Open Source Python Data Analytics Tools for Linux
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Python Data Analytics Tools for Linux
View 19 business solutionsBrowse free open source Python Data Analytics Tools for Linux and projects below. Use the toggles on the left to filter open source Python Data Analytics Tools for Linux by OS, license, language, programming language, and project status.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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.
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HEALPix
Data Analysis, Simulations and Visualization on the Sphere
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/ -
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pandas
Fast, flexible and powerful Python data analysis toolkit
pandas is a Python data analysis library that provides high-performance, user friendly data structures and data analysis tools for the Python programming language. It enables you to carry out entire data analysis workflows in Python without having to switch to a more domain specific language. With pandas, performance, productivity and collaboration in doing data analysis in Python can significantly increase. pandas is continuously being developed to be a fundamental high-level building block for doing practical, real world data analysis in Python, as well as powerful and flexible open source data analysis/ manipulation tool for any language. -
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Orange Data Mining
Orange: Interactive data analysis
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. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
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F1 Race Replay
An interactive Formula 1 race visualisation and data analysis tool
F1 Race Replay is an interactive replay viewer that lets users watch and analyze recorded Formula 1 race sessions with precise control over camera angles, timing, and telemetry overlay, offering a rich experience beyond standard broadcast replays. It ingests official timing and positional data, then renders vehicle movements through track maps and 3D visualizations so fans, analysts, and engineers can review strategy, overtakes, tire degradation effects, and pit stop impacts in detail. Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. The viewer also supports annotations and bookmark capabilities so users can mark moments of interest for future review or comparison. -
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Python for Data Analysis
Materials and IPython notebooks for "Python for Data Analysis"
Python for Data Analysis is the official companion repository for Python for Data Analysis, 3rd Edition by Wes McKinney. It contains the datasets, examples, and IPython notebooks used throughout the book. The repository helps readers practice Python data analysis concepts directly in Jupyter Notebook. Its chapters cover Python basics, NumPy, pandas, data loading, cleaning, wrangling, visualization, time series, modeling libraries, and full analysis examples. The project includes setup options using uv or Conda, with dependency files to reproduce the working environment. It is best suited for learners, analysts, and developers who want hands-on practice with the modern Python data stack. -
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QtiPlot is a user-friendly, platform independent data analysis and visualization application similar to the non-free Windows program Origin.
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$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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AUR Malware Check
Detection tools for the June 2026 atomic-lockfile AUR supply-chain
AUR Malware Check is a community repository for detecting exposure to the June 2026 atomic-lockfile supply-chain attack against the Arch User Repository. It collects scattered indicators, affected package lists, and detection scripts into one place for easier review and contribution. The project helps users compare installed AUR packages against known compromised package lists. It also includes checks for related package-manager cache artifacts and supports broader historical scans through pacman logs. The repository provides shell-based tooling, a Python 3.14+ implementation, consolidated indicators, source notes, and testable detection resources. It is useful for Arch users, maintainers, and incident responders who need a focused way to investigate possible local exposure. -
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Dash
Build beautiful web-based analytic apps, no JavaScript required
Dash is a Python framework for building beautiful analytical web applications without any JavaScript. Built on top of Plotly.js, React and Flask, Dash easily achieves what an entire team of designers and engineers normally would. It ties modern UI controls and displays such as dropdown menus, sliders and graphs directly to your analytical Python code, and creates exceptional, interactive analytics apps. Dash apps are very lightweight, requiring only a limited number of lines of Python or R code; and every aesthetic element can be customized and rendered in the web. It’s also not just for dashboards. You have full control over the look and feel of your apps, so you can style them to look any way you want. -
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Crystalsim - XRD hkl simulation
X-ray diffraction (XRD) analysis for hkl simulation of any crystal.
Crystalsim is a simple freeware program with a neat graphical user interface for X-ray diffraction (XRD) data analysis . It can simulates all possible {hkl} planes data for the selected crystal. Crystallographic Information File (.cif) can also be used. Analyze both powder diffraction and single crystal data . Indexed at International Union of Crystallography (IUCR). Crystalline lattice parameters such as ‘a’, ‘b’, ‘c’ as well as interfacial angles such as alpha, beta, gamma can also be entered manually. Processed data can be saved as .csv file format. Designed by M Kanagasabapathy, Associate Professor, Department of Chemistry, Rajus' College, Affiliated to Madurai Kamaraj University Rajapalayam (TN) India email: rrcmks(at)gmail.com -
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relax
Molecular dynamics by NMR data analysis
The software package 'relax' is designed for the study of molecular dynamics through the analysis of experimental NMR data. Organic molecules, proteins, RNA, DNA, sugars, and other biomolecules are all supported. It supports exponential curve fitting for the calculation of the R1 and R2 relaxation rates, calculation of the NOE, reduced spectral density mapping, the Lipari and Szabo model-free analysis, study of domain motions via the N-state model and frame order dynamics theories using anisotropic NMR parameters such as RDCs and PCSs, the investigation of stereochemistry in dynamic ensembles, and the analysis of relaxation dispersion data. -
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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...
