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Best-of Python
A ranked list of awesome Python open-source libraries
This curated list contains 390 awesome open-source projects with a total of 1.4M stars grouped into 28 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! Ranked list of awesome python libraries for web... -
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Python for Data Analysis
Materials and IPython notebooks for "Python for Data Analysis"
Python for Data Analysis is the official companion repository for Python for Data Analysis, 3rd Edition by Wes McKinney. It contains the datasets, examples, and IPython notebooks used throughout the book. The repository helps readers practice Python data analysis concepts directly in Jupyter Notebook. Its chapters cover Python basics, NumPy, pandas, data loading, cleaning, wrangling, visualization, time series, modeling libraries, and full analysis examples. The project includes setup... -
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GPUCompiler.jl
Reusable compiler infrastructure for Julia GPU backends
Reusable compiler infrastructure for Julia GPU backends. This package offers reusable compiler infrastructure and tooling for implementing GPU compilers in Julia. It is not intended for end users! Instead, you should use one of the packages that builds on GPUCompiler.jl, such as CUDA.jl or AMDGPU.jl. -
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LLVM.jl
Julia wrapper for the LLVM C API
A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language. -
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ProtoBuf.jl
Julia protobuf implementation
This is a Julia package that provides a compiler and a codec for Protocol Buffers. Protocol Buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data. -
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JuliaSyntax
A Julia frontend, written in Julia
A Julia compiler frontend, written in Julia. Read the documentation for more information. JuliaSyntax.jl is used as the new default Julia parser in Julia 1.10. It's highly compatible with Julia's older femtoliter-based parser - It parses all of Base, the standard libraries and the General registry. Some minor differences remain where we've decided to fix bugs or strange behaviors in the reference parser. The AST and tree data structures are usable but their APIs will evolve as we try out... -
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Finch.jl
Sparse tensors in Julia and more
Finch is a cutting-edge Julia-to-Julia compiler specially designed for optimizing loop nests over sparse or structured multidimensional arrays. Finch empowers users to write conventional for loops which are transformed behind-the-scenes into fast sparse code. -
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pandas
Fast, flexible and powerful Python data analysis toolkit
pandas is a Python data analysis library that provides high-performance, user friendly data structures and data analysis tools for the Python programming language. It enables you to carry out entire data analysis workflows in Python without having to switch to a more domain specific language. With pandas, performance, productivity and collaboration in doing data analysis in Python can significantly increase. pandas is continuously being developed to be a fundamental high-level building... -
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PythonCall & JuliaCall
Python and Julia in harmony
Bringing Python® and Julia together in seamless harmony. Call Python code from Julia and Julia code from Python via a symmetric interface. Simple syntax, so the Python code looks like Python and the Julia code looks like Julia. Intuitive and flexible conversions between Julia and Python: anything can be converted, you are in control. Fast non-copying conversion of numeric arrays in either direction: modify Python arrays (e.g. bytes, array. array, numpy.ndarray) from Julia or Julia arrays... -
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BetaML.jl
Beta Machine Learning Toolkit
...Thanks to its JIT compiler, Julia is indeed in the sweet spot where we can easily write models in a high-level language and still have them running efficiently. -
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JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling... -
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Pathway
Python ETL framework for stream processing, real-time analytics, LLM
Pathway is an open-source framework designed for building real-time data applications using reactive and declarative paradigms. It enables seamless integration of live data streams and structured data into analytical pipelines with minimal latency. Pathway is especially well-suited for scenarios like financial analytics, IoT, fraud detection, and logistics, where high-velocity and continuously changing data is the norm. Unlike traditional batch processing frameworks, Pathway continuously... -
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scikit-learn
Machine learning in Python
scikit-learn is an open source Python module for machine learning built on NumPy, SciPy and matplotlib. It offers simple and efficient tools for predictive data analysis and is reusable in various contexts. -
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XY
Ultra-fast and customizable Python charts
XY is an interactive Python visualization library designed to handle both everyday charts and extremely large datasets. Charts can be created through a declarative API or familiar Matplotlib-style conventions. A Rust processing core dynamically determines how much data the current screen resolution actually needs to display. Large datasets can be rendered as bounded density or decimated views, while zooming retrieves finer details and original rows. Interactive capabilities include panning,... -
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DuckDB
DuckDB is an in-process SQL OLAP Database Management System
DuckDB is a high-performance analytical database system. It is designed to be fast, reliable and easy to use. DuckDB provides a rich SQL dialect, with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs), and more. For more information on the goals of DuckDB, please refer to the Why DuckDB page on our website. Processing and storing tabular datasets, e.g. from CSV or Parquet files. Interactive... -
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes,... -
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marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots,... -
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CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will... -
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geemap
A Python package for interactive geospaital analysis and visualization
A Python package for interactive geospatial analysis and visualization with Google Earth Engine. Geemap is a Python package for geospatial analysis and visualization with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE... -
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Dash
Build beautiful web-based analytic apps, no JavaScript required
Dash is a Python framework for building beautiful analytical web applications without any JavaScript. Built on top of Plotly.js, React and Flask, Dash easily achieves what an entire team of designers and engineers normally would. It ties modern UI controls and displays such as dropdown menus, sliders and graphs directly to your analytical Python code, and creates exceptional, interactive analytics apps. Dash apps are very lightweight, requiring only a limited number of lines of Python or... -
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Catbird Linux
Linux for content creation, web scraping, coding, and data analysis.
Catbird Linux is a USB pluggable Live Linux operating system built for media creation, web scraping, and software coding. It is the daily driver you want for retrieving data, making videos or podcasts, and making software tools to automate the repetitive tasks. It is ready for work in Python, Lua, and Go languages, with numerous packages for web scraping or downloading data via API calls. -
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Positron
Positron, a next-generation data science IDE
Positron is a next-generation integrated development environment (IDE) created by Posit PBC (formerly RStudio Inc) specifically tailored for data science workflows in Python, R, and multi-language ecosystems. It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding... -
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PyCall.jl
Package to call Python functions from the Julia language
Package to call Python functions from the Julia language. This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them. -
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reticulate
R Interface to Python
reticulate is an R package from Posit that creates seamless interoperability between R and Python. It lets you call Python modules, classes, and functions from within R, automatically translating between R and Python data structures. Useful for combining Python tooling with R projects, data analysis, and RMarkdown reports. -
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folium
Python data, Leaflet.js maps
folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via folium. folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map. The library has a number of...
