Awesome Python Data Science Overview

Probably the best curated list of data science software in Python.

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Awesome Python Data Science

Probably the best curated list of data science software in Python

Contents

• Contents
• Machine Learning
  • General Purpose Machine Learning
  • Gradient Boosting
  • Ensemble Methods
  • Imbalanced Datasets
  • Random Forests
  • Kernel Methods
• Deep Learning
  • PyTorch
  • TensorFlow
  • JAX
  • Others
• Automated Machine Learning
• Natural Language Processing
• Computer Audition
• Computer Vision
• Time Series
• Reinforcement Learning
• Graph Machine Learning
• Learning-to-Rank & Recommender Systems
• Probabilistic Graphical Models
• Probabilistic Methods
• Model Explanation
• Optimization
• Genetic Programming
• Feature Engineering
  • General
  • Feature Selection
• Visualization
  • General Purposes
  • Interactive plots
  • Map
  • Automatic Plotting
  • NLP
• Data Manipulation
  • Data Frames
  • Pipelines
  • Data-centric AI
  • Synthetic Data
• Deployment
• Statistics
• Distributed Computing
• Experimentation
• Data Validation
• Evaluation
• Computations
• Web Scraping
• Spatial Analysis
• Quantum Computing
• Conversion
• Contributing
• License

Machine Learning

General Purpose Machine Learning

• scikit-learn - Machine learning in Python.
• PyCaret (⭐9.1k) - An open-source, low-code machine learning library in Python.
• Shogun (⭐3k) - Machine learning toolbox.
• xLearn (⭐3.1k) - High Performance, Easy-to-use, and Scalable Machine Learning Package.
• cuML (⭐4.4k) - RAPIDS Machine Learning Library.
• modAL (⭐2.3k) - Modular active learning framework for Python3.
• Sparkit-learn (⭐1.2k) - PySpark + scikit-learn = Sparkit-learn.
• mlpack (⭐5.3k) - A scalable C++ machine learning library (Python bindings).
• dlib (⭐14k) - Toolkit for making real-world machine learning and data analysis applications in C++ (Python bindings).
• MLxtend (⭐5k) - Extension and helper modules for Python's data analysis and machine learning libraries.
• hyperlearn (⭐2k) - 50%+ Faster, 50%+ less RAM usage, GPU support re-written Sklearn, Statsmodels.
• Reproducible Experiment Platform (REP) (⭐691) - Machine Learning toolbox for Humans.
• scikit-multilearn (⭐929) - Multi-label classification for python.
• seqlearn (⭐693) - Sequence classification toolkit for Python.
• pystruct (⭐665) - Simple structured learning framework for Python.
• sklearn-expertsys (⭐487) - Highly interpretable classifiers for scikit learn.
• RuleFit (⭐413) - Implementation of the rulefit.
• metric-learn (⭐1.4k) - Metric learning algorithms in Python.
• pyGAM (⭐887) - Generalized Additive Models in Python.
• causalml (⭐5.2k) - Uplift modeling and causal inference with machine learning algorithms.

Gradient Boosting

• XGBoost (⭐27k) - Scalable, Portable, and Distributed Gradient Boosting.
• LightGBM (⭐17k) - A fast, distributed, high-performance gradient boosting.
• CatBoost (⭐8.3k) - An open-source gradient boosting on decision trees library.
• ThunderGBM (⭐693) - Fast GBDTs and Random Forests on GPUs.
• NGBoost (⭐1.7k) - Natural Gradient Boosting for Probabilistic Prediction.
• TensorFlow Decision Forests (⭐673) - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

Ensemble Methods

• ML-Ensemble - High performance ensemble learning.
• Stacking (⭐223) - Simple and useful stacking library written in Python.
• stacked_generalization (⭐117) - Library for machine learning stacking generalization.
• vecstack (⭐691) - Python package for stacking (machine learning technique).

Imbalanced Datasets

• imbalanced-learn (⭐6.9k) - Module to perform under-sampling and over-sampling with various techniques.
• imbalanced-algorithms (⭐236) - Python-based implementations of algorithms for learning on imbalanced data.

