Seaborn

Explanation of Seaborn

This notebook provides a comprehensive guide to various data visualization techniques using the SeaBorn library in Python.

Table of Contents

Table of Contents

1. Relational Plots
2. Categorical Plots
3. Distribution Plots
4. Regression Plots
5. Matrix Plots

Relational Plots

Relational plots are used to understand the relationships between two or more variables in your data.

• scatterplot: Scatter plots to represent pairwise relationships.
• lineplot: Line plots for visualizing trends in data.

Categorical Plots

Categorical plots are used for visualizing data that can be categorized based on some qualitative properties.

• catplot: A versatile interface for drawing categorical plots.
• boxplot: Display the distribution of a categorical variable using boxes and whiskers.
• violinplot: Combines aspects of boxplots and KDE plots for categorical data.
• stripplot: A scatter plot for categorical variables.
• swarmplot: A categorical scatter plot with non-overlapping points.
• barplot: Bar charts to represent an estimate of central tendency for a numeric variable.
• countplot: Histograms for categorical variables.

Distribution Plots

Distribution plots are designed to show the distribution of a dataset and to visualize it in various ways.

• histplot: Histograms and binnings for visualizing distributions.
• kdeplot: Kernel Density Estimation plots.
• ecdfplot: Empirical Cumulative Distribution Functions.
• jointplot: Combines multiple plots for bivariate analysis.
• pairplot: Plot pairwise relationships in a dataset.

Regression Plots

Regression plots are utilized for observing the relationship between dependent and independent variables and fitting a regression model to the data.

• regplot: Scatter plots with linear regression fits.
• lmplot: Combines regplot and FacetGrid.

Matrix Plots

Matrix plots allow you to plot data as color-encoded matrices and can also be used to indicate clusters within the data.

• heatmap: Represent data as a colored matrix.
• clustermap: Clustered heatmap with hierarchical clustering.

Contributing

We welcome contributions to this project. To contribute:

1. Fork the project.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

Contact Information

For any questions or inquiries, please contact [email protected] - Subject: Github Repo Q, Seaborn. For a full article walkthrough please visit > https://www.pyfi.com/blog < and learn more about PyFi's award winning Python for Finance courses which have been trusted by the top financial institutions in the United States and Canada multiple years running here >> https://www.pyfi.com <<

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We hope this notebook helps you understand the basics of data visualization with Matplotlib and inspires you to create your own visualizations.