Fantaxtic - Nested Bar Plots for Phyloseq Data

demir.ai Dataset Operations

Interactive Jupyter notebooks showcasing data visualization with Matplotlib and Seaborn for learners and data practitioners.

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Package for automation of statistics that are widely used in metabolomics.

Tool for analyzing network traffic written in Java

Matplotlib Tutorial

This app allows users to explore key factors for employee attrition. Survey data can be filtered by gender, age, and department.

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Youtube Trend Dashboard - Find out what key characteristics make a particular youtube video land in the trending section

This repository houses data visualization with Python.

An app displaying surgical wait times in different health authorities of British Columbia

A Shiny App designed to help nutrition conscious individuals track their macronutrient intake.

Basic Barplot with Matplotlib . A barplot shows the relationship between a numeric and a categoric variable.

Assignment 2 Set 2

MDS-Winery Dashboard for American wines

A simple visualization dashboard for tsunami events since 1800 using plotly dash and Python.

Seaborn Visualization on Titanic Dataset Visual exploration of different features on No. of people survived or otherwise Visualization using FacetGrid function, Lambda function and criterion function Visualization of subplots

Plotting two different categories- box plot, barplot, histogram. Plotting single category- Pie chart, bar chart. Different Plots- Scatter Plot, Histogram, Box Plot, Violin Plot

A dashboard visualizing Covid-19 statistics at both global and country scales using JHU CSSE COVID-19 Data

Python matplotlib, seaborn and plotly plots cheat sheet

R_basics- Earth Quake data

This Data set consists of information about an employee, There are attributes such as education level, experience level, age, salary, gender, department, degree, ratings, work ethics, current company working experience, job level, job role, attrition rate, employee id, employee satisfaction etc to take some serious important decisions for the company regarding the company.

Comparison between Marvel and DC in terms of their Characters Popularity, their Gender, Hair Color, Eye Color, Character Alignment, Appearances, Launch day, names, etc. I have used Seaborn, matplotlib, networkx, and plotly to visualize Interactive plots

An analysis and visualization of Olympic Games data from the year of 2000 to 2016.

Data Visualization with Base R Graphics Package

DATA551 Dash Project in Python

How to deploy an dashr app to heroku

nyankomicro is a in-house package that aims to make the plots more beautiful.

Assignment Advance Stats