Random Forests

• rpforest (⭐223) - A forest of random projection trees.
• sklearn-random-bits-forest (⭐9) - Wrapper of the Random Bits Forest program written by (Wang et al., 2016).
• rgf_python (⭐380) - Python Wrapper of Regularized Greedy Forest.

Kernel Methods

• pyFM (⭐924) - Factorization machines in python.
• fastFM (⭐1.1k) - A library for Factorization Machines.
• tffm (⭐782) - TensorFlow implementation of an arbitrary order Factorization Machine.
• liquidSVM (⭐67) - An implementation of SVMs.
• scikit-rvm (⭐234) - Relevance Vector Machine implementation using the scikit-learn API.
• ThunderSVM (⭐1.6k) - A fast SVM Library on GPUs and CPUs.

Deep Learning

PyTorch

• PyTorch (⭐87k) - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
• pytorch-lightning (⭐29k) - PyTorch Lightning is just organized PyTorch.
• ignite (⭐4.6k) - High-level library to help with training neural networks in PyTorch.
• skorch (⭐6k) - A scikit-learn compatible neural network library that wraps PyTorch.
• Catalyst (⭐3.3k) - High-level utils for PyTorch DL & RL research.
• ChemicalX (⭐727) - A PyTorch-based deep learning library for drug pair scoring.

TensorFlow

• TensorFlow (⭐188k) - Computation using data flow graphs for scalable machine learning by Google.
• TensorLayer (⭐7.3k) - Deep Learning and Reinforcement Learning Library for Researcher and Engineer.
• TFLearn (⭐9.6k) - Deep learning library featuring a higher-level API for TensorFlow.
• Sonnet (⭐9.8k) - TensorFlow-based neural network library.
• tensorpack (⭐6.3k) - A Neural Net Training Interface on TensorFlow.
• Polyaxon (⭐3.6k) - A platform that helps you build, manage and monitor deep learning models.
• tfdeploy (⭐354) - Deploy TensorFlow graphs for fast evaluation and export to TensorFlow-less environments running numpy.
• tensorflow-upstream (⭐690) - TensorFlow ROCm port.
• TensorFlow Fold (⭐1.8k) - Deep learning with dynamic computation graphs in TensorFlow.
• TensorLight (⭐11) - A high-level framework for TensorFlow.
• Mesh TensorFlow (⭐1.6k) - Model Parallelism Made Easier.
• Ludwig (⭐11k) - A toolbox that allows one to train and test deep learning models without the need to write code.
• Keras - A high-level neural networks API running on top of TensorFlow.
• keras-contrib (⭐1.6k) - Keras community contributions.
• Hyperas (⭐2.2k) - Keras + Hyperopt: A straightforward wrapper for a convenient hyperparameter.
• Elephas (⭐1.6k) - Distributed Deep learning with Keras & Spark.
• qkeras (⭐551) - A quantization deep learning library.

JAX

• JAX (⭐31k) - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.
• FLAX (⭐6.3k) - A neural network library for JAX that is designed for flexibility.
• Optax (⭐1.8k) - A gradient processing and optimization library for JAX.

Others

• transformers (⭐139k) - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
• Tangent (⭐2.3k) - Source-to-Source Debuggable Derivatives in Pure Python.
• autograd (⭐7.1k) - Efficiently computes derivatives of numpy code.
• Caffe (⭐34k) - A fast open framework for deep learning.
• nnabla (⭐2.7k) - Neural Network Libraries by Sony.

Automated Machine Learning

• auto-sklearn (⭐7.7k) - An AutoML toolkit and a drop-in replacement for a scikit-learn estimator.
• Auto-PyTorch (⭐2.4k) - Automatic architecture search and hyperparameter optimization for PyTorch.
• AutoKeras (⭐9.2k) - AutoML library for deep learning.
• AutoGluon (⭐8.4k) - AutoML for Image, Text, Tabular, Time-Series, and MultiModal Data.
• TPOT (⭐9.8k) - AutoML tool that optimizes machine learning pipelines using genetic programming.
• MLBox (⭐1.5k) - A powerful Automated Machine Learning python library.

