papermill
cheatsheets
papermill | cheatsheets | |
---|---|---|
26 | 60 | |
5,630 | 5,612 | |
0.6% | 0.6% | |
8.0 | 7.6 | |
6 days ago | about 14 hours ago | |
Python | TeX | |
BSD 3-clause "New" or "Revised" License | Creative Commons Attribution 4.0 |
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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
cheatsheets
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Tools a Data Scientist should know:
If you're an R user, stringr + its cheatsheet gets you very close to remembering what to do without needing to look further!
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JSON to PDF Magic: Harnessing LaTeX and JSON for Effortless Customization and Dynamic PDF Generation
For more information on how to use ggplot2 and create charts consult the ggplot2 official page or the ggplot2 cheat graphic.
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Best packages to learn?
I'd suggest you have a look at cheatsheets (or download them from GitHub) if you want to get to know your way around a package or set if functions, it saves you a lot of time.
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How do I make these shapes (pictured below) in ggplot?
You could use geom_hline and geom_vline, geom_abline, or geom_segment for this. (The ggplot cheat sheet is very useful for answering these kinds of questions, BTW.)
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Why does my scatter plot look like this?
I can't say for sure because I don't know what your ultimate aim is for your visualization. Check out the cheat sheet for ggplot2 here.
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Import from Excel
Finally just do your analysis. You should also should give a try and see the cheat sheet for data importing on the tidyverse package.
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[Request] How to best visualize percentages with R?
That said, when I’m trying to come up with an interesting way to visualize data, I find the ggplot cheat sheet very helpful: https://github.com/rstudio/cheatsheets/raw/main/data-visualization-2.1.pdf
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Need help with variables
Here's a cheat sheet: https://github.com/rstudio/cheatsheets/blob/main/strings.pdf
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Data manipulation in R
The cheat sheet of the stringr package should give you good overview of string manipulation/ regex in R.
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I'm trying to recreate this plot but I keep failing
I would very highly recommend that rather than trying to get started by translating an existing graph, you check out some documentation about ggplot first. If nothing else, the ggplot cheat sheet from RStudio should help explain what the component parts of the code are, and that might help you figure out what you actually want to do.
What are some alternatives?
nbconvert - Jupyter Notebook Conversion
tidytuesday - Official repo for the #tidytuesday project
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
forcats - 🐈🐈🐈🐈: tools for working with categorical variables (factors)
airflow-notebook - This repository is no longer maintained.
mostly-adequate-guide - Mostly adequate guide to FP (in javascript)
nbdev - Create delightful software with Jupyter Notebooks
ggplot2-book - ggplot2: elegant graphics for data analysis
voila - Voilà turns Jupyter notebooks into standalone web applications
mech - 🦾 Main repository for the Mech programming language. Start here!
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
ggplot2 - An implementation of the Grammar of Graphics in R