Reverse Dependencies of seaborn

The following projects have a declared dependency on seaborn:

• atlaspy — Python library for working with brain atlases
• atlassianhw — Atlassian application homework
• atmospy — Data analysis and visualization for air quality data.
• atombench — atombench
• AtomProNet — A Python package for pre and post-process VASP/Quantum ESPRESSO data into machine learning interatomic potential (MLIP) format.
• atomqc — atomqc
• atomvision — atomvision
• atonixcore — At AtonixCorp, we're pioneering the future with cutting-edge technology solutions across agriculture, fintech, medical research, security, big data, and cloud computing. Our innovative approaches and dedicated expertise drive advancements and empower industries to reach new heights.
• Attention-Maps-Extraction — A package for extracting attention maps.
• AttentionMOI — A Denoised Multi-omics Integration Framework for Cancer Subtype Classification and Survival Prediction.
• attribench — A benchmark for feature attribution techniques
• atts — Train_test splitter with adversarial validation
• aucome — Automatic Comparison of Metabolism
• audplot — A Python plotting package
• auroris — Data Curation in Polaris
• autil — Some Python snippets for a researcher's daily use
• auto-feature-extraction — "Automatic Feature Extraction in Images and Texts using Transfer Learning"
• auto-machine-learning — This is an python Library for AutoML which works for prediction and classification tasks.
• Auto-ML-C — A small example package
• auto-paired-test — An automatic paired test
• auto-prep — AutoML with enhanced preprocessing and explainability.
• Auto-Taste-ML — A small example package
• auto-ts — Automatically Build Multiple Time Series models fast - now with Facebook Prophet!
• autoballs — Python package for segmentation of axons and morphological analysis.
• autobmt — a modeling tool that automatically builds scorecards and tree models.
• autoboost — A thin wrapper for step-wise parameter optimization of boosting algorithms.
• AutoDataPre — The package of Auto-DP ( Automated System for Data Preparation).
• AutoDataPreprocess — A high-level library for automatic preprocessing of tabular data
• autodl-gpu — Automatic Deep Learning, towards fully automated multi-label classification for image, video, text, speech, tabular data.
• AutoEis — A tool for automated EIS analysis by proposing statistically plausible ECMs.
• autoesda — A Python package that automates the exploratory spatial data analysis (ESDA) process by summarizing the results in an HTML report
• AutoFeatures — PySpark Auto Feature Selector
• autofracture — Support the intelligent fracturing process.
• Autogasuptake — ::A tool to automatically treat the data and plot the gas uptake curve::
• autogluon.eda — AutoML for Image, Text, and Tabular Data
• autograd-lib — Library to simplify autograd computations in PyTorch
• autoimpute — Imputation Methods in Python
• autoinsights — An automatic dataset exploration and visualization package
• autokattis — Updated Kattis API wrapper
• AutoLinePlotter — This repo support auto line plot for multi-seed event file from TensorBoard
• autolingua — autolingua description
• automate-machinelearning — A library package used to automate the machine learning on regression and classification problems.
• automate-ML — A python module to solve machine learning problems in a mechanized way. The repository is able to preprocess the data and output the results in numerical as well as in the graphical form.
• automated-machineLearning-methods — A small example package for machine learning operations
• AutomatedCleaning — Automated Data Cleaning Library
• automationobjectdetection-sandeepjena7 — no summary
• automize_science — Automize Science is a Python package designed to elaborate data into graphs coming from lipid extractions (LC/MS).
• automl-alex — State-of-the art Automated Machine Learning python library for Tabular Data
• automl-tools — automl_tools
• AutoMLBench — A Python package for automated ML model benchmarking and comparison
• automlclassifier — Automation of machine learning with visualizations and report generation.
• autoMLF — autoML for training and inference Deep Learning model
• automlkiller — Auto machine learning, deep learning library in Python.
• autoMMM — no summary
• automotifs — A wrapper for automatic Motif Detection
• automs — Automatic Model Selection Using Cluster Indices
• autoneuro-pypi — Template python package
• autonon — Organon Automated ML Platform
• autopetroleum — Support the intelligent fracturing process.
• autoprognosis — A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
• autoPyTorch — Auto-PyTorch searches neural architectures using smac
• autoqtl — Automated Quantitative Trait Locus Analysis Tool
• AutoRAG — Automatically Evaluate RAG pipelines with your own data. Find optimal structure for new RAG product.
• AutoReduce — Python based automated model reduction tools for SBML models
• AutoSteper — Automated Stepwise Addition Procedure for Extrafullerene.
• autotime — Automated ML-based predictive analytics framework for time-series data.
• autotm — Automatic hyperparameters tuning for topic models (ARTM approach) using evolutionary algorithms
• autotonne — Auto machine learning, deep learning library in Python.
• autotransformers — a Python package for automatic training and benchmarking of Language Models.
• AutoTS — Automated Time Series Forecasting
• autoviml — Automatically Build Variant Interpretable ML models fast - now with CatBoost!
• autoviz — Automatically Visualize any dataset, any size with a single line of code
• autowoe — Library for automatic interpretable model building (Whitebox AutoML)
• autoxeda — An automated and dynamic exploratory data analysis (EDA) library for streamlined data insights using Large Language Model.
• AutoZeekWatch — Network Intrusion Detection using Zeek logs
• autrainer — A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks.
• avaframe — The Open Avalanche Framework
• avalon-rl — Avalon: A Benchmark for RL Generalization Using Procedurally Generated Worlds
• avatarpy — avatar analysis module
• avi-mmdet — Custom OpenMMLab Detection Toolbox and Benchmark
• avn — Package for zebra finch song analysis.
• awesome-panel-extensions — A package of awesome Panel extensions. Provided by awesome-panel.org
• awherepy — Python package built to work with weather and agriculture data in the aWhere API.
• axel-lab-to-nwb — NWB conversion scripts and tutorials.
• axoden — axoden simplifies the quantification of axonal projections in neuroscience.
• axon-synthesis — A package to synthesize artificial axons
• azulejo — tile phylogenetic space with subtrees
• azureml-designer-classic-modules — A variety of modules for data processing, model training, inferencing and evaluation.
• azutils — Utilities for Azure
• BABACHI — no summary
• babino2020masks — Code used in https://arxiv.org/abs/2006.05532
• babypy — A simplified Python library for beginners
• BacDiving — Bacdiving accesses the Bacterial Diversity Metadatabase BacDive and provides various visualization options.
• backbone-learn — A Library for Scaling Mixed-Integer Optimization-Based Machine Learning.
• backend-central-dev — no summary
• backstrip — backstrip adds color-coordinated fill behind matplotlib boxplots.
• backtester272 — A backtesting project where you can also request binance and yfinance assets, and customize your strategies
• Backtesting-Framework — A comprehensive backtesting framework designed to evaluate and compare various investment strategies using historical data. This framework enables users to implement, test, and analyze trading strategies by providing detailed performance metrics and customizable visualizations. It supports data input in CSV or Parquet formats and offers multiple visualization backends, including matplotlib, seaborn, and plotly.
• backtracks — Python package to fit relative astrometry with background star motion tracks.
• baconian — model-based reinforcement learning toolbox