除了频次直方图,我们还可以用KDE(kernel density estimation)获取变量分布的平滑估计。具体请见下一篇:Matplotlib学习---用seaborn画直方图/核密度图(histogram, kdeplot)。
In this Python tutorial we will explore how to create a Density Plot using theMatplotlib Graphing Library. We will discuss a variety of different methods, each with it’s own unique twist. But before that, what exactly is a Density plot? A density plot isa representation of the distribution ...
import numpy as np import matplotlib.pyplot as plt ## 实现版本1,根据公式代码实现 def K_func(x,s,h): kval = np.exp(-((x - s) / h) ** 2 / 2) / (h * np.sqrt(2 * np.pi)) return kval n = 6 S = [65,75,67,79,81,91] X = np.linspace(50, 99,num=50) h = 5....
title("Python Matplotlib - Density Scatter Plot", fontproperties=font_latex2, pad=12 ) # 文本的位置是根据数据坐标来确定的 ax.text(x=-5, y=4.5, s=r'$\ {R^2} = 0.522$', usetex=True, fontsize=14, fontweight="bold" ) # 显示网格 虚线和透明度 plt.grid(alpha=0.360, ls="--", ...
sns.kdeplot(df['sepal_width'], shade=True, bw=.05, color="olive"); 1. 2. 3. 多个变量的核密度图绘制 Density plot of several variables # 有时需要比较多个变量的核密度,可以通过matplotlib创建两个子图,也可以直接画在一张图上 p1=sns.kdeplot(df['sepal_width'], shade=True, color="r") ...
最后一步是绘制密度图。使用Matplotlib,我们可以将计算出的密度值可视化。 AI检测代码解析 plt.figure(figsize=(10,6))# 设置绘图尺寸plt.plot(x_values,density_values,label='Density Function',color='b')# 绘制密度图plt.title('Density Plot')# 标题plt.xlabel('Value')# x轴标签plt.ylabel('Density')...
3. 多个变量的核密度图绘制 Density plot of several variables # 有时需要比较多个变量的核密度,可以通过matplotlib创建两个子图,也可以直接画在一张图上p1=sns.kdeplot(df['sepal_width'],shade=True,color="r")p1=sns.kdeplot(df['sepal_length'],shade=True,color="b") ...
importmpl_scatter_density, then create Matplotlib axes as usual but adding aprojection='scatter_density'option (if your reaction is 'wait, what?', seehere). This will return aScatterDensityAxesinstance that has ascatter_densitymethod in addition to all the usual methods (scatter,plot, etc.)....
matplotlib >= 2.0.0 scatter : A scatterplot colored by the data density. Dense regions are merged into bins. Sparse region is represented by as single dots. mesh : 2D-histogram colored by the data density in the region scatter_mesh : 2D-histogram with original data values plotted as dots...
matplotlib: for plotting seaborn: for making the plots prettier importmatplotlib.pyplotaspltimportseabornassns Dataset The dataset that we will use is theirisdataset that we can load usingseaborn. df=sns.load_dataset('iris') Plot multiple elements ...