)# 标准化数据fromsklearn.preprocessingimportMinMaxScaler scaler=MinMaxScaler()data=scaler.fit_transform(data)# 创建训练和测试数据集train_size=int(len(data)*0....
df_train = data[['Date','Close']] df_train = df_train.rename(columns={"Date":"ds","Close":"y"}) m = Prophet() m.fit(df_train) future = m.make_future_dataframe(periods=period) forecast = m.predict(future) # Show and plot foreca...
#Using scipy:Subtract the line of best fitfrom scipy import signal #处理信号df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'])detrended = signal.detrend(df.value.values) #用于去趋势化(detrend)#df.value 返回的是一个 pandas Series...
1#模型构建2print('---')3model= ARIMA(ndf, order=(1, 1, 2)).fit()4print(model.params)5print(model.summary()) 仅管之前进行了差分运算,但这里采用的是差分运算前的时间序列数据,只需要令ARIMA差分阶数的值即可,Python会自动运算差分! 六.模型后检验 6.1残差检验 残差检验是在统计学中经常用于检测线...
def lineplot(x_data, y_data, x_label="", y_label="", title=""): # Create the plot object _, ax = plt.subplots() # Plot the best fit line, set the linewidth (lw), color and # transparency (alpha) of the line ax.plot(x_data, y_data, lw = 2, color = '#539caf', alp...
3.1 Data Visualization 3.1.1 Plot2DData 3.1.2 Generate Stata Graph in Python 3.2 Scientific Computation 3.2.1 Optimization Toolbox 3.2.2 Probability Distributions 3.2.3 lllustrative Example 3.2.4 Quadrature Integration 3.2.5 Ordinary Differential...
下面我们把数据分成data和label,如下形式: 代码如下: AI检测代码解析 X = df[["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"]].values y = df['Species'].values enc = LabelEncoder() label_encoder = enc.fit(y) y = label_encoder.transform(y) + 1 1. 2. 3. 4. 5. 6...
数据处理：pandas、numpy 数据建模：scipy、scikit-learn、statesmodel、keras 数据可视化：matplotlib、seabor...
forest.fit(X_train, Y_train) #print model accuracy on the training data. print('[0]Logistic Regression Training Accuracy:', log.score(X_train, Y_train)) print('[1]K Nearest Neighbor Training Accuracy:', knn.score(X_train, Y_train)) ...