diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index a6fe8df..2d19244 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -19,16 +19,19 @@ jobs:
with:
fetch-depth: 2
+ - name: Install uv
+ uses: astral-sh/setup-uv@v4
+
- name: Set up Python
- uses: actions/setup-python@v5.3.0
- with:
- python-version: "3.10"
+ run: uv python install
- - name: Install uv
- uses: astral-sh/setup-uv@v5
+ - name: Install dependencies
+ run: uv sync --all-extras --dev
+
+ - name: Install Quarto
+ uses: quarto-dev/quarto-actions/setup@v2
with:
- # Install a specific version of uv.
- version: "0.5.15"
+ version: "1.5.57"
- name: Check if there is a parent commit
id: check-parent-commit
@@ -63,16 +66,16 @@ jobs:
run: |
sudo apt-get -y install openssl graphviz nano texlive graphviz-dev unzip build-essential
- - name: Install dependencies
+ - name: build the book
run: |
- uv sync
-
- - name: Build book
- run: uv run jupyter-book build . --verbose
+ uv run quarto render --execute
+ env:
+ # This forces Quarto to use the Python inside your uv venv
+ QUARTO_PYTHON: ${{ github.workspace }}/.venv/bin/python
- name: Publish
if: steps.check-version.outputs.tag
- run: uv run ghp-import -n -p -f _build/html
+ run: uv run quarto publish gh-pages --no-render --no-browser
- name: Publish the release notes
uses: release-drafter/release-drafter@v6.0.0
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 73d860e..cac3d2d 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -1,8 +1,13 @@
name: tests
on:
- - pull_request
- - push
+ push:
+ branches: [main]
+ pull_request:
+
+concurrency:
+ group: tests-${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
jobs:
pre-commit:
@@ -22,16 +27,19 @@ jobs:
with:
fetch-depth: 2
- - name: Set up Python
- uses: actions/setup-python@v5.3.0
- with:
- python-version: "3.10"
-
- name: Install uv
- uses: astral-sh/setup-uv@v5
+ uses: astral-sh/setup-uv@v4
+
+ - name: Set up Python
+ run: uv python install
+
+ - name: Install dependencies
+ run: uv sync --all-extras --dev
+
+ - name: Install Quarto
+ uses: quarto-dev/quarto-actions/setup@v2
with:
- # Install a specific version of uv.
- version: "0.5.2"
+ version: "1.5.57"
- name: set timezone
run: |
@@ -42,13 +50,9 @@ jobs:
run: |
sudo apt-get -y install openssl graphviz nano texlive graphviz-dev unzip build-essential
- - name: Install dependencies
- run: |
- uv sync
-
- name: build the book
run: |
- uv run jupyter-book build . --verbose
+ uv run quarto render --execute
- name: success
run: |
diff --git a/.gitignore b/.gitignore
index 09507ca..af04659 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,12 @@
# Mac
.DS_Store
-# jupyterbook
-_build/**
+# quarto
+_book/
+/.quarto/
+**/*.quarto_ipynb
+_freeze/
+.panache*
# Byte-compiled / optimized / DLL files
__pycache__/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ccf8257..7b71d2a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -28,3 +28,7 @@ repos:
# Run the formatter.
- id: ruff-format
types_or: [python, pyi, jupyter]
+ - repo: https://github.com/jolars/panache
+ rev: v2.32.0 # Use the latest release version
+ hooks:
+ - id: panache-format
diff --git a/README.md b/README.md
index 989893d..2c7dc63 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ When you make a pull request, pre-commit and build will run automatically, and f
## Installing the development environment locally
-You will need installations of Python 3.10 and [**uv**](https://docs.astral.sh/uv/). **uv** can be used to install certain distributions of Python through the `uv python install 3.10` command but you can use other Python installations.
+You will need installations of Python 3.10 and [**uv**](https://docs.astral.sh/uv/). **uv** can be used to install certain distributions of Python through the `uv python install 3.12` command but you can use other Python installations.
Clone this repository.
@@ -26,15 +26,15 @@ To install the development environment, run `uv sync` from the project root. Thi
## Building the book
-The book is compiled from source markdown and Jupyter notebook files [**jupyter-book**](https://jupyterbook.org/en/stable/) package.
+The book is compiled from source markdown and Jupyter notebook files using [**Quarto**](https://quarto.org/). You will need Quarto installed on your system — see the [Quarto installation guide](https://quarto.org/docs/get-started/).
To build the book, run
```bash
-uv run jupyter-book build .
+uv run quarto render --execute
```
-Once this command is run, you should be able to look at the HTML files for the book locally on your computer. They will be in `_build`. The project is configured to stop the build if any errors are encountered. This is a frequent occurrence! You'll need to look at the logs to work out what might have gone wrong.
+Once this command is run, you should be able to look at the HTML files for the book locally on your computer. They will be in `_book`. The `freeze: auto` setting means only notebooks whose source has changed will be re-executed on subsequent builds.
## Uploading the book
@@ -46,10 +46,10 @@ This repo is configured such that new versions automatically build and upload th
You shouldn't need to upload the book if you are a regular contributor. There are times when you might need to as an admin, but normally the book will be updated automatically upon release of a new version.
-See [here](https://jupyterbook.org/publish/gh-pages.html) for how to upload revised HTML files, but the key command is
+First build the book, then publish with:
```bash
-uv run ghp-import -n -p -f _build/html
+uv run quarto publish gh-pages --no-render --no-browser
```
## Code hygiene
@@ -78,7 +78,7 @@ on your staged files. Ensure pre-commit reports all tests as having passed befor
## Running and developing in a Docker container
-There is a Dockerfile associated with this project. Pre-reqs
+There is a Dockerfile associated with this project.
To use it:
1. Pre-reqs: docker installed, VS Code installed, VS Code docker and Remote Explorer extensions installed.
@@ -90,5 +90,5 @@ There is a Dockerfile associated with this project. Pre-reqs
If you wish to copy any files (eg the built HTML files) back to your local machine to check them, use
```bash
-docker cp CONTAINER:app/_build/html/ temp_dir/
+docker cp CONTAINER:app/_book/ temp_dir/
```
diff --git a/_config.yml b/_config.yml
deleted file mode 100644
index 824e3fc..0000000
--- a/_config.yml
+++ /dev/null
@@ -1,53 +0,0 @@
-# Book settings
-# Learn more at https://jupyterbook.org/customize/config.html
-
-title: Python for Data Science
-author: The Py4DS Community
-logo: logo.png
-exclude_patterns: [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints", ".venv", "README.md"]
-# Force re-execution of notebooks on each build.
-# See https://jupyterbook.org/content/execute.html
-execute:
- execute_notebooks: force
- timeout: 120
- exclude_patterns: ["README.md"]
- allow_errors: false
- nb_output_stderr: show
-
-
-# Define the name of the latex output file for PDF builds
-latex:
- latex_documents:
- targetname: book.tex
-
-# Add a bibtex file so that we can create citations
-bibtex_bibfiles:
- - references.bib
-
-# Information about where the book exists on the web
-repository:
- url: https://github.com/aeturrell/python4DS # Online location of your book
- branch: main # Which branch of the repository should be used when creating links (optional)
-
-# Add GitHub buttons to your book
-# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
-html:
- use_issues_button: true
- use_repository_button: true
- favicon: "favicon.ico" # A path to a favicon image
- analytics:
- google_analytics_id: "G-LXJC37BJVX" # A GA id that can be used to track book views.
-
-launch_buttons:
- colab_url: "https://colab.research.google.com"
- notebook_interface: "jupyterlab" # or "classic"
- binderhub_url: "https://mybinder.org"
-
-sphinx:
- config:
- html_js_files:
- - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js
- bibtex_reference_style: author_year
- suppress_warnings: ["mystnb.unknown_mime_type"]
- nb_execution_show_tb: true
- nb_execution_raise_on_error: true # Make build fail any content errors (don't want to publish if errors)
diff --git a/_quarto.yml b/_quarto.yml
new file mode 100644
index 0000000..51699c0
--- /dev/null
+++ b/_quarto.yml
@@ -0,0 +1,94 @@
+project:
+ type: book
+ output-dir: _book
+
+book:
+ title: "Python for Data Science"
+ author: "The Py4DS Community"
+ favicon: favicon.ico
+ cover-image: logo.png
+ repo-url: https://github.com/aeturrell/python4DS
+ repo-branch: main
+ repo-actions: [edit, issue]
+ google-analytics: "G-LXJC37BJVX"
+ sidebar:
+ style: docked
+ collapse-level: 1
+ chapters:
+ - index.md
+ - part: "Introduction"
+ chapters:
+ - introduction.ipynb
+ - prerequisites.ipynb
+ - part: "Quick Start"
+ chapters:
+ - whole-game.ipynb
+ - data-visualise.ipynb
+ - workflow-basics.ipynb
+ - data-transform.ipynb
+ - workflow-style.ipynb
+ - data-tidy.ipynb
+ - workflow-writing-code.ipynb
+ - data-import.ipynb
+ - workflow-packages-and-environments.qmd
+ - workflow-help.qmd
+ - part: "Visualise"
+ chapters:
+ - visualise.qmd
+ - vis-layers.ipynb
+ - exploratory-data-analysis.ipynb
+ - communicate-plots.ipynb
+ - part: "Transform"
+ chapters:
+ - boolean-data.ipynb
+ - numbers.ipynb
+ - strings.ipynb
+ - regex.ipynb
+ - categorical-data.ipynb
+ - dates-and-times.ipynb
+ - missing-values.ipynb
+ - joins.ipynb
+ - part: "Import"
+ chapters:
+ - spreadsheets.ipynb
+ - databases.ipynb
+ - rectangling.ipynb
+ - webscraping-and-apis.ipynb
+ - part: "Programme"
+ chapters:
+ - functions.ipynb
+ - iteration.ipynb
+ - command-line.md
+ - part: "Communicate"
+ chapters:
+ - markdown.md
+ - quarto.qmd
+ appendices:
+ - references.md
+
+bibliography: references.bib
+search: true
+twitter-card: true
+
+format:
+ html:
+ theme:
+ light: flatly
+ dark: superhero
+ toc: true
+ toc-depth: 3
+ toc-expand: false
+ html-math-method: katex
+ code-copy: true
+ code-link: true
+ code-overflow: wrap
+ number-sections: false
+ include-in-header:
+ - text: |
+
+
+execute:
+ freeze: auto
+ error: false
+
+jupyter: python3
diff --git a/_toc.yml b/_toc.yml
deleted file mode 100644
index 99d5015..0000000
--- a/_toc.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-# Table of contents
-
-format: jb-book
-root: welcome
-parts:
-- caption: Introduction
- chapters:
- - file: introduction
- - file: prerequisites
-- caption: Quick Start
- numbered: true
- chapters:
- - file: whole-game
- - file: data-visualise
- - file: workflow-basics
- - file: data-transform
- - file: workflow-style
- - file: data-tidy
- - file: workflow-writing-code
- - file: data-import
- - file: workflow-packages-and-environments
- - file: workflow-help
-- caption: Visualise
- numbered: true
- chapters:
- - file: visualise
- - file: vis-layers
- - file: exploratory-data-analysis
- - file: communicate-plots
-- caption: Transform
- numbered: true
- chapters:
- - file: boolean-data
- - file: numbers
- - file: strings
- - file: regex
- - file: categorical-data
- - file: dates-and-times
- - file: missing-values
- - file: joins
-- caption: Import
- numbered: true
- chapters:
- - file: spreadsheets
- - file: databases
- - file: rectangling
- - file: webscraping-and-apis
-- caption: Programme
- numbered: true
- chapters:
- - file: functions
- - file: iteration
- - file: command-line
-- caption: Communicate
- numbered: true
- chapters:
- - file: markdown
- - file: quarto
-- caption: Appendix
- chapters:
- - file: zreferences
\ No newline at end of file
diff --git a/boolean-data.ipynb b/boolean-data.ipynb
index c8ba880..931bbb2 100644
--- a/boolean-data.ipynb
+++ b/boolean-data.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(boolean-data)=\n",
- "# Boolean Data\n",
+ "# Boolean Data {#sec-boolean-data}\n",
"\n",
"## Introduction\n",
"\n",
@@ -101,9 +100,9 @@
"id": "e4367753",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"What does `not (not True)` evaluate to?\n",
- "```"
+ ":::"
]
},
{
@@ -194,9 +193,9 @@
"id": "e044d14a",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Check if \"a\" is in the string \"Walloping weasels\" using `in`. Is \"a\" `in` \"Anodyne\"?\n",
- "```"
+ ":::"
]
},
{
@@ -235,9 +234,9 @@
"source": [
"Note that this does nothing if the score is between 11 and 90, and prints a message otherwise.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Create a new `if` ... `elif` ... `else` statement that prints \"well done\" if a score is over 90, \"good\" if between 40 and 90, and \"bad luck\" otherwise.\n",
- "```\n",
+ ":::\n",
"\n",
"One nice feature of Python is that you can make multiple boolean comparisons in a single line."
]
@@ -715,7 +714,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/categorical-data.ipynb b/categorical-data.ipynb
index 7a670c9..5796a26 100644
--- a/categorical-data.ipynb
+++ b/categorical-data.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(categorical-data)=\n",
- "# Categorical Data\n",
+ "# Categorical Data {#sec-categorical-data}\n",
"\n",
"## Introduction\n",
"\n",
@@ -150,7 +149,7 @@
"id": "76dc9dd6",
"metadata": {},
"source": [
- "Note that NaNs appear for any value that *isn't* in the categories we specified—you can find more on this in {ref}`missing-values`."
