Search Results for "code library"
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Showing 30 open source projects for "code library"
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1
NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. -
2
alpha_vantage
A python wrapper for Alpha Vantage API for financial data.
...This module implements a python interface to the free API provided by Alpha Vantage. You can have a look at all the API calls available in their API documentation. For code-less access to the APIs, you may also consider the official Google Sheet Add-on or the Microsoft Excel Add-on by Alpha Vantage. To get data from the API, simply import the library and call the object with your API key. Next, get ready for some awesome, free, realtime finance data. Your API key may also be stored in the environment variable ALPHAVANTAGE_API_KEY. ... -
3
NautilusTrader
A high-performance algorithmic trading platform
NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the... -
4
Matplotlib
matplotlib: plotting with Python
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib ships with several add-on toolkits, including 3D plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist. A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, ...), and a... -
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5
Optopsy
A nimble options backtesting library for Python
Optopsy is a Python-based, nimble backtesting and statistics library focused on evaluating options trading strategies like calls, puts, straddles, spreads, and more, using pandas-driven analysis. The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. -
6
Google Spreadsheets Python
Google Sheets Python API
...To access spreadsheets via Google Sheets API you need to authenticate and authorize your application. Older versions of gspread have used oauth2client. Google has deprecated it in favor of google-auth. If you’re still using oauth2client credentials, the library will convert these to google-auth for you, but you can change your code to use the new credentials to make sure nothing breaks in the future. If you familiar with the Jupyter Notebook, Google Colaboratory is probably the easiest way to get started using gspread. -
7
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... -
8
SageMaker Training Toolkit
Train machine learning models within Docker containers
...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 reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. ... -
9
Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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10
AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
aws-sdk-pandas (formerly AWS Data Wrangler) bridges pandas with the AWS analytics stack so DataFrames flow seamlessly to and from cloud services. With a few lines of code, you can read from and write to Amazon S3 in Parquet/CSV/JSON/ORC, register tables in the AWS Glue Data Catalog, and query with Amazon Athena directly into pandas. The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling boilerplate. ... -
11
bridgex
Convert files like docx, xlsx, pptx, html, and more to MarkDown
Bridgex is an open‑source graphical interface for converting files to Markdown, built in Python and based on Pyside6 (Qt for Python). Its objective is to simplify access to the Markitdown library through a straightforward, modular visual experience. Features ✨ - Cross‑platform graphical interface. - Efficient file‑to‑Markdown conversion. - Modularity: easy to adapt and extend. - Support for multiple input formats. - Lightweight editing prior to saving. Supported Formats... -
12
Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is... -
13
LibrePLM
LibrePLM integrates CAD editors in Odoo / LibrERP
Save and manage Engineering informations from CAD system directly in Odoo or LibrERP. Integrated in CAD system, allows complete Product Lifecycle control. Reduce time to market, improve your business and avoid trivial errors. Server New Release : 2025 Q3 - Release on Odoo version 19.0. Client New Release : 2026 Q1 - Added Draftsight integration. Supported: Windows 11. Added new tool GetNodeID to help you asking activation code. Have more info :... -
14
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. -
15
club
Software for small organisations like clubs.
Simplify management of bar, kitchen and memberships of small organisations, that don't need complicated management programs. Made in python, MongoDB is required. Source code: https://github.com/NrtypmForhp/club -
16
SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve 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. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where the... -
17
Robin-Stocks API Library
This is a library to use with Robinhood Financial App
This is a library to use with Robinhood Financial App. It currently supports trading crypto-currencies, options, and stocks. In addition, it can be used to get real-time ticker information, assess the performance of your portfolio, and can also get tax documents, total dividends paid, and more. The code is simple to use, easy to understand, and easy to modify. -
18
OpenOffice.org Utility Library
Library modules for creating ODF documents.
....) - Set column and row attributes including width and height I am currently hosting the new development code at the following URL: https://github.com/josephcolton/ooolib-python I will be posting releases here on SourceForge as well as GitHub, so you can come to either place for the releases. The newest code will only be available using Git on GitHub. -
19
AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
...These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
20
TA-Lib.git: Technical Analysis Library
Mirror of the TA-Lib project using a Git repository
This project is intended to provide Git access to the code of the original project, TA-Lib, which uses Subversion. It is intended for system integrators wishing to use TA-Lib in their Git-managed project through Git submodules or subtrees. No actual development is being done here; all development happens in the original project. -
21
StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. -
22
SageMaker Containers
Create SageMaker-compatible Docker containers
...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 reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. ... -
23
FuzzyWuzzy
Fuzzy string matching in Python
...To pick an example completely at random, Cirque du Soleil has a show running in New York called “Zarkana”. When we scour the web to find tickets for sale, mostly those tickets are identified by a title, date, time, and venue. We’ve built up a library of “fuzzy” string matching routines to help us along. And good news! We’re open sourcing it. The library is called “Fuzzywuzzy”, the code is pure python, and it depends only on the (excellent) difflib python library. -
24
Optimal BPM
The beat of your business
...It incorporates many different open source projects that relates to interoperability, transformation, context and process control. It has no business engine, instead it generates Python code and leaves the engine mechanics to its scripting engine. This enables it to: * be very flexible * be truly multi-platform * draw from all the features available to the language and its sprawling ecosystem * partake in that ecosystem as a library. It has a strong focus on ease of use and low maintenance. Anyone should be able to get started with minimal outside(consultants) help and quickly get the system working. ... -
25
Error Propagation Calculator
Easily propagate measurement errors through mathematical operations.
Error Propagation Calculator is essentially a cross-platform GUI front-end for the Python Uncertainties library that does not require knowledge of Python to use. Simply point and click to enter values and errors, input your expression and press compute. The program evaluates native Python code, but the user does not need to know the syntax as common operators are provided as buttons, similar to a scientific calculator. -Evaluate expressions with as much as 26 different variable and error pairs. ...
