Open Source Python Data Analytics Tools - Page 2
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Python Data Analytics Tools
View 250 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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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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CorNetMap
A tool for Gene Expression Correlation Network
Capabilities of CorNetMap: 1. Read data as tab-delimited text file. Can be used for analysis of any data set beyond gene expression. 2. Capable of both two-dimensional and multidimensional data analysis. 3. Calculate Pearson correlation and cross-correlation for analysis data with phase difference. 4. Generate correlation Heat-map and draws network map. 5. Save correlation data as text file. How to use and doccumentation: https://sourceforge.net/projects/cornetmap/files/Documentation_corNetMap.pdf Sample data for testing: https://sourceforge.net/projects/cornetmap/files/Test%20Data/ Citation: Cite CornetMap as " Khaund, A. Bose, B. CorNetMap. https://sourceforge.net/projects/cornetmap " Credit: Abhigyan Khaund, Biplab Bose, IIT Guwahati -
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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DSTK - DataScience ToolKit
DSTK - DataScience ToolKit for All of Us
DSTK - DataScience ToolKit is an opensource free software for statistical analysis, data visualization, text analysis, and predictive analytics. Newer version and smaller file size can be found at: https://sourceforge.net/projects/dstk3/ It is designed to be straight forward and easy to use, and familar to SPSS user. While JASP offers more statistical features, DSTK tends to be a broad solution workbench, including text analysis and predictive analytics features. Of course you may specify JASP for advanced data editing and RapidMiner for advanced prediction modeling. DSTK is written in C#, Java and Python to interface with R, NLTK, and Weka. It can be expanded with plugins using R Scripts. We have also created plugins for more statistical functions, and Big Data Analytics with Microsoft Azure HDInsights (Spark Server) with Livy. License: R, RStudio, NLTK, SciPy, SKLearn, MatPlotLib, Weka, ... each has their own licenses. -
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Data Preprocessing Automate
Data Preprocessing Automation: A GUI for easy data cleaning & visualiz
Data Preprocessing Automation is a Python-based GUI application designed to simplify and automate data preprocessing tasks. It allows users to upload Excel files, automatically handle missing values, remove duplicates, and detect and remove outliers using statistical methods. The application provides data visualization tools, including box plots for distribution analysis and scatter plots for exploring relationships between variables. Users can download the processed data for further analysis. Built with Tkinter, Pandas, Matplotlib, and Seaborn, it ensures an intuitive interface and efficient performance. Additionally, it features a custom logo, a clean UI with a green-blue theme, and options for licensing and public release. This tool is ideal for data analysts, researchers, and professionals looking to automate preprocessing without coding. 🚀 -
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Data analysis for ADEXL
Create Excel reports from results of Cadence ADE-XL simulations
*** Required files from "Excel COM API for Python" project *** The scripts allows post processing of data saved in CSV format from "Detail - Transposed" view of ADE-XL -
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DataPrep
Python-based data preprocessing tool
DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal strategies with customizable parameter setting options. -
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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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Electrophysiology & circular stats tools
Data analysis and circular statistics with OpenElectrophy and R
Set of tools for basic analysis of electrophysiological data. The Python classes show how to call OpenElectrophy functions and save data. The R library applies circular statistics to spike phase data and saves the best von Mises fit and the Rayleigh statistics on the disk. The wavelet coherence analysis is done in R by the package "sowas". Check the module R_coherence to see how we solved that problem. This packages may be useful for people who start using OpenElectrophy and circular statistics in R. If you find errors, please report them. The project is still in development, so in the future you'll get updates. -
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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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GUI for DEDA
GUI for DEmography Data Analysis
<This project has been completely rewrote and transformed into a new one: https://sourceforge.net/projects/deday/. 2013/06/26> The graphic user interface for DEDA (DEmography Data Analysis), a scientific software package fitting survivalship data to a number of distributions using maximum likelihood (ML) method. Currently, Weibull (2p), Gompertz and Gompertz-Makeham are supported. IMPORTANT NOTICE: Only the GUI is provided here. In order to perform the analysis, one also need the DEDA computation core program. Please email a request to me, entitled: 'Request for DEDA computation core', if you wish to have a copy. -
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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.
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JS Analyzer
Burp Suite extension for JavaScript static analysis
JS Analyzer is a powerful static analysis tool implemented as a Burp Suite extension that helps security researchers and web developers automatically uncover important artifacts in JavaScript files during web application testing. It parses JavaScript responses intercepted by Burp Suite and intelligently extracts API endpoints, full URLs (including cloud storage links), secrets like API keys or tokens, and email addresses while filtering out noise from irrelevant code patterns. The extension is designed to reduce manual effort when analyzing large or obfuscated JavaScript assets, helping testers find security vulnerabilities and sensitive information faster and more reliably. It also includes UI features such as live search, result filtering, and the ability to export findings in JSON format for further processing. The underlying engine can be used independently in Python, enabling integration into custom workflows or automated pipelines outside Burp Suite. -
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MoDAVE aims at providing data analysis and visualization extensions to Mosaic datasets, including cubed sphere data, to the Climate Data Analysis Tools (CDATA) in particular.
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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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Pathomx
Workflow-based data analysis built on IPython
Pathomx is a workflow-based tool for the analysis and visualisation of experimental data. Initially created as a tool for metabolomic data analysis is has been extended and can now be used for any scientific and non-scientific data analysis. -
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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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PyNanoLab
data analysis and Visualization with matplotlib
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. -
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Pyclamp
A Python package used to extract and analyse electrophysiological data
Pyclamp is Python package used to extract and analyse electrophysiological data. A graphical user interface has been developed to allow a user to run the pack- age without requiring any knowledge of Python code. Presently, Pyclamp is designed to perform very specific forms of analysis on evoked synaptic responses: Data analysis (under development) : This is a highly user-interactive en-vironment that can be used to discriminate synaptic events, obtain various measures of their kinetics and size, and output the results. Quantal analysis : This part of the package performs simple variance-mean analysis and Bayesian quantal analysis to estimate the quantal sizea dn number of release sites among other measures. -
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Pyphant is a framework for scientific data analysis. It features a flexible plugin architecture allowing for the application in many different fields. For the analysis a graph of processing steps is constructed, that can be applied via the cli as well.
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Python Laboratory Operations Toolkit
many useful snippets for using python in a laboratory
A toolkit of Python software useful in a laboratory data acquisition and analysis environment. Includes support for such protocols as VXI-11 (and its extension, LXI), Vernier LabPro (now very old), and National Instruments DSTP (now very old). Also includes data analysis and modelling tidbits. Python3 updates are on the way in the very near future for the biggest packages. the vxi11 package is fully up-to-date, although see the blog post about python 3.13 -
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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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QME-DEV Workbench (wxScipy) is a data analysis workbench based on Python, SciPy, Numpy and MatPlotLib(PyLab) and uses wxPython for the GUI. It can be used for non-linear analysis with a large set of experimental or generated data. Load/Acquire / Save/ Gr
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QuickNXS
Polarized ToF reflectivity raw data analysis tool
Data evaluation tool for the magnetism reflectometer at the spallation neutron source (BL-4A@SNS). Reads raw nexus files (HDF5) of histogrammed or event mode data to create reflectivity curves and 2D Q-maps.
