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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 statisticsThis repository, matplotlib/mplfinance, contains a new matplotlib finance API that makes it easier to create financial plots. It interfaces nicely with Pandas DataFrames. More importantly, the new API automatically does the extra matplotlib work that the user previously had to do "manually" with the old API. (The old API is still available within this package; see below).

finance market-data matplotlib candlestick candlestick-chart ohlc intraday-data ohlcv ohlc-chart ohlc-plot mplfinance trading-days ohlc-data candlestickchartWelcome to matplotlib-cpp, possibly the simplest C++ plotting library. It is built to resemble the plotting API used by Matlab and matplotlib.

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell (à la MATLAB or Mathematica), web application servers, and various graphical user interface toolkits.

plotting-library charting-library charts bar-charts histogram graphsMisc 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 plotData visualization can help programmers and scientists identify trends in their data and efficiently communicate these results with their peers. Modern C++ is being used for a variety of scientific applications, and this environment can benefit considerably from graphics libraries that attend the typical design goals toward scientific data visualization. Besides the option of exporting results to other environments, the customary alternatives in C++ are either non-dedicated libraries that depend on existing user interfaces or bindings to other languages. Matplot++ is a graphics library for data visualization that provides interactive plotting, means for exporting plots in high-quality formats for scientific publications, a compact syntax consistent with similar libraries, dozens of plot categories with specialized algorithms, multiple coding styles, and supports generic backends. This formula is a contribution to vcpkg by @myd7349.

HyperTools 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-seriesThe chart-fx charting library is an extension in the spirit of Oracle's XYChart and performance/time-proven JDataViewer charting functionalities. Emphasis was put on plotting performance for both large number of data points and real-time displays, as well as scientific accuracies leading to error bar/surface plots, and other scientific plotting features (parameter measurements, fitting, multiple axes, zoom, ...). The library also contains a small set of math routines that can operate directly on the DataSet primitive for fitting, computing spectra, linear algebra, FIR/IIR filtering, and other functionalities common to signal processing.

javafx scientific-visualization data-visualisation hacktoberfest large-dataset chart-fx charting-librariesLets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language. The design of Lets-Plot library is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics.

kotlin data-science jupyter plot data-visualization pycharm plot-library jupyter-notebooks statistical-data geo-spatial ggplot datalore sciview sciview-pluginChartify is a Python library that aims to make it as easy as possible for data scientists to create charts. Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format. Chartify is built on top of Bokeh, so if you do need more control you can always fall back on Bokeh's API.

visualization data-science plots bokeh plotting charts charting-libraryBokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

visualization javascript python plots jupyter visualisation data-visualisation bokeh plotting notebooks interactive-plots numfocus charting-library chartsSpyder 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 scientificXChart is a light-weight and convenient library for plotting data designed to go from data to chart in the least amount of time possible and to take the guess-work out of customizing the chart style.

charts tool graph visualization chart-library-java charts-library charting-libraryEach 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 rasterization
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