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Create delightful software with Jupyter Notebooks

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Top Related Projects

4,994

Create delightful software with Jupyter Notebooks

11,913

Jupyter Interactive Notebook

JupyterLab computational environment.

VS Code Jupyter extension

6,226

📘 The interactive computing suite for you! ✨

Quick Overview

nbdev is a powerful library for creating Python packages and documentation using Jupyter Notebooks. It allows developers to write, test, and document code in a single environment, streamlining the development process and promoting literate programming practices.

Pros

  • Seamless integration of code, tests, and documentation in Jupyter Notebooks
  • Automatic generation of Python modules and documentation from notebooks
  • Built-in support for continuous integration and version control
  • Encourages best practices in software development and documentation

Cons

  • Learning curve for developers not familiar with Jupyter Notebooks
  • May require adjustments to existing development workflows
  • Limited customization options for generated documentation
  • Potential performance overhead when working with large projects

Code Examples

  1. Creating a new nbdev project:
from nbdev.cli import nbdev_new
nbdev_new("my_project")
  1. Exporting notebooks to Python modules:
from nbdev.export import notebook2script
notebook2script()
  1. Building documentation:
from nbdev.cli import nbdev_build_docs
nbdev_build_docs()
  1. Running tests:
from nbdev.test import test_nb
test_nb("my_notebook.ipynb")

Getting Started

To get started with nbdev, follow these steps:

  1. Install nbdev:
pip install nbdev
  1. Create a new project:
nbdev_new my_project
cd my_project
  1. Edit the settings.ini file to configure your project.

  2. Create and edit Jupyter Notebooks in the nbs/ directory.

  3. Export notebooks to Python modules:

nbdev_build_lib
  1. Build documentation:
nbdev_build_docs
  1. Commit changes and push to GitHub to trigger CI/CD.

Competitor Comparisons

4,994

Create delightful software with Jupyter Notebooks

Pros of nbdev

  • More active development and recent updates
  • Larger community and contributor base
  • Better documentation and examples

Cons of nbdev

  • Potentially more complex setup and configuration
  • May have more features than needed for smaller projects
  • Steeper learning curve for new users

Code Comparison

nbdev:

from nbdev import *
setup_git_hooks()
nbdev_build_lib()
nbdev_clean_nbs()
nbdev_test_nbs()

nbdev>:

from nbdev_core import *
nbdev_export()
nbdev_test()
nbdev_docs()

Both repositories are related to the nbdev project, which is a tool for developing Python libraries using Jupyter Notebooks. The main difference appears to be that nbdev> is a fork or alternative version of the original nbdev project. The code comparison shows slight differences in function names and available commands, with nbdev offering more specific functions while nbdev> seems to have more generalized commands. Overall, nbdev appears to be the more established and actively maintained project, while nbdev> might offer a simplified or alternative approach to the same concept.

11,913

Jupyter Interactive Notebook

Pros of notebook

  • Well-established, widely adopted interactive computing environment
  • Extensive ecosystem of extensions and integrations
  • Supports multiple programming languages beyond Python

Cons of notebook

  • Limited version control and collaboration features
  • Can lead to non-reproducible workflows and "hidden state" issues
  • Lacks built-in tools for creating Python packages from notebooks

Code comparison

notebook:

from notebook import notebookapp
notebookapp.main()

nbdev:

from nbdev.cli import nbdev_build_lib
nbdev_build_lib()

The notebook code snippet shows how to launch the Jupyter Notebook application, while the nbdev code demonstrates how to build a Python library from notebooks.

nbdev focuses on creating Python packages from Jupyter notebooks, offering features like automated documentation generation and two-way sync between notebooks and source code. It's designed to streamline the development process for data scientists and researchers who work primarily in notebooks.

notebook, on the other hand, provides a more general-purpose interactive computing environment that's not specifically tailored for package development. It offers a flexible platform for exploratory data analysis, visualization, and prototyping across various programming languages.

JupyterLab computational environment.

