Open Source Python Data Analytics Tools
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Python Data Analytics Tools
View 253 business solutionsBrowse free open source Python Data Analytics Tools and projects below. Use the toggles on the left to filter open source Python Data Analytics Tools by OS, license, language, programming language, and project status.
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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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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. -
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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/ -
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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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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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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Timesketch
Collaborative forensic timeline analysis
Timesketch is a collaborative forensic timeline analysis platform used to investigate security incidents by turning diverse evidence into a single, searchable chronology. Analysts ingest logs and artifacts from many sources—endpoints, servers, cloud services—and Timesketch normalizes them into events on a unified timeline. Powerful search, aggregations, and saved views help you pivot quickly, highlight anomalies, and preserve investigative steps for later review. The system supports tagging, sketch notes, and story building so teams can annotate findings and share context without losing the raw data trail. Integrations with popular DFIR pipelines make ingestion repeatable, while role-based access and audit logs support enterprise workflows. By combining scale, collaboration, and reproducibility, Timesketch moves incident response beyond ad-hoc spreadsheets to a durable, team-oriented investigation record. -
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Astropy
Repository for the Astropy core package
The Astropy Project is a community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages. Astropy is a Python library for use in astronomy. Learn Astropy provides a portal to all of the Astropy educational material through a single dynamically searchable web page. It allows you to filter tutorials by keywords, search for filters, and make search queries in tutorials and documentation simultaneously. The Anaconda Python Distribution includes Astropy and is the recommended way to install both Python and the Astropy package. The astropy package contains key functionality and common tools needed for performing astronomy and astrophysics with Python. It is at the core of the Astropy Project, which aims to enable the community to develop a robust ecosystem of affiliated packages covering a broad range of needs for astronomical research, data processing, and data analysis. -
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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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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic). File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others). -
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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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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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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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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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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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Datasette
An open source multi-tool for exploring and publishing data
Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size, analyze and explore it, and publish it as an interactive website and accompanying API. Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world. It is part of a wider ecosystem of tools and plugins dedicated to making working with structured data as productive as possible. Try a demo and explore 33,000 power plants around the world, then take a look at some other examples of Datasette in action. Then read how to get started with Datasette, subscribe to the monthly-ish newsletter and consider signing up for office hours for an in-person conversation about the project. -
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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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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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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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xrayutilities
a package with useful scripts for X-ray diffraction physicists
xrayutilities is a python package used to analyze x-ray diffraction data. It can support with performing diffraction experiments and used for common steps in the data analysis. It can read experimental data from several data formats (spec, edf, xrdml, ...); convert them to reciprocal space for arbitrary goniometer geometries and different detector systems (point, linear as well as area detectors); for further processing the data can be gridded (transformed to a regular grid). More detailed description as well as documentation can be found at webpage http://xrayutilities.sourceforge.io/. Downloads for windows can be found on http://pypi.python.org/pypi/xrayutilities Development is performed on github: https://github.com/dkriegner/xrayutilities -
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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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FreeSEM
Free and open-source desktop application designed for SEM
FreeSEM is a free, open-source desktop application designed for researchers and students to perform Structural Equation Modeling (SEM) for statistical and research analysis. It allows users to visually build models using a drag-and-drop interface to create path diagrams and analyze relationships between observed and latent variables. The software supports methods such as exploratory factor analysis, covariance-based SEM, partial least squares SEM, and meta-SEM, and it provides model fit statistics like CFI, TLI, RMSEA, SRMR, and chi-square to evaluate models. It also enables exporting analysis results and reports to formats like Word, Excel, CSV, and PDF, making it useful for academic research and data analysis workflows. -
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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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Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). Note : if you have downloaded version 3.12 before the 8th of february, a patch exists for a minor bug on TOutputFileKey file, don't hesitate to ask us. -
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i-Map - Plot Geolocation from Images
Automatically plots latitude, longitude from images on Google maps.
i-Map is a Photo metadata forensic tool for Geo-location analysis of images that are clicked from GPS enabled devices. In this tool, you can load 100s of images from a suspect's device and analyze them to know various locations where photos were clicked on mobile phone/tablet. After loading images, with a single click, iMap plots all the images on World Map to visually check where they have been captured, generate timeline and activity of suspect and match them with CDR (Call Detail Record) Details. To generate a report, you can export this data into PDF or Excel file according to your requirements.