+ "Note that NaNs appear for any value that *isn't* in the categories we specified—you can find more on this in @sec-missing-values."
]
},
{
@@ -379,7 +378,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/command-line.md b/command-line.md
index 25fcc67..1079842 100644
--- a/command-line.md
+++ b/command-line.md
@@ -1,16 +1,4 @@
----
-jupytext:
- formats: md:myst
- text_representation:
- extension: .md
- format_name: myst
-kernelspec:
- display_name: py4ds2e
- language: python
- name: python3
----
-(command-line)=
-# The Command Line
+# The Command Line {#sec-command-line}
In this chapter, you'll meet the *command line* and learn how to use it. Beyond a few key commands like `uv add ` you don't strictly need to know how to use the command line to follow the rest of this book. However, even a tiny bit of knowledge of the command line goes a long way in coding and will serve you well.
@@ -79,9 +67,9 @@ here `head` is the command that looks at the start of the file, `-n` is an optio
The flags or options, such as `-n` in the example above, typically begin with a dash (`-`) or, occasionally, a double dash (`--`). They can also be chained together, for example `ls -la` combines `ls -a` and `ls -l`.
-```{warning}
+::: {.callout-warning}
Spaces take on a special role when using the command line. For this reason, it's good practice to avoid spaces in file names. If you need to refer to a filename with spaces in, you’ll need to use quotes or escape the spaces in the file names using a `\`, for example `this is my file.txt` becomes `this\ is\ my\ file.txt`
-```
+:::
To run programmes from the command line, all you need is the name of the programme as the command: in fact, commands *are* programmes. The `date` command refers to an actual programme on your computer that you can find. And this also explains a bit of what's going on when you *run a script from the command line* (more on that later).
@@ -111,14 +99,14 @@ To find out "where" you are when you open a terminal, you can use the `pwd` comm
The table below shows some useful commands for moving around your computer using the command line. Note that `cd` accepts a location *relative* to your current directory.
-| Command | What it does |
-| ----------- | ----------- |
-| `pwd` | Shows current directory |
-| `cd` | Change directory command |
-| `cd ..` | Go up one level in the directory (`cd ../..` for two levels) |
-| `cd ~` | Go to your home directory |
-| `cd -` | Go to the previous directory |
-| `cd documents/papers` | Go directly to a directory named 'papers' |
+ | Command | What it does |
+ | --------------------- | ------------------------------------------------------------ |
+ | `pwd` | Shows current directory |
+ | `cd` | Change directory command |
+ | `cd ..` | Go up one level in the directory (`cd ../..` for two levels) |
+ | `cd ~` | Go to your home directory |
+ | `cd -` | Go to the previous directory |
+ | `cd documents/papers` | Go directly to a directory named 'papers' |
## Using Python on the command line
@@ -146,39 +134,39 @@ which python
Now we'll see some useful commands for the terminal.
-| Command | What it does |
-| ----------- | ----------- |
-| `man ` | Shows a manual for the given command |
-| `touch ` | Creates an empty file named `` |
-| `code ` | Open a file in VS Code (creating it, if it does not exist) |
-| `mkdir ` | creates a new folder called `foldername`|
-| `echo ` | Prints `` |
-| `cat ` | Print the full contents of `` |
-| `head ` | Print the start of a file |
-| `tail ` | Print the end of a file |
-| `> ` | Redirects output from screen to ``. For example, `echo "Hello World" > hello.txt` |
-| `>> ` | Redirects output from screen to the end of ``, ie appends output rather than overwrites it |
-| `|` | The pipe symbol: uses output from one command as input into another. For example, `head -n 10 data.csv | > hello_world.txt` would write the first 10 lines of data.csv into a file called hello_world.txt
-| `less ` | Print out the contents of a file in paginated form. Use `ctrl+v` and `Alt+v` (or `⌘+v` and `⌥+v` on Mac) to move up and down. Press `q` to quit.|
-| `wc -l` | Returns number of lines in input, for example `cat | wc -l`. Use `wc` alone for word count. |
-| `sort` | Arrange lines in a file in alphabetical order |
-| `uniq` | Remove duplicate lines from input, for example `cat | uniq` or `uniq -d` to show duplicate files |
-| `mv` | Move or rename a file; for example, `mv file1 file2` would rename `file1` to `file2` while `mv file1 ~` would move `file1` to the home directory|
-| `cp` | Copy a file; for example, `cp file1 file2` would copy `file1` to `file2` while `cp file1 ~` would make a copy of `file1` in the home directory|
-| `rm ` | Permanently remove a file |
-| `rmdir ` | Permanently remove an empty directory |
-| `rm -rf ` | ⚠ Permanently remove everything in a directory ⚠ |
-| `grep ` | Search for a given term, for example `cat hello_world.txt | grep world` |
-| `ls` | Basically, this means list stuff (files and folders) in the current directory |
-| `ls -a` | List stuff in the current directory even if it's hidden |
-| `ls -l` | List stuff in a more readable format and show permissions |
-| `ls -S` | List stuff by size |
-| `file ` | Give information on the file type of ``|
-| `find` | Find specific files on your computer, can be piped into other commands for example `find *.md -size +5k -type f | xargs wc -l` will count the number of lines `wc -l` of all files, `-type f`, ending in `.md` that are greater than 5 kilobytes in size, `-size +5k`. |
-| `diff -u ` | Show a single summary of the differences between two files. |
+ | Command | What it does |
+ | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ |
+ | `man ` | Shows a manual for the given command |
+ | `touch ` | Creates an empty file named `` |
+ | `code ` | Open a file in VS Code (creating it, if it does not exist) |
+ | `mkdir ` | creates a new folder called `foldername` |
+ | `echo ` | Prints `` |
+ | `cat ` | Print the full contents of `` |
+ | `head ` | Print the start of a file |
+ | `tail ` | Print the end of a file |
+ | `> ` | Redirects output from screen to ``. For example, `echo "Hello World" > hello.txt` |
+ | `>> ` | Redirects output from screen to the end of ``, ie appends output rather than overwrites it |
+ | ` | ` | The pipe symbol: uses output from one command as input into another. For example, `head -n 10 data.csv | > hello_world.txt` would write the first 10 lines of data.csv into a file called hello_world.txt |
+ | `less ` | Print out the contents of a file in paginated form. Use `ctrl+v` and `Alt+v` (or `⌘+v` and `⌥+v` on Mac) to move up and down. Press `q` to quit. |
+ | `wc -l` | Returns number of lines in input, for example `cat | wc -l`. Use `wc` alone for word count. |
+ | `sort` | Arrange lines in a file in alphabetical order |
+ | `uniq` | Remove duplicate lines from input, for example `cat | uniq` or `uniq -d` to show duplicate files |
+ | `mv` | Move or rename a file; for example, `mv file1 file2` would rename `file1` to `file2` while `mv file1 ~` would move `file1` to the home directory |
+ | `cp` | Copy a file; for example, `cp file1 file2` would copy `file1` to `file2` while `cp file1 ~` would make a copy of `file1` in the home directory |
+ | `rm ` | Permanently remove a file |
+ | `rmdir ` | Permanently remove an empty directory |
+ | `rm -rf ` | ⚠ Permanently remove everything in a directory ⚠ |
+ | `grep ` | Search for a given term, for example `cat hello_world.txt | grep world` |
+ | `ls` | Basically, this means list stuff (files and folders) in the current directory |
+ | `ls -a` | List stuff in the current directory even if it's hidden |
+ | `ls -l` | List stuff in a more readable format and show permissions |
+ | `ls -S` | List stuff by size |
+ | `file ` | Give information on the file type of `` |
+ | `find` | Find specific files on your computer, can be piped into other commands for example `find *.md -size +5k -type f | xargs wc -l` will count the number of lines `wc -l` of all files, `-type f`, ending in `.md` that are greater than 5 kilobytes in size, `-size +5k`. |
+ | `diff -u ` | Show a single summary of the differences between two files. |
-*More details of the grep command, by [@b0rk](https://twitter.com/b0rk).*
+*More details of the grep command, by [\@b0rk](https://twitter.com/b0rk).*
You can write for loops in bash (remember, it's a language). The general structure is
diff --git a/communicate-plots.ipynb b/communicate-plots.ipynb
index 01b7435..8531e79 100644
--- a/communicate-plots.ipynb
+++ b/communicate-plots.ipynb
@@ -5,14 +5,13 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(communicate-plots)=\n",
- "# Graphics for Communication\n",
+ "# Graphics for Communication {#sec-communicate-plots}\n",
"\n",
"## Introduction\n",
"\n",
"In this chapter, you'll learn about using visualisation to communicate.\n",
"\n",
- "In {ref}`exploratory-data-analysis`, you learned how to use plots as tools for *exploration*.\n",
+ "In @sec-exploratory-data-analysis, you learned how to use plots as tools for *exploration*.\n",
"When you make exploratory plots, you know—even before looking—which variables the plot will display.\n",
"You made each plot for a purpose, quickly looked at it, and then moved on to the next plot.\n",
"In the course of most analyses, you'll produce tens or hundreds of plots, most of which are immediately thrown away.\n",
@@ -771,7 +770,7 @@
},
"outputs": [],
"source": [
- "# remove-input\n",
+ "# | echo: false\n",
"cmaps = [\n",
" (\n",
" \"Perceptually Uniform Sequential\",\n",
@@ -1332,7 +1331,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1346,7 +1345,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/conversion_scripts/fix_kernels.py b/conversion_scripts/fix_kernels.py
new file mode 100644
index 0000000..2c33ab8
--- /dev/null
+++ b/conversion_scripts/fix_kernels.py
@@ -0,0 +1,33 @@
+import glob
+
+import nbformat
+
+# Find all notebooks
+notebooks = glob.glob("**/*.ipynb", recursive=True)
+
+for nb_path in notebooks:
+ if ".ipynb_checkpoints" in nb_path:
+ continue
+
+ with open(nb_path, "r", encoding="utf-8") as f:
+ try:
+ nb = nbformat.read(f, as_version=4)
+ print(f"Fixing {nb_path}...")
+
+ # Wipe and replace kernelspec
+ nb.metadata["kernelspec"] = {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3",
+ }
+
+ # Wipe language_info version (this prevents the 3.10.17 mismatch)
+ if "language_info" in nb.metadata:
+ nb.metadata["language_info"]["version"] = "3"
+
+ with open(nb_path, "w", encoding="utf-8") as f_out:
+ nbformat.write(nb, f_out)
+ except Exception as e:
+ print(f"Could not process {nb_path}: {e}")
+
+print("Successfully standardized all notebooks.")
diff --git a/conversion_scripts/jb_to_quarto.py b/conversion_scripts/jb_to_quarto.py
new file mode 100644
index 0000000..3eec10a
--- /dev/null
+++ b/conversion_scripts/jb_to_quarto.py
@@ -0,0 +1,261 @@
+"""Convert MyST/Jupyter Book syntax to Quarto syntax.
+
+Handles both .qmd (markdown) files and .ipynb (notebook) files.
+Idempotent: running twice on an already-converted file is a no-op.
+
+Usage:
+ python scripts/jb_to_quarto.py --dry-run *.qmd
+ python scripts/jb_to_quarto.py --apply *.qmd *.ipynb
+"""
+
+import argparse
+import difflib
+import json
+import re
+import sys
+from pathlib import Path
+
+# --- Title → callout-type mapping for {admonition} ---
+TITLE_MAP = {
+ "exercise": "tip",
+ "exercises": "tip",
+ "tip": "tip",
+ "tips": "tip",
+ "important": "important",
+ "warning": "warning",
+ "caution": "caution",
+ "note": "note",
+ "hint": "tip",
+}
+
+
+def convert_text(text: str) -> str:
+ """Apply all MyST→Quarto transformations to a block of text."""
+ lines = text.split("\n")
+ out_lines = []
+ i = 0
+
+ # Track open callout fences for proper closing
+ fence_stack = [] # list of (backtick_count, is_callout)
+
+ while i < len(lines):
+ line = lines[i]
+
+ # --- Pass A: Label definitions ---
+ # Pattern: (label-name)= on its own line, followed by a heading
+ label_match = re.match(r"^\(([a-zA-Z0-9_-]+)\)=\s*$", line)
+ if label_match and i + 1 < len(lines):
+ label = label_match.group(1)
+ next_line = lines[i + 1]
+ heading_match = re.match(r"^(#{1,6})\s+(.+)$", next_line)
+ if heading_match:
+ hashes = heading_match.group(1)
+ title = heading_match.group(2)
+ # Remove any existing {#...} from title
+ title = re.sub(r"\s*\{#[^}]+\}\s*$", "", title)
+ out_lines.append(f"{hashes} {title} {{#sec-{label}}}")
+ i += 2
+ continue
+ # Label not followed by heading — emit as-is (shouldn't happen often)
+
+ # --- Pass B: Fence-aware callout conversion ---
+ # Match opening fences: ```{note}, ````{admonition} Title, etc.