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GXSM
Scanning Probe Microscopy Controller and Data Visualization Software
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 github.com/pyzahl/Gxsm4 (source code) or as binary for Ubuntu on https://launchpad.net/~totto/+archive/ubuntu/gxsm -
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LaueTools
open source python packages for X-ray MicroLaue Diffraction analysis
LaueTools is an open-source project for white beam Laue x-ray microdiffraction data analysis including tools in image processing, peaks searching & indexing, crystal structure solving (orientation & strain) and data & grain mapping visualisation. Python 3 Code and new features are now at: https://gitlab.esrf.fr/micha/lauetools -
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sadsa
SADSA (Software Application for Data Science and Analytics)
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. -
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ExAM-Exome_Analysis_And_Mining
A whole exome sequencing analysis package and its graphical interface
During the past few years, whole exome sequencing has imposed itself for genetic research, largely due to its use for detection of causative mutations responsible for Mendelian disorders. As a consequence of their power and of the rapidly decreasing cost of these technologies, massive amount of exome sequencing data are generated and becoming available to a broadening community of scientists. However, these data remain difficult to analyze and interpret by the general scientific community, due to the limited bioinformatics resources that are currently freely available for their analysis and the partial information that they provide. Here, we present an interactive and free analysis package, ExAM, dedicated to whole exome sequencing data analysis. It is accessible to non-computer scientists and provides a complete set of information, as well as decision support to prioritize candidate disease causing variants. -
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ThermV
Comprehensive thermal analysis software package
ThermV thermal analysis software package aims to provide the most sophisticated automatic analysis of thermal analysis data (TG/DTG, DTA and DSC). It offers new algorithm for concurrent peak deconvolution at different heating rates and provides full kinetic analysis of these data, including isoconversional methods for Ea, determination of reaction model and full kinetic triplet, Avrami coefficients, and dimensionality of crystal growth for reactions in the solid state. The project is currently in alpha stage, where individual modules will be provided for data analysis. The modules for peak deconvolution, peak profile analysis and determination of Ea and lnA will be provided first. Full GUI will be provided in beta stage. Due to computational limitations, the code is partly programmed in Python and partly in R. Python code will eventually be fully integrated into GUI. R code might remain standalone, although it will be integrated to a highest possible degree. Distributed under GPL -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community, we-media, and online earning community, with a QQ group of more than 10,000 people and at least 10,000 subscribers. The number of Github Stars exceeds 60k, and it ranks in the top 100 of all Github organizations. The daily up of all its websites exceeds 4k, and the peak of Alexa ranking is 20k. Our core members are certified as CSDN blog experts and short-book programmers as excellent authors. We have established ApacheCN, a non-profit document, and tutorial translation project. -
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A collection of tools for working with the comparative data analysis ontology including import/export facilities for common phylogenetic file formats, and also a triple-store framework.
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CalPyLab is an open source data acquisition and (light) analysis software suite for scientific laboratories. The goal of this project is to provide a lightweight replacement for lab-view, to provide a quick and easy way to connect to your equipment.
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Collaborative Computing Project for NMR
Collaborative Computing Project for NMR (CCPN)
The Collaborative Computational Project for NMR (CCPN) provides tools and knowledge to maximise the impact of the biological NMR studies. The CCPN software facilitates data analysis and software integration. The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields. -
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression. -
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CrimeKgAssitant
Crime assistant including crime type prediction
CrimeKgAssitant is a Chinese-language legal NLP project that combines offense prediction, consultation classification, automated answers, and knowledge graph queries. It organizes data around criminal charges, sentencing cases, legal question-and-answer pairs, and related legal information. A multiclass model predicts likely offense categories from written case descriptions using document embeddings and a support vector machine. Separate classifiers sort consultation questions into predefined legal categories before retrieving or generating relevant responses from the prepared knowledge base. The repository includes training scripts, inference programs, dictionaries, models, and utilities for building the question-answer database. Its published experiments use millions of case records and hundreds of thousands of consultation pairs. The software is intended for research and demonstration and does not replace qualified legal advice.
