A Brief Introduction of the Violin Plot and Box Plot

Date Author Version Note
2024.03.03 Dog Tao V1.0 Release the note.

文章目录

  • [A Brief Introduction of the Violin Plot and Box Plot](#A Brief Introduction of the Violin Plot and Box Plot)
    • [Box Plot](#Box Plot)
    • [Violin Plot](#Violin Plot)
    • [Histogram with Error Bar](#Histogram with Error Bar)
    • Comparison
    • [Example 1](#Example 1)
    • [Example 2](#Example 2)

A Brief Introduction of the Violin Plot and Box Plot

Box Plot

A Box Plot, also known as a Box-and-Whisker Plot, provides a visual summary of a data set's central tendency, variability, and skewness. The "box" represents the interquartile range (IQR) where the middle 50% of data points lie, with a line inside the box indicating the median value. The "whiskers" extend from the box to show the range of the data, typically to 1.5 * IQR beyond the quartiles, though this can vary. Data points outside of the whiskers are often considered outliers.

Violin Plot

A Violin Plot combines features of the Box Plot with a kernel density plot, which shows the distribution shape of the data. The width of the violin at different values indicates the kernel density estimation of the data at that value, providing a deeper insight into the distribution of the data, including multimodality (multiple peaks). It includes a marker for the median of the data and often includes a box plot inside the violin.

Histogram with Error Bar

A Histogram is a graphical representation of the distribution of numerical data, where the data is divided into bins, and the frequency of data points within each bin is depicted. An Error Bar can be added to a histogram to represent the variability of the data. The error bars typically represent the standard deviation, standard error, or confidence interval for the data.

Comparison

  • Violin Plot: This plot provides a visual summary of the data distribution along with its probability density. The width of the plot at different values indicates the density of the data at that point, showing where the data is more concentrated.

  • Box Plot: This plot shows the median (central line), interquartile range (edges of the box), and potential outliers (dots outside the 'whiskers'). It's useful for identifying the central tendency and spread of the data, as well as outliers.

  • Histogram with Error Bar: The histogram shows the frequency distribution of the data across different bins. The error bars on each bin represent the variability of the data within that bin, using the standard error of the mean to give an idea of the uncertainty around the count in each bin.

Example 1

python 复制代码
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Generating a random dataset
np.random.seed(10)
data = np.random.normal(loc=0, scale=1, size=100)

# Setting up the matplotlib figure
plt.figure(figsize=(14, 6))

# Creating a subplot for the Violin Plot
plt.subplot(1, 3, 1)
sns.violinplot(data=data, inner="quartile", color="lightgray")
plt.title('Violin Plot')

# Creating a subplot for the Box Plot
plt.subplot(1, 3, 2)
sns.boxplot(data=data, width=0.3, color="skyblue")
plt.title('Box Plot')

# Creating a subplot for the Histogram with Error Bar
plt.subplot(1, 3, 3)
mean = np.mean(data)
std = np.std(data)
count, bins, ignored = plt.hist(data, bins=10, color="pink", edgecolor='black', alpha=0.7)
plt.errorbar((bins[:-1] + bins[1:]) / 2, count, yerr=std / np.sqrt(count), fmt='o', color='red', ecolor='lightgray', elinewidth=3, capsize=0)
plt.title('Histogram with Error Bar')

plt.tight_layout()
plt.show()

Example 2

python 复制代码
# Generating two random datasets for comparison
np.random.seed(10)
data1 = np.random.normal(loc=0, scale=1, size=100)  # Dataset 1
data2 = np.random.normal(loc=1, scale=1.5, size=100)  # Dataset 2

# Setting up the matplotlib figure
plt.figure(figsize=(14, 6))

### Creating a customized Violin Plot
plt.subplot(1, 3, 1)
sns.violinplot(data=[data1, data2], inner="quartile", split=True, palette=["lightblue", "lightgreen"], orient="h")
plt.title('Customized Violin Plot')

### Creating a customized Box Plot
plt.subplot(1, 3, 2)
sns.boxplot(data=[data1, data2], width=0.5, palette=["skyblue", "lightgreen"], orient="h", showmeans=True, notch=True, meanprops={"marker":"o", "markerfacecolor":"red", "markeredgecolor":"black"})
plt.title('Customized Box Plot')

### Creating a customized Histogram with Error Bars
plt.subplot(1, 3, 3)

# Histogram for Dataset 1
count1, bins1, ignored1 = plt.hist(data1, bins=10, color="skyblue", edgecolor='black', alpha=0.5, label='Dataset 1')

# Histogram for Dataset 2
count2, bins2, ignored2 = plt.hist(data2, bins=10, color="lightgreen", edgecolor='black', alpha=0.5, label='Dataset 2')

# Error Bars for Dataset 1
std1 = np.std(data1)
plt.errorbar((bins1[:-1] + bins1[1:]) / 2, count1, yerr=std1 / np.sqrt(count1), fmt='o', color='blue', ecolor='lightgray', elinewidth=3, capsize=0)

# Error Bars for Dataset 2
std2 = np.std(data2)
plt.errorbar((bins2[:-1] + bins2[1:]) / 2, count2, yerr=std2 / np.sqrt(count2), fmt='o', color='green', ecolor='lightgray', elinewidth=3, capsize=0)

# Mean Lines and Legend
plt.axvline(np.mean(data1), color='blue', linestyle='dashed', linewidth=1)
plt.axvline(np.mean(data2), color='green', linestyle='dashed', linewidth=1)
plt.legend()

plt.title('Customized Histogram with Error Bars')

plt.tight_layout()
plt.show()
相关推荐
小白银子3 小时前
零基础从头教学Linux(Day 52)
linux·运维·服务器·python·python3.11
wb043072014 小时前
性能优化实战:基于方法执行监控与AI调用链分析
java·人工智能·spring boot·语言模型·性能优化
AAA小肥杨4 小时前
基于k8s的Python的分布式深度学习训练平台搭建简单实践
人工智能·分布式·python·ai·kubernetes·gpu
lichong9516 小时前
Git 检出到HEAD 再修改提交commit 会消失解决方案
java·前端·git·python·github·大前端·大前端++
Tiny番茄6 小时前
31.下一个排列
数据结构·python·算法·leetcode
mit6.8246 小时前
[Agent可视化] 配置系统 | 实现AI模型切换 | 热重载机制 | fsnotify库(go)
开发语言·人工智能·golang
Percent_bigdata7 小时前
百分点科技发布中国首个AI原生GEO产品Generforce,助力品牌决胜AI搜索新时代
人工智能·科技·ai-native
Gloria_niki7 小时前
YOLOv4 学习总结
人工智能·计算机视觉·目标跟踪
小白学大数据7 小时前
实战:Python爬虫如何模拟登录与维持会话状态
开发语言·爬虫·python
FriendshipT7 小时前
目标检测:使用自己的数据集微调DEIMv2进行物体检测
人工智能·pytorch·python·目标检测·计算机视觉