Seaborn is one of the go-to tools for statistical data visualization in python. It has been actively developed since 2012 and in July 2018, the author released version 0.9. This version of Seaborn has several new plotting features, API changes and documentation updates which combine to enhance an already great library. This article will walk through a few of the highlights and show how to use the new scatter and line plot functions for quickly creating very useful visualizations of data.

Chart.js Box Plots and Violin Plot Charts

Development version of vioplot R package (CRAN maintainer)

The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.

In this data set we have perform classification or clustering and predict the intention of the Online Customers Purchasing Intention. The data set was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.

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A simple workaround to produce violin plot with highcharts.js

Two Data Visualization about the relationship between PROMs scores after surgery and weather conditions on the day of compilation. Made with the matplotlib library.

A development version of the vioplot R package. This has been migrated to "vioplot" as version 0.3.

Comprehensive Machine Learning Techniques: Metrics, Classifiers, and Evaluation

The objective of this work is to investigate factors affecting borrower rate and loan amount.

A simple example on creating violin plots using Seaborn library in Python

:notebook: Visualization and training a basic ML model on the Iris dataset

Employed hyper-parameter tuning (Gridsearch CV) and ensemble methods (Voting Classifier) to combine the results of the best models. Data Cleaning and Exploration using Pandas. Stratified Cross Validation to model and validate the training data

Python EDA and Visualization Using , Matplotlib, Seaborn,Plotly and Bokeh. Map visualization using Folium

Visualization using Matplotlib and Seaborn

Strip Plot, Grouping with Strip Plot, Swarm Plot, Box and Violin Plot, placing plots together, Combining the plots, Joint Plot, Density Plot, Pair Plot

📊 Upload CSV/Excel files to generate boxplots, run ANOVA, and auto-calculate Tukey HSD — no coding needed.

EDA - Pre processing | Feature Engineering

R package to allow the storage of several datasets of RNAseq data and the generation of graphical representations of genes in those datasets (violinplots or heatmaps) with a shiny app

:blue_book: Ejemplos de gráficos con R

Presentación html con scripts de R para poder crear gráficos de violín, usando los datos penguins

Exploratory Data Analysis on Haberman Dataset

A collection of Jupyter Notebook exercises covering key statistical concepts for Data Science, featuring interactive visualizations and real-world datasets.

Interactive dashboard app for Airbnb data developed using Python and Dash

Used libraries and functions as follows:

Minimal single-panel Matplotlib violin plot recipe for single-column figures in two-column papers

Visualisation of birth trends in England and Wales: Relation with age group, deprivation and country of birth

Tidy Tuesday: Bench press results by month (men), 1990-2019

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