Natural Language Processing

• torchtext (⭐3.5k) - Data loaders and abstractions for text and NLP.
• KerasNLP (⭐844) - Modular Natural Language Processing workflows with Keras.
• spaCy - Industrial-Strength Natural Language Processing.
• NLTK (⭐14k) - Modules, data sets, and tutorials supporting research and development in Natural Language Processing.
• CLTK (⭐846) - The Classical Language Toolkik.
• gensim - Topic Modelling for Humans.
• pyMorfologik (⭐18) - Python binding for Morfologik.
• skift (⭐233) - Scikit-learn wrappers for Python fastText.
• Phonemizer (⭐1.3k) - Simple text-to-phonemes converter for multiple languages.
• flair (⭐14k) - Very simple framework for state-of-the-art NLP.

Computer Audition

• torchaudio (⭐2.6k) - An audio library for PyTorch.
• librosa (⭐7.4k) - Python library for audio and music analysis.
• Yaafe (⭐244) - Audio features extraction.
• aubio (⭐3.4k) - A library for audio and music analysis.
• Essentia (⭐3k) - Library for audio and music analysis, description, and synthesis.
• LibXtract (⭐227) - A simple, portable, lightweight library of audio feature extraction functions.
• Marsyas (⭐411) - Music Analysis, Retrieval, and Synthesis for Audio Signals.
• muda (⭐233) - A library for augmenting annotated audio data.
• madmom (⭐1.4k) - Python audio and music signal processing library.

Computer Vision

• torchvision (⭐17k) - Datasets, Transforms, and Models specific to Computer Vision.
• PyTorch3D (⭐9.1k) - PyTorch3D is FAIR's library of reusable components for deep learning with 3D data.
• KerasCV (⭐1k) - Industry-strength Computer Vision workflows with Keras.
• OpenCV (⭐81k) - Open Source Computer Vision Library.
• Decord (⭐2k) - An efficient video loader for deep learning with smart shuffling that's super easy to digest.
• MMEngine (⭐1.2k) - OpenMMLab Foundational Library for Training Deep Learning Models.
• scikit-image (⭐6.2k) - Image Processing SciKit (Toolbox for SciPy).
• imgaug (⭐15k) - Image augmentation for machine learning experiments.
• imgaug_extension - Additional augmentations for imgaug.
• Augmentor (⭐5.1k) - Image augmentation library in Python for machine learning.
• albumentations (⭐15k) - Fast image augmentation library and easy-to-use wrapper around other libraries.
• LAVIS (⭐10k) - A One-stop Library for Language-Vision Intelligence.

Time Series

• sktime (⭐8.2k) - A unified framework for machine learning with time series.
• skforecast (⭐1.3k) - Time series forecasting with machine learning models
• darts (⭐8.3k) - A python library for easy manipulation and forecasting of time series.
• statsforecast (⭐4.1k) - Lightning fast forecasting with statistical and econometric models.
• mlforecast (⭐961) - Scalable machine learning-based time series forecasting.
• neuralforecast (⭐3.3k) - Scalable machine learning-based time series forecasting.
• tslearn (⭐2.9k) - Machine learning toolkit dedicated to time-series data.
• tick (⭐497) - Module for statistical learning, with a particular emphasis on time-dependent modeling.
• greykite (⭐1.8k) - A flexible, intuitive, and fast forecasting library next.
• Prophet (⭐19k) - Automatic Forecasting Procedure.
• PyFlux (⭐2.1k) - Open source time series library for Python.
• bayesloop (⭐156) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
• luminol (⭐1.2k) - Anomaly Detection and Correlation library.
• dateutil - Powerful extensions to the standard datetime module
• maya (⭐3.4k) - makes it very easy to parse a string and for changing timezones
• Chaos Genius (⭐749) - ML powered analytics engine for outlier/anomaly detection and root cause analysis

