This repository contains sample code scripts for creating awesome data visualizations from scratch using different python libraries (such as matplotlib, plotly, seaborn) with the help of example notebooks. For sample code with datasets, please check individual folder.
Python libraries for data visualization
- altair - Declarative statistical visualizations, based on Vega-Lite.
- bokeh - Interactive Web Plotting for Python.
- bqplot - plotting library for IPython/Jupyter notebooks - front-end in d3
- Chartify - Bokeh wrapper that makes it easy for data scientists to create charts.
- dash - Dash is a Python framework for building analytical web applications
- diagram - Text mode diagrams using UTF-8 characters
- ggplot - plotting system based on R's ggplot2.
- glumpy - OpenGL scientific visualizations library.
- holoviews - Complex and declarative visualizations from annotated data.
- mayai - interactive scientific data visualization and 3D plotting in Python.
- matplotlib - 2D plotting library.
- missingno - provides flexible toolset of data-visualization utilities that allows quick visual summary of the completeness of your dataset, based on matplotlib.
- plotly - Interactive web based visualization built on top of plotly.js
- PyQtGraph - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
- PyVista – 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)
- seaborn - A library for making attractive and informative statistical graphics.
- toyplot - The kid-sized plotting toolkit for Python with grownup-sized goals.
- three.py - Easy to use 3D library based on PyOpenGL. Inspired by Three.js.
- veusz - Python multiplatform GUI plotting tool and graphing library
- VisPy - High-performance scientific visualization based on OpenGL.
- vtk - 3D computer graphics, image processing, and visualization that includes a Python interface.