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Add quarto-script-python (#96)
* Migrate jump start quarto-script-python * Do not .gitignore manifest.json * Update min Quarto version in manifest * Adjust min Python version * Adjust min Python version in manifest * Add tags, quarto requires * Update manifest to remove .Rprofile * Use example category * Rename as script-python and refactor some of the gallery language * Fix homepage link --------- Co-authored-by: Jordan Jensen <[email protected]>
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‎.github/workflows/extensions.yml‎

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quarto-stock-report-r: extensions/quarto-stock-report-r/**
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stock-api-fastapi: extensions/stock-api-fastapi/**
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quarto-presentation: extensions/quarto-presentation/**
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script-python: extensions/script-python/**
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connectwidgets-example: extensions/connectwidgets-example/**
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plumbertableau-example: extensions/plumbertableau-example/**
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fastapitableau-example: extensions/fastapitableau-example/**

‎extensions/script-python/.gitignore‎

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/.quarto/
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/env/
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*.csv
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*.json
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script.html
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script_files
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/email-preview/
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.output_metadata.json
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# But do not ignore manifest.json
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!manifest.json

‎extensions/script-python/README.md‎

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# Run a Python Script using Quarto
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## About this example
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This data transformation script uses Quarto and Python. It is an example
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of how you might automate regular imports and transformations from a data
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source. JSON and CSV files are exported and made available for download.
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The script also generates a custom email, with the generated CSV files as
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attachments.
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## Learn more
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* [Quarto](https://quarto.org)
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* [Quarto: Rendering Script Files](https://quarto.org/docs/computations/render-scripts.html)
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## Requirements
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* Quarto version 1.4 or higher
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* Python version 3.8 or higher

‎extensions/script-python/_quarto.yml‎

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project:
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title: "Penguin data transformations"

‎extensions/script-python/manifest.json‎

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{
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"version": 1,
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"locale": "en_US.UTF-8",
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"metadata": {
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"appmode": "quarto-static"
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},
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"extension": {
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"name": "script-python",
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"title": "Run a Python Script",
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"description": "This data transformation Python script is an example of how you might automate regular imports and transformations from a data source. JSON and CSV files are exported and made available for download. Quarto is used to render the script.",
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"homepage": "https://github.com/posit-dev/connect-extensions/tree/main/extensions/script-python",
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"category": "example",
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"minimumConnectVersion": "2025.04.0",
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"version": "1.0.0"
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},
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"environment": {
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"python": {
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"requires": "~=3.8"
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},
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"quarto": {
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"requires": "~=1.4"
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}
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},
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"quarto": {
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"version": "1.4.557",
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"engines": [
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"jupyter"
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]
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},
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"python": {
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"version": "3.8.20",
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"package_manager": {
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"name": "pip",
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"version": "23.0.1",
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"package_file": "requirements.txt"
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}
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},
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"files": {
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"requirements.txt": {
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"checksum": "5a2845f6ec11bcefce420b49ef94c152"
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},
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".gitignore": {
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"checksum": "42b3cf8550e12acf456cef1254151634"
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},
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"README.md": {
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"checksum": "87c06eb724cdf54ed602741c3a82cc71"
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},
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"_quarto.yml": {
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"checksum": "e5bfdf389c2ae3bc115d8f4add41fc2f"
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},
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"script.py": {
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"checksum": "46d490184f5d440c884c6fd28460b54f"
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},
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"script.quarto_ipynb": {
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"checksum": "e032e1bd96459a32494df48ea2e58194"
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}
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}
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}

‎extensions/script-python/requirements.txt‎

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# jupyter
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notebook==7.2.2
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qtconsole==5.5.1
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jupyter-console==6.6.3
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nbconvert==7.16.1
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ipykernel==6.29.3
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ipywidgets==8.1.2
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palmerpenguins==0.1.4