Pros of JupyterLab

  • More comprehensive and feature-rich IDE-like environment
  • Supports multiple file types and programming languages
  • Highly extensible with a wide range of plugins and extensions

Cons of JupyterLab

  • Steeper learning curve for beginners
  • Heavier resource usage, especially for large projects
  • Less integrated with version control and software development workflows

Code Comparison

JupyterLab (Python cell in a notebook):

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

nbdev (Python code in a notebook cell):

#| export
import numpy as np
import matplotlib.pyplot as plt

def plot_sine_wave():
    x = np.linspace(0, 10, 100)
    y = np.sin(x)
    plt.plot(x, y)
    plt.show()

JupyterLab focuses on providing an interactive environment for data exploration and visualization, while nbdev emphasizes literate programming and seamless integration between notebooks and Python modules. The code examples illustrate how JupyterLab is typically used for direct data manipulation and visualization, whereas nbdev encourages creating reusable functions that can be easily exported to Python modules.

VS Code Jupyter extension

Pros of vscode-jupyter

  • Seamless integration with Visual Studio Code, providing a familiar IDE experience
  • Rich set of features for interactive Python development and data science workflows
  • Extensive language support beyond Python, including R and Julia

Cons of vscode-jupyter

  • Heavier resource usage due to the full VS Code environment
  • Steeper learning curve for users not familiar with VS Code
  • Less focus on literate programming and documentation generation

Code Comparison

vscode-jupyter:

# Interactive Python code execution in VS Code
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.show()

nbdev:

# #hide
from nbdev.showdoc import *
# #export
def plot_data(data):
    """Plot the given data"""
    plt.plot(data)
    plt.show()

The vscode-jupyter example shows interactive plotting within VS Code, while the nbdev example demonstrates its focus on documentation and code export. nbdev uses special comments for hiding cells and exporting functions, emphasizing its literate programming approach.

6,226

📘 The interactive computing suite for you! ✨

Pros of nteract

  • More comprehensive interactive computing environment with support for multiple languages
  • Offers a desktop application for a standalone experience
  • Provides a rich set of components for building interactive interfaces

Cons of nteract

  • Larger and more complex project, potentially harder to contribute to
  • Less focused on literate programming and documentation generation
  • May have a steeper learning curve for new users

Code Comparison

nteract example (JavaScript):

import { ContentRef } from "@nteract/types";
import { actions } from "@nteract/core";

const contentRef: ContentRef = "some-content-ref";
dispatch(actions.fetchContent({ contentRef }));

nbdev example (Python):

from nbdev.showdoc import show_doc
from nbdev.export import notebook2script

show_doc(notebook2script)
notebook2script('00_export.ipynb', 'nbdev/export.py')

nteract focuses on building interactive interfaces and managing notebook state, while nbdev emphasizes literate programming and code generation from notebooks. nteract's codebase is primarily JavaScript/TypeScript, whereas nbdev is Python-based. nteract offers a more comprehensive environment for interactive computing across languages, while nbdev specializes in Python development workflows and documentation generation.

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README

Getting Started

CI

nbdev is a notebook-driven development platform. Simply write notebooks with lightweight markup and get high-quality documentation, tests, continuous integration, and packaging for free!

nbdev makes debugging and refactoring your code much easier than in traditional programming environments since you always have live objects at your fingertips. nbdev also promotes software engineering best practices because tests and documentation are first class.

  • Documentation is automatically generated using Quarto and hosted on GitHub Pages. Docs support LaTeX, are searchable, and are automatically hyperlinked (including out-of-the-box support for many packages via nbdev-index)
  • Publish packages to PyPI and conda as well as tools to simplify package releases. Python best practices are automatically followed, for example, only exported objects are included in __all__
  • Two-way sync between notebooks and plaintext source code allowing you to use your IDE for code navigation or quick edits
  • Tests written as ordinary notebook cells are run in parallel with a single command
  • Continuous integration out-of-the-box with GitHub Actions that run your tests and rebuild your docs
  • Git-friendly notebooks with Jupyter/Git hooks that clean unwanted metadata and render merge conflicts in a human-readable format
  • … and much more!

Install

nbdev works on macOS, Linux, and most Unix-style operating systems. It works on Windows under WSL, but not under cmd or Powershell.

You can install nbdev with pip:

pip install nbdev

… or with conda (or mamba):

conda install -c fastai nbdev

Note that nbdev must be installed into the same Python environment that you use for both Jupyter and your project.

How to use nbdev

The best way to learn how to use nbdev is to complete either the written walkthrough or video walkthrough:

Alternatively, there’s a shortened version of the video walkthrough with coding sections sped up using the unsilence Python library – it’s 27 minutes faster, but a bit harder to follow.

