Reverse Dependencies of seaborn
The following projects have a declared dependency on seaborn:
- datascienv — Data Science package for setup data science environment in single line
- DataScrubber — A data cleaning package and visualisation tool for data science projects
- dataset-format-benchmark — Image dataset format benchmark
- dataset-shuffler — Data engineering tool for learning-based computer vision.
- datasette-seaborn — Statistical visualizations for Datasette using Seaborn
- datasist — A Python library for easy data analysis, visualization, exploration and modeling
- DataStand — A python package to help Data Scientists, Machine Learning Engineers and Analysts better understand data. Gives quick insights about given data; general dataset statistics, shape of dataset, number of unique data types, number of numerical and non-numerical columns, missing data statistics, missing data heatmap and provides methodology to impute missing data.
- DataSynthesizer — Generate synthetic data that simulate a given dataset.
- datatoolkit — A collection of tools for visualization and data processing for exploratory data analysis
- datatools-mikdowd — datatools is a python package for doing basic data summaries and other tasks
- datavis-cli — Output data loading and visualization boilerplate
- DataVizML — A package to explore and visualise a dataset in preparation for an ML project
- datawaza — Datawaza is a collection of tools for data exploration, visualization, data cleaning, pipeline creation, model iteration, and evaluation.
- datawhispers — This is a library to solve regression problems or statistical analysis for the DHBW Mannheim courses Advanced Programming and Data Visualisation
- datawhsipers — This is a library to solve regression problems or statistical analysis for the DHBW Mannheim courses Advanced Programming and Data Visualisation
- datawindow — Simplify data tasks in Python with interactive interface for effortless data interaction. Empower your data journey today.
- datexplore — A package for exploratory data analysis and data cleaning.
- datto — Data Tools (Dat To)
- db-classifier — A Probabilistic Approach to Multiclass Classification with Unknown Instances
- dbaae — Adversarial Autoencoder with dynamic batching package
- dbgpt — DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure.
- DBGSOM — A Python implementation of the Directed Batch Growing Self-Organizing Map
- DBRetina — DBRetina Python Package
- dbspace — Library for data-congruent, model-centric DBS Research
- DCACP — A Dyeing Clustering Algorithm based on Ant Colony Path-finding
- dcapy — Oil and Gas DCA Workflows
- dcclab — A Python library to read, transform, manipulate images and manage databases
- dcl-stats-n-plots — coming soon
- dclustval — A package for performing dense cluster validation
- dCRE — Python package for CRE processing
- dcss — Utilities for the book Doing Computational Social Science
- dddm — Direct Detection of Dark Matter: Probing the complementarity of several targets for dark matter detection
- ddmra — A Python package for distance-dependent motion-related artifact (DDMRA) analysis.
- Ddnet — This is a test. This project is not very useful!!!
- ddpaw — Extract APM metrics from DataDog
- ddpm-proteins — Denoising Diffusion Probabilistic Models - for Proteins - Pytorch
- ddpro — Data Discovery Pro for Automated EDA and ML
- ddr-p — data-driven research papers, making use of Python and LaTeX for automation and reproducibility.
- ddrage — Simulator for ddRADseq (double digest restriction site associated DNA sequencing) datasets. Generates reads (FASTQ format) that can be analyzed and validated using a ground truth file (YAML).
- ddu-dirty-mnist — Dirty-MNIST from "Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty"
- deafrica-tools — Functions and algorithms for analysing Digital Earth Africa data.
- debarcer — A package for de-barcoding and error correction of sequencing data containing molecular barcodes.
- decalmlutils — Useful functions when working with Machine Learning in Python
- decare — Detection of spatial chromatin accessibility patterns with inter-cellular correlations
- decavision — A package to easily train powerful image classification models using colab's free TPUs.
- decodanda — Geometric decoding of neural data with built-in best practices.