Reinforcement Learning

• Gymnasium (⭐8.2k) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym (⭐35k)).
• PettingZoo (⭐2.8k) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.
• MAgent2 (⭐265) - An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments.
• Stable Baselines3 (⭐9.8k) - A set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
• Shimmy (⭐151) - An API conversion tool for popular external reinforcement learning environments.
• EnvPool (⭐1.1k) - C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
• RLlib - Scalable Reinforcement Learning.
• Tianshou (⭐8.2k) - An elegant PyTorch deep reinforcement learning library.
• Acme (⭐3.6k) - A library of reinforcement learning components and agents.
• Catalyst-RL (⭐46) - PyTorch framework for RL research.
• d3rlpy (⭐1.4k) - An offline deep reinforcement learning library.
• DI-engine (⭐3.2k) - OpenDILab Decision AI Engine.
• TF-Agents (⭐2.9k) - A library for Reinforcement Learning in TensorFlow.
• TensorForce (⭐3.3k) - A TensorFlow library for applied reinforcement learning.
• TRFL (⭐3.1k) - TensorFlow Reinforcement Learning.
• Dopamine (⭐11k) - A research framework for fast prototyping of reinforcement learning algorithms.
• keras-rl (⭐5.5k) - Deep Reinforcement Learning for Keras.
• garage (⭐1.9k) - A toolkit for reproducible reinforcement learning research.
• Horizon (⭐3.6k) - A platform for Applied Reinforcement Learning.
• rlpyt (⭐2.2k) - Reinforcement Learning in PyTorch.
• cleanrl (⭐6.3k) - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG).
• Machin (⭐406) - A reinforcement library designed for pytorch.
• SKRL (⭐655) - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym.
• Imitation (⭐1.4k) - Clean PyTorch implementations of imitation and reward learning algorithms.

Graph Machine Learning

• pytorch_geometric (⭐22k) - Geometric Deep Learning Extension Library for PyTorch.
• pytorch_geometric_temporal (⭐2.7k) - Temporal Extension Library for PyTorch Geometric.
• PyTorch Geometric Signed Directed (⭐133) - A signed/directed graph neural network extension library for PyTorch Geometric.
• dgl (⭐14k) - Python package built to ease deep learning on graph, on top of existing DL frameworks.
• Spektral (⭐2.4k) - Deep learning on graphs.
• StellarGraph (⭐3k) - Machine Learning on Graphs.
• Graph Nets (⭐5.4k) - Build Graph Nets in Tensorflow.
• TensorFlow GNN (⭐1.4k) - A library to build Graph Neural Networks on the TensorFlow platform.
• Auto Graph Learning (⭐1.1k) -An autoML framework & toolkit for machine learning on graphs.
• PyTorch-BigGraph (⭐3.4k) - Generate embeddings from large-scale graph-structured data.
• Auto Graph Learning (⭐1.1k) - An autoML framework & toolkit for machine learning on graphs.
• Karate Club (⭐2.2k) - An unsupervised machine learning library for graph-structured data.
• Little Ball of Fur (⭐707) - A library for sampling graph structured data.
• GreatX (⭐85) - A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
• Jraph (⭐1.4k) - A Graph Neural Network Library in Jax.

Learning-to-Rank & Recommender Systems

• LightFM (⭐4.8k) - A Python implementation of LightFM, a hybrid recommendation algorithm.
• Spotlight - Deep recommender models using PyTorch.
• Surprise (⭐6.5k) - A Python scikit for building and analyzing recommender systems.
• RecBole (⭐3.6k) - A unified, comprehensive and efficient recommendation library.
• allRank (⭐910) - allRank is a framework for training learning-to-rank neural models based on PyTorch.
• TensorFlow Recommenders (⭐1.9k) - A library for building recommender system models using TensorFlow.
• TensorFlow Ranking (⭐2.8k) - Learning to Rank in TensorFlow.

Probabilistic Graphical Models

• pomegranate (⭐3.4k) - Probabilistic and graphical models for Python.
• pgmpy (⭐2.8k) - A python library for working with Probabilistic Graphical Models.
• pyAgrum - A GRaphical Universal Modeler.

