Data-analysis Libraries

86 Components & Libraries

Sortby

Data-analysis Libraries

Apache Superset is a Data Visualization and Data Exploration Platform

A modern, enterprise-ready business intelligence web application. Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intellige…

scikit-learn: machine learning in Python

It is currently maintained by a team of volunteers. scikit-learn requires: You can check the latest sources with the command: The project is currently maintained by a team of volunteers.

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

It just takes a few lines of Python to create a beautiful demo like the one above, so let's get started 💫 You can run Gradio in your favorite code editor, Jupyter notebook, Google Colab, or anywhere …

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

Open Machine Learning Course

mlcourse.ai is still in self-paced mode but we offer you Bonus Assignments with solutions for a contribution of $17/month. The idea is that you pay for ~1-5 months while studying the course materials…

PandasAI is a Python library that integrates generative artificial intelligence capabilities into pandas, making dataframes conversational

Try out PandasAI in your browser: PandasAI is designed to be used in conjunction with pandas. It makes pandas conversational, allowing you to ask questions to your data in natural language. For examp…

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

cleanlab is: cleanlab supports Linux, macOS, and Windows and runs on Python 3.7+. cleanlab can find label issues from any model's predicted class probabilities if you can produce them yourself. This …

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn requires the following dependencies: Additionally, imbalanced-learn requires the following optional dependencies: The examples will requires the following additional dependencies: im…

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.

There are many ways to get involved in Flyte, including: Flyte is available under the Apache License 2.0. Use it wisely.

🛠 All-in-one web-based IDE specialized for machine learning and data science.

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on you…

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

xLearn is developed by high-performance C++ code with careful design and optimizations. Our system is designed to maximize CPU and memory utilization, provide cache-aware computation, and support loc…

Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..

Lance is a modern columnar data format that is optimized for ML workflows and datasets. Lance is perfect for: The key features of Lance include: Download the sift1m subset Convert it to Lance Build …

Data Science Roadmap from A to Z

Pandas

App to easily query, script, and visualize data from every database, file, and API.

DataStation is an open-source data IDE for developers. It allows you to easily build graphs and tables with data pulled from SQL databases, logging databases, metrics databases, HTTP servers, and all…

🙌 Welcome open-source Python mini-project contributions!

A collection of easy Python small projects to help you improve your programming skills. As a Python newbie, I understand the problems that people face when they first begin studying and attempting to…

The official repository for ROOT: analyzing, storing and visualizing big data, scientifically

The ROOT system provides a set of modules with all the functionality needed to handle and analyze large amounts of data in a very efficient way. Having the data defined as a set of objects, specializ…

Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.

These lecture notes are intended for introductory linear algebra courses, suitable for university students, programmers, data analysts, algorithmic traders and etc. The lectures notes are loosely bas…

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Currently, you can use DataPrep to: DataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of…

Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the most must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

The reference book for these and other Spark related topics is: The following notebooks can be examined individually, although there is a more or less linear 'story' when followed in sequence. By usi…

Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.

Because NannyML can estimate performance, it is possible to weed out data drift alerts that do not impact expected performance, combatting alert fatigue. Besides linking data drift issues to drops in…

Modern columnar data format for ML implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..

fklearn: Functional Machine Learning

To install via pip: You can also install from the source:

Multi-class confusion matrix library in Python

Fig1. ConfusionMatrix Block Diagram After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of seven overall benchmar…

A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.

A comprehensive list of +180 YouTube Channels (in no particular order) for Data Science, Data Engineering, Machine Learning, Deep Learning, Artificial Intelligence, Computer Science, programming, sof…

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

With Intel(R) Extension for Scikit-learn, you can: To install Intel(R) Extension for Scikit-learn, run: The software acceleration is achieved through patching. It means, replacing the stock scikit-le…

Your single tool to express data, ML, and LLM pipelines with simple python functions. Runs anywhere that python runs, E.G. spark, airflow, jupyter, fastapi, etc. Incrementally adoptable. Use Hamilton to build testable, reusable, and self-documenting dataflows with lineage and metadata out of the box.

Hamilton is a novel paradigm for specifying a flow of delayed execution in python. It works on python objects of any type and dataflows of any complexity. Core to the design of Hamilton is a clear ma…