Draw Point Plot and Show Standard Deviation with Seaborn in Python Pandas

Point Plot in Seaborn is used to show point estimates and confidence intervals using scatter plot glyphs. The seaborn.pointplot() is used for this. Display Standard Deviation of Observations using confidence interval ci parameter value "sd" in the pointplot() method.

Let’s say the following is our dataset in the form of a CSV file − Cricketers.csv

At first, import the required libraries −

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\Cricketers.csv")

Plotting point plot with “Academy” and “Age”. Display Standard Deviation of Observations using confidence interval parameter value "sd"

sb.pointplot( x = 'Academy',y = 'Age', data = dataFrame, ci = "sd")

Example

Following is the complete code −

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\Cricketers.csv")

sb.set_theme(style="darkgrid")

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

# display
plt.show()

Output

This will produce the following output −