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Data Visualization and Analysis With Seaborn Library

Visualization with Point Plots

Explore how to visualize categorical data with Seaborn point plots. Learn to plot central tendency estimates like mean and standard deviation, customize plots with hue, ordering, and orientation, and adjust confidence intervals and error bars for detailed insights.

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Overview

Point plot represents the central tendency estimates for numerical variables using scatter plot points, such as mean, standard deviation, and so on. We can understand how the value of one variable changes across another variable.

Plotting point plots

We import the required libraries pandas, seaborn, numpy, and matplotlib. Next, we set the seaborn default theme using sns.set_theme() and import the diamonds dataset. We use the head() function to view the first five records, as shown in the code below:

Python
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt   
sns.set_theme()
diamonds_df = sns.load_dataset('diamonds')
print(diamonds_df.head())

To begin, let’s plot a point plot between cut and depth using the sns.pointplot() function. The resulting plot is shown below. We can ...