+ fence_open = re.match(
+ r"^(`{3,})\{(note|tip|warning|caution|important|exercise|solution)\}\s*(.*)$",
+ line,
+ )
+ admonition_open = re.match(r"^(`{3,})\{admonition\}\s*(.+)$", line)
+
+ if fence_open:
+ backticks = fence_open.group(1)
+ directive = fence_open.group(2)
+ extra = fence_open.group(3).strip()
+
+ if directive == "solution":
+ callout_type = "note"
+ title_attr = ' title="Solution" collapse="true"'
+ elif directive == "exercise":
+ callout_type = "tip"
+ title_attr = ' title="Exercise"'
+ else:
+ callout_type = directive
+ title_attr = ""
+
+ if extra:
+ title_attr = f' title="{extra}"'
+
+ out_lines.append(f"::: {{.callout-{callout_type}{title_attr}}}")
+ fence_stack.append((len(backticks), True))
+ i += 1
+ continue
+
+ elif admonition_open:
+ backticks = admonition_open.group(1)
+ title = admonition_open.group(2).strip()
+ callout_type = TITLE_MAP.get(title.lower(), "note")
+ out_lines.append(f'::: {{.callout-{callout_type} title="{title}"}}')
+ fence_stack.append((len(backticks), True))
+ i += 1
+ continue
+
+ # Match code-cell fences (MyST executable cells in .md files)
+ code_cell_match = re.match(r"^(`{3,})\{code-cell\}(?: ipython3)?\s*$", line)
+ if code_cell_match:
+ backticks = code_cell_match.group(1)
+ out_lines.append(f"{backticks}{{python}}")
+ fence_stack.append((len(backticks), False))
+ i += 1
+ continue
+
+ # Match closing fences
+ close_fence = re.match(r"^(`{3,})\s*$", line)
+ if close_fence and fence_stack:
+ backtick_count = len(close_fence.group(1))
+ # Check if this closes the most recent fence
+ top_count, is_callout = fence_stack[-1]
+ if backtick_count >= top_count:
+ fence_stack.pop()
+ if is_callout:
+ out_lines.append(":::")
+ i += 1
+ continue
+ # For code-cell→python fences, keep the closing backticks
+ out_lines.append(line)
+ i += 1
+ continue
+
+ # --- Pass A continued: Inline substitutions ---
+ converted = line
+
+ # Citations: {cite:t}`key` → @key
+ converted = re.sub(r"\{cite:t\}`([^`]+)`", r"@\1", converted)
+ # Citations: {cite:p}`key` → [@key]
+ converted = re.sub(r"\{cite:p\}`([^`]+)`", r"[@\1]", converted)
+ # Citations: {cite:ps}`key` → [@key]
+ converted = re.sub(r"\{cite:ps\}`([^`]+)`", r"[@\1]", converted)
+
+ # Cross-references: {ref}`text ` → [text](#sec-label)
+ converted = re.sub(
+ r"\{ref\}`([^<`]+?)\s*<([a-zA-Z0-9_-]+)>`",
+ r"[\1](#sec-\2)",
+ converted,
+ )
+ # Cross-references: {ref}`label` → @sec-label
+ converted = re.sub(r"\{ref\}`([a-zA-Z0-9_-]+)`", r"@sec-\1", converted)
+
+ out_lines.append(converted)
+ i += 1
+
+ return "\n".join(out_lines)
+
+
+def process_qmd_file(path: Path, apply: bool) -> bool:
+ """Process a .qmd file. Returns True if changes were made."""
+ original = path.read_text(encoding="utf-8")
+ converted = convert_text(original)
+
+ if original == converted:
+ return False
+
+ if apply:
+ path.write_text(converted, encoding="utf-8")
+ else:
+ diff = difflib.unified_diff(
+ original.splitlines(keepends=True),
+ converted.splitlines(keepends=True),
+ fromfile=str(path),
+ tofile=str(path) + " (converted)",
+ )
+ sys.stdout.writelines(diff)
+
+ return True
+
+
+def process_ipynb_file(path: Path, apply: bool) -> bool:
+ """Process a .ipynb file by converting markdown cells. Returns True if changes were made."""
+ original_bytes = path.read_bytes()
+ nb = json.loads(original_bytes)
+
+ changed = False
+ for cell in nb.get("cells", []):
+ if cell.get("cell_type") != "markdown":
+ continue
+
+ source = cell.get("source", [])
+ if not source:
+ continue
+
+ # Join source lines, convert, then split back
+ text = "".join(source)
+ converted = convert_text(text)
+
+ if text != converted:
+ changed = True
+ # Preserve the list-of-strings format
+ # Split on newlines but keep the newlines (except possibly the last)
+ new_source = []
+ conv_lines = converted.split("\n")
+ for j, cline in enumerate(conv_lines):
+ if j < len(conv_lines) - 1:
+ new_source.append(cline + "\n")
+ else:
+ new_source.append(cline)
+ cell["source"] = new_source
+
+ if not changed:
+ return False
+
+ if apply:
+ # Write with consistent formatting
+ output = json.dumps(nb, ensure_ascii=False, indent=1)
+ # Ensure file ends with newline
+ if not output.endswith("\n"):
+ output += "\n"
+ path.write_text(output, encoding="utf-8")
+ else:
+ original_text = original_bytes.decode("utf-8")
+ new_text = json.dumps(nb, ensure_ascii=False, indent=1) + "\n"
+ diff = difflib.unified_diff(
+ original_text.splitlines(keepends=True),
+ new_text.splitlines(keepends=True),
+ fromfile=str(path),
+ tofile=str(path) + " (converted)",
+ )
+ sys.stdout.writelines(diff)
+
+ return True
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Convert MyST/JB syntax to Quarto")
+ parser.add_argument("files", nargs="+", help="Files to process")
+ group = parser.add_mutually_exclusive_group(required=True)
+ group.add_argument("--dry-run", action="store_true", help="Show diffs only")
+ group.add_argument("--apply", action="store_true", help="Apply changes in-place")
+ args = parser.parse_args()
+
+ total_changed = 0
+ for filepath in args.files:
+ path = Path(filepath)
+ if not path.exists():
+ print(f"WARNING: {path} does not exist, skipping", file=sys.stderr)
+ continue
+
+ if path.suffix in (".qmd", ".md"):
+ if process_qmd_file(path, args.apply):
+ total_changed += 1
+ print(f"{'Applied' if args.apply else 'Would change'}: {path}")
+ elif path.suffix == ".ipynb":
+ if process_ipynb_file(path, args.apply):
+ total_changed += 1
+ print(f"{'Applied' if args.apply else 'Would change'}: {path}")
+ else:
+ print(f"Skipping unsupported file type: {path}", file=sys.stderr)
+
+ print(
+ f"\nTotal files {'changed' if args.apply else 'that would change'}: {total_changed}"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/data-import.ipynb b/data-import.ipynb
index 590110d..59363eb 100644
--- a/data-import.ipynb
+++ b/data-import.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(data-import)=\n",
- "# Data Import\n",
+ "# Data Import {#sec-data-import}\n",
"\n",
"## Introduction\n",
"\n",
@@ -437,7 +436,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/data-tidy.ipynb b/data-tidy.ipynb
index 96251e5..e0ba7a7 100644
--- a/data-tidy.ipynb
+++ b/data-tidy.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(data-tidy)=\n",
- "# Tidy Data\n",
+ "# Tidy Data {#sec-data-tidy}\n",
"\n",
"## Introduction\n",
"\n",
@@ -139,9 +138,9 @@
"id": "e62978fd",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Perform a `melt()` that uses `job` as the id instead of `first` and `last`.\n",
- "```\n",
+ ":::\n",
"\n",
"How does this relate to tidy data? Sometimes you'll have a variable spread over multiple columns that you want to turn tidy. Let's look at this example that uses cases of [tuburculosis from the World Health Organisation](https://www.who.int/teams/global-tuberculosis-programme/data).\n",
"\n",
@@ -334,9 +333,9 @@
"id": "185eae1f",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"What happens if you unstack to `level=1` instead? What about applying `unstack()` twice?\n",
- "```"
+ ":::"
]
},
{
@@ -453,14 +452,14 @@
"id": "841c4821",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Why is the first entry NaN?\n",
- "```\n",
+ ":::\n",
"\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Perform a `pivot()` that applies to both the `variable` and `category` columns in the example from above where category is defined such that `df[\"category\"] = np.random.choice([\"type1\", \"type2\", \"type3\", \"type4\"], 20). (Hint: remember that you will need to pass multiple objects via a list.)\n",
- "```"
+ ":::"
]
}
],
@@ -472,7 +471,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -486,7 +485,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/data-transform.ipynb b/data-transform.ipynb
index 3e772d8..55f2df7 100644
--- a/data-transform.ipynb
+++ b/data-transform.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(data-transform)=\n",
- "# Data Transformation\n",
+ "# Data Transformation {#sec-data-transform}\n",
"\n",
"## Introduction\n",
"\n",
@@ -504,9 +503,9 @@
"\n",
"This section will show you how to apply various operations you may need to columns in your data frame.\n",
"\n",
- "```{note}\n",
+ "::: {.callout-note}\n",
"Some **pandas** operations can apply either to columns or rows, depending on the syntax used. For example, accessing values by position can be achieved in the same way for rows and columns via `.iloc` where to access the ith row you would use `df.iloc[i]` and to access the jth column you would use `df.iloc[:, j]` where `:` stands in for 'any row'.\n",
- "```"
+ ":::"
]
},
{
@@ -554,9 +553,9 @@
"id": "14763861",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"What happens if you try to use assignment where the right-hand side values are longer or shorter than the length of the data frame?\n",
- "```\n",
+ ":::\n",
"\n",
"By passing a list within the square brackets, we can actually create more than one new column:"
]
@@ -619,14 +618,14 @@
"id": "c531df3e",
"metadata": {},
"source": [
- "````{note}\n",
+ "::: {.callout-note}\n",
"A lambda function is like any normal function in Python except that it has no name, and it tends to be contained in one line of code. A lambda function is made of an argument, a colon, and an expression, like the following lambda function that multiplies an input by three.\n",
"\n",
"```python\n",
"lambda x: x*3\n",
"```\n",
"\n",
- "````"
+ ":::"
]
},
{
@@ -927,13 +926,12 @@
"\n",
"which picks out two rows and two columns via the lists `[\"row0\", \"row1\"]` and `[\"column0\", \"column2\"]`. Because there are lists alongside the usual system of selecting values, there are two sets of square brackets.\n",
"\n",
- "```{admonition} Tip\n",
- ":class: tip\n",
+ "::: {.callout-tip title=\"Tip\"}\n",
"\n",
"If you only want to remember one syntax for accessing rows and columns by name, use the pattern `df.loc[[\"row0\", \"row1\", ...], [\"col0\", \"col1\", ...]]`. This also works with a single row or a single column (or both).\n",
"\n",
"If you only want to remember one syntax for accessing rows and columns by position, use the pattern `df.iloc[[0, 1, ...], [0, 1, ...]]`. This also works with a single row or a single column (or both).\n",
- "```\n"
+ ":::\n"
]
},
{
@@ -1209,7 +1207,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/data-visualise.ipynb b/data-visualise.ipynb
index 5abfd3c..0335c48 100644
--- a/data-visualise.ipynb
+++ b/data-visualise.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(data-visualise)=\n",
- "# Data Visualisation\n",
+ "# Data Visualisation {#sec-data-visualise}\n",
"\n",
"## Introduction\n",
"\n",
@@ -36,9 +35,9 @@
"id": "792902c7",
"metadata": {},
"source": [
- "```{note}\n",
+ "::: {.callout-note}\n",
"The command line can be opened within Visual Studio Code and Codespaces by going to View -> Terminal.\n",
- "```\n",
+ ":::\n",
"\n",
"Note that you only need to install a package once in each Python environment."
]
@@ -97,9 +96,9 @@
"id": "e4eb9c4f",
"metadata": {},
"source": [
- "### The `penguins` data frame\n",
+ "### The penguins data frame\n",
"\n",
- "You can test your answers to those questions with the penguins data frame found in palmerpenguins (a.k.a. `from palmerpenguins import load_penguins`). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). `penguins` contains 344 observations collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER.{cite:p}`horst2020palmerpenguins`.\n",
+ "You can test your answers to those questions with the penguins data frame found in palmerpenguins (a.k.a. `from palmerpenguins import load_penguins`). A data frame is a rectangular collection of variables (in the columns) and observations (in the rows). `penguins` contains 344 observations collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER [@horst2020palmerpenguins].\n",
"\n",
"To make the discussion easier, let's define some terms:\n",
"\n",
@@ -1122,7 +1121,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/databases.ipynb b/databases.ipynb
index 7d42e63..f14914c 100644
--- a/databases.ipynb
+++ b/databases.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(databases)=\n",
- "# Databases\n",
+ "# Databases {#sec-databases}\n",
"\n",
"## Introduction\n",
"\n",
@@ -379,7 +378,7 @@
"\n",
"Finally, there's some training for SQL that uses **Datasette** available [here](https://datasette.io/tutorials/learn-sql).\n",
"\n",
- "````{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Using the Chinook database on your own computer, run the SQL query with the join from before, ie:\n",
"\n",
"```sql\n",
@@ -396,7 +395,7 @@
" 5\n",
"```\n",
"\n",
- "````"
+ ":::"
]
},
{
@@ -780,7 +779,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -794,7 +793,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/dates-and-times.ipynb b/dates-and-times.ipynb
index 8fbb5db..4fe0c68 100644
--- a/dates-and-times.ipynb
+++ b/dates-and-times.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(dates-and-times)=\n",
- "# Dates and Times\n",
+ "# Dates and Times {#sec-dates-and-times}\n",
"\n",
"## Introduction\n",
"\n",
@@ -101,9 +100,9 @@
"source": [
"We can see that the variable we created has methods such as `year`, `month`, `day`, and so on, down to `microsecond`. When calling these methods on the `now` object we created, they will return the relevant detail. \n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Try calling the year, month, and day functions on an instance of `datetime.now()`.\n",
- "```\n",
+ ":::\n",
"\n",
"Note that, once created, `now` does not refresh itself: it's frozen at the time that it was made."
