Python Pandas – 用Seaborn绘制条形图并显示观察值的标准差

Seaborn中的条形图用于展示矩形条作为点估计和置信区间。使用seaborn.barplot()实现此功能。使用置信区间ci参数值展示观测值的标准差 sd 。

假设以下是我们的数据集，以CSV文件的形式呈现 − Cricketers2.csv

首先，导入所需的库−

import seaborn as sb
import pandas as pd
import matplotlib.pyplot as plt

从CSV文件中加载数据并将其放入Pandas DataFrame中−

dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers2.csv")

使用学院和比赛来绘制条形图。使用置信区间参数值”sd”显示观察值的标准差−

sb.barplot(x = "Academy", y = "Matches",data = dataFrame, ci = "sd")

更多Pandas相关文章，请阅读：Pandas 教程

示例

以下为代码−

import seaborn as sb
import pandas as pd
import matplotlib.pyplot as plt

# Load data from a CSV file into a Pandas DataFrame
dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers2.csv")

sb.set_theme(style="darkgrid")

# plotting bar plot with Academy and Matches
# Display Standard Deviation of Observations using confidence interval parameter value "sd"
sb.barplot(x = "Academy", y = "Matches",data = dataFrame, ci = "sd")

# display
plt.show()