Open Source Python Data Management Systems - Page 6
Sort By:
Python Data Management Systems
View 4423 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.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
1
AquaTerm is a Mac OS X grahics renderer. It allows command line applications written in ObjC, C, FORTRAN, Lisp, Perl or Python to display vector graphics, text and images using a simple API. Adapters for gnuplot, PGPLOT, and PLplot exists as well.
-
2
The 3D Slicer is freely available, open-source software for visualization, registration, segmentation, and quantification of medical image data. Follow the link for more info: http://slicer.org
-
3
ADOMA stands for: Alternative Display Of Multiple Alignment. ADOMA can create four different displays of a multiple sequence alignment: a ClustalW alignment in HTML format, a simplified ClustalW alignment in HTML and/or txt format and a colored ClustalW alignment in HTML format. For examples of these outputfiles check the screenshots. ADOMA uses ClustalW to create the multiple alignment from DNA or protein sequences and displays them slightly different than the normal output of ClustalW. ADOMA is a commandline program that can easily be used in pipelines. For more information check the README.md in the Files section. How to cite ADOMA: Zaal, D. and Nota, B. (2016), ADOMA: A Command Line Tool to Modify ClustalW Multiple Alignment Output. Mol. Inf., 35: 42–44. doi: 10.1002/minf.201500083 http://onlinelibrary.wiley.com/doi/10.1002/minf.201500083/abstract
-
4
AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. The project includes ready-to-use applications that showcase these agents in action, such as an exploratory data analysis copilot that generates reports, a pandas data analyst that combines wrangling and plotting, and SQL database agents that can query business databases and output results directly. -
$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.
-
5
Simplified interface to the Augmented Reality library ARToolKit for Python and other programming languages.
-
6
AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
aws-sdk-pandas (formerly AWS Data Wrangler) bridges pandas with the AWS analytics stack so DataFrames flow seamlessly to and from cloud services. With a few lines of code, you can read from and write to Amazon S3 in Parquet/CSV/JSON/ORC, register tables in the AWS Glue Data Catalog, and query with Amazon Athena directly into pandas. The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling boilerplate. It also supports Redshift, OpenSearch, and other services, enabling ETL tasks that blend SQL engines and Python transformations. Operational helpers handle IAM, sessions, and concurrency while exposing knobs for encryption, versioning, and catalog consistency. The result is a productive workflow that keeps your analytics in Python while leveraging AWS-native storage and query engines at scale. -
7
AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
8
Agaseous-NES is a graphic app for creating integrated circuit layers. Developers can import Agaseous for SuperWikia Alpha .AJA (Agaseous Joint Application) text files to automate & create asauna plates. Also imports and intergrate Adobe .PSD files.
-
9
Apache Airflow Provider
Great Expectations Airflow operator
Due to apply_default decorator removal, this version of the provider requires Airflow 2.1.0+. If your Airflow version is 2.1.0, and you want to install this provider version, first upgrade Airflow to at least version 2.1.0. Otherwise, your Airflow package version will be upgraded automatically, and you will have to manually run airflow upgrade db to complete the migration. This operator currently works with the Great Expectations V3 Batch Request API only. If you would like to use the operator in conjunction with the V2 Batch Kwargs API, you must use a version below 0.1.0. This operator uses Great Expectations Checkpoints instead of the former ValidationOperators. Because of the above, this operator requires Great Expectations >=v0.13.9, which is pinned in the requirements.txt starting with release 0.0.5. -
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.
-
10
AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>. -
11
Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
12
The aim of this project is to translate the geostatistical BMELib Matlab Toolbox (http://www.unc.edu/depts/case/BMELIB/) into Python.
-
13
BasisViewer
Browse and visualize your downloaded Basis Band B1 biometric data
This application allows you to graph your Basis data in several ways and allows you to easily move across dates, plot mulitple attributes simultaneously, and understand trends and answer questions. This application assumes you've already run the BasisRetriever application and downloaded your metrics in csv format. -
14
An Open Source IEC 61131-3 Integrated Development Environment, providing PLCOpen SoftPLC programming, CanOpen IO's, and SVG based HMI.
