Open Source Python Data Management Systems - Page 10
Sort By:
Python Data Management Systems
View 4298 business solutionsBrowse free open source Python Data Management Systems and projects below. Use the toggles on the left to filter open source Python Data Management Systems by OS, license, language, programming language, and project status.
-
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
1
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 -
2
Data logger, manipulator and visualizer. Receives data in CSV format from stdio.
-
3
Procup is a system that allows the user to do a centralized backup from a group of servers. It also indexes the information of the backup to be able to do fast searching into the data.
-
4
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. -
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.
-
5
Software written in Python to take oscilloscope data for a data transmit channel and a data clock as inputs, and output an eye diagram. The eye diagram helps evaluate the quality of the transmit data.
-
6
Data Migration Tools for RDBMS
DataMigrator for 14 major databases
Touch and go Windows command line data migration tool for 14 databases: 1. Sybase ASE 2. Informix Innovator C 3. Sybase SQL Anywhere 4. DB2 UDB 5. SQLServer 6. MariaDB 7. Sybase IQ 8. PostgreSQL 9. MySQL 10. Informix IDS 11. TimesTen 12. Oracle 13. SQL Lite 14. Exadata -
7
Data Migrator for Oracle
Migrate/Copy your data between Oracle database and 13 major DBs.
Command line data Copy/Migration tool for Oracle. Supports Oracle 7.3, Oracle 8i, Oracle 9i, Oracle 10G, Oracle 11G and 13 major databases. 1. Exadata 2. Sybase ASE 3. Informix Innovator C 4. Sybase SQL Anywhere 5. DB2 UDB 6. CSV 7. SQLServer 8. MariaDB 9. Sybase IQ 10. PostgreSQL 11. MySQL 12. Informix IDS 13. TimesTen -
8
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. 🚀 -
9
Data Science Notes
Curated collection of data science learning materials
Data Science Notes is a large, curated collection of data science learning materials, with explanations, code snippets, and structured notes across the typical end-to-end workflow. It spans foundational math and statistics through data wrangling, visualization, machine learning, and practical project organization. The content emphasizes hands-on understanding by pairing narrative notes with runnable examples, making it useful for both self-study and classroom settings. Because it aggregates topics in one place, learners can move linearly or jump into specific areas as needed during projects. The notes also highlight common pitfalls and good practices, which helps beginners adopt professional habits early. It’s a living resource that many students consult when revising fundamentals or exploring adjacent tools in the ecosystem. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
10
Data Spooler For Oracle #SaveUkraine
Simplified turbo spooler for Oracle.
#SaveUkraine #StopRussia #FreeUkraine #StopPutin #CrimeaIsUkraine #UnitedForUkraine #RussiaInvadedUkraine Exports/Spools scalar data on disk for a given Oracle table. Turbo mode spools 5x faster. -
11
Data Spooler for SQLServer #SaveUkraine
Extracts table or query data from SQL Server 2005, 2008, 2012
#SaveUkraine #StopRussia #FreeUkraine #StopPutin #CrimeaIsUkraine #UnitedForUkraine #RussiaInvadedUkraine Spools/extracts/dump table or query data from SQL Server 2015, 2008,2012. Serial spool creates single dump file. Turbo mode offers 5x spool performance improvement. Sharded turbo more creates multiple files. -
12
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 -
13
DataCopy For SQLServer
Data Copy tool for SQL Server and Oracle
Migrate your data from SQLServer to Oracle without creating single dump file. Input is a SQLServer query file defining dataset you want to copy to Oracle. Target table has to exist for copy to go through. Turbo mode offers 5x copy performance improvement. -
14
DataMule
Extract-Copy-Load (ECL) tool for 14 databases.
Extract, Copy and Load operations for: 1. Sybase ASE 2. Informix Innovator C 3. Sybase SQL Anywhere 4. DB2 UDB 5. SQLServer 6. MariaDB 7. Sybase IQ 8. PostgreSQL 9. MySQL 10. Informix IDS 11. TimesTen 12. Oracle 13. SQL Lite 14. Exadata Total 224 data copy vectors. CSV -> DB. DB->DB DB->CSV -
15
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. -
16
DataSync Suite is an open source platform for integrating tools like Zimbra, SugarCRM, and Drupal. The tool is focused on a single sign-on, application data integration, and fast, flexible deployment.
-
17
A group a subprojects for Data Cleaning projects, mainly as a step of a Data Mining Project. Visit www.datacleaningopensource.com to review our current applications or if you want to add yours. NOTE: PROGRAMMING SKILLS ARE REQUIRED.
-
18
Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
Datumaro is a flexible Python-based dataset management framework and command-line tool for building, analyzing, transforming, and converting computer vision datasets in many popular formats. It supports importing and exporting annotations and images across a wide variety of standards like COCO, PASCAL VOC, YOLO, ImageNet, Cityscapes, and many more, enabling easy integration with different training pipelines and tools. Datumaro makes it easy to merge datasets, split them into training/validation/test subsets, filter or transform annotations, and validate annotation quality — all while preserving metadata and supporting detailed statistics. It’s especially useful when you’re dealing with heterogeneous data sources or need to prepare complex datasets for machine learning workflows, freeing you from writing custom scripts for every format conversion. -
19
DiaCanvas is a library for drawing diagrams. It is completely Model/View/Controller based and has a Visio (DIA) look. The package also contains a Python wrapper for the library.
-
20
Django-dataplot enables developers using the Django web framework to seamlessly integrate data-driven graphical plots into their web pages.
-
21
A 3D data visualization tool, specializing in the display of robotically collected point, range, and volumetric data. Provides a very flexible framework for different file formats and visualization parameters.
-
22
Drop is a graphical interface for the GPL project Wets (hosted in SourceForge). Wets is an ETL software.
-
23
Electronic Constructor
Programs for electronics study and research
-
24
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. -
25
Elementary
Open-source data observability for analytics engineers
Elementary is an open-source data observability solution for data & analytics engineers. Monitor your dbt project and data in minutes, and be the first to know of data issues. Gain immediate visibility, detect data issues, send actionable alerts, and understand the impact and root cause. Generate a data observability report, host it or share with your team. Monitoring of data quality metrics, freshness, volume and schema changes, including anomaly detection. Elementary data monitors are configured and executed like native tests in dbt your project. Uploading and modeling of dbt artifacts, run and test results to tables as part of your runs. Get informative notifications on data issues, schema changes, models and tests failures. Inspect upstream and downstream dependencies to understand impact and root cause of data issues.