Probabilistic Methods

• pyro (⭐8.7k) - A flexible, scalable deep probabilistic programming library built on PyTorch.
• PyMC (⭐8.9k) - Bayesian Stochastic Modelling in Python.
• ZhuSuan - Bayesian Deep Learning.
• GPflow - Gaussian processes in TensorFlow.
• InferPy (⭐148) - Deep Probabilistic Modelling Made Easy.
• PyStan (⭐348) - Bayesian inference using the No-U-Turn sampler (Python interface).
• sklearn-bayes (⭐515) - Python package for Bayesian Machine Learning with scikit-learn API.
• skpro (⭐257) - Supervised domain-agnostic prediction framework for probabilistic modelling by The Alan Turing Institute.
• PyVarInf (⭐358) - Bayesian Deep Learning methods with Variational Inference for PyTorch.
• emcee (⭐1.5k) - The Python ensemble sampling toolkit for affine-invariant MCMC.
• hsmmlearn (⭐83) - A library for hidden semi-Markov models with explicit durations.
• pyhsmm (⭐555) - Bayesian inference in HSMMs and HMMs.
• GPyTorch (⭐3.6k) - A highly efficient and modular implementation of Gaussian Processes in PyTorch.
• sklearn-crfsuite (⭐426) - A scikit-learn-inspired API for CRFsuite.

Model Explanation

• dalex (⭐1.4k) - moDel Agnostic Language for Exploration and explanation.
• Shapley (⭐218) - A data-driven framework to quantify the value of classifiers in a machine learning ensemble.
• Alibi (⭐2.5k) - Algorithms for monitoring and explaining machine learning models.
• anchor (⭐798) - Code for "High-Precision Model-Agnostic Explanations" paper.
• aequitas (⭐706) - Bias and Fairness Audit Toolkit.
• Contrastive Explanation (⭐45) - Contrastive Explanation (Foil Trees).
• yellowbrick (⭐4.3k) - Visual analysis and diagnostic tools to facilitate machine learning model selection.
• scikit-plot (⭐2.4k) - An intuitive library to add plotting functionality to scikit-learn objects.
• shap (⭐23k) - A unified approach to explain the output of any machine learning model.
• ELI5 (⭐2.8k) - A library for debugging/inspecting machine learning classifiers and explaining their predictions.
• Lime (⭐12k) - Explaining the predictions of any machine learning classifier.
• FairML (⭐362) - FairML is a python toolbox auditing the machine learning models for bias.
• L2X (⭐124) - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
• PDPbox (⭐850) - Partial dependence plot toolbox.
• PyCEbox (⭐165) - Python Individual Conditional Expectation Plot Toolbox.
• Skater - Python Library for Model Interpretation.
• model-analysis (⭐1.3k) - Model analysis tools for TensorFlow.
• themis-ml (⭐125) - A library that implements fairness-aware machine learning algorithms.
• treeinterpreter (⭐753) - Interpreting scikit-learn's decision tree and random forest predictions.
• AI Explainability 360 (⭐1.7k) - Interpretability and explainability of data and machine learning models.
• Auralisation (⭐42) - Auralisation of learned features in CNN (for audio).
• CapsNet-Visualization (⭐395) - A visualization of the CapsNet layers to better understand how it works.
• lucid (⭐4.7k) - A collection of infrastructure and tools for research in neural network interpretability.
• Netron (⭐29k) - Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
• FlashLight - Visualization Tool for your NeuralNetwork.
• tensorboard-pytorch (⭐7.9k) - Tensorboard for PyTorch (and chainer, mxnet, numpy, ...).

Genetic Programming

• gplearn (⭐1.7k) - Genetic Programming in Python.
• PyGAD (⭐2k) - Genetic Algorithm in Python.
• DEAP (⭐6k) - Distributed Evolutionary Algorithms in Python.
• karoo_gp (⭐162) - A Genetic Programming platform for Python with GPU support.
• monkeys (⭐122) - A strongly-typed genetic programming framework for Python.
• sklearn-genetic (⭐324) - Genetic feature selection module for scikit-learn.