]
@@ -897,10 +896,11 @@
"outputs": [],
"source": [
"# Get stock market data\n",
- "import pandas_datareader as web\n",
+ "import yfinance as yf\n",
"\n",
- "xf = web.DataReader(\"AAPL\", \"stooq\", start=\"2017-01-01\", end=\"2019-06-01\")\n",
- "xf = xf.sort_index()"
+ "xf = yf.download(\"AAPL\", start=\"2017-01-01\", end=\"2019-06-01\")\n",
+ "xf = xf.sort_index()\n",
+ "xf.columns = xf.columns.droplevel(1)"
]
},
{
@@ -1089,7 +1089,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -1103,7 +1103,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/exploratory-data-analysis.ipynb b/exploratory-data-analysis.ipynb
index a81d948..2bf323b 100644
--- a/exploratory-data-analysis.ipynb
+++ b/exploratory-data-analysis.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(exploratory-data-analysis)=\n",
- "# Exploratory Data Analysis\n",
+ "# Exploratory Data Analysis {#sec-exploratory-data-analysis}\n",
"\n",
"## Introduction\n",
"\n",
@@ -826,9 +825,9 @@
"source": [
"By default, `background_gradient()` highlights each number relative to the others in its column; you can highlight by row using `axis=1` or relative to all table values using `axis=0`. And of course `plasma` is just one of [many available colormaps](https://matplotlib.org/stable/tutorials/colors/colormaps.html)!\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Do a new cross-tabulation using a different colourmap.\n",
- "```\n",
+ ":::\n",
"\n",
"Here are a couple of other styling tips for data frames.\n",
"\n",
@@ -1120,7 +1119,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.13"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/functions.ipynb b/functions.ipynb
index 4e06889..7171d21 100644
--- a/functions.ipynb
+++ b/functions.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(functions)=\n",
- "# Functions\n",
+ "# Functions {#sec-functions}\n",
"\n",
"## Introduction\n",
"\n",
@@ -106,18 +105,17 @@
"id": "5c4c6cdf",
"metadata": {},
"source": [
- "```{admonition} Arguments and keyword arguments\n",
- ":class: tip\n",
+ "::: {.callout-note title=\"Arguments and keyword arguments\"}\n",
"\n",
"*arguments* are the variables that functions *always* need, so `a` and `b` in `def add(a, b): return a + b`. The function won't work without them! Function arguments are sometimes referred to as *args*.\n",
"\n",
"*Keyword arguments* are the variables that are optional for functions, so `c` in `def add(a, b, c=5): return a + b - c`. If you do not provide a value for `c` when calling the function, it will automatically revert to `c=5`. Keyword arguments are sometimes referred to as *kwargs*.\n",
- "```\n",
+ ":::\n",
"\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"What is the return type of a function with multiple return values separated by commas following the `return` statement?\n",
- "```\n",
+ ":::\n",
"\n",
"In that last example, you'll notice that we added some text to the function. This is a doc-string, or documentation string. It's there to help users (and, most likely, future you) to understand what the function does. Let's see how this works in action by calling `help()` on the `score_message` function:"
]
@@ -137,13 +135,13 @@
"id": "6e466fcd",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Write a function that returns a high five unicode character if the input is equal to \"coding for economists\" and a sad face, \":-/\" otherwise.\n",
"\n",
"Add a second argument that takes a default argument of an empty string but, if used, is added (concatenated) to the return message. Use it to create the return output, \":-/ here is my message.\"\n",
"\n",
"Write a doc-string for your function and call `help` on it.\n",
- "```\n",
+ ":::\n",
"\n",
"To learn more about args and kwargs, check out these [short video tutorials](https://calmcode.io/args-kwargs/introduction.html)."
]
@@ -496,7 +494,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/welcome.md b/index.md
similarity index 98%
rename from welcome.md
rename to index.md
index 02d5b43..965c550 100644
--- a/welcome.md
+++ b/index.md
@@ -1,3 +1,8 @@
+---
+aliases:
+ - welcome.html
+---
+
# Welcome
[](https://zenodo.org/doi/10.5281/zenodo.10518241) 
diff --git a/introduction.ipynb b/introduction.ipynb
index c4c686a..60bfa83 100644
--- a/introduction.ipynb
+++ b/introduction.ipynb
@@ -6,8 +6,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(introduction)=\n",
- "# First Steps\n",
+ "# First Steps {#sec-introduction}\n",
"\n",
"Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of “Python for Data Science” is to help you learn the most important tools and workflows in Python that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges!\n",
"\n",
@@ -43,7 +42,7 @@
},
"outputs": [],
"source": [
- "# remove-input\n",
+ "# | echo: false\n",
"import graphviz\n",
"\n",
"dot = graphviz.Digraph(comment=\"Data science workflow\")\n",
@@ -148,7 +147,7 @@
},
"outputs": [],
"source": [
- "# remove-input\n",
+ "# | echo: false\n",
"import sys\n",
"\n",
"print(\"Compiled with Python version:\", sys.version)"
@@ -174,7 +173,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -188,7 +187,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/iteration.ipynb b/iteration.ipynb
index defbd71..fc1452a 100644
--- a/iteration.ipynb
+++ b/iteration.ipynb
@@ -5,13 +5,12 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(iteration)=\n",
- "# Iteration\n",
+ "# Iteration {#sec-iteration}\n",
"\n",
"## Introduction\n",
"\n",
"\n",
- "In {ref}`functions`, we talked about how important it is to reduce duplication in your code by creating functions instead of copying-and-pasting. Reducing code duplication has three main benefits:\n",
+ "In @sec-functions, we talked about how important it is to reduce duplication in your code by creating functions instead of copying-and-pasting. Reducing code duplication has three main benefits:\n",
"\n",
"1. It's easier to see the intent of your code, because your eyes are drawn to what's different, not what stays the same.\n",
"\n",
@@ -142,9 +141,9 @@
"id": "2a080093",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Write a for loop that prints out \"Python for Data Science\" so that each word is printed in a successive iteration.\n",
- "```\n",
+ ":::\n",
"\n",
"A useful trick with for loops is the `enumerate()` keyword, which runs through an index that keeps track of the place of items in a list:"
]
@@ -209,9 +208,9 @@
"source": [
"Note that we added `.items()` to the end of the dictionary. And note that we didn't *have* to call the key `key`, or the value `value`: these are set by their position. But part of best practice in writing code is that *there should be no surprises*, and writing key, value makes it really clear that you're using values from a dictionary.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Write a dictionary that maps four cities you know into their respective countries and print the results using the `key, value` iteration trick.\n",
- "```\n",
+ ":::\n",
"\n",
"Another useful type of for loop is provided by the `zip()` function. You can think of the `zip()` function as being like a zipper, bringing elements from two different iterators together in turn. Here's an example:"
]
@@ -237,11 +236,11 @@
"source": [
"The zip function is super useful in practice.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Zip together the first names from above with this jumbled list of surnames: `['Babbage', 'Hopper', 'Smith', 'Lovelace']`.\n",
"\n",
"(Hint: you have seen a trick to help re-arrange lists earlier on in the Chapter.)\n",
- "```"
+ ":::"
]
},
{
@@ -276,11 +275,11 @@
"\n",
"Note that lists are not the only wrapping you can provide to this kind of structure. A `(` and `)` to make it a generator (don't worry about what this is for now), a `{` and `}` to make it a set (an object that only contains unique values), or it's possible to create a dictionary from a comprehension too! List comprehensions are the most common, so if you only remember one kind, remember them.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Create a list comprehension that multiplies numbers in the range from 1 to 10 by 5.\n",
"\n",
"Did you get the range right?\n",
- "```\n",
+ ":::\n",
"\n",
"Let's now see how to include a condition within a list comprehension. Say we had a list of numbers and wanted to filter it according to whether the numbers divided by 3 or not using the modulo operator:"
]
@@ -408,9 +407,9 @@
"id": "d18be80b",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Create a nested list comprehension that results in a list of lists of strings equal to `[['a0', 'b0', 'c0'], ['a1', 'b1', 'c1'], ['a2', 'b2', 'c2']]` (ie a combination of the first three integers and letters of the alphabet). You may find that you need to convert numbers to strings using `str(x)` to do this.\n",
- "```\n",
+ ":::\n",
"\n",
"If you'd like to learn more about list comprehensions, check out these [short video tutorials](https://calmcode.io/comprehensions/introduction.html).\n",
"\n",
@@ -443,9 +442,9 @@
"\n",
"You can use the keyword `break` to break out of a while loop, for example if it's reached a certain number of iterations without converging.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Making use of `import string` and then `string.ascii_lowercase` to get the characters in the alphabet, write a while loop that iterates backwards through the alphabet (starting at \"z\") before printing \"execution complete\".\n",
- "```"
+ ":::"
]
},
{
@@ -457,7 +456,7 @@
"\n",
"For loops, while loops, and comprehensions all work on **pandas** data frames, but they are generally a bad way to get things done because they are slow and not memory efficient. To aid cases where iteration is needed, **pandas** has built-in methods for iteration depending on what you need to do.\n",
"\n",
- "These built-in methods for iteration have an overlap with what we've seen in {ref}`data-transform` but we'll dig a little deeper into `assign()`/assignment operations, `apply()`, and `eval()` here.\n",
+ "These built-in methods for iteration have an overlap with what we've seen in @sec-data-transform but we'll dig a little deeper into `assign()`/assignment operations, `apply()`, and `eval()` here.\n",
"\n"
]
},
@@ -688,7 +687,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/joins.ipynb b/joins.ipynb
index 2bb4f52..e58d2d3 100644
--- a/joins.ipynb
+++ b/joins.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(joins)=\n",
- "# Joins\n",
+ "# Joins {#sec-joins}\n",
"\n",
"## Introduction\n",
"\n",
@@ -95,7 +94,7 @@
"source": [
"Note that the `keys` argument is optional, but is useful for keeping track of origin data frames within the merged data frame.\n",
"\n",
- "````{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Concatenate the follow two data frames:\n",
"\n",
"```python\n",
@@ -106,7 +105,7 @@
" columns=['letter', 'number'])\n",
"```\n",
"\n",
- "````"
+ ":::"
]
},
{
@@ -203,7 +202,7 @@
"source": [
"Now we can see that the products of all key combinations are here. The `indicator=True` option has caused an extra column to be added, called '_merge', that tells us which data frame the keys on that row came from.\n",
"\n",
- "````{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"\n",
"Merge the following two data frames using the `left_on` and `right_on` keyword arguments to specify a join on `lkey` and `rkey` respectively:\n",
"\n",
@@ -213,9 +212,9 @@
"df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],\n",
" 'value': [5, 6, 7, 8]})\n",
"```\n",
- "````\n",
+ ":::\n",
"\n",
- "````{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"\n",
"Merge the following two data frames on `\"a\"` using `how=\"left\"` as a keyword argument:\n",
"\n",
@@ -225,7 +224,7 @@
"```\n",
"\n",
"What do you notice about the position `.loc[1, \"c\"]` in the merged data frame? \n",
- "````\n",
+ ":::\n",
"\n",
"For more on the options for merging, see **pandas**' comprehensive [merging documentation](https://pandas.pydata.org/docs/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging)."
]
@@ -256,7 +255,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/markdown.md b/markdown.md
index 12c2627..a5ad933 100644
--- a/markdown.md
+++ b/markdown.md
@@ -1,5 +1,4 @@
-(markdown)=
-# Markdown
+# Markdown {#sec-markdown}
## Introduction
@@ -154,11 +153,11 @@ The basic syntax to create tables is
which becomes
-| Cheese | Country | Cost per kg |
-|---------------------|-----------------|-------------|
-| Appleby's Cheshire | UK | £30 |
-| Edam | Netherlands | £8 |
-| Pélardon | France | £37 |
+ | Cheese | Country | Cost per kg |
+ | ------------------ | ----------- | ----------- |
+ | Appleby's Cheshire | UK | £30 |
+ | Edam | Netherlands | £8 |
+ | Pélardon | France | £37 |
but you will rarely want to write these out yourself! In practice, it's easiest to export a markdown file from a **pandas** data frame using `df.to_markdown()` or use the handy website, [markdown table generator](https://www.tablesgenerator.com/markdown_tables).
@@ -230,9 +229,15 @@ Footnotes can be be created using `[^1]` followed by `[^2]`, and so on, or by co
```
[^1]: First footnote.
-[^2]: Every new line in a footnote should be prefixed with 2 spaces.
- This allows you to have a footnote with multiple lines.
-[^note]: Named footnotes will still render with numbers instead of the text but allow easier identification and linking.
+
+ [^2]:
+
+ Every new line in a footnote should be prefixed with 2 spaces.
+ This allows you to have a footnote with multiple lines.
+
+ [^note]:
+
+ Named footnotes will still render with numbers instead of the text but allow easier identification and linking.