You can also run nbdev_help from the terminal to see the full list of available commands:

!nbdev_help
nbdev_bump_version        Increment version in settings.ini by one
nbdev_changelog           Create a CHANGELOG.md file from closed and labeled GitHub issues
nbdev_clean               Clean all notebooks in `fname` to avoid merge conflicts
nbdev_conda               Create a `meta.yaml` file ready to be built into a package, and optionally build and upload it
nbdev_create_config       Create a config file.
nbdev_docs                Create Quarto docs and README.md
nbdev_export              Export notebooks in `path` to Python modules
nbdev_filter              A notebook filter for Quarto
nbdev_fix                 Create working notebook from conflicted notebook `nbname`
nbdev_help                Show help for all console scripts
nbdev_install             Install Quarto and the current library
nbdev_install_hooks       Install Jupyter and git hooks to automatically clean, trust, and fix merge conflicts in notebooks
nbdev_install_quarto      Install latest Quarto on macOS or Linux, prints instructions for Windows
nbdev_merge               Git merge driver for notebooks
nbdev_migrate             Convert all markdown and notebook files in `path` from v1 to v2
nbdev_new                 Create an nbdev project.
nbdev_prepare             Export, test, and clean notebooks, and render README if needed
nbdev_preview             Preview docs locally
nbdev_proc_nbs            Process notebooks in `path` for docs rendering
nbdev_pypi                Create and upload Python package to PyPI
nbdev_readme              Create README.md from readme_nb (index.ipynb by default)
nbdev_release_both        Release both conda and PyPI packages
nbdev_release_gh          Calls `nbdev_changelog`, lets you edit the result, then pushes to git and calls `nbdev_release_git`
nbdev_release_git         Tag and create a release in GitHub for the current version
nbdev_requirements        Writes a `requirements.txt` file to `directory` based on settings.ini.
nbdev_sidebar             Create sidebar.yml
nbdev_test                Test in parallel notebooks matching `path`, passing along `flags`
nbdev_trust               Trust notebooks matching `fname`
nbdev_update              Propagate change in modules matching `fname` to notebooks that created them
nbdev_update_license      Allows you to update the license of your project.

FAQ

Q: What is the warning “Found a cell containing mix of imports and computations. Please use separate cells”?

A: You should not have cells that are not exported, and contain a mix of import statements along with other code. For instance, don’t do this in a single cell:

import some_module
some_module.something()

Instead, split this into two cells, one which does import some_module, and the other which does some_module.something().

The reason for this is that when we create your documentation website, we ensure that all of the signatures for functions you document are up to date, by running the imports, exported cells, and show_doc functions in your notebooks. When you mix imports with other code, that other code will be run too, which can cause errors (or at least slowdowns) when creating your website.

Q: Why is nbdev asking for root access? How do I install Quarto without root access?

A: When you setup your first project, nbdev will attempt to automatically download and install Quarto for you. This is the program that we use to create your documentation website.

Quarto’s standard installation process requires root access, and nbdev will therefore ask for your root password during installation. For most people, this will work fine and everything will be handled automatically – if so, you can skip over the rest of this section, which talks about installing without root access.

If you need to install Quarto without root access on Linux, first cd to wherever you want to store it, then download Quarto, and type:

dpkg -x quarto*.deb .
mv opt/quarto ./
rmdir opt
mkdir -p ~/.local/bin
ln -s "$(pwd)"/quarto/bin/quarto ~/.local/bin

To use this non-root version of Quarto, you’ll need ~/.local/bin in your PATH environment variable. (Alternatively, change the ln -s step to place the symlink somewhere else in your path.)

Q: Someone told me not to use notebooks for “serious” software development!

A: Watch this video. Don’t worry, we still get this too, despite having used nbdev for a wide range of “very serious” software projects over the last three years, including deep learning libraries, API clients, Python language extensions, terminal user interfaces, and more!

Contributing

If you want to contribute to nbdev, be sure to review the contributions guidelines. This project adheres to fastai’s code of conduct. By participating, you are expected to uphold this code. In general, we strive to abide by generally accepted best practices in open-source software development.

Make sure you have nbdev’s git hooks installed by running nbdev_install_hooks in the cloned repository.

Copyright

Copyright © 2019 onward fast.ai, Inc. Licensed under the Apache License, Version 2.0 (the “License”); you may not use this project’s files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.