diff --git a/.github/labels.yml b/.github/labels.yml
new file mode 100644
index 0000000..f7f83aa
--- /dev/null
+++ b/.github/labels.yml
@@ -0,0 +1,66 @@
+---
+# Labels names are important as they are used by Release Drafter to decide
+# regarding where to record them in changelog or if to skip them.
+#
+# The repository labels will be automatically configured using this file and
+# the GitHub Action https://github.com/marketplace/actions/github-labeler.
+- name: breaking
+ description: Breaking Changes
+ color: bfd4f2
+- name: bug
+ description: Something isn't working
+ color: d73a4a
+- name: build
+ description: Build System and Dependencies
+ color: bfdadc
+- name: ci
+ description: Continuous Integration
+ color: 4a97d6
+- name: dependencies
+ description: Pull requests that update a dependency file
+ color: 0366d6
+- name: documentation
+ description: Improvements or additions to documentation
+ color: 0075ca
+- name: duplicate
+ description: This issue or pull request already exists
+ color: cfd3d7
+- name: enhancement
+ description: New feature or request
+ color: a2eeef
+- name: github_actions
+ description: Pull requests that update Github_actions code
+ color: "000000"
+- name: good first issue
+ description: Good for newcomers
+ color: 7057ff
+- name: help wanted
+ description: Extra attention is needed
+ color: 008672
+- name: invalid
+ description: This doesn't seem right
+ color: e4e669
+- name: performance
+ description: Performance
+ color: "016175"
+- name: python
+ description: Pull requests that update Python code
+ color: 2b67c6
+- name: question
+ description: Further information is requested
+ color: d876e3
+- name: refactoring
+ description: Refactoring
+ color: ef67c4
+- name: