Search Results for "google-visualization-python" - Page 5
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Showing 2286 open source projects for "google-visualization-python"
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Accessors.jl
Update immutable data
The goal of Accessors.jl is to make updating immutable data simple. It is the successor of Setfield.jl. -
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SymEngine.jl
Julia wrappers of SymEngine
Julia Wrappers for SymEngine, a fast symbolic manipulation library, written in C++. -
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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 experience along with specialized panes and tooling for variable inspection, data-frame viewing, plotting previews, and interactive consoles designed for analytical work. ... -
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nb-clean
Clean Jupyter notebooks of outputs, metadata, and empty cells
...It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines. nb-clean can also be used as a pre-commit hook. You may prefer this to the Git filter if your project already uses the pre-commit framework. Note that the Git filter and pre-commit hook work differently, with different effects on your working directory. ... -
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SymbolicNumericIntegration.jl
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
SymbolicNumericIntegration.jl is a hybrid symbolic/numerical integration package that works on the Julia Symbolics expressions. -
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Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
...It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two features. popmon can automatically flag and alert on changes observed over time, such as trends, shifts, peaks, outliers, anomalies, changing correlations, etc, using monitoring business rules. Advanced users can leverage popmon's modular data pipeline to customize their workflow. Visualization of the pipeline can be useful when debugging or for didactic purposes. There is a script included with the package that you can use. -
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Tables.jl
An interface for tables in Julia
The Tables.jl package provides simple, yet powerful interface functions for working with all kinds of tabular data. -
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Metal.jl
Metal programming in Julia
With Metal.jl it's possible to program GPUs on macOS using the Metal programming framework. The package is a work in progress. There are bugs, functionality is missing, and performance hasn't been optimized. Expect to have to make changes to this package if you want to use it. PRs are very welcome. These requirements are fairly strict, and are due to our limited development resources (manpower, hardware). Technically, they can be relaxed. If you are interested in contributing to this, see... -
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DSP.jl
Filter design, periodograms, window functions
DSP.jl provides a number of common digital signal processing routines in Julia. -
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ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
The ModelingToolkit Standard Library is a standard library of components to model the world and beyond. -
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Java Tablesaw
Java dataframe and visualization library
...Tablesaw supports data visualization by providing a wrapper for the Plot.ly JavaScript plotting library. Here are a few examples of the new library in action. Descriptive stats: mean, min, max, median, sum, product, standard deviation, variance, percentiles, geometric mean, skewness, kurtosis, etc. Add tablesaw-core to your project. You can find the version number for the latest release in the release notes. -
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KernelDensity.jl
Kernel density estimators for Julia
Kernel density estimators for Julia. -
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CommonMark.jl
A CommonMark-compliant parser for Julia
A CommonMark-compliant parser for Julia. -
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DiffEqBayes.jl
Extension functionality which uses Stan.jl, DynamicHMC.jl
This repository is a set of extension functionality for estimating the parameters of differential equations using Bayesian methods. It allows the choice of using CmdStan.jl, Turing.jl, DynamicHMC.jl and ApproxBayes.jl to perform a Bayesian estimation of a differential equation problem specified via the DifferentialEquations.jl interface. -
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Registrator.jl
Julia package registration bot
Registrator is a GitHub app that automates the creation of registration pull requests for your Julia packages to the General registry. -
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NNlib.jl
Neural Network primitives with multiple backends
This package provides a library of functions useful for neural networks, such as softmax, sigmoid, batched multiplication, convolutions and pooling. Many of these are used by Flux.jl, which loads this package, but they may be used independently. -
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CategoricalArrays.jl
Arrays for working with categorical data
This package provides tools for working with categorical variables, both with unordered (nominal variables) and ordered categories (ordinal variables), optionally with missing values. -
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TaylorSeries.jl
Taylor polynomial expansions in one and several independent variables
A Julia package for Taylor polynomial expansions in one or more independent variables. -
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Parsers.jl
fast parsing machinery for basic types in Julia
A collection of type parsers and utilities for Julia. Installation: at the Julia REPL, import Pkg; Pkg.add("Parsers") Parsers is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators. -
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CxxWrap
Package to make C++ libraries available in Julia
This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. -
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DataStructures.jl
Julia implementation of Data structures
Julia implementation of Data structures. -
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ZMQ.jl
Julia interface to ZMQ
A Julia interface to ZeroMQ. ZMQ.jl is a Julia interface to ZeroMQ, The Intelligent Transport Layer. -
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MultivariateStats.jl
A Julia package for multivariate statistics and data analysis
A Julia package for multivariate statistics and data analysis (e.g. dimensionality reduction). -
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Distributions.jl
A Julia package for probability distributions and associated functions
A Julia package for probability distributions and associated functions. -
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Obsidian Visual Skills Pack
Generate Canvas, Excalidraw, and Mermaid diagrams from text
LLM-TLDR is a Python-based tool designed to dramatically reduce the amount of code a large language model needs to read by extracting the essential structure and context from a codebase and presenting only the most relevant parts to the model. Traditional approaches often dump entire files into a model’s context, which quickly exceeds token limits; LLM-TLDR instead indexes project structure, traces dependencies, and summarizes code in a way that preserves semantic relevance while shrinking input size by up to 95 %. ...