-
15
BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention. -
16
Biggles is a 2D scientific plotting package for Python, geared toward the production of publication-quality plots.
-
17
Blender Multibool, Searching for Fractal
Multibool function is slow ;)
Booleans all meshes on the screen. Plus: A fractal generator and attractor generator with a delicious SEARCH function ;) <meta name="google-site-verification" content="cULQewNGfx-aXCQR9fTJZ1Z7DXbLA5GjqjvAfF_oH5I" /> -
18
-
19
Bloxs
Build dashboards in Jupyter Notebook with numeric and chart boxes
Bloxs is a simple Python package that helps you display information in an attractive way (formed in blocks). Perfect for building dashboards, reports and apps in the notebook. -
20
Blueprint MCP
Diagram generation for understanding codebases and system architecture
Blueprint MCP is a modular control plane designed for managing and orchestrating multiple game-server clusters in real time, giving operators fine-grained control over scaling, configuration, and deployment workflows across distributed infrastructure. It provides a central management REST API and dashboard where teams can view cluster health, adjust instance fleets, set auto-scaling policies, and monitor usage metrics in a unified interface. Blueprint-MCP also supports templated server configurations so game environments can be versioned, replicated, and deployed consistently across regions or cloud providers. The control plane includes hooks for event-driven automation, allowing rules like “scale up at peak hours” or “restart unhealthy nodes automatically” to be codified and managed without manual intervention. Security and access control are built in so administrators can assign roles, manage secrets, and enforce network policies across cluster resources. -
21
Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. Bytewax is a Python framework and Rust distributed processing engine that uses a dataflow computational model to provide parallelizable stream processing and event processing capabilities similar to Flink, Spark, and Kafka Streams. You can use Bytewax for a variety of workloads from moving data à la Kafka Connect style all the way to advanced online machine learning workloads. Bytewax is not limited to streaming applications but excels anywhere that data can be distributed at the input and output. -
22
CAPLET
GDS visualization and parallelized capacitance extraction
Project CAPLET is a capacitance extraction toolkit that extract capacitance at field-solver accuracy. CAPLET can directly handle GDS2 layout files into capacitance matrices in both GUI and command line interfaces. The internal extraction algorithm is specialized for VLSI interconnect structures but not exclusively, as long as the structure is of Manhattan geometry and embedded in a uniform dielectric material. -
23
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.
-
24
COV2HTML
A visualization and analysis tool of Bacterial NGS data for Biologists
COV2HTML provides an easy and 'in home' web interface for biologists that allows coverage visualization of the NGS alignment needed for the analysis. It combines two essential processes: (i) MAP2COV, a tool that converts the huge NGS mapping or coverage files into light specific coverage files which contains genetic elements informations. (ii) COV2HTML, a visualization interface allowing a real-time analysis of data with selected criteria. Thus this interface offers a visualization of NGS mapping coverage data (DNA-seq, RNA-seq, ChIP-seq and TSS) performed in different prokaryotic organisms (bacteria, viruses...) or different experimental conditions (mutant versus wild type strains or different growth states…) facilitating studies. -
25
CSV*Extractor Pro (Windows command line)
Spool you scalar data in CSV format from 14 major Databases.
Command line tool for data export from major relational data stores (RDBMS). DB2 Advanced Enterprise Server DB2 Advanced Workgroup Server DB2 Developer Edition DB2 Enterprise Server DB2 Express DB2 Express C DB2 Workgroup Server Exadata Infobright Informix IDS Informix Innovator C MariaDB MySQL Oracle Oracle XE PostgreSQL SAP Sybase ASE SQL Lite SQL Server Enterprise SQL Server Express Sybase IQ Sybase SQL Anywhere TimesTen
