papermill - 📚 Parameterize, execute, and analyze notebooks

  •        9

Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. To parameterize your notebook designate a cell with the tag parameters.



Related Projects

gophernotes - The Go kernel for Jupyter notebooks and nteract.

  •    Go

Acknowledgements - This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. Important Note - gomacro relies on the plugin package when importing third party libraries. This package works reliably on Mac OS X only with Go 1.10.2+ as long as you never execute the command strip gophernotes. If you can only compile gophernotes with Go <= 1.10.1 on Mac, consider using the Docker install and run gophernotes/Jupyter in Docker.

nteract - 📘 The interactive computing suite for you! ✨

  •    Javascript

nteract is first and foremost a dynamic tool to give you flexibility when writing code, exploring data, and authoring text to share insights about the data. Edit code, write prose, and visualize.

IfSharp - F# for Jupyter Notebooks

  •    Jupyter

This is the F# implementation for Jupyter. View the Feature Notebook for some of the features that are included.You can use Jupyter F# Notebooks for free (with free server-side execution) at Azure Notebooks. If you select "Show me some samples", then there is an "Introduction to F#" which guides you through the language and its use in Jupyter.

spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

  •    Jupyter

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

hydrogen - :atom: Run code interactively, inspect data, and plot

  •    Javascript

Hydrogen is an interactive coding environment that supports Python, R, JavaScript and other Jupyter kernels. Checkout our Documentation and Medium blog post to see what you can do with Hydrogen.

PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks

  •    Jupyter

This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.

bokeh-notebooks - Interactive Web Plotting with Bokeh in IPython notebook

  •    Jupyter

Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. These Jupyter notebooks provide useful Bokeh examples and a tutorial to get started. You can visualize the rendered Jupyter notebooks on NBViewer or download the repository and execute jupyter notebook from your terminal.

CADL - Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

  •    Jupyter

This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow. It also contains a python package containing all the code developed during all three courses. The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results. - Instructional notebooks on music information retrieval.

  •    Jupyter

stanford-mir is now This repository contains instructional Jupyter notebooks related to music information retrieval (MIR). Inside these notebooks are Python code snippets that illustrate basic MIR systems.

dashboards - Jupyter Dashboards Layout Extension

  •    Jupyter

The dashboards layout extension is an add-on for Jupyter Notebook. It lets you arrange your notebook outputs (text, plots, widgets, ...) in grid- or report-like layouts. It saves information about your layouts in your notebook document. Other people with the extension can open your notebook and view your layouts. For a sample of what's possible with the dashboard layout extension, have a look at the demo dashboard-notebooks in this repository.

deep-learning-with-python-notebooks - Jupyter notebooks for the code samples of the book "Deep Learning with Python"

  •    Jupyter

This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Publications). Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments.These notebooks use Python 3.6 and Keras 2.0.8. They were generated on a p2.xlarge EC2 instance.

ipython-notebooks - A collection of IPython notebooks covering various topics.

  •    Jupyter

This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subjects that I find interesting. I've included notebook viewer links below. Click the link to see a live rendering of the notebook. These notebooks contain introductory content such as an overview of the language and a review of IPython's functionality.

qiskit-tutorial - A collection of Jupyter notebooks using Qiskit

  •    Jupyter

In this repository, we've put together a collection of Jupyter notebooks aimed at teaching people who want to use the QISKit for writing quantum computing programs and executing them on one of several backends (online quantum processors, online simulators, and local simulators). The online quantum processors connects to the IBM Q devices. Please refer to this installation for installing and setting up QISKit and tutorials on your own machine.

scikit-learn-videos - Jupyter notebooks from the scikit-learn video series

  •    Jupyter

This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. It was featured on Kaggle's blog in 2015. There are 9 video tutorials totaling 4 hours, each with a corresponding Jupyter notebook. The notebook contains everything you see in the video: code, output, images, and comments.

numerical-linear-algebra - Free online textbook of Jupyter notebooks for fast

  •    Jupyter

This course was taught in the University of San Francisco's Masters of Science in Analytics program, summer 2017 (for graduate students studying to become data scientists). The course is taught in Python with Jupyter Notebooks, using libraries such as Scikit-Learn and Numpy for most lessons, as well as Numba (a library that compiles Python to C for faster performance) and PyTorch (an alternative to Numpy for the GPU) in a few lessons. Accompanying the notebooks is a playlist of lecture videos, available on YouTube. If you are ever confused by a lecture or it goes too quickly, check out the beginning of the next video, where I review concepts from the previous lecture, often explaining things from a new perspective or with different illustrations, and answer questions.

jupyterhub - Multi-user server for Jupyter notebooks

  •    Python

With JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high performance computing group.

emacs-ipython-notebook - Jupyter and IPython 2.x/3.x notebook client in Emacs

  •    Emacs

EIN works with IPython 2.x, 3.x, and Jupyter! Note that remote and password protected logins are working with IPython 3.x, but have not been tested with Jupyter. The code for testing EIN is horribly broken, but I regularly hand check the code running against IPython's suite of sample notebooks. It's a worse-is-better solution to problem requiring a time-consuming solution.

learn-python3 - Jupyter notebooks for teaching/learning Python 3

  •    Python

This repository contains a collection of materials for teaching/learning Python 3 (3.5+). If you can not access Python and/or Jupyter Notebook on your machine, you can still follow the web based materials. However, you should be able to use Jupyter Notebook in order to complete the exercises.