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Showing 512 open source projects for "compiler python linux"
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prettymaps
A small set of Python functions to draw pretty maps from OpenStreetMap
A Python package to draw maps with customizable styles from OpenStreetMap data. Created using the osmnx, matplotlib, shapely and vsketch packages. -
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Conda.jl
https://github.com/JuliaPy/Conda.jl
This package allows one to use conda as a cross-platform binary provider for Julia for other Julia packages, especially to install binaries that have complicated dependencies like Python. conda is a package manager that started as the binary package manager for the Anaconda Python distribution, but it also provides arbitrary packages. Instead of the full Anaconda distribution, Conda.jl uses the miniconda Python environment, which only includes conda and its dependencies. -
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The PyPlot module for Julia
Plotting for Julia based on matplotlib.pyplot
This module provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy). (See also PythonPlot.jl for a version of PyPlot.jl using the alternative PythonCall.jl package.) This package takes advantage of Julia's multimedia I/O API to display plots in any Julia graphical backend, including... -
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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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Run Page
Make your own running home page
GitHub Actions manages automatic synchronization of runs and generation of new pages. Gatsby-generated static pages, fast. Support for Vercel (recommended) and GitHub Pages automated deployment. React Hooks. Mapbox for map display. Supports most sports apps such as nike strava. Automatically backup gpx data for easy backup and uploading to other software. -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets. popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two... -
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Sweetviz
Visualize and compare datasets, target values and associations
Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Shows how a target value (e.g. "Survived" in the Titanic... -
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PipeRider
Code review for data in dbt
PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence. PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more. PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report. PipeRider generates a static HTML report each time it runs, which can be viewed... -
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whylogs
The open standard for data logging
whylogs is an open-source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond... -
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FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to... -
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data-diff
Efficiently diff rows across two different databases
We're excited to announce the launch of a new open-source product, data-diff that makes comparing datasets across databases fast at any scale. data-diff automates data quality checks for data replication and migration. In modern data platforms, data is constantly moving between systems, and at the modern data volume and complexity, systems go out of sync all the time. Until now, there has not been any tooling to ensure that when the data is correctly copied. Replicating data at scale, across... -
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electricityMap
A real-time visualisation of the CO2 emissions of electricity
Real-time visualization of the Greenhouse Gas (in terms of CO2 equivalent) footprint of electricity consumption built with d3.js and mapbox GL. Real-time data is defined as a data source with an hourly (or better) frequency, delayed by less than 2hrs. It should provide a breakdown by generation type. Often fossil fuel generation (coal/gas/oil) is combined under a single heading like 'thermal' or 'conventional', this is not a problem. Citizens should not be responsible for the emissions... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
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Great Expectations
Always know what to expect from your data
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations... -
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tsfresh
Automatic extraction of relevant features from time series
tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python... -
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Recommenders
Best practices on recommendation systems
The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several... -
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CocoIndex
ETL framework to index data for AI, such as RAG
CocoIndex is an open-source framework designed for building powerful, local-first semantic search systems. It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search... -
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Graphs.jl
An optimized graphs package for the Julia programming language
The goal of Graphs.jl is to offer a performant platform for network and graph analysis in Julia, following the example of libraries such as NetworkX in Python. Offers a set of simple, concrete graph implementations – SimpleGraph (for undirected graphs) and SimpleDiGraph (for directed graphs), an API for the development of more sophisticated graph implementations under the AbstractGraph type, and a large collection of graph algorithms with the same requirements as this API. -
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InteractiveViz.jl
Interactive visualization tools for Julia
.... To render perceptually accurate summaries at large scale, allowing drill down to individual data points. To allow generation of data points on demand through a graphics pipeline, requiring computation only at a level of detail appropriate for display at the viewing resolution. Additional data points can be generated on demand when zooming or panning. This package was partly inspired by the excellent Datashader package available in the Python ecosystem. -
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rudderstack
Privacy and Security focused Segment-alternative, in Golang
Quickly deploy flexible, powerful customer data pipelines, then send the data to your entire stack—without the engineering headache. Our complete toolset makes it easy to level-up your customer data stack. Spare your data engineers the headache. Our 180+ integrations, along with custom webhook sources and destinations, save data teams hundred of hours. Say goodbye to different versions of the truth. Our SDKs track anonymous and known users at the source and reconcile users in your warehouse... -
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Dolphin Scheduler
A distributed and extensible workflow scheduler platform
... definition operations are visualized, Visualization process defines key information at a glance, One-click deployment. Support multi-tenant. Support many task types e.g., spark,flink,hive, mr, shell, python, sub_process. Support custom task types, Distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
.... Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its... -
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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.