‎extensions/script-python/script.py‎

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# %% [markdown]
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# ---
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# title: "Penguin data transformations"
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# subtitle: "Exported as JSON and CSV"
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# format: email
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# email-attachments:
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# - "adelie-m.csv"
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# - "gentoo-f.csv"
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# ---
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# %% [markdown]
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# ## Setup
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# %%
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import palmerpenguins
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penguins = palmerpenguins.load_penguins()
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# %% [markdown]
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# ## Filtering
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# %%
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gentoo_f = penguins[
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(penguins["species"] == "Gentoo") &
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(penguins["sex"] == "female")
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]
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adelie_m = penguins[
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(penguins["species"] == "Adelie") &
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(penguins["sex"] == "male")
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]
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# %% [markdown]
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# ## Statistics (Gentoo)
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# %%
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gentoo_f.describe()
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# %% [markdown]
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# ## Statistics (Adelie)
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# %%
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adelie_m.describe()
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# %% [markdown]
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# ## Export
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# %%
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gentoo_f.to_csv("gentoo-f.csv")
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gentoo_f.to_json("gentoo-f.json", orient="records")
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adelie_m.to_csv("adelie-m.csv")
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adelie_m.to_json("adelie-m.json", orient="records")
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# %% [markdown]
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# # Exported data
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#
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# * [gentoo-f.json](gentoo-f.json)
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# * [gentoo-f.csv](gentoo-f.csv)
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# * [adelie-m.json](adelie-m.json)
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# * [adelie-m.csv](adelie-m.csv)
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# %% [markdown]
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#
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# ::: {.email}
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# ::: {.subject}
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# Penguin data files
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# :::
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# %%
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#| echo: false
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#| output: asis
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print("Identified", len(gentoo_f), "female Gentoo penguins.")
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print("Identified", len(adelie_m), "male Adelie penguins.")
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print("")
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print("CSV files are attached.")
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# %% [markdown]
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# :::

‎extensions/script-python/script.quarto_ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"---\n",
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"title: \"Penguin data transformations\"\n",
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"subtitle: \"Exported as JSON and CSV\"\n",
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"format: email\n",
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"email-attachments:\n",
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" - \"adelie-m.csv\"\n",
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" - \"gentoo-f.csv\"\n",
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"---\n",
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"\n",
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"\n",
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"## Setup\n"
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],
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"id": "05b2b162"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"import palmerpenguins\n",
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"\n",
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"penguins = palmerpenguins.load_penguins()"
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],
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"id": "d0398bb8",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Filtering\n"
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],
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"id": "d9632eb2"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"gentoo_f = penguins[\n",
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" (penguins[\"species\"] == \"Gentoo\") &\n",
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" (penguins[\"sex\"] == \"female\")\n",
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"]\n",
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"adelie_m = penguins[\n",
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" (penguins[\"species\"] == \"Adelie\") &\n",
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" (penguins[\"sex\"] == \"male\")\n",
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"]"
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],
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"id": "180dd4e3",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Statistics (Gentoo)\n"
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],
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"id": "2c56f743"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"gentoo_f.describe()"
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],
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"id": "19650928",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Statistics (Adelie)\n"
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],
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"id": "f957bb3e"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"adelie_m.describe()"
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],
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"id": "9ee32e66",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Export\n"
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],
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"id": "b71c1828"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"gentoo_f.to_csv(\"gentoo-f.csv\")\n",
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"gentoo_f.to_json(\"gentoo-f.json\", orient=\"records\")\n",
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"adelie_m.to_csv(\"adelie-m.csv\")\n",
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"adelie_m.to_json(\"adelie-m.json\", orient=\"records\")"
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],
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"id": "7edff85c",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exported data\n",
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"\n",
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"* [gentoo-f.json](gentoo-f.json)\n",
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"* [gentoo-f.csv](gentoo-f.csv)\n",
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"* [adelie-m.json](adelie-m.json)\n",
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"* [adelie-m.csv](adelie-m.csv)\n",
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"\n",
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"\n",
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"::: {.email}\n",
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"::: {.subject}\n",
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"Penguin data files\n",
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":::\n"
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],
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"id": "89f8e637"
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},
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{
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"cell_type": "code",
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"metadata": {},
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"source": [
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"#| echo: false\n",
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"#| output: asis\n",
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"print(\"Identified\", len(gentoo_f), \"female Gentoo penguins.\")\n",
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"print(\"Identified\", len(adelie_m), \"male Adelie penguins.\")\n",
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"print(\"\")\n",
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"print(\"CSV files are attached.\")"
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],
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"id": "eed89b62",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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":::\n"
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],
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"id": "c6118388"
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}
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],
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"metadata": {
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"kernelspec": {
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"name": "python3",
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"language": "python",
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"display_name": "Python 3 (ipykernel)",
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"path": "/Users/christophergraham/.pyenv/versions/3.11.7/share/jupyter/kernels/python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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