Creating Statistical Visualizations with Seaborn

Seaborn builds on Matplotlib and makes it easier to create statistical charts. It works particularly well with Pandas DataFrames because columns can be assigned directly to chart variables.

Preparing a Dataset

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

df = pd.DataFrame({

"Department": ["Sales", "IT", "HR", "Sales", "IT", "HR"],

"Experience": [2, 5, 3, 7, 8, 4],

"Salary": [35000, 65000, 40000, 60000, 85000, 45000]

})

sns.set_theme(style="whitegrid")

The dataset contains employee departments, years of experience, and salaries. It can be used to compare groups and investigate relationships.

Creating a Bar Plot

sns.barplot(data=df, x="Department", y="Salary")

plt.title("Average Salary by Department")

plt.show()

By default, Seaborn's barplot() estimates a summary statistic, typically the mean, for each category. It also displays uncertainty intervals by default. This differs from Matplotlib's bar(), which plots the values supplied directly.

Creating a Scatter Plot

sns.scatterplot(

data=df,

x="Experience",

y="Salary",

hue="Department"

)

plt.title("Experience vs Salary")

plt.show()

The hue parameter assigns different visual colors to categories, helping identify group-level patterns.

Creating a Box Plot

sns.boxplot(data=df, x="Department", y="Salary")

plt.title("Salary Distribution by Department")

plt.show()

A box plot displays the median, quartiles, and potential outliers. It is useful when comparing how numerical values vary across groups.