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Practice

Python Library for Visualization, Seaborn

Data visualization plays a crucial role in understanding data intuitively. Graphs make it easier to identify patterns than raw numbers alone.

Seaborn is a Python library specialized in data visualization, built on Matplotlib, allowing for the easy creation of intuitive and sophisticated graphs.

With Seaborn, you can create various visualizations such as Bar plots, Distribution plots, and Box plots with simple code.


Installing Seaborn​

Seaborn can be installed with the following command:

Installing Seaborn
pip install seaborn

In a practice environment, instead of the above command, use await piplite.install('seaborn') to install it in a way optimized for practice.


Why Seaborn is Widely Used​

Seaborn provides many features that make data visualization straightforward.

  • DataFrame-based visualization: It integrates smoothly with Pandas DataFrame.

  • Elegant default styles: Generates visually appealing graphs without additional style settings.

  • Offers advanced graphs: Includes advanced visualization features like heatmap, violin plot, and pairplot.

  • Built-in statistical analysis: Use functions like kdeplot, histplot to analyze data distribution and relationships.


How to Use Seaborn​

To use Seaborn, you first need to import the library.

The code below is an example of how to import Seaborn and set up basic configurations for graphing.

1. Importing the Seaborn Library​

Use Python's import statement to load the Seaborn library.

Importing Seaborn Library
import seaborn as sns
import matplotlib.pyplot as plt

# Set style
sns.set_theme()

2. Creating Basic Graphs with the Penguins Dataset​

In the example below, we perform visualization using the Penguins dataset included in Seaborn.

Basic Graph Example with Seaborn
import seaborn as sns
import matplotlib.pyplot as plt

# Load sample data
penguins = sns.load_dataset("penguins")

# Bar plot of penguin species count
sns.countplot(x="species", data=penguins)

# Display graph
plt.show()

Running the above code will generate a bar plot showing the count according to species (penguin species).


3. Visualizing the Relationship Between Two Variables​

Using Seaborn's scatterplot, you can visually check the relationship between two variables.

Visualizing Relationship Between Variables
sns.scatterplot(x="bill_length_mm", y="bill_depth_mm", hue="species", data=penguins)

# Display graph
plt.show()

This plot shows the relationship between bill_length_mm and bill_depth_mm, with colors representing different penguin species.


With Seaborn, you can effectively visualize complex data with simple code.

Developing the habit of using Seaborn to first check patterns before data analysis will make your data analysis much more intuitive and efficient.

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