Tools for diffing and merging of Jupyter notebooks.

Overview

Installation | Documentation | Contributing | Development Install | Testing | License | Getting help

nbdime Jupyter Notebook Diff and Merge tools

Build Status codecov.io Documentation Status Google Group

nbdime provides tools for diffing and merging of Jupyter Notebooks.

  • nbdiff compare notebooks in a terminal-friendly way
  • nbmerge three-way merge of notebooks with automatic conflict resolution
  • nbdiff-web shows you a rich rendered diff of notebooks
  • nbmerge-web gives you a web-based three-way merge tool for notebooks
  • nbshow present a single notebook in a terminal-friendly way

Diffing notebooks in the terminal:

terminal-diff

Merging notebooks in a browser:

web-merge

Installation

Install nbdime with pip:

pip install nbdime

See the installation docs for more installation details and development installation instructions.

Documentation

See the latest documentation at https://nbdime.readthedocs.io.

See also description and discussion in the Jupyter Enhancement Proposal.

Contributing

If you would like to contribute to the project, please read our contributor documentation and the CONTRIBUTING.md.

Development Install

To install a development version of nbdime, you will need npm installed and available on your PATH while installing.

For a development install, enter on the command line:

pip install -e git+https://github.com/jupyter/nbdime#egg=nbdime

See installation documentation for additional detail, particularly related to performing a dev install for working on the browser script code.

Testing

Install the test requirements:

pip install nbdime[test]

To run Python tests locally, enter on the command line: pytest

To run Javascript tests locally, enter: npm test

Install the codecov browser extension to view test coverage in the source browser on github.

See testing documentation for additional detail.

License

We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.

All code is licensed under the terms of the revised BSD license.

Getting help

We encourage you to ask questions on the mailing list.

Resources

Owner
Project Jupyter
Interactive Computing
Project Jupyter
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Data Science on AWS - O'Reilly Book Get the book on Amazon.com Book Outline Quick Start Workshop (4-hours) In this quick start hands-on workshop, you

Data Science on AWS 2.8k Jan 03, 2023
Bayesian Additive Regression Trees For Python

BartPy Introduction BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1]. Reasons to use BART

187 Dec 16, 2022
Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

Criteo 419 Jan 01, 2023
Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

Keivan Ipchi Hagh 1 Nov 22, 2021
Decision Tree Regression algorithm implemented on Python from scratch.

Decision_Tree_Regression I implemented the decision tree regression algorithm on Python. Unlike regular linear regression, this algorithm is used when

1 Dec 22, 2021
Time series changepoint detection

changepy Changepoint detection in time series in pure python Install pip install changepy Examples from changepy import pelt from cha

Rui Gil 92 Nov 08, 2022
Machine Learning Techniques using python.

👋 Hi, I’m Fahad from TEXAS TECH. 👀 I’m interested in Optimization / Machine Learning/ Statistics 🌱 I’m currently learning Machine Learning and Stat

FAHAD MOSTAFA 1 Jan 19, 2022
Lseng-iseng eksplor Machine Learning dengan menggunakan library Scikit-Learn

Kalo dengar istilah ML, biasanya rada ambigu. Soalnya punya beberapa kepanjangan, seperti Mobile Legend, Makan Lontong, Ma**ng L*v* dan lain-lain. Tapi pada repo ini membahas Machine Learning :)

Alfiyanto Kondolele 1 Apr 06, 2022
The unified machine learning framework, enabling framework-agnostic functions, layers and libraries.

The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv

Ivy 8.2k Dec 31, 2022
A Python Module That Uses ANN To Predict A Stocks Price And Also Provides Accurate Technical Analysis With Many High Potential Implementations!

Stox A Module to predict the "close price" for the next day and give "technical analysis". It uses a Neural Network and the LSTM algorithm to predict

Stox 31 Dec 16, 2022
A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Allan Barcelos 8 Aug 10, 2022
Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray

A unified Data Analytics and AI platform for distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray What is Analytics Zoo? Analytics Zo

2.5k Dec 28, 2022
CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

CorrProxies - Optimizing Machine Learning Inference Queries with Correlative Proxy Models

ZhihuiYangCS 8 Jun 07, 2022
Predict the income for each percentile of the population (Python) - FRENCH

05.income-prediction Predict the income for each percentile of the population (Python) - FRENCH Effectuez une prédiction de revenus Prérequis Pour ce

1 Feb 13, 2022
GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

Generator of Rad Names from Decent Paper Acronyms

264 Nov 08, 2022
PySpark + Scikit-learn = Sparkit-learn

Sparkit-learn PySpark + Scikit-learn = Sparkit-learn GitHub: https://github.com/lensacom/sparkit-learn About Sparkit-learn aims to provide scikit-lear

Lensa 1.1k Jan 04, 2023
K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

K Means Algorithm What is K Means This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of pr

1 Nov 01, 2021
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
Pragmatic AI Labs 421 Dec 31, 2022
Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining

**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** S

Sebastian Raschka 4k Dec 30, 2022