Finally, to insert a line-break use
@@ -242,7 +247,7 @@ Finally, to insert a line-break use
To produce this line-break:
-***
+--------------------------------------------------------------------------------
### Other Markdown Resources
diff --git a/missing-values.ipynb b/missing-values.ipynb
index c39b798..262f015 100644
--- a/missing-values.ipynb
+++ b/missing-values.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(missing-values)=\n",
- "# Missing Values\n",
+ "# Missing Values {#sec-missing-values}\n",
"\n",
"## Introduction\n",
"\n",
@@ -597,7 +596,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/numbers.ipynb b/numbers.ipynb
index d2dd681..388db20 100644
--- a/numbers.ipynb
+++ b/numbers.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(numbers)=\n",
- "# Numbers\n",
+ "# Numbers {#sec-numbers}\n",
"\n",
"## Introduction\n",
"\n",
@@ -793,7 +792,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/panache.toml b/panache.toml
new file mode 100644
index 0000000..0915bb9
--- /dev/null
+++ b/panache.toml
@@ -0,0 +1,12 @@
+flavor = "quarto"
+
+[format]
+wrap = "preserve"
+blank-lines = "collapse"
+
+[lint.rules]
+missing-chunk-labels = false
+undefined-reference-label = false
+
+[formatters]
+python = "ruff"
diff --git a/prerequisites.ipynb b/prerequisites.ipynb
index 8fd4b7b..68bea6d 100644
--- a/prerequisites.ipynb
+++ b/prerequisites.ipynb
@@ -6,8 +6,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(prereq)=\n",
- "# Prerequisites\n",
+ "# Prerequisites {#sec-prereq}\n",
"\n",
"Congratulations on starting your data science journey! In this chapter, we're going to help you install or access the tools you need to start learning and doing data science. We've made a few assumptions about what you already know in order to get the most out of this book. You should be generally numerically literate, and it's helpful if you have some programming experience already.\n",
"\n",
@@ -131,9 +130,9 @@
"\n",
"There are many ways to do data science in the cloud, but we're going to share with you the absolute simplest. For this, you will need to sign up for a [Github Account](https://github.com/). Github is an organisation that's owned by Microsoft and which provides a range of services including a way to back-up code on the cloud, and cloud computing. One of the services offered is *Github Codespaces*. A GitHub Codespace is an online cloud computer that you connect to from your browser window. It has a generous 60 hours free of computing per month.\n",
"\n",
- "```{note}\n",
+ "::: {.callout-note}\n",
"If you go over the free tier hours on Github Codespaces, your credit card will be charged for any further hours of GitHub Codespaces you use.\n",
- "```\n",
+ ":::\n",
"\n",
"Once you've signed up for a Github account, head to [Github Codespaces](https://github.com/codespaces) and click on \"Get Started for Free\". You should see a menu of \"quick start templates\". Under where it says \"Jupyter Notebook\", hit \"Use this template\".\n",
"\n",
@@ -223,11 +222,10 @@
"\n",
"Now return to your script, put your cursor on the line with `print(\"Hello world!\")` on, and hit Shift +Enter . You should see \"Hello world!\" appear again, only this time, it was much easier.\n",
"\n",
- "```{admonition} Running code in the terminal instead\n",
- ":class: dropdown\n",
+ "::: {.callout-note title=\"Running code in the terminal instead\"}\n",
"\n",
"The interactive window isn't the only way to run code; you can do it in the terminal too. This is less popular for data science, but it does occasionally have its uses. If you want to do this, right-click on the selected code and choose \"Run Python -> Run Selection/Line in Terminal\".\n",
- "```\n",
+ ":::\n",
"\n",
"Let's make more use of the *interactive window*. At the bottom of it, there is a box that says 'Type code here and press shift-enter to run'. Go ahead and type `print('Hello World!')` directly in there to achieve the same effect as running the line from your script. Also, any variables you run in the interactive window (from your script or directly by entering them in the box) will persist.\n",
"\n",
@@ -235,14 +233,13 @@
"\n",
"This shows the two ways of working with the interactive window--running (segments) from a script, or writing code directly in the entry box. It doesn't matter which way you entered variables, they will all be remembered within that session in your interactive window.\n",
"\n",
- "```{admonition} Start interactive windows and terminals within your project directory\n",
- ":class: dropdown\n",
+ "::: {.callout-note title=\"Start interactive windows and terminals within your project directory\"}\n",
"In Visual Studio Code, you can ensure that the interactive window starts in the root directory of your project by setting \"Jupyter: Notebook File Root\" to `${workspaceFolder}` in the Settings menu. For the integrated command line, change \"Terminal › Integrated: Cwd\" to `${workspaceFolder}` too.\n",
- "```\n",
+ ":::\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Create a new script that, when run, prints \"Welcome to Python for Data Science\" and run it in an interactive window.\n",
- "```"
+ ":::"
]
},
{
@@ -264,7 +261,7 @@
"\n",
"You can see what packages you have previously installed by entering `uv pip list` into the command line.\n",
"\n",
- "There'll be more on installing and using packages in {ref}`workflow-packages-and-environments`.\n",
+ "There'll be more on installing and using packages in @sec-workflow-packages-and-environments.\n",
"\n",
"If you have problems installing, make sure that you are connected to the internet, and that [PyPI](https://pypi.org/) (the Python package index) isn't blocked by your firewall or proxy."
]
@@ -308,7 +305,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.10.0"
},
"toc-showtags": true
},
diff --git a/pyproject.toml b/pyproject.toml
index 9a6f6a1..9bb8502 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,18 +3,17 @@ name = "python4ds"
version = "1.0.4"
description = "The online book that teaches you how to use Python for data science."
readme = "README.md"
-requires-python = ">=3.10.0"
+requires-python = ">=3.12.0,<3.13"
dependencies = [
"beautifulsoup4>=4.12.3",
- "ghp-import>=2.1.0",
"graphviz>=0.20.3",
"ibis-framework[sqlite]>=9.5.0",
"ipykernel>=6.29.5",
"jupyter>=1.1.1",
- "jupyter-book>=1.0.3",
"jupyterlab>=4.3.4",
"lets-plot==4.5.2",
"nbclient>=0.10.2",
+ "nbformat>=5.0",
"nbstripout>=0.8.1",
"numpy>=2.2.1",
"openpyxl>=3.1.5",
@@ -36,6 +35,8 @@ dependencies = [
"statsmodels>=0.14.4",
"toml>=0.10.2",
"watermark>=2.5.0",
+ "wbgapi>=1.0.14",
+ "yfinance>=1.2.1",
]
diff --git a/quarto.md b/quarto.qmd
similarity index 80%
rename from quarto.md
rename to quarto.qmd
index e65340f..7d2561d 100644
--- a/quarto.md
+++ b/quarto.qmd
@@ -1,7 +1,6 @@
-(quarto)=
-# Quarto
+# Quarto {#sec-quarto}
-In this chapter, you'll get more in-depth with [**Quarto**](https://quarto.org/), following a brief introduction in {ref}`workflow-writing-code`. **Quarto** is a tool that allows you to combine code and text (in the form of markdown, covered in {ref}`markdown`) and create rich outputs, like reports and presentations.
+In this chapter, you'll get more in-depth with [**Quarto**](https://quarto.org/), following a brief introduction in @sec-workflow-writing-code. **Quarto** is a tool that allows you to combine code and text (in the form of markdown, covered in @sec-markdown) and create rich outputs, like reports and presentations.
## Introduction
@@ -9,11 +8,11 @@ Quarto is a unified authoring framework for data science, combining your code, i
Quarto markdown is designed to be used in three ways:
-1. For communicating to decision makers, who want to focus on the conclusions, not the code behind the analysis.
+1. For communicating to decision makers, who want to focus on the conclusions, not the code behind the analysis.
-2. For collaborating with other data scientists (including future you!), who are interested in both your conclusions, and how you reached them (i.e. the code).
+2. For collaborating with other data scientists (including future you!), who are interested in both your conclusions, and how you reached them (i.e. the code).
-3. As an environment in which to *do* data science, as a modern day lab notebook where you can capture not only what you did, but also what you were thinking.
+3. As an environment in which to *do* data science, as a modern day lab notebook where you can capture not only what you did, but also what you were thinking.
For example, a use case would be a technical report exploring recent trade statistics in which the code part pulls down the latest data and plots it. Or you could do the same analysis, but push it to a website. Best of all, you can decide whether to hide or show the code parts in the final outputs (or, with html outputs, allow users to choose whether to see the code or not). In more detail, use cases include:
@@ -43,8 +42,8 @@ Quarto can be used to create *output* documents and slides in a wide variety of
You can write the *input* documents (include code snippets) in two possible ways:
-1. A special kind of markdown file, with file extension `.qmd`. For more on markdown, see {ref}`markdown`. Code blocks that have a special syntax get executed and their results are included in any outputs.
-
+1. A special kind of markdown file, with file extension `.qmd`. For more on markdown, see @sec-markdown. Code blocks that have a special syntax get executed and their results are included in any outputs.
+
2. Jupyter Notebooks, with file extension `.ipynb`. Code cells are executed and their results are included in any outputs.
You optionally add code (e.g. Python, R, JavaScript, etc.) to the documents to dynamically create figures, tables, etc. and then render the documents to their final format using Quarto.
@@ -71,7 +70,7 @@ jupyter: python3
For a demonstration of a line plot on a polar axis, see @fig-polar.
-```{python}
+```{{python}}
#| label: fig-polar
#| fig-cap: "A line plot on a polar axis"
@@ -90,9 +89,9 @@ plt.show()
This example contains three important types of content:
-1. A **YAML header** surrounded by `---`s.
-2. **Chunks** of Python code surrounded by ```` ``` ````.
-3. Markdown mixed with simple text formatting like `# heading` and `_italics_`.
+1. A **YAML header** surrounded by `---`s.
+2. **Chunks** of Python code surrounded by ```` ``` ````.
+3. Markdown mixed with simple text formatting like `# heading` and `_italics_`.
In this 'raw' input quarto markdown `.qmd` file, `{python}` tells **Quarto** that a code chunk is in Python and should be executed, and `jupyter: python3` tells **Quarto** what installation of Jupyter Notebooks to use. If you're unsure what your installation of Jupyter is called, you can see a list by running `jupyter kernelspec list` on the command line.
@@ -106,13 +105,12 @@ quarto render report.qmd
Remember that if you are using the Visual Studio Code [quarto extension](https://marketplace.visualstudio.com/items?itemName=quarto.quarto) (recommended), you can hit the render button instead (but you'll need to choose PDF as the output).
-```{admonition} Exercise
-
+::: {.callout-tip title="Exercise"}
Successfuly create a PDF by saving the markdown above into a file called `report.qmd`.
If you get an error about not being able to find the Jupyter kernel, first check you have Jupyter Lab installed and then check what your Jupyter kernel is called using `jupyter kernelspec list` on the command line. You need to specify the name of your Jupyter kernel correct in the header document (in the example above, it's called 'python3', which is the default).
-```
+:::
Now, because we specified `pdf` in the 'header' of our file we automatically got a pdf. But a wide range of output formats are available. For example, HTML
@@ -130,13 +128,12 @@ One slight frustration with the conversion to Word Documents is that tables from
The basic syntax is to write `--to outputformat` at the end of the render command.
-```{admonition} Exercise
-
+::: {.callout-tip title="Exercise"}
Successfully create a HTML report by saving the markdown above into a file called `report.qmd` and then running the quarto render command with the to html option.
What happens to the menu on the right-hand side as you add extra headings using the `##` markdown syntax?
-```
+:::
### Code Block Execution Options
@@ -145,7 +142,7 @@ There are different options for how the code block is executed. To include a cod
For an example of a code output where the input is not shown, the code below will *only* show the output table by using the `echo: false` option.
````
-```{python}
+```{{python}}
#| echo: false
import pandas as pd
import seaborn as sns
@@ -157,16 +154,16 @@ pd.crosstab(df["species"], [df["island"], df["sex"]], margins=True)
The table below gives all of the options for code blocks.
-| Option | Description |
-|---|---|
-| `eval` | Evaluate the code chunk (if false, just echos the code into the output). |
-| `echo` | Include the source code in output |
-| `output` | Include the results of executing the code in the output (true, false, or asis to indicate that the output is raw markdown and should not have any of Quarto’s standard enclosing markdown). |
-| `warning` | Include warnings in the output. |
-| `error` | Include errors in the output (note that this implies that errors executing code will not halt processing of the document). |
-| `include` | Catch all for preventing any output (code or results) from being included (e.g. include: false suppresses all output from the code block). |
-| `true` | True |
-| `false` | False |
+ | Option | Description |
+ | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+ | `eval` | Evaluate the code chunk (if false, just echos the code into the output). |
+ | `echo` | Include the source code in output |
+ | `output` | Include the results of executing the code in the output (true, false, or asis to indicate that the output is raw markdown and should not have any of Quarto’s standard enclosing markdown). |
+ | `warning` | Include warnings in the output. |
+ | `error` | Include errors in the output (note that this implies that errors executing code will not halt processing of the document). |
+ | `include` | Catch all for preventing any output (code or results) from being included (e.g. include: false suppresses all output from the code block). |
+ | `true` | True |
+ | `false` | False |
It's also **possible to insert code results in-line with text**. Here's a minimal example of that.
@@ -186,7 +183,7 @@ For a demonstration of a line plot on a polar axis, see @fig-polar.
For an example of a code output where the input is not shown, the code below will *only* show the output table by using the `echo: false` option.
-```{python}
+```{{python}}
#| echo: false
from IPython.display import display, Markdown
import pandas as pd
@@ -205,11 +202,10 @@ We find that the heaviest penguin, out of a total of `python f"{number}"` pengui
Note that, in this example, the `:2f` part of `{big_pen:.2f}` is an instruction to report the given number to 2 decimal places.
-```{admonition} Exercise
-
+::: {.callout-tip title="Exercise"}
Create a HTML report with an in-line number using the above example but change the formatting of the heaviest penguin to not show any decimal places.
-```
+:::
There are, of course, lots of extra features that go beyond this example.
@@ -244,11 +240,10 @@ The main difference when it comes to Jupyter Notebooks is that you must decide w
quarto render jupyter-report.ipynb --execute
```
-```{admonition} Exercise
-
+::: {.callout-tip title="Exercise"}
Create a new Jupyter Notebook called `jupyter-report.ipynb` with the header above. Re-use the blocks of code and text from the `qmd` example in the previous section. Render it with the command above.
