importnb imports notebooks as python modules.

if you're here, then there is a chance you have a notebook (.ipynb) in a directory saved as Untitled.ipynb. it is just sitting there, but what if it could be used as a python module? importnb is here to answer that question.

basic example

use importnb's Notebook finder and loader to import notebooks as modules

import importnb
with the explicit API
with importnb.Notebook():     import Untitled
with the extensible API
with importnb.imports("ipynb"):     import Untitled

What does this snippet do?

the snippet begins with a context manager that modifies the files python can discover.

it will find the Untitled.ipynb notebook and import it as a module with the name

of Untitled. the file description will have .ipynb as an extension.

maybe when we give notebooks new life they eventually earn a better name than Untitled?

run a notebook as a script

the importnb command line interface mimics python's. it permits running notebook files, modules, and raw json data.

the commands below execute a notebook module and file respectively.

importnb -m Untitled         # call the Untitled module as main
importnb Untitled.ipynb      # call the Untitled file as main

installing importnb

use either pip or sconda/mamba

pip install importnb
or
conda install -c conda-forge importnb
or
mamba install -c conda-forge importnb

importnb features

• importnb.Notebook offers parameters to customize how modules are imported
• imports Jupyter notebooks as python modules

• works with top-level await statements
• integration with pytest, IPython, and coverage
• extensible machinery and entry points
• translates Jupyter notebook files (i.e. .ipynb files) line-for-line to python source providing natural error messages
• command line interface for running notebooks as python scripts
• has no required dependencies

customizing parameters

the Notebook object has a few features that can be toggled:

• lazy:bool=False lazy load the module, the namespace is populated when the module is access the first time.
• position:int=0 the relative position of the import loader in the sys.path_hooks
• includefuzzyfinder:bool=True use fuzzy searching syntax when underscores are encountered.
• includemarkdowndocstring:bool=True markdown blocks preceding a class or def become docstrings.
• includenondefs:bool=True import only function and class definitions. ignore intermediate \* expressions.
• no_magic:bool=False execute IPython magic statements from the loader.

• name:str | None=None a module name for the imported source
• path:str | None=None a path to a source file
• extensions:tuple[str, ...]=(".ipy", ".ipynb") file extensions to be considered importable
• module_type:type[M]=SourceModule the class used to store a module

importing notebooks

the primary goal of this library is to make it easy to reuse python code in notebooks. below are a few ways to invoke python's import system within the context manager.

import importnb
from importlib import import_module
with importnb.imports("ipynb"):     import Untitled     import Untitled as nb     import("Untitled")     import_module("Untitled")

import data files

importnb can import more than notebooks. json-like data from disk can be loaded and stored on the module with rich representations.

import importnb
with importnb.imports("toml", "json", "yaml"):     pass

all the available entry points are found with:

from importnb.entrypoints import listaliases
list_aliases()

loading directly from file

from importnb import Notebook
Untitled = Notebook.load("Untitled.ipynb")

fuzzy finding

often notebooks have names that are not valid python files names that are restricted alphanumeric characters and an _. the importnb fuzzy finder converts python's import convention into globs that will find modules matching specific patters. consider the statement:

import importnb
with importnb.Notebook():     import Utitl_d  # Utitl*d.ipynb

importnb translates Utitl_d to a glob format that matches the pattern Utitl*d.ipynb when searching for the source. that means that importnb should find Untitled.ipynb as the source for the import.

import importnb
with importnb.Notebook():     import _ntitled   # *ntitled.ipynb     import __d        # **d.ipynb     import U__        # U**.ipynb

a motivation for this feature is naming notebooks as if they were blog posts using the YYYY-MM-DD-title-here.ipynb convention. there are a few ways we could this file explicitly. the fuzzy finder syntax means all of the following are equivalent:

import importnb
with importnb.Notebook():     import _titlehere     import YYYYMMDDtitlehere     import _MMDDtitlehere

fuzzy name ambiguity

it is possible that a fuzzy import may be ambiguous and return multiple files. the importnb fuzzy finder will prefer the most recently changed file.

ambiguity can be avoided by using more explicit fuzzy imports to reduce collisions. another option is to use python's explicit import functions.

from importlib import import_module
import importnb
with importnb.Notebook():     import("YYYY-MM-DD-title-here")     import_module("YYYY-MM-DD-title-here")

importing your most recently changed notebook

an outcome of resolving the most recently changed is that you can import your most recent notebook with:

import importnb
with importnb.Notebook():
    import __                        # **.ipynb

integrations

pytest

since importnb transforms notebooks to python documents we can use these as source for tests. importnbs pytest extension is not fancy, it only allows for conventional pytest test discovery, and must be explicitly enabled.

pytest -pimportnb.utils.pytest_importnb

PYTESTPLUGINS=importnb.utils.pytestimportnb pytest

[tool.pytest.ini_options]
  addopts = ["-pimportnb.utils.pytest_importnb"]

pytest_plugins = [
      "importnb.utils.pytest_importnb",
  ]

coverage

coverage can tell you how much of your code runs.

[tool.coverage.run]
  plugins = ["importnb.utils.coverage"]

extensible

the importnb.Notebook machinery is extensible. it allows other file formats to be used. for example, pidgy uses importnb to import markdown files as compiled python code.

import importnb
class MyLoader(importnb.Notebook):     pass

appendix

line-for-line translation and natural error messages

a challenge with Jupyter notebooks is that they are json data. this poses problems:

• every valid line of code in a Jupyter notebook is a quoted json string
• json parsers don't have a reason to return line numbers.

the problem with quoted code

line-for-line json parser

python's json module is not pluggable in the way we need to find line numbers. since importnb is meant to be dependency free on installation we couldn't look to any other packages like ujson or json5.

the need for line numbers is enough that we ship a stand-alone json grammar parser. to do this without extra dependencies we use the lark grammar package at build time:

• we've defined a minimal grammar in json.g
• we invoke lark-standalone that generates a stand-alone parser for the grammar.

the result of importnb is json data translated into vertically sparse, valid python code.

reproducibility caution with the fuzzy finder

⚠️ fuzzy finding is not reproducible as your system will change over time. in python, "explicit is better than implicit" so defining strong fuzzy strings is best practice if you MUST use esoteric names. an alternative option is to use the importlib.import_module machinery

[pixi]: https://pixi.sh