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JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots. Joyplots are stacked, partially overlapping density plots, simple as that. They are a nice way to plot data to visually compare distributions, especially those that change across one dimension (e.g., over time). Though hardly a new technique, they have become very popular lately thanks to the R package ggjoy (which is clearly much better developed/maintained than this one -- and I strongly suggest you to use that if you can use R and ggplot.) Update: the ggjoy package has now been renamed ggridges.

https://github.com/sbebo/joypyTags | data-visualization matplotlib plotting |

Implementation | Jupyter Notebook |

License | MIT |

Platform |

Stop plotting your data - annotate your data and let it visualize itself. HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple. With HoloViews, you can usually express what you want to do in very few lines of code, letting you focus on what you are trying to explore and convey, not on the process of plotting.

visualization analysis bokeh matplotlib interactive data-science exploratory-data-analysis pandasChris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.Online documentation is available at seaborn.pydata.org. Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.

data-visualization visualization statisticsGramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library by Hadley Wickham. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab. Gramm is a data visualization toolbox for Matlab that allows to produce publication-quality plots from grouped data easily and flexibly. Matlab can be used for complex data analysis using a high-level interface: it supports mixed-type tabular data via tables, provides statistical functions that accept these tables as arguments, and allows users to adopt a split-apply-combine approach (Wickham 2011) with rowfun(). However, the standard plotting functionality in Matlab is mostly low-level, allowing to create axes in figure windows and draw geometric primitives (lines, points, patches) or simple statistical visualizations (histograms, boxplots) from numerical array data. Producing complex plots from grouped data thus requires iterating over the various groups in order to make successive statistical computations and low-level draw calls, all the while handling axis and color generation in order to visually separate data by groups. The corresponding code is often long, not easily reusable, and makes exploring alternative plot designs tedious.

matlab visualization stats plot data-visualization statisticsWelcome to matplotlib-cpp, possibly the simplest C++ plotting library. It is built to resemble the plotting API used by Matlab and matplotlib.

Misc data visualization projects, examples, and demos: mostly Python (pandas + matplotlib) and JavaScript (leaflet).

i-sight is a scientific data visualization / plotting / mesh visualization software that can plot data fields, contours, streamlines (much like TecPlot), and also has a 3D mesh visualizer that can cut a mesh, perform shadow visualization and related stuf

gnuplot is a command-driven interactive function plotting program. It can be used to plot functions and data points in both two- and three-dimensional plots in many different formats. It is designed primarily for the visual display of scientific data.

graphics chart visualization plotting-engineScikit-plot is the result of an unartistic data scientist's dreadful realization that visualization is one of the most crucial components in the data science process, not just a mere afterthought. Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a single-line dump of numbers enclosed in brackets. Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel.

scikit-learn visualization machine-learning data-science plotting plotBokeh is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open-source scientific computing community. If you like Bokeh and would like to support our mission, please consider making a donation. Bokeh is an interactive visualization library for Python that enables beautiful and meaningful visual presentation of data in modern web browsers. With Bokeh, you can quickly and easily create interactive plots, dashboards, and data applications.

bokeh interactive-plots visualization plotting plotsHyperTools is designed to facilitate dimensionality reduction-based visual explorations of high-dimensional data. The basic pipeline is to feed in a high-dimensional dataset (or a series of high-dimensional datasets) and, in a single function call, reduce the dimensionality of the dataset(s) and create a plot. The package is built atop many familiar friends, including matplotlib, scikit-learn and seaborn. Our package was recently featured on Kaggle's No Free Hunch blog. For a general overview, you may find this talk useful (given as part of the MIND Summer School at Dartmouth). Check the repo of Jupyter notebooks from the HyperTools paper.

data-visualization high-dimensional-data topic-modeling text-vectorization data-wrangling visualization time-seriesSpyder is a Python development environment with advanced editing, interactive testing, debugging and introspection features. It is especially recommended for scientific computing thanks to NumPy (linear algebra), SciPy (signal and image processing), matplotlib (interactive 2D/3D plotting) and MayaVi’s mlab (interactive 3D visualization) support.

ide integrated-development-environment text-editor python-ide scientificEach record is projected into zero or more bins of a nominal plotting grid shape, based on a specified glyph. Reductions are computed for each bin, compressing the potentially large dataset into a much smaller aggregate array.

visualization data-science plotting heatmap rasterizationOpenchart2 is based on the JOpenChart library. It provides a simple interface for Java programmers to create two-dimensional charts and plots. This library features an assortment of graph styles, including advanced scatter plots, bar graphs, pie charts, Radar charts, Dot plots. All chart types support dynamic zooming. Simple arrays or full database sources can provide data to the plotting routines.

chart tool graph visualization chart-library-javaGosl is a Go library to develop Artificial Intelligence and High-Performance Scientific Computations. The library tries to be as general and easy as possible. Gosl considers the use of both Go concurrency routines and parallel computing using the message passing interface (MPI). Gosl has several modules (sub-packages) for a variety of tasks in scientific computing, image analysis, and data post-processing.

scientific-computing visualization linear-algebra differential-equations sparse-systems plotting mkl parallel-computations computational-geometry graph-theory tensor-algebra fast-fourier-transform eigenvalues eigenvectors hacktoberfest machine-learning artificial-intelligence optimization optimization-algorithms linear-programmingA weekly data project aimed at the R ecosystem. An emphasis will be placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.

Spyre is a Web Application Framework for providing a simple user interface for Python data projects. Spyre runs on the minimalist python web framework, cherrypy, with jinja2 templating. Spyre is all about data and data visualization, so you'll also need pandas and matplotlib.

Vegas aims to be the missing MatPlotLib for the Scala and Spark world. Vegas wraps around Vega-Lite but provides syntax more familiar (and type checked) for use within Scala. And then use the following code to render a plot into a pop-up window (see below for more details on controlling how and where Vegas renders).

plotting datascience
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