-```
+:::
To change the output type, add another instruction to the command using `--to`:
@@ -276,12 +271,13 @@ The image below shows an example of using preview side-by-side with a Jupyter No
-````{admonition} Exercise
+::: {.callout-tip title="Exercise"}
Take the Jupyter example from the previous exercise and change the format to HTML. Then add both a new text cell and a new figure (from a code cell) while in preview mode, making use of the `quarto preview` command above. If you need inspiration for the new figure, here's a simple scatter plot:
```python
import numpy as np
import matplotlib.pyplot as plt
+
fig, ax = plt.subplots()
ax.scatter(
[1, 2, 3, 4, 5, 6],
@@ -294,7 +290,7 @@ ax.scatter(
plt.show()
```
-````
+:::
## Automated Slides with **Quarto**
@@ -320,7 +316,7 @@ format: revealjs
## Here Are Some Code Outputs
-```{python}
+```{{python}}
#| echo: false
import numpy as np
import matplotlib.pyplot as plt
@@ -337,10 +333,10 @@ plt.show()
Note that this will not show the code, only the figure, as we have set `#| echo: false` for the code chunk. You could also set `echo: false` for the whole deck in the header.
-```{admonition} Exercise
+::: {.callout-tip title="Exercise"}
Render this slide example in all three of the main formats
-```
+:::
-```{admonition} Exercise
+::: {.callout-tip title="Exercise"}
Add outputs from in-line code into your rendered deck, adapting the heaviest penguin example from earlier.
-```
+:::
diff --git a/rectangling.ipynb b/rectangling.ipynb
index afd529e..f0bcf47 100644
--- a/rectangling.ipynb
+++ b/rectangling.ipynb
@@ -5,12 +5,11 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(rectangling)=\n",
- "# Nested Data\n",
+ "# Nested Data {#sec-rectangling}\n",
"\n",
"## Introduction\n",
"\n",
- "In this chapter, you'll learn about **nested data**, working with data that is fundamentally tree-like and (often) converting it into a rectangular data frames made up of rows and columns. This is important because nested data is surprisingly common, especially when working with data that comes from a web API (such as you will see in {ref}`webscraping-and-apis`).\n",
+ "In this chapter, you'll learn about **nested data**, working with data that is fundamentally tree-like and (often) converting it into a rectangular data frames made up of rows and columns. This is important because nested data is surprisingly common, especially when working with data that comes from a web API (such as you will see in @sec-webscraping-and-apis).\n",
"\n",
"To learn about rectangling, you'll first learn about lists, dictionaries, and the JSON format, as these are the data structures that are most often used to work with hierarchical data in Python. Then you'll learn about some functions that can help you turn hierarchical data into 'tidy' data in columns and rows. We'll then show you a few case studies, applying these simple function multiple times to solve real complex problems.\n"
]
@@ -109,9 +108,9 @@
"id": "920d19f4",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"How might you access the penultimate entry in a list object if you didn't know how many elements it had?\n",
- "```"
+ ":::"
]
},
{
@@ -178,9 +177,9 @@
"id": "39f80c9e",
"metadata": {},
"source": [
- "````{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Slice the `list_example` from earlier to get only the first five entries.\n",
- "````"
+ ":::"
]
},
{
@@ -614,7 +613,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -628,7 +627,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/references.md b/references.md
new file mode 100644
index 0000000..925f7c4
--- /dev/null
+++ b/references.md
@@ -0,0 +1,4 @@
+# References {.unnumbered}
+
+::: {#refs}
+:::
diff --git a/regex.ipynb b/regex.ipynb
index 1020b09..a8055fc 100644
--- a/regex.ipynb
+++ b/regex.ipynb
@@ -4,8 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "(text-regex)=\n",
- "# Regular Expressions, aka regex\n",
+ "# Regular Expressions, aka regex {#sec-text-regex}\n",
"\n",
"This chapter covers how to regular expressions to perform processing of text.\n",
"\n",
@@ -100,9 +99,9 @@
"| + | 1 or more. Same as {1,} | \"4 323 hello\" | `\\d+` | \"4\", \"323\" |\n",
"| ? | Optional, so 0 or 1. Same as {,1}. | \"4 323 hello\" | `\\d?\\s` | \"4\" |\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Find a single regex that will pick out only the percentage numbers from both \"Inflation in year 3 was 2 percent\" and \"Interest rates were as high as 12 percent\". \n",
- "```\n",
+ ":::\n",
"\n",
"## Metacharacters\n",
"\n",
@@ -117,9 +116,9 @@
"\n",
"Because so many characters have special meaning in regex, if you want to look for, say, a dollar sign or a dot, you need to escape the character first with a backward slash. So `\\${1}\\d+` would look for a single dollar sign followed by some digits and would pick up the '\\$50' in 'she made \\$50 dollars'.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Find the regex that will pick out only the first instance of the word 'money' and any word subsequent to 'money' from the following: \"money supply has grown considerably. money demand has not kept up.\". \n",
- "```\n",
+ ":::\n",
"\n",
"## Ranges\n",
"\n",
@@ -210,9 +209,9 @@
"\n",
"But the capture group is only the subset of the match that is the number range-we discard most of the rest. Note also that other numbers, even if they are followed by 'per', are not picked up. `(?:)` begins a *non-capture group*, which matches only but does not capture, so that although `(?:\\s?per)` looks for \" per\" after a salary (with the space optional due to the second `?`), it does not get returned.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Find a regex that captures the wage range from \"Salary Pay in range $9.00 - $12.02 but you must start at 8.00 - 8.30 every morning.\". \n",
- "```\n",
+ ":::\n",
"\n",
"This has been a whirlwind tour of regexes. Although regex looks a lot like gobbledygook, it is a really useful tool to be able to deploy for more complex string cleaning and extraction tasks."
]
@@ -220,13 +219,21 @@
],
"metadata": {
"kernelspec": {
- "display_name": "py4ds2e",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
"name": "python",
- "version": "3.10.12"
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.12"
}
},
"nbformat": 4,
diff --git a/spreadsheets.ipynb b/spreadsheets.ipynb
index e2f2c97..12f1672 100644
--- a/spreadsheets.ipynb
+++ b/spreadsheets.ipynb
@@ -5,12 +5,11 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(spreadsheets)=\n",
- "# Spreadsheets\n",
+ "# Spreadsheets {#sec-spreadsheets}\n",
"\n",
"## Introduction\n",
"\n",
- "This chapter will show you how to work with spreadsheets, for example Microsoft Excel files, in Python. We already saw how to import csv (and tsv) files in {ref}`data-import`. In this chapter we will introduce you to tools for working with data in Excel spreadsheets and Google Sheets.\n",
+ "This chapter will show you how to work with spreadsheets, for example Microsoft Excel files, in Python. We already saw how to import csv (and tsv) files in @sec-data-import. In this chapter we will introduce you to tools for working with data in Excel spreadsheets and Google Sheets.\n",
"\n",
"If you or your collaborators are using spreadsheets for organising data that will be ingested by an analytical tool like Python, we recommend reading the paper \"Data Organization in Spreadsheets\" by Karl Broman and Kara Woo {cite}`broman2018data`. The best practices presented in this paper will save you much headache down the line when you import the data from a spreadsheet into Python to analyse and visualise. (For spreadsheets that are meant to be read by humans, we recommend the [good practice tables](https://github.com/best-practice-and-impact/gptables) package.)"
]
@@ -439,7 +438,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/strings.ipynb b/strings.ipynb
index 7c36cf4..e401876 100644
--- a/strings.ipynb
+++ b/strings.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(strings)=\n",
- "# Strings and Text\n",
+ "# Strings and Text {#sec-strings}\n",
"\n",
"## Introduction\n",
"\n",
@@ -187,9 +186,9 @@
"source": [
"Note that there are many built-in functions for using strings in Python, you can find a comprehensive list [here](https://www.w3schools.com/python/python_ref_string.asp).\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Reverse the string `\"gnirts desrever a si sihT\"` using indexing operations.\n",
- "```"
+ ":::"
]
},
{
@@ -545,7 +544,7 @@
"\n",
"Replacing characters is extremely useful in certain situations, most commonly when you wish to remote all punctuation prior to doing other text analysis. You can use the built-in `string.punctuation` for this.\n",
"\n",
- "Let's see how to use it to remove all of the vowels from some text. With apologies to economist Lisa Cook, we'll use the abstract from {cite:t}`cook2011inventing` as the text we'll modify and we'll first create a dictionary of translations of vowels to nothing, i.e. `\"\"`."
+ "Let's see how to use it to remove all of the vowels from some text. With apologies to economist Lisa Cook, we'll use the abstract from @cook2011inventing as the text we'll modify and we'll first create a dictionary of translations of vowels to nothing, i.e. `\"\"`."
]
},
{
@@ -585,9 +584,9 @@
"id": "1c0359ed",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Use `translate` to replace all puncuation from the following sentence with spaces: \"The well-known story I told at the conferences [about hypocondria] in Boston, New York, Philadelphia,...and Richmond went as follows: It amused people who knew Tommy to hear this; however, it distressed Suzi when Tommy (1982--2019) asked, \\\"How can I find out who yelled, 'Fire!' in the theater?\\\" and then didn't wait to hear Missy give the answer---'Dick Tracy.'\"\n",
- "```"
+ ":::"
]
},
{
@@ -924,9 +923,9 @@
"id": "e6bb64b2",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Using vectorised operations, create a new column with the index position where the first vowel occurs for each row in the `names` column.\n",
- "```\n",
+ ":::\n",
"\n",
"Here's an example of using a regex function with **pandas**:"
]
@@ -1049,9 +1048,9 @@
"\n",
"In the above, the delimiter for different rows of the data frame is set as \"\\n\", which means new line, but you could use whatever delimiter you prefer.\n",
"\n",
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Download the file 'smith_won.txt' using this [link](https://github.com/aeturrell/coding-for-economists/blob/main/data/smith_won.txt) (use right-click and save as). Then read the text in using **pandas**.\n",
- "```\n",
+ ":::\n",
"\n",
"### CSV file\n",
"\n",
@@ -1090,7 +1089,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/uv.lock b/uv.lock
index 952cff9..8316606 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,54 +1,28 @@
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-resolution-markers = [
- "python_full_version < '3.11'",
- "python_full_version == '3.11.*'",
- "python_full_version >= '3.12'",
-]
-
-[[package]]
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- { name = "pygments" },
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-]
+revision = 3
+requires-python = "==3.12.*"
[[package]]
name = "anyio"
version = "4.8.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
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{ name = "idna" },
{ name = "sniffio" },
- { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+ { name = "typing-extensions" },
]
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diff --git a/vis-layers.ipynb b/vis-layers.ipynb
index e98fd0c..ba22bb2 100644
--- a/vis-layers.ipynb
+++ b/vis-layers.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(vis-layers)=\n",
- "# Layers\n"
+ "# Layers {#sec-vis-layers}\n"
]
},
{
@@ -466,7 +465,7 @@
"source": [
"## Facets\n",
"\n",
- "In {ref}`data-visualise`, you learned about faceting with `facet_wrap()`, which splits a plot into subplots that each display one subset of the data based on a categorical variable.\n"
+ "In @sec-data-visualise, you learned about faceting with `facet_wrap()`, which splits a plot into subplots that each display one subset of the data based on a categorical variable.\n"
]
},
{
@@ -996,7 +995,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/visualise.md b/visualise.qmd
similarity index 78%
rename from visualise.md
rename to visualise.qmd
index fefe0f0..da58088 100644
--- a/visualise.md
+++ b/visualise.qmd
@@ -1,27 +1,12 @@
----
-jupytext:
- cell_metadata_filter: -all
- formats: md:myst
- text_representation:
- extension: .md
- format_name: myst
- format_version: '0.8'
- jupytext_version: 1.5.0
-kernelspec:
- display_name: python3
- language: python
- name: python3
----
-(visualise)=
-# Visualisation
-
-After reading the first part of the book, you understand the basics of the most important tools for doing data science. Now it’s time to start diving into the details. In this part of the book, you’ll learn about visualising data in further depth (in {ref}`vis-layers`), and get further stuck into the details of the different kinds of data visualisation (in {ref}`exploratory-data-analysis` and {ref}`communicate-plots`). In this short chapter, we discuss the different ways to create visualisations, and the different purposes of visualisations.
+# Visualisation {#sec-visualise}
+
+After reading the first part of the book, you understand the basics of the most important tools for doing data science. Now it’s time to start diving into the details. In this part of the book, you’ll learn about visualising data in further depth (in @sec-vis-layers), and get further stuck into the details of the different kinds of data visualisation (in @sec-exploratory-data-analysis and @sec-communicate-plots). In this short chapter, we discuss the different ways to create visualisations, and the different purposes of visualisations.
## Philosophies of data visualisation
There are broadly two categories of approach to using code to create data visualisations: *imperative* (build what you want from individual elements) and *declarative* (say what you want from a list of pre-existing options). Choosing which to use involves a trade-off: imperative libraries offer you flexibility but at the cost of some verbosity; declarative libraries offer you a quick way to plot your data, but only if it’s in the right format to begin with, and customisation to special chart types is more difficult.
-Python has many excellent plotting packages, including perhaps the most powerful imperative plotting package around, **matplotlib**, and an amazing declarative library that we already saw, **lets-plot**. These two libraries will get you a long way, and each could be worthy of an entire book themselves. Fortunately for us, though, we can do 95% of what we need with a small number of commands from one or the other of them. In general, to keep this book as light as possible, we've opted to use **lets-plot** wherever possible—and {ref}`vis-layers` is going to take you on a more in-depth tour of how to use it yourself.