Optimization

• Optuna (⭐11k) - A hyperparameter optimization framework.
• pymoo (⭐2.4k) - Multi-objective Optimization in Python.
• pycma (⭐1.1k) - Python implementation of CMA-ES.
• Spearmint (⭐1.6k) - Bayesian optimization.
• BoTorch (⭐3.2k) - Bayesian optimization in PyTorch.
• scikit-opt (⭐5.4k) - Heuristic Algorithms for optimization.
• sklearn-genetic-opt (⭐328) - Hyperparameters tuning and feature selection using evolutionary algorithms.
• SMAC3 (⭐1.1k) - Sequential Model-based Algorithm Configuration.
• Optunity (⭐418) - Is a library containing various optimizers for hyperparameter tuning.
• hyperopt (⭐7.3k) - Distributed Asynchronous Hyperparameter Optimization in Python.
• hyperopt-sklearn (⭐1.6k) - Hyper-parameter optimization for sklearn.
• sklearn-deap (⭐772) - Use evolutionary algorithms instead of gridsearch in scikit-learn.
• sigopt_sklearn (⭐75) - SigOpt wrappers for scikit-learn methods.
• Bayesian Optimization (⭐8.1k) - A Python implementation of global optimization with gaussian processes.
• SafeOpt (⭐143) - Safe Bayesian Optimization.
• scikit-optimize (⭐2.8k) - Sequential model-based optimization with a scipy.optimize interface.
• Solid (⭐575) - A comprehensive gradient-free optimization framework written in Python.
• PySwarms (⭐1.3k) - A research toolkit for particle swarm optimization in Python.
• Platypus (⭐589) - A Free and Open Source Python Library for Multiobjective Optimization.
• GPflowOpt (⭐270) - Bayesian Optimization using GPflow.
• POT (⭐2.5k) - Python Optimal Transport library.
• Talos (⭐1.6k) - Hyperparameter Optimization for Keras Models.
• nlopt (⭐2k) - Library for nonlinear optimization (global and local, constrained or unconstrained).
• OR-Tools - An open-source software suite for optimization by Google; provides a unified programming interface to a half dozen solvers: SCIP, GLPK, GLOP, CP-SAT, CPLEX, and Gurobi.

Feature Engineering

General

• Featuretools (⭐7.4k) - Automated feature engineering.
• Feature Engine (⭐2k) - Feature engineering package with sklearn-like functionality.
• OpenFE (⭐764) - Automated feature generation with expert-level performance.
• skl-groups (⭐41) - A scikit-learn addon to operate on set/"group"-based features.
• Feature Forge (⭐381) - A set of tools for creating and testing machine learning features.
• few (⭐51) - A feature engineering wrapper for sklearn.
• scikit-mdr (⭐125) - A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.
• tsfresh (⭐8.6k) - Automatic extraction of relevant features from time series.
• dirty_cat (⭐17) - Machine learning on dirty tabular data (especially: string-based variables for classifcation and regression).
• NitroFE (⭐106) - Moving window features.
• sk-transformer (⭐10) - A collection of various pandas & scikit-learn compatible transformers for all kinds of preprocessing and feature engineering steps

Feature Selection

• scikit-feature (⭐1.5k) - Feature selection repository in Python.
• boruta_py (⭐1.6k) - Implementations of the Boruta all-relevant feature selection method.
• BoostARoota (⭐220) - A fast xgboost feature selection algorithm.
• scikit-rebate (⭐415) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
• zoofs (⭐245) - A feature selection library based on evolutionary algorithms.

Visualization

General Purposes

• Matplotlib (⭐21k) - Plotting with Python.
• seaborn (⭐13k) - Statistical data visualization using matplotlib.
• prettyplotlib (⭐1.7k) - Painlessly create beautiful matplotlib plots.
• python-ternary (⭐749) - Ternary plotting library for Python with matplotlib.
• missingno (⭐4k) - Missing data visualization module for Python.
• chartify (⭐3.6k) - Python library that makes it easy for data scientists to create charts.
• physt (⭐133) - Improved histograms.

Interactive plots

• animatplot (⭐413) - A python package for animating plots built on matplotlib.
• plotly - A Python library that makes interactive and publication-quality graphs.
• Bokeh (⭐20k) - Interactive Web Plotting for Python.
• Altair - Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
• bqplot (⭐3.6k) - Plotting library for IPython/Jupyter notebooks
• pyecharts (⭐15k) - Migrated from Echarts (⭐62k), a charting and visualization library, to Python's interactive visual drawing library.

Map

• folium - Makes it easy to visualize data on an interactive open street map
• geemap (⭐3.6k) - Python package for interactive mapping with Google Earth Engine (GEE)

Automatic Plotting

• HoloViews (⭐2.8k) - Stop plotting your data - annotate your data and let it visualize itself.
• AutoViz (⭐1.8k): Visualize data automatically with 1 line of code (ideal for machine learning)
• SweetViz (⭐3k): Visualize and compare datasets, target values and associations, with one line of code.

