Open Source Python Data Management Systems for Mac - Page 5
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Python Data Management Systems for Mac
View 621 business solutionsBrowse free open source Python Data Management Systems for Mac and projects below. Use the toggles on the left to filter open source Python Data Management Systems for Mac by OS, license, language, programming language, and project status.
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PyOSG is a python wrapper for the OpenSceneGraph. Version number corresponds to the version of OpenSceneGraph that PyOSG should work with (e.g. PyOSG 2.8 should work with OpenSceneGraph 2.8.x). Still in early stages, so email mday299 if any problems!
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QuickPlot
Simple user interface for gnuplot aimed for reflectometry data
Graphical user interface for gnuplot to create publication quality figure very quickly. It supports templates for fast formatting of graphics, different plot styles, insets, axis and label options. One important feature is storing metadata in png and pdf files that can be used to reload any graph saved with QuickPlot. -
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Metropolis is a visualization tool for TRANSIMS traffic simulation output. It specializes in viewing the dynamic outputs of the micro-simulator, such as vehicle motion and other time-varying data.
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pyBoids is a free/open-source project that implements (in Python/TKinter) Craig Reynold's famous boids algorithm. This algorithm intelligently simulates flocking, herding, swarming, and schooling behavior as found in nature.
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Workshop resource manager
Easy and fast tool for managing home workshop resources
Storekeeper is small tool that helps you to keep an eye on your resources in home lab or workshop. It can be used in several places and, thanks to it's single-file and synchronize option, merge data between users. -
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A python-based system to diagnosis and make weather maps (wxmap) of numerical weather prediction models using the Grid Analysis and Display System (GrADS) from opengrads.org
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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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AWS Data Wrangler
Pandas on AWS, easy integration with Athena, Glue, Redshift, etc.
An AWS Professional Service open-source python initiative that extends the power of Pandas library to AWS connecting DataFrames and AWS data-related services. Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON, and EXCEL). Built on top of other open-source projects like Pandas, Apache Arrow and Boto3, it offers abstracted functions to execute usual ETL tasks like load/unload data from Data Lakes, Data Warehouses, and Databases. Convert the column name to be compatible with Amazon Athena and the AWS Glue Catalog. Run a query against AWS CloudWatchLogs Insights and convert the results to Pandas DataFrame. Get QuickSight dashboard ID given a name and fails if there is more than 1 ID associated with this name. List IAM policy assignments in the current Amazon QuickSight account. -
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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. -
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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. -
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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>. -
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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. -
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The aim of this project is to translate the geostatistical BMELib Matlab Toolbox (http://www.unc.edu/depts/case/BMELIB/) into Python.
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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. -
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic tour notebook to learn how to use the package's most important features. Take a look at the advanced tour notebook to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more. Explore the options exemplifying the balance between exploration and exploitation and how to control it. Explore the domain reduction notebook to learn more about how search can be sped up by dynamically changing parameters' bounds. -
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An Open Source IEC 61131-3 Integrated Development Environment, providing PLCOpen SoftPLC programming, CanOpen IO's, and SVG based HMI.
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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. -
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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. -
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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. -
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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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Parse, analyze and -- most importantly -- use COBOL data definitions. This gives you access to COBOL data from Python programs. Write data analyzers, one-time data conversion utilities and Python programs that are part of COBOL systems. Really.
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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. -
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CellTypist
A tool for semi-automatic cell type classification, harmonization
CellTypist is an automated tool for cell type classification, harmonization, and integration. Classification, transfer cell type labels from the reference to query dataset. Harmonization, match and harmonize cell types defined by independent datasets. integration, integrate cell and cell types with supervision from harmonization. CellTypist recapitulates cell type structure and biology of independent datasets. Regularised linear models with Stochastic Gradient Descent provide a fast and accurate prediction. Scalable and flexible. Python-based implementation is easy to integrate into existing pipelines. A community-driven encyclopedia for cell types. -
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Cellicone is a project to develop an artificial life organism with the necessary components to make it comparable to biological life as we know it. This includes components ranging from proteins to cells to organs to limbs, and many steps between.
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Chartshare is a computer based system for the production, analysis, and distribution of Standard Celeration Charts (http://www.celeration.org).