- deconvolawrence — Automated deconvolution of mass spectra datasets
- deep-xf — DEEPXF - An open-source, low-code explainable forecasting and nowcasting library with state-of-the-art deep neural networks and Dynamic Factor Model. Now available with additional addons like Denoising TS signals with ensembling of filters, TS signal similarity test with Siamese Neural Networks
- deepBreaks — deepBreaks: a machine learning tool for identifying and prioritizing genotype-phenotype associations
- deepchecks — Package for validating your machine learning model and data
- deepcomp — DeepCoMP: Self-Learning Dynamic Multi-Cell Selection for Coordinated Multipoint (CoMP)
- deepcpg — Deep learning for predicting CpG methylation
- deepdow — Portfolio optimization with deep learning
- deepeda — Useful PyTorch Layers
- deepehr — Deep Learning for EHR Analysis
- deepehrgraph — no summary
- deepfastmlu — Machine learning utilities to help speed up your prototyping process.
- deepfastvision — A Python library for rapid prototyping of deep transfer learning vision models
- deepfeatx — "Automatic Feature Extraction in Images and Texts using Transfer Learning"
- deephaven-plugin-matplotlib — Deephaven Plugin for matplotlib
- deeplense-domain-adaptation — A PyTorch-based collection of Unsupervised Domain Adaptation methods applied to strong gravitational lenses
- deepmol — DeepMol: a python-based machine and deep learning framework for drug discovery
- deepobs — Deep Learning Optimizer Benchmark Suite
- deeprecsys — deeprecsys is an open tool belt to speed up the development of modern data science projects at an enterprise level
- deepreplay — "Hyper-parameters in Action!" visualizing tool for Keras models.
- deepsardl — This is a main package for processeing data for DeepSAR
- DeepSecE — A Deep Learning Framework for Multi-class Secreted Effector Prediction in Gram-negative Bacteria.
- deepsemhist — deep_semantic_histology: Deep Semantic Representations for Cancer Histology Images
- deepsensor — A Python package for modelling xarray and pandas data with neural processes.
- deepss_unsupervised — Tools for unsupervised classification of acoustic signals.
- deepsurvk — Implementation of DeepSurv using Keras
- deepsvr — Automated Somatic Variant Refinement by Deep Learning
- deeptexture — deep_texture_histology: Deep Texture Representations for Cancer Histology Images
- deepvelo — Deep Velocity
- defSim — The Discrete Event Framework for Social Influence Models
- degex — Detect Gene Expression in Single-CEll data
- Dellingr — Error supression and variant calling pipeline for Illumina sequencing data
- delphai-ml-utils — A Python package to manage delphai machine learning operations.
- delphi.ai — Package for Robust Statistics
- delpytools — Implementation of scripts to automate a data science project
- demand-pred-model — An ML model to predict demand in E-commerce
- demuxEM — demuxEM is the demultiplexing module of Pegasus
- demyst-report — no summary
- denmune — This is the package for DenMune Clustering Algorithm published in paper https://doi.org/10.1016/j.patcog.2020.107589
- depict — Business grade visualizations in seconds
- depro — a package for Decomposition of Profile Data
- derivslib — Provides pricing tools and data for various derivative assets. I am not an attorney, accountant or financial advisor, nor am I holding myself out to be, and the information and tools contained in this package is not a substitute for financial advice from a professional who is aware of the facts and circumstances of your individual situation.
- descartes-rpa — descartes_rpa: Extract pathway features from Single-Cell
- descript-audio-codec — A high-quality general neural audio codec.
- DescTC — The package includes methods that condense large amounts of information about each variable of your dataset into easy-to-understand formats (table and charts) that clearly and effectively communicate important points.
- DeSide — A DEep-learning and SIngle-cell based DEconvolution method for solid tumors
- designed — Module for Supporting the Creation of University Entrance Examination Mathematics Proofs Using a Symbolic Computation Module
- desk — The DESK is an SED-fitting python scripts for fitting data from evolved stars
- detectools — Overlay of PyTorch to generalize trainning and inference processes for detection & instance segmentation tasks.
- deTELpy — Python package of the deTEL translation error detection pipeline from mass-spectrometry data
- detprocess — Detector Data Processing Package
- dev-hi-chem — dev-hi-chem - A template Python package for Harms Informatics
- devbio-napari — A bundle of napari plugins useful for 3D+t image processing and analysis for studying developmental biology.
- devcellpy — devCellPy -- hierarchical multilayered classification of cells based on scRNA-seq
- devos-py — A tool to Depict Vocabulary Summaries