Open Source Python Data Analytics Tools for Linux

A data visualization and processing tool for scanning probe microscopy (SPM, i.e. AFM, STM, MFM, SNOM/NSOM, ...) and profilometry data, useful also for general image and 2D data analysis.

SciDAVis is a user-friendly data analysis and visualization program primarily aimed at high-quality plotting of scientific data. It strives to combine an intuitive, easy-to-use graphical user interface with powerful features such as Python scriptability.

Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight! When teaching data mining, we like to illustrate rather than only explain.

Software for pixelization, hierarchical indexation, synthesis, analysis, and visualization of data on the sphere. Please acknowledge HEALPix by quoting the web page http://healpix.sourceforge.net (or https://healpix.sourceforge.io) and publication: K.M. Gorski et al., 2005, Ap.J., 622, p.759 Full software documentation available at https://healpix.sourceforge.io/documentation.php Wiki Pages: https://sourceforge.net/p/healpix/wiki/Home Exchanging Data with HEALPix (in FITS files): https://sourceforge.net/p/healpix/wiki/Exchanging%20Data%20with%20HEALPix/ GDL and FL users should read https://sourceforge.net/p/healpix/wiki/HEALPix%20and%20GDL/

LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.

Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Shows how a target value (e.g. "Survived" in the Titanic dataset) relates to other features. Sweetviz integrates associations for numerical (Pearson's correlation), categorical (uncertainty coefficient) and categorical-numerical (correlation ratio) datatypes seamlessly, to provide maximum information for all data types. Automatically detects numerical, categorical and text features, with optional manual overrides. min/max/range, quartiles, mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness.

QtiPlot is a user-friendly, platform independent data analysis and visualization application similar to the non-free Windows program Origin.

This is a sophisticated & integrated simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems). It supports symbolic math, optimization, continuation, data analysis, biological apps...

Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), is a powerful and complete front-end to a rich set of visual-data exploration and analysis capabilities well suited for climate-data analysis problems.

GXSM -- Gnome X Scanning Microscopy: A multi-channel image and vector-probe data acquisition and visualization system designed for SPM techniques (STM,AFM..), but also SPA-LEED/LEED/LEEM data analysis. A plug-in interface allows any user add-on data-processing and special hardware and instrument support. Latest: NC-AFM and related explorative methods as SQDM can be configured. High-Speed external PAC-PLL hardware option with digital DSP link. Based on several hardware options it supports a commercially available DSP hardware and provided also Open Source Code for all the low level signal processing tasks and instrument controls in a most flexible and adaptable manner. All latest software is available via SVN only or Live Demo/Install CD: http://www.ventiotec.de/linux/GXSM-Linux.iso

IVuPy is a set of Python bindings for the Coin3D C++ class libraries which serve to create software for 3D visualization in science and engineering. IVuPy aims to be a solid basis for large Python programs for 3D visualization and data analysis.

PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI.

Zherlock has now changed name to SciCraft and no longer hosted on sourceforge. Visit our new site at http://www.scicraft.org

Python project with goal to provide FOSS library to extract, analyse and visualise data in a 3D fashion. The instance will connect to a data source, ods sheet, csv, sql DB, pyodbc the instance will analyse and/or transform the data to be presented to the visualisation functionality the instance will visualise the data in a 3D fashion, likely using third party FOSS

SADSA (Software Application for Data Science and Analytics) is a Python-based desktop application designed to simplify statistical analysis, machine learning, and data visualization for students, researchers, and data professionals. Built using Python for the GUI, SADSA provides a menu-driven interface for handling datasets, applying transformations, running advanced statistical tests, machine learning algorithms, and generating insightful plots — all without writing code.