+Python has many excellent plotting packages, including perhaps the most powerful imperative plotting package around, **matplotlib**, and an amazing declarative library that we already saw, **lets-plot**. These two libraries will get you a long way, and each could be worthy of an entire book themselves. Fortunately for us, though, we can do 95% of what we need with a small number of commands from one or the other of them. In general, to keep this book as light as possible, we've opted to use **lets-plot** wherever possible—and @sec-vis-layers is going to take you on a more in-depth tour of how to use it yourself.
## Purposes of data visualisation
@@ -37,10 +22,8 @@ Let's look at each in a bit more detail.
The first of the three kinds is *exploratory data visualisation*, and it's the kind that you do when you're looking and data and trying to understand it. Just plotting the data is a really good strategy for getting a feel for any issues there might be. This is perhaps most famously demonstrated by Anscombe's quartet: four different datasets with the same mean, standard deviation, and correlation but very different data distributions.
-```{code-cell} ipython3
----
-tags: [remove-input]
----
+```{python}
+#| echo: false
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
@@ -105,11 +88,11 @@ plt.show()
Exploratory visualisation is usually quick and dirty, and flexible too. Some exploratory data viz can be automated, and there's a whole host of packages to help with this, including [**skimpy**](https://aeturrell.github.io/skimpy/).
-Beyond you and perhaps your co-authors/collaborators, however, not many other people should be seeing your exploratory visualisation! They will typically be worked up quickly, be numerous, and be throw-away. We'll look more at this in {ref}`exploratory-data-analysis`.
+Beyond you and perhaps your co-authors/collaborators, however, not many other people should be seeing your exploratory visualisation! They will typically be worked up quickly, be numerous, and be throw-away. We'll look more at this in @sec-exploratory-data-analysis.
### Scientific Data Viz
-The second kind, scientific data visualisation, is the prime cut of your exploratory visualisation. It's the kind of plot you might include in a more technical paper, the picture that says a thousand words. I often think of the first image of a black hole {cite:t}`akiyama2019first` as a prime example of this. You can get away with having a high density of information in a scientific plot and, in short format journals, you may need to. The journal Physical Review Letters, which has an 8 page limit, has a classic of this genre in more or less every issue. Ensuring that important values can be accurately read from the plot is especially important in these kinds of charts. But they can also be the kind of plot that presents the killer results in a study; they might not be exciting to people who don't look at charts for a living, but they might be exciting and, just as importantly, understandable by your peers.
+The second kind, scientific data visualisation, is the prime cut of your exploratory visualisation. It's the kind of plot you might include in a more technical paper, the picture that says a thousand words. I often think of the first image of a black hole @akiyama2019first as a prime example of this. You can get away with having a high density of information in a scientific plot and, in short format journals, you may need to. The journal Physical Review Letters, which has an 8 page limit, has a classic of this genre in more or less every issue. Ensuring that important values can be accurately read from the plot is especially important in these kinds of charts. But they can also be the kind of plot that presents the killer results in a study; they might not be exciting to people who don't look at charts for a living, but they might be exciting and, just as importantly, understandable by your peers.
This type of visualisation is especially popular in the big science journals like *Nature* and *Science*, where space is at a premium. We won't cover this type of plot in this book, because it tends to be very bespoke.
@@ -117,4 +100,4 @@ This type of visualisation is especially popular in the big science journals lik
The third and final kind is narrative data visualisation. This is the one that requires the most thought in the step where you go from the first view to the end product. It's a visualisation that doesn't just show a picture, but gives an insight. These are the kind of visualisations that you might see in the *Financial Times*, *The Economist*, or on the *BBC News* website. They come with aids that help the viewer focus on the aspects that the creator wanted them to (you can think of these aids or focuses as doing for visualisation what bold font does for text). They're well worth using in your work, especially if you're trying to communicate a particular narrative, and especially if the people you're communicating with don't have deep knowledge of the topic. You might use them in a paper that you hope will have a wide readership, in a blog post summarising your work, or in a report intended for a policymaker.
-You can find more information on the topic of communicating via data visualisations in the {ref}`communicate-plots` chapter.
+You can find more information on the topic of communicating via data visualisations in the @sec-communicate-plots chapter.
diff --git a/webscraping-and-apis.ipynb b/webscraping-and-apis.ipynb
index 1baee98..9366eb6 100644
--- a/webscraping-and-apis.ipynb
+++ b/webscraping-and-apis.ipynb
@@ -6,8 +6,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(webscraping-and-apis)=\n",
- "# Webscraping and APIs\n",
+ "# Webscraping and APIs {#sec-webscraping-and-apis}\n",
"\n",
"## Introduction\n",
"\n",
@@ -68,7 +67,7 @@
"source": [
"### Prerequisites\n",
"\n",
- "You will need to install the **pandas** package for this chapter. We'll use **seaborn** too, which you should already have installed. You will also need to install the **beautifulsoup** and **pandas-datareader** packages in your terminal using `uv add beautifulsoup4` and `uv add pandas-datareader` respectively. We'll also use two built-in packages, **textwrap** and **requests**.\n",
+ "You will need to install the **pandas** package for this chapter. We'll use **seaborn** too, which you should already have installed. You will also need to install the **beautifulsoup**, **pandas-datareader**, and **wbgapi** packages in your terminal using `uv add beautifulsoup4`, `uv add pandas-datareader`, and `uv add wbgapi` respectively. We'll also use two built-in packages, **textwrap** and **requests**.\n",
"\n",
"To kick off, let's import some of the packages we need (it's always good practice to import the packages you need at the top of a script or notebook)."
]
@@ -125,9 +124,9 @@
"\n",
"Because they are designed to work with any tool, you don't actually need a programming language to interact with an API, it's just a *lot* easier if you do.\n",
"\n",
- "```{note}\n",
+ "::: {.callout-note}\n",
"An API key is needed in order to access some APIs. Sometimes all you need to do is register with site, in other cases you may have to pay for access.\n",
- "```\n",
+ ":::\n",
"\n",
"To see this, let's directly use an API to get some time series data. We will make the call out to the internet using the **requests** package.\n",
"\n",
@@ -207,7 +206,7 @@
"id": "0613aefb",
"metadata": {},
"source": [
- "And, because it's also a really useful one, let's also see how to use **pandas-datareader** to access World Bank data."
+ "And, because it's also a really useful one, let's see how to use the [**wbgapi**](https://pypi.org/project/wbgapi/) package to access World Bank data. (**pandas-datareader** used to provide a World Bank reader too, but it has not been actively maintained, so we prefer **wbgapi** for new work.)"
]
},
{
@@ -219,20 +218,25 @@
},
"outputs": [],
"source": [
- "# World Bank total greenhouse emissions (CO2e metric tons per capita)\n",
+ "# World Bank CO2 equivalent emissions (metric tons per capita)\n",
"# https://data.worldbank.org/indicator/EN.GHG.ALL.PC.CE.AR5\n",
"# country and region codes at http://api.worldbank.org/v2/country\n",
- "from pandas_datareader import wb\n",
+ "import wbgapi as wb\n",
"\n",
- "df = wb.download( # download the data from the world bank\n",
- " indicator=\"EN.GHG.ALL.PC.CE.AR5\", # indicator code\n",
- " country=[\"US\", \"CHN\", \"IND\", \"Z4\", \"Z7\"], # country codes\n",
- " start=2019, # start year\n",
- " end=2019, # end year\n",
+ "indicator_code = \"EN.GHG.ALL.PC.CE.AR5\"\n",
+ "df = (\n",
+ " wb.data.DataFrame(\n",
+ " indicator_code,\n",
+ " [\"USA\", \"CHN\", \"IND\", \"EAS\", \"ECS\"], # country and region codes\n",
+ " time=range(2019, 2020),\n",
+ " labels=True,\n",
+ " )\n",
+ " .rename(columns={\"Country\": \"country\", \"YR2019\": indicator_code})\n",
+ " .reset_index(drop=True)\n",
")\n",
- "df = df.reset_index() # remove country as index\n",
"df[\"country\"] = df[\"country\"].apply(lambda x: textwrap.fill(x, 10)) # wrap long names\n",
- "df = df.sort_values(\"EN.GHG.ALL.PC.CE.AR5\") # re-order"
+ "df = df.sort_values(indicator_code) # re-order\n",
+ "df.head()"
]
},
{
@@ -243,14 +247,14 @@
"outputs": [],
"source": [
"(\n",
- " ggplot(df, aes(x=\"country\", y=\"EN.ATM.CO2E.PC\"))\n",
+ " ggplot(df, aes(x=\"country\", y=indicator_code))\n",
" + geom_bar(aes(fill=\"country\"), color=\"black\", alpha=0.8, stat=\"identity\")\n",
" + scale_fill_discrete()\n",
" + theme_minimal()\n",
" + theme(legend_position=\"none\")\n",
" + ggsize(600, 400)\n",
" + labs(\n",
- " subtitle=\"Carbon dioxide (metric tons per capita)\",\n",
+ " subtitle=\"Greenhouse gases (CO2-equivalent metric tons per capita, 2019)\",\n",
" title=\"The USA leads the world on per-capita emissions\",\n",
" y=\"\",\n",
" )\n",
@@ -525,7 +529,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": ".venv",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -539,7 +543,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.0"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/whole-game.ipynb b/whole-game.ipynb
index 69eb024..eee6c2c 100644
--- a/whole-game.ipynb
+++ b/whole-game.ipynb
@@ -6,8 +6,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(whole-game)=\n",
- "# Whole Game\n",
+ "# Whole Game {#sec-whole-game}\n",
"\n",
"Our goal in this part of the book is to give you a rapid overview of the main tools of data science: importing, cleaning, transforming, and visualising data, as shown in the figure below. We want to show you the “whole game” of data science giving you just enough of all the major pieces so that you can tackle real, if simple, datasets. The later parts of the book will hit each of these topics in more depth, increasing the range of data science challenges that you can tackle."
]
@@ -39,7 +38,7 @@
},
"outputs": [],
"source": [
- "# remove-input\n",
+ "# | echo: false\n",
"import graphviz\n",
"\n",
"dot = graphviz.Digraph(comment=\"Data science workflow\")\n",
@@ -69,17 +68,17 @@
"source": [
"After this chapter, we have four main chapters that focus on the tools of data science:\n",
"\n",
- "- Visualisation is a great place to start with Python programming because the pay-off is so clear: you get to make elegant and informative plots that help you understand data. In {ref}`data-visualise`, you’ll dive into visualisation, learning the basic structure of a plot, and powerful techniques for turning data into plots.\n",
- "- Visualisation alone is typically not enough, so in {ref}`data-transform`, you’ll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.\n",
- "- In {ref}`data-tidy`, you’ll learn about cleaning data and specifically \"tidy\" data, a consistent way of storing tabular data that makes transformation, visualisation, and modelling easier. You’ll learn the underlying principles, and how to get your data into a \"tidy\" format.\n",
- "- Before you can transform and visualise your data, you need to first get your data into a Python session. In {ref}`data-import` you’ll learn the basics of getting .csv files into your Python session.\n",
+ "- Visualisation is a great place to start with Python programming because the pay-off is so clear: you get to make elegant and informative plots that help you understand data. In @sec-data-visualise, you’ll dive into visualisation, learning the basic structure of a plot, and powerful techniques for turning data into plots.\n",
+ "- Visualisation alone is typically not enough, so in @sec-data-transform, you’ll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.\n",
+ "- In @sec-data-tidy, you’ll learn about cleaning data and specifically \"tidy\" data, a consistent way of storing tabular data that makes transformation, visualisation, and modelling easier. You’ll learn the underlying principles, and how to get your data into a \"tidy\" format.\n",
+ "- Before you can transform and visualise your data, you need to first get your data into a Python session. In @sec-data-import you’ll learn the basics of getting .csv files into your Python session.\n",
"\n",
"These are interspersed with four other chapters that focus on your Python workflow:\n",
"\n",
- "- In {ref}`workflow-basics`, {ref}`workflow-style`, and {ref}`workflow-writing-code`, you'll learn good workflow practices for writing and organising your code.\n",
- "- In {ref}`workflow-packages-and-environments`, you'll learn more about packages and isolating your projects in separate code environments.\n",
+ "- In @sec-workflow-basics, @sec-workflow-style, and @sec-workflow-writing-code, you'll learn good workflow practices for writing and organising your code.\n",
+ "- In @sec-workflow-packages-and-environments, you'll learn more about packages and isolating your projects in separate code environments.\n",
"\n",
- "Finally, {ref}`workflow-help` contains some short advice on how to get help and keep learning."
+ "Finally, @sec-workflow-help contains some short advice on how to get help and keep learning."
