papermill
typer
papermill | typer | |
---|---|---|
26 | 87 | |
5,630 | 14,398 | |
0.6% | - | |
8.0 | 8.7 | |
6 days ago | 2 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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papermill
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Spreadsheet errors can have disastrous consequences – yet we keep making them
Pandas docs > Comparison with spreadsheets: https://pandas.pydata.org/docs/getting_started/comparison/co...
Pandas docs > I/O > Excel files: https://pandas.pydata.org/docs/user_guide/io.html#excel-file...
nteract/papermill: https://github.com/nteract/papermill :
> papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. [...]
> This opens up new opportunities for how notebooks can be used. For example:
> - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier.
"The World Excel Championship is being broadcast on ESPN" (2022) https://news.ycombinator.com/item?id=32420925 :
> Computational notebook speedrun ideas:
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Jupyter Kernel Architecture
There is Papermill ... https://github.com/nteract/papermill
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Git and Jupyter Notebooks Guide
https://github.com/jupyter/enhancement-proposals/pull/103#is...
Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/
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JupyterLab 4.0
You may be interested in papermill to address the parametrized analysis problem [1]. I think (but I'm not positive) this is what the data team at a previous job used to automate running notebooks for all sorts nightly reports.
[1] https://papermill.readthedocs.io/en/latest/#
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Show HN: Mercury – convert Jupyter Notebooks to Web Apps without code rewriting
I'm using Papermill to operationalize Notebooks (https://github.com/nteract/papermill), it e.g. also has airflow support. I'm really happy with papermill for automatic notebook execution, in my field it's nice that we can go very quickly from analysis to operations -- while having super transparent "logging" in the executed notebooks.
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What's the best thing/library you learned this year ?
papermill bcpandas fastapi
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Does the Jupyter API allow using Jupyter from the CL?
But you can execute your notebook using Jupyter-run or papermill.
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Running Jupyter notebooks in parallel
As a first option, we will use Papermill, which has a Python API that allows us to run different notebooks using some functions:
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Tips for using Jupyter Notebooks with GitHub
Papermill can also target cloud storage outputs for hosting rendered notebooks, execute notebooks from custom Python code, and even be used within distributed data pipelines like Dagster (see Dagstermill). For more information, see the papermill documentation.
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Three Tools for Executing Jupyter Notebooks
Papermill Source Code
typer
- Typer: Python library for building CLI applications
- Copilot for your GitHub stars
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Things I've learned about building CLI tools in Python
I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.
https://typer.tiangolo.com/
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Things to do with standalone script
Adding CLI capabilities. My preferred library here is typer.
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Where to start for managing a Python code base for public distribution
I just heard about this but it seems to be pretty much the type of thing you want and want fast.
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Help on Docstrings
Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.
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Which best practices do you follow to build robust & extensible ETL jobs?
Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).
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Devs on teams that deploy anytime you want, what does your SDLC workflow look like?
So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.
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The different uses of Python type hints
Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.
[1] https://typer.tiangolo.com/
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Command line parser library, which one do you like the most, regardless of language?
interesting that you hate python, but love Click. Did you try Typer which uses Click underneath?
What are some alternatives?
nbconvert - Jupyter Notebook Conversion
click - Python composable command line interface toolkit
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
airflow-notebook - This repository is no longer maintained.
Gooey - Turn (almost) any Python command line program into a full GUI application with one line
nbdev - Create delightful software with Jupyter Notebooks
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
voila - Voilà turns Jupyter notebooks into standalone web applications
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
cement - Application Framework for Python