Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Plotnine | 3,677 | 127 | 134 | 3 months ago | 20 | November 06, 2023 | 74 | mit | Python | |
A Grammar of Graphics for Python | ||||||||||
Matplotplusplus | 3,496 | 5 months ago | 1 | March 03, 2021 | 46 | mit | C++ | |||
Matplot++: A C++ Graphics Library for Data Visualization 📊🗾 | ||||||||||
Aachartkit Swift | 2,343 | 1 | 2 months ago | 17 | May 31, 2022 | 148 | mit | Swift | ||
📈📊📱💻🖥️An elegant modern declarative data visualization chart framework for iOS, iPadOS and macOS. Extremely powerful, supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types. 极其精美而又强大的现代化声明式数据可视化图表框架,支持柱状图、条形图、折线图、曲线图、折线填充图、曲线填充图、气泡图、扇形图、环形图、散点图、雷达图、混合图等各种类型的多达几十种的信息图图表,完全满足工作所需. | ||||||||||
Plots.jl | 1,775 | 189 | 2 months ago | March 15, 2023 | 668 | other | Julia | |||
Powerful convenience for Julia visualizations and data analysis | ||||||||||
Statistical Analysis Python Tutorial | 1,233 | 9 years ago | 1 | HTML | ||||||
Statistical Data Analysis in Python | ||||||||||
Uniplot | 288 | 10 | 2 months ago | 35 | September 02, 2023 | 2 | mit | Python | ||
Lightweight plotting to the terminal. 4x resolution via Unicode. | ||||||||||
Volbx | 227 | 7 months ago | 5 | lgpl-3.0 | C++ | |||||
Graphical tool for data manipulation written in C++/Qt. | ||||||||||
Ida | 179 | 7 years ago | 13 | R | ||||||
Introduction to Data Analysis, using R (2013) | ||||||||||
Visualize_ml | 160 | 8 years ago | 3 | August 04, 2016 | mit | Python | ||||
Python package for consolidated and extensive Univariate,Bivariate Data Analysis and Visualization catering to both categorical and continuous datasets. | ||||||||||
Seaborn Tutorial | 76 | 3 years ago | mit | Jupyter Notebook | ||||||
This repository is my attempt to help Data Science aspirants gain necessary Data Visualization skills required to progress in their career. It includes all the types of plot offered by Seaborn, applied on random datasets. |