]
}
],
@@ -108,7 +107,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/workflow-basics.ipynb b/workflow-basics.ipynb
index 7ed34e3..a0a7e47 100644
--- a/workflow-basics.ipynb
+++ b/workflow-basics.ipynb
@@ -6,8 +6,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(workflow-basics)=\n",
- "# Workflow: Basics\n",
+ "# Workflow: Basics {#sec-workflow-basics}\n",
"\n",
"If you followed the previous chapters, you will now have some experience running Python code. We didn't give you many details, but you've obviously figured out the basics, or you would've thrown this book away in frustration! Frustration is natural when you start programming in Python, because it is such a stickler for punctuation, and even one character out of place will cause it to complain. But while you should expect to be a little frustrated, take comfort in that this experience is both typical and temporary: it happens to everyone, and the only way to get over it is to keep trying.\n",
"\n",
@@ -259,9 +258,9 @@
"id": "8df398a0",
"metadata": {},
"source": [
- "```{admonition} Exercise\n",
+ "::: {.callout-tip title=\"Exercise\"}\n",
"Try creating the object `age` and assigning it the value `10`. What happens when you type `Age` into your console?\n",
- "```"
+ ":::"
]
},
{
@@ -529,7 +528,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/workflow-help.md b/workflow-help.qmd
similarity index 95%
rename from workflow-help.md
rename to workflow-help.qmd
index a75fe9d..443bc32 100644
--- a/workflow-help.md
+++ b/workflow-help.qmd
@@ -1,16 +1,4 @@
----
-jupytext:
- formats: md:myst
- text_representation:
- extension: .md
- format_name: myst
-kernelspec:
- display_name: py4ds2e
- language: python
- name: python3
----
-(workflow-help)=
-# Postscript: Getting Further Help
+# Postscript: Getting Further Help {#sec-workflow-help}
This book is not an island; there is no single resource that will allow you to master Python for Data Science. As you begin to apply the techniques described in this book to your own data, you will soon find questions that we do not answer. This section describes a few tips on how to get help, and to help you keep learning.
@@ -41,7 +29,7 @@ A good reprex makes it easier for other people to help you, and often you'll fig
- First, you need to make your code reproducible. This means that you need to capture everything, i.e., include any packages you used and create all necessary objects. The easiest way to make sure you've done this is to use the [**watermark**](https://github.com/rasbt/watermark) package alongside whatever else you are doing:
-```{code-cell} ipython3
+```{python}
import pandas as pd
import numpy as np
import watermark.watermark as watermark
@@ -66,7 +54,7 @@ There are several things you need to include to make your example reproducible:
- **Data**: as others won't be able to easily download the data you're working with, it's often best to create a small amount of data from code that still have the same problem as you're finding with your actual data. Between **numpy** and **pandas**, it's quite easy to generate data from code; here's an example:
-```{code-cell} ipython3
+```{python}
df = pd.DataFrame(
data=np.reshape(range(36), (6, 6)),
index=["a", "b", "c", "d", "e", "f"],
diff --git a/workflow-packages-and-environments.md b/workflow-packages-and-environments.qmd
similarity index 88%
rename from workflow-packages-and-environments.md
rename to workflow-packages-and-environments.qmd
index 47de523..b4f6464 100644
--- a/workflow-packages-and-environments.md
+++ b/workflow-packages-and-environments.qmd
@@ -1,16 +1,4 @@
----
-jupytext:
- formats: md:myst
- text_representation:
- extension: .md
- format_name: myst
-kernelspec:
- display_name: python3
- language: python
- name: python3
----
-(workflow-packages-and-environments)=
-# Workflow: Packages and Environments
+# Workflow: Packages and Environments {#sec-workflow-packages-and-environments}
In this chapter, you're going to learn about packages and how to install them plus virtual coding environments that keep your packages isolated and your projects reproducible.
@@ -20,7 +8,7 @@ In this chapter, you're going to learn about packages and how to install them pl
Packages (also called libraries) are key to extending the functionality of Python. It won't be long before you'll need to install some. There are packages for geoscience, for building websites, for analysing genetic data, for economics—pretty much for anything you can think of. Packages are typically not written by the core maintainers of the Python language but by enthusiasts, firms, researchers, academics, all sorts! Because anyone can write packages, they vary widely in their quality and usefulness. There are some that you'll be seeing them again and again.
-
+
The three Python packages **numpy**, **pandas**, and **maplotlib**, which respectively provide numerical, data analysis, and plotting functionality, are ubiquitous. So many scripts begin by importing all three of them, as in the tweet above!
@@ -34,19 +22,19 @@ When you issue an install command for a specific package, it is automatically do
### The Command Line in Brief
-The *terminal* or *command line* or sometimes the *command prompt* was labelled 4 in the screenshot of Visual Studio Code from the chapter on {ref}`introduction`. The terminal is a text-based way to issue all kinds of commands to your computer (not just Python commands) and knowing a little bit about it is really useful for coding (and more) because managing packages, environments (which we haven't yet discussed), and version control (ditto) can all be done via the terminal. We'll come to these in due course in the chapter on {ref}`command-line`, but for now, a little background on what the terminal is and what it does.
+The *terminal* or *command line* or sometimes the *command prompt* was labelled 4 in the screenshot of Visual Studio Code from the chapter on @sec-introduction. The terminal is a text-based way to issue all kinds of commands to your computer (not just Python commands) and knowing a little bit about it is really useful for coding (and more) because managing packages, environments (which we haven't yet discussed), and version control (ditto) can all be done via the terminal. We'll come to these in due course in the chapter on @sec-command-line, but for now, a little background on what the terminal is and what it does.
-```{note}
+::: {.callout-note}
To open up the command line within Visual Studio Code, use the ⌃ + \` keyboard shortcut (Mac) or ctrl + \` (Windows/Linux), or click "View > Terminal".
If you want to open up the command line independently of Visual Studio Code, search for "Terminal" on Mac and Linux, and "Powershell" on Windows.
-```
+:::
Firstly, everything you can do by clicking on icons to launch programmes on your computer, you can also do via the terminal, also known as the command line. For many programmes, a lot of their functionality can be accessed using the command line, and other programmes *only* have a command line interface (CLI), including some that are used for data science.
-```{tip}
+::: {.callout-tip}
The command line interacts with your operating system and is used to create, activate, or change Python installations.
-```
+:::
Use Visual Studio Code to open a terminal window by clicking Terminal -> New Terminal on the list of commands at the very top of the window. If you have installed uv on your computer, your terminal should look something like this as your 'command prompt':
@@ -76,9 +64,9 @@ To install extra Python packages, the default and easiest way is to use `uv add
`uv add ...` will install packages into the special Python environment in your current folder (it sits in a subdirectory called ".venv" which will be hidden by default on most systems.) It's really helpful and good practice to have one Python environment per project, and **uv** does this automatically for you.
-```{admonition} Exercise
+::: {.callout-tip title="Exercise"}
Try installing the **matplotlib**, **pandas**, **statsmodels**, and **skimpy** packages using `uv add`.
-```
+:::
### Using Packages
@@ -102,7 +90,7 @@ Many programming languages now come with an option to install packages and a ver
You can see all of the packages in the environment created in your current folder by running `uv pip list` on the command line. Here's an example of looking at the installed packages within this very book, filtering them just to the ones beginning with "s".
-```{code-cell} bash
+```{bash}
uv run pip list | grep ^s
```
@@ -110,7 +98,7 @@ uv run pip list | grep ^s
You may have noticed that a file called `pyproject.toml` has been created.
-```{code-cell} ipython
+```{python}
import toml
from rich import print_json
diff --git a/workflow-style.ipynb b/workflow-style.ipynb
index 63f6078..ab2eb53 100644
--- a/workflow-style.ipynb
+++ b/workflow-style.ipynb
@@ -5,8 +5,7 @@
"id": "95f0a171",
"metadata": {},
"source": [
- "(workflow-style)=\n",
- "# Workflow: Style\n",
+ "# Workflow: Style {#sec-workflow-style}\n",
"\n",
"Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. Even as a very new programmer it's a good idea to work on your code style. Use a consistent style makes it easier for others (including future-you!) to read your work, and is particularly important if you need to get help from someone else.\n",
"\n",
@@ -28,7 +27,7 @@
"\n",
"As well as this general tip, Python has conventions on naming different kinds of variables. The naming convention for almost all objects is lower case separated by underscores, e.g. `a_variable=10` or ‘this_is_a_script.py’. This style of naming is also known as snake case. There are different naming conventions though—[Allison Horst](https://twitter.com/allison_horst) made this fantastic cartoon of the different conventions that are in use.\n",
"\n",
- " Different naming conventions. Artwork by @allison_horst.\n",
+ " Different naming conventions. Artwork by \\@allison_horst.\n",
"\n",
"There are three exceptions to the snake case convention: classes, which should be in camel case, eg `ThisIsAClass`; constants, which are in capital snake case, eg `THIS_IS_A_CONSTANT`; and packages, which are typically without spaces or underscores and are lowercase `thisisapackage`.\n",
"\n",
@@ -204,10 +203,9 @@
"\n",
"Some practical ways to apply DRY in practice are to use functions, to put functions or code that needs to be executed multiple times by multiple different scripts into another script (eg called `utilities.py`) and then import it, and to think carefully if another way of writing your code would be more concise (yet still readable).\n",
"\n",
- "```{admonition} Tip\n",
- ":class: tip\n",
+ "::: {.callout-tip title=\"Tip\"}\n",
"If you're using Visual Studio Code, you can [automatically send code into a function](https://code.visualstudio.com/docs/editor/refactoring) by right-clicking on code and using the 'Extract to method' option.\n",
- "```\n",
+ ":::\n",
"\n",
"### KISS (Keep It Simple, Stupid)\n",
"\n",
@@ -270,7 +268,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.13"
+ "version": "3.12.12"
},
"toc-showtags": true
},
diff --git a/workflow-writing-code.ipynb b/workflow-writing-code.ipynb
index 89e3572..28e2edd 100644
--- a/workflow-writing-code.ipynb
+++ b/workflow-writing-code.ipynb
@@ -4,8 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "(workflow-writing-code)=\n",
- "# Workflow: Writing Code\n",
+ "# Workflow: Writing Code {#sec-workflow-writing-code}\n",
"\n",
"In this chapter, we'll look at the different ways you can both write and run code. This is something that can be very confusing if you're just getting into coding.\n",
"\n",
@@ -79,7 +78,7 @@
"\n",
"If you get stuck with this tutorial, there's a more in-depth VS Code and Jupyter tutorial available [here](https://code.visualstudio.com/docs/python/jupyter-support).\n",
"\n",
- "Create a new file in Visual Studio Code and save it as `hello_world.ipynb`. Close the file and re-open it. The notebook interface should automatically load and you'll see options to create cells with plus signs labelled 'Code' and 'Markdown'. A cell is an independent chunk of either code or text. Text cells have markdown in them, a lightweight language for creating text outputs that you will find out more about in {ref}`markdown`. For now, create a markdown cell containing the following:\n",
+ "Create a new file in Visual Studio Code and save it as `hello_world.ipynb`. Close the file and re-open it. The notebook interface should automatically load and you'll see options to create cells with plus signs labelled 'Code' and 'Markdown'. A cell is an independent chunk of either code or text. Text cells have markdown in them, a lightweight language for creating text outputs that you will find out more about in @sec-markdown. For now, create a markdown cell containing the following:\n",
"\n",
"```markdown\n",
"# This is a title\n",
@@ -99,7 +98,7 @@
"\n",
"Jupyter Notebooks are versatile and popular for early exploration of ideas, especially in fields like data science. This entire book is written in a combination of Jupyter Notebooks and executable markdown (more on that in a moment). Jupyter Notebooks can easily be run in the cloud using a browser too (via Binder or Google Colab) without any prior installation. Although it's not got very much code in, the page you're reading now can be loaded into Google Colab as a Jupyter Notebook by clicking 'Colab' under the rocket icon at the top of the page.\n",
"\n",
- "One really nice feature of Jupyter Notebooks is that you can use them as the input files for **Quarto** instead of using `.qmd` files, and this opens up many export options and possibilities (like hiding some code inputs). You can find more information [here](https://quarto.org/docs/computations/python.html) (look for the guidance on Jupyter Notebooks aka `.ipynb` files) or look ahead to the chapters on {ref}`markdown` and {ref}`quarto`.\n",
+ "One really nice feature of Jupyter Notebooks is that you can use them as the input files for **Quarto** instead of using `.qmd` files, and this opens up many export options and possibilities (like hiding some code inputs). You can find more information [here](https://quarto.org/docs/computations/python.html) (look for the guidance on Jupyter Notebooks aka `.ipynb` files) or look ahead to the chapters on @sec-markdown and @sec-quarto.\n",
"\n",
"You can try a Jupyter Notebook without installing anything online at [https://jupyter.org/try](https://jupyter.org/try). Click on Try Classic Notebook for a tutorial.\n",
"\n",
@@ -128,7 +127,7 @@
"\n",
"When you have much more text combined code, even using Jupyter Notebooks can feel a bit onerous and, historically, editing the text in a notebook was a bit tedious - especially if you wanted to move cells around a lot. Markdown makes for a much more pleasant writing experience. But markdown on its own cannot execute code—but imagine you want to combine reproducibility, text, and code + code outputs: however, there is a tool called **Quarto** that allows you to do this by adding executable code chunks to markdown.\n",
"\n",
- "As this is a bit more of an advanced topic, and as much about communication as it is about writing code, we'll come back to how to do it in {ref}`quarto`.\n"
+ "As this is a bit more of an advanced topic, and as much about communication as it is about writing code, we'll come back to how to do it in @sec-quarto.\n"
]
}
],
@@ -137,7 +136,8 @@
"hash": "c4570b151692b3082981c89d172815ada9960dee4eb0bedb37dc10c95601d3bd"
},
"kernelspec": {
- "display_name": "Python 3.8.12 64-bit ('codeforecon': conda)",
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -150,7 +150,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.10.0"
}
},
"nbformat": 4,
diff --git a/zreferences.md b/zreferences.md
deleted file mode 100644
index 7807423..0000000
--- a/zreferences.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-jupytext:
- formats: md:myst
- text_representation:
- extension: .md
- format_name: myst
-kernelspec:
- display_name: py4ds2e
- language: python
- name: python3
----
-
-# Bibliography
-
-```{bibliography} references.bib
-:style: unsrtalpha
-```