UNIT-III
SYLLABUS:Seaborn: Features of seaborn, Creating plots with seaborn, Visualization examples
SEABORN
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-
level interface for creating attractive and informative statistical graphics. It is especially useful
for visualizing complex datasets because it works seamlessly with Pandas DataFrames and offers
built-in themes and color palettes that improve the appearance of plots automatically.
Features of Seaborn
● Seaborn provides several advanced features that make data visualization easier and more
effective.
● It offers built-in support for statistical plots such as distribution plots, categorical plots,
and relationship plots.
● Seaborn integrates tightly with Pandas, allowing direct plotting from DataFrames
without extra preprocessing.
● It automatically applies aesthetically pleasing themes and color palettes, reducing the
need for manual styling.
● Seaborn also simplifies complex visualizations like heatmaps and pair plots, which are
difficult to create using basic Matplotlib functions.
Example 1:
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
sns.histplot(tips['total_bill'])
plt.title("Histogram using Seaborn")
plt.show()
This example demonstrates Seaborn’s ability to create a visually appealing statistical plot with
minimal code.
, Example 2
import seaborn as sns
import matplotlib.pyplot as plt
sns.boxplot(x='day', y='total_bill', data=tips)
plt.title("Box Plot using Seaborn")
plt.show()
This example highlights Seaborn’s built-in support for categorical and statistical visualization.
SYLLABUS:Seaborn: Features of seaborn, Creating plots with seaborn, Visualization examples
SEABORN
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-
level interface for creating attractive and informative statistical graphics. It is especially useful
for visualizing complex datasets because it works seamlessly with Pandas DataFrames and offers
built-in themes and color palettes that improve the appearance of plots automatically.
Features of Seaborn
● Seaborn provides several advanced features that make data visualization easier and more
effective.
● It offers built-in support for statistical plots such as distribution plots, categorical plots,
and relationship plots.
● Seaborn integrates tightly with Pandas, allowing direct plotting from DataFrames
without extra preprocessing.
● It automatically applies aesthetically pleasing themes and color palettes, reducing the
need for manual styling.
● Seaborn also simplifies complex visualizations like heatmaps and pair plots, which are
difficult to create using basic Matplotlib functions.
Example 1:
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
sns.histplot(tips['total_bill'])
plt.title("Histogram using Seaborn")
plt.show()
This example demonstrates Seaborn’s ability to create a visually appealing statistical plot with
minimal code.
, Example 2
import seaborn as sns
import matplotlib.pyplot as plt
sns.boxplot(x='day', y='total_bill', data=tips)
plt.title("Box Plot using Seaborn")
plt.show()
This example highlights Seaborn’s built-in support for categorical and statistical visualization.
