plt.plot(x_data, slope * x_data + intercept, color='red', label='Fitted Line') # 添加标题和图例 plt.title('Linear Regression Fitting') plt.legend() # 显示图形 plt.show() ``` 通过以上步骤,我们成功地利用线性回归方法拟合了 Python 散点图数据,并得到了一条最佳拟合直线。这条直线更好地反...
# 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', alpha = 1) # Label the axes and provide a title ax.set_title(title) ax....
plt.scatter(x,y,label='Data')# 绘制散点图plt.plot(x,linear_func(params_fit,x),'r',label='Fit Line')# 绘制拟合直线plt.legend()# 添加图例plt.show()# 显示图形 1. 2. 3. 4. 在上面的代码中,我们使用scatter函数绘制散点图,使用plot函数绘制拟合直线,并使用legend函数添加图例。最后,我们使用...
plt.scatter(x,y,color="red",label="Sample Point",linewidth=3)#画样本点x=np.linspace(0,10,1000) y=k*x+b plt.plot(x,y,color="orange",label="Fitting Line",linewidth=2)#画拟合直线plt.legend() plt.show() https://www.bilibili.com/read/cv4971766/...
df=pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv")df_select=df.loc[df.cyl.isin([4,8]),:]# Plot sns.set_style("white")gridobj=sns.lmplot(x="displ",y="hwy",hue="cyl",data=df_select,height=7,aspect=1.6,robust=True,palette='tab10',scat...
(cache_file,'r') as f: line = f.readline() d = dict(yaml.safe_load(line)) keys = d.keys() for i, key in enumerate(keys): yvals[i] = d[key] # user input config N = 30 st = pd.to_datetime('20230414') cache_file = None cache_file = './tmp/saved_weight_2.json' ...
（1）s.plot(kind=‘line’)绘制单条折线图，kind指的是图表类型 （2）传递一个代表图像宽和高的...
plt.tight_layout() plt.title("Scatterplot with line of best fit grouped by number ofcylinders") plt.show() draw_scatter("F:\数据杂坛\datasets\mpg_ggplot2.csv") 实现效果: 在散点图上添加趋势线(线性拟合线)反映两个变量是正相关、负相关或者无相关关系。红蓝两组数据分别绘制出最佳的线性拟合线。
model.fit(X, y) return model.coef_, model.intercept_ 4. 数据可视化层 数据可视化层使用Matplotlib、Seaborn和Plotly等库将分析结果以图表形式展示。 import matplotlib.pyplot as plt import seaborn as sns # 可视化示例 def plot_yield_trend(years, yields): plt.figure(figsize=(10, 6)) sns.lineplot(...
np.arange(1, 17, 1) y = np.array([4.00, 6.40, 8.00, 8.80, 9.22, 9.50, 9.70, 10.86, 10.00, 10.20, 10.32, 10.42, 10.50, 11.55, 12.58, 13.60]) # 计算并绘制 w, b = fit(x, y) pred_y = w * x + b plt.scatter(x, y) plt.plot(x, pred_y, c='r', label='line') ...