Seaborn

Seaborn is a powerful data visualization library in Python that helps you create attractive and informative statistical graphics. It is built on top of Matplotlib, another popular visualization library, and provides a high-level interface for drawing plots.

Some key features of Seaborn include:

1. Attractive Plots: Seaborn offers a range of default themes and color palettes that make your plots look appealing and professional. It also provides tools for customizing the appearance of your plots to suit your preferences.

2. Integration with Pandas: Seaborn works seamlessly with Pandas, a popular data manipulation library in Python. You can easily create visualizations from Pandas dataframes using Seaborn functions.

3. Statistical Visualizations: Seaborn specializes in creating statistical graphics, such as scatter plots, line plots, bar plots, and heatmaps. It also provides tools for visualizing distributions, relationships between variables, and categorical data.

4. Facet Grids: Seaborn allows you to create multi-plot visualizations called facet grids. These grids make it easy to compare multiple subsets of data or variables in a single figure.

5. Regression Plots: Seaborn provides functions for visualizing linear regression models, making it easy to explore relationships between variables and identify trends in your data.

To use Seaborn, you first need to install it using a package manager like pip or conda. Once installed, you can import the library into your Python code using the following line:

import seaborn as sns

After importing Seaborn, you can access its various functions and modules to create visualizations. For example, you can create a scatter plot using the scatterplot() function:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()

This code will create a scatter plot of the “total_bill” and “tip” variables from the “tips” dataset.

Seaborn is widely used in fields such as data science, machine learning, and scientific research. By learning how to use Seaborn, you can gain valuable skills for visualizing and communicating insights from data.

Let’s start with 30 examples using Seaborn, and then we’ll cover 30 examples using SciPy.

Seaborn Examples:

First, let’s import the necessary modules:

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

1. Line Plot

   sns.lineplot(x=[1, 2, 3, 4], y=[1, 4, 9, 16])
   plt.show()
   

2. Scatter Plot

   sns.scatterplot(x=[1, 2, 3, 4], y=[1, 4, 9, 16])
   plt.show()
   

3. Bar Plot

   sns.barplot(x=['A', 'B', 'C'], y=[1, 3, 2])
   plt.show()
   

4. Box Plot

   sns.boxplot(x='species', y='sepal_length', data=sns.load_dataset("iris"))
   plt.show()
   

5. Violin Plot

   sns.violinplot(x='species', y='sepal_length', data=sns.load_dataset("iris"))
   plt.show()
   

6. Swarm Plot

   sns.swarmplot(x='species', y='sepal_length', data=sns.load_dataset("iris"))
   plt.show()
   

7. Heat Map

   sns.heatmap(np.random.rand(10, 10))
   plt.show()
   

8. Pair Plot

   sns.pairplot(sns.load_dataset("iris"))
   plt.show()
   

9. Joint Plot

   sns.jointplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"))
   plt.show()
   

10. Regression Plot

    sns.regplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"))
    plt.show()
    

11. Residual Plot

    sns.residplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"))
    plt.show()
    

12. KDE Plot

    sns.kdeplot(data=sns.load_dataset("iris")['sepal_length'])
    plt.show()
    

13. Facet Grid

    g = sns.FacetGrid(sns.load_dataset("iris"), col="species")
    g.map(sns.histplot, "sepal_length")
    plt.show()
    

14. Categorial Plot

    sns.catplot(x="species", y="sepal_length", kind="box", data=sns.load_dataset("iris"))
    plt.show()
    

15. Count Plot

    sns.countplot(x='species', data=sns.load_dataset("iris"))
    plt.show()
    

16. Dist Plot

    sns.distplot(sns.load_dataset("iris")['sepal_length'])
    plt.show()
    

17. Strip Plot

    sns.stripplot(x='species', y='sepal_length', data=sns.load_dataset("iris"))
    plt.show()
    

18. Cluster Map

    sns.clustermap(sns.load_dataset("iris").drop('species', axis=1))
    plt.show()
    

19. Lm Plot

    sns.lmplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"))
    plt.show()
    

20. Rel Plot

    sns.relplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"))
    plt.show()
    

21. Point Plot

    sns.pointplot(x='species', y='sepal_length', data=sns.load_dataset("iris"))
    plt.show()
    

22. Rug Plot

    sns.rugplot(sns.load_dataset("iris")['sepal_length'])
    plt.show()
    

23. Setting Style

    sns.set_style("darkgrid")
    sns.lineplot(x=[1, 2, 3, 4], y=[1, 4, 9, 16])
    plt.show()
    

24. Color Palette

    sns.set_palette("husl")
    sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=sns.load_dataset("iris"))
    plt.show()
    

25. Figure Size

    sns.set(rc={'figure.figsize':(11.7,8.27)})
    sns.lineplot(x=[1, 2, 3, 4], y=[1, 4, 9, 16])
    plt.show()
    

26. Despine

    sns.lineplot(x=[1, 2, 3, 4], y=[1, 4, 9, 16])
    sns.despine()
    plt.show()
    

27. Joint KDE

    sns.jointplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"), kind='kde')
    plt.show()
    

28. Hex Bin Plot

    sns.jointplot(x='sepal_length', y='sepal_width', data=sns.load_dataset("iris"), kind='hex')
    plt.show()
    

29. Pair Grid

    g = sns.PairGrid(sns.load_dataset("iris"))
    g.map_diag(sns.histplot)
    g.map_offdiag(sns.scatterplot)
    plt.show()
    

30. Animated Plot (using FuncAnimation)

    import matplotlib.animation as animation
    
    fig, ax = plt.subplots()
    x = np.linspace(0, 10, 100)
    line, = ax.plot(x, np.sin(x))
    
    def animate(i):
        line.set_ydata(np.sin(x + i/10))
        return line,
    
    ani = animation.FuncAnimation(fig, animate, frames=100, interval=50)
    plt.show()