NLP

• pyLDAvis (⭐1.8k): Visualize interactive topic model

Deployment

• fastapi - Modern, fast (high-performance), a web framework for building APIs with Python
• streamlit - Make it easy to deploy the machine learning model
• streamsync (⭐1.4k) - No-code in the front, Python in the back. An open-source framework for creating data apps.
• gradio (⭐36k) - Create UIs for your machine learning model in Python in 3 minutes.
• Vizro (⭐2.8k) - A toolkit for creating modular data visualization applications.
• datapane - A collection of APIs to turn scripts and notebooks into interactive reports.
• binder - Enable sharing and execute Jupyter Notebooks

Statistics

• pandas_summary (⭐513) - Extension to pandas dataframes describe function.
• Pandas Profiling (⭐13k) - Create HTML profiling reports from pandas DataFrame objects.
• statsmodels (⭐10k) - Statistical modeling and econometrics in Python.
• stockstats (⭐1.3k) - Supply a wrapper StockDataFrame based on the pandas.DataFrame with inline stock statistics/indicators support.
• weightedcalcs (⭐107) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
• scikit-posthocs (⭐357) - Pairwise Multiple Comparisons Post-hoc Tests.
• Alphalens (⭐3.5k) - Performance analysis of predictive (alpha) stock factors.

Data Manipulation

Data Frames

• pandas - Powerful Python data analysis toolkit.
• polars (⭐32k) - A fast multi-threaded, hybrid-out-of-core DataFrame library.
• Arctic (⭐3.1k) - High-performance datastore for time series and tick data.
• datatable (⭐1.8k) - Data.table for Python.
• pandas_profiling (⭐13k) - Create HTML profiling reports from pandas DataFrame objects
• cuDF (⭐8.7k) - GPU DataFrame Library.
• blaze (⭐3.2k) - NumPy and pandas interface to Big Data.
• pandasql (⭐1.3k) - Allows you to query pandas DataFrames using SQL syntax.
• pandas-gbq (⭐458) - pandas Google Big Query.
• xpandas (⭐26) - Universal 1d/2d data containers with Transformers .functionality for data analysis by The Alan Turing Institute.
• pysparkling (⭐268) - A pure Python implementation of Apache Spark's RDD and DStream interfaces.
• modin (⭐10k) - Speed up your pandas workflows by changing a single line of code.
• swifter (⭐2.6k) - A package that efficiently applies any function to a pandas dataframe or series in the fastest available manner.
• pandas-log (⭐215) - A package that allows providing feedback about basic pandas operations and finds both business logic and performance issues.
• vaex (⭐8.3k) - Out-of-Core DataFrames for Python, ML, visualize and explore big tabular data at a billion rows per second.
• xarray (⭐3.7k) - Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.

Pipelines

• pdpipe (⭐718) - Sasy pipelines for pandas DataFrames.
• SSPipe - Python pipe (|) operator with support for DataFrames and Numpy, and Pytorch.
• pandas-ply (⭐197) - Functional data manipulation for pandas.
• Dplython (⭐764) - Dplyr for Python.
• sklearn-pandas (⭐2.8k) - pandas integration with sklearn.
• Dataset (⭐202) - Helps you conveniently work with random or sequential batches of your data and define data processing.
• pyjanitor (⭐1.4k) - Clean APIs for data cleaning.
• meza (⭐418) - A Python toolkit for processing tabular data.
• Prodmodel (⭐59) - Build system for data science pipelines.
• dopanda (⭐477) - Hints and tips for using pandas in an analysis environment.
• Hamilton (⭐2k) - A microframework for dataframe generation that applies Directed Acyclic Graphs specified by a flow of lazily evaluated Python functions.

Data-centric AI

• cleanlab (⭐10k) - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
• snorkel (⭐5.8k) - A system for quickly generating training data with weak supervision.
• dataprep (⭐2.1k) - Collect, clean, and visualize your data in Python with a few lines of code.

Synthetic Data

• ydata-synthetic (⭐1.5k) - A package to generate synthetic tabular and time-series data leveraging the state-of-the-art generative models.

