Awesome Python Data Science

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Visual analysis and diagnostic tools to facilitate machine learning model selection.

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

Performance analysis of predictive (alpha) stock factors

Missing data visualization module for Python.

N-D labeled arrays and datasets in Python

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

A Python package for interactive geospatial analysis and visualization with Google Earth Engine.

Vizro is a low-code toolkit for building high-quality data visualization apps.

a library for audio and music analysis

Python library that makes it easy for data scientists to create charts.

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

C++ library for audio and music analysis, description and synthesis, including Python bindings

Fast, flexible and easy to use probabilistic modelling in Python.

Datetimes for Humans™

Accelerated deep learning R&D

Python Toolkit for Causal and Probabilistic Reasoning

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

The machine learning toolkit for time series analysis in Python

Visualize and compare datasets, target values and associations, with one line of code.

High performance datastore for time series and tick data

StellarGraph - Machine Learning on Graphs

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. https://deepnote.com/

Data manipulation and transformation for audio signal processing, powered by PyTorch

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

POT : Python Optimal Transport

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Graph Neural Networks with Keras and Tensorflow 2.

A modular active learning framework for Python

Feature engineering and selection open-source Python library compatible with sklearn.

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Natural Gradient Boosting for Probabilistic Prediction

A Python package for manipulating 2-dimensional tabular data structures

Genetic Programming in Python, with a scikit-learn inspired API

A high performance Python graph library implemented in Rust.

A distributed task scheduler for Dask

Python audio and music signal processing library

The Python ensemble sampling toolkit for affine-invariant MCMC

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

OpenMMLab Foundational Library for Training Deep Learning Models

Python interface for igraph

No-code in the front, Python in the back. An open-source framework for creating data apps.

Metric learning algorithms in Python

Scalable machine 🤖 learning for time series forecasting.

:chart_with_upwards_trend: Adaptive: parallel active learning of mathematical functions

Fast NumPy array functions written in C

PySpark + Scikit-learn = Sparkit-learn