Distributed Computing

• Horovod (⭐14k) - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
• PySpark - Exposes the Spark programming model to Python.
• Veles (⭐907) - Distributed machine learning platform.
• Jubatus (⭐709) - Framework and Library for Distributed Online Machine Learning.
• DMTK (⭐2.7k) - Microsoft Distributed Machine Learning Toolkit.
• PaddlePaddle (⭐22k) - PArallel Distributed Deep LEarning.
• dask-ml (⭐916) - Distributed and parallel machine learning.
• Distributed (⭐1.6k) - Distributed computation in Python.

Experimentation

• mlflow (⭐20k) - Open source platform for the machine learning lifecycle.
• Neptune - A lightweight ML experiment tracking, results visualization, and management tool.
• dvc (⭐14k) - Data Version Control | Git for Data & Models | ML Experiments Management.
• envd (⭐2.1k) - 🏕️ machine learning development environment for data science and AI/ML engineering teams.
• Sacred (⭐4.3k) - A tool to help you configure, organize, log, and reproduce experiments.
• Ax (⭐2.4k) - Adaptive Experimentation Platform.

Data Validation

• great_expectations (⭐10k) - Always know what to expect from your data.
• pandera (⭐3.6k) - A lightweight, flexible, and expressive statistical data testing library.
• deepchecks (⭐3.7k) - Validation & testing of ML models and data during model development, deployment, and production.
• evidently (⭐5.7k) - Evaluate and monitor ML models from validation to production.
• TensorFlow Data Validation (⭐768) - Library for exploring and validating machine learning data.
• DataComPy (⭐505)- A library to compare Pandas, Polars, and Spark data frames. It provides stats and lets users adjust for match accuracy.

Evaluation

• recmetrics (⭐576) - Library of useful metrics and plots for evaluating recommender systems.
• Metrics (⭐1.6k) - Machine learning evaluation metric.
• sklearn-evaluation (⭐3) - Model evaluation made easy: plots, tables, and markdown reports.
• AI Fairness 360 (⭐2.5k) - Fairness metrics for datasets and ML models, explanations, and algorithms to mitigate bias in datasets and models.

Computations

• numpy - The fundamental package needed for scientific computing with Python.
• Dask (⭐13k) - Parallel computing with task scheduling.
• bottleneck (⭐1.1k) - Fast NumPy array functions written in C.
• CuPy (⭐9.9k) - NumPy-like API accelerated with CUDA.
• scikit-tensor (⭐403) - Python library for multilinear algebra and tensor factorizations.
• numdifftools (⭐262) - Solve automatic numerical differentiation problems in one or more variables.
• quaternion (⭐622) - Add built-in support for quaternions to numpy.
• adaptive (⭐1.2k) - Tools for adaptive and parallel samping of mathematical functions.
• NumExpr (⭐2.3k) - A fast numerical expression evaluator for NumPy that comes with an integrated computing virtual machine to speed calculations up by avoiding memory allocation for intermediate results.

Web Scraping

• BeautifulSoup: The easiest library to scrape static websites for beginners
• Scrapy: Fast and extensible scraping library. Can write rules and create customized scraper without touching the core
• Selenium: Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
• Pattern (⭐8.8k): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization
• twitterscraper (⭐2.4k): Efficient library to scrape Twitter

Spatial Analysis

• GeoPandas (⭐4.6k) - Python tools for geographic data.
• PySal (⭐1.4k) - Python Spatial Analysis Library.

Quantum Computing

• qiskit (⭐5.7k) - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules.
• cirq (⭐4.4k) - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
• PennyLane (⭐2.5k) - Quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
• QML (⭐202) - A Python Toolkit for Quantum Machine Learning.

Conversion

• sklearn-porter (⭐1.3k) - Transpile trained scikit-learn estimators to C, Java, JavaScript, and others.
• ONNX (⭐18k) - Open Neural Network Exchange.
• MMdnn (⭐5.8k) - A set of tools to help users inter-operate among different deep learning frameworks.
• treelite (⭐751) - Universal model exchange and serialization format for decision tree forests.

Contributing

Contributions are welcome! 😎
Read the <a href=https://github.com/krzjoa/awesome-python-datascience/blob/master/CONTRIBUTING.md>contribution (⭐2.7k) guideline.

License

This work is licensed under the Creative Commons Attribution 4.0 International License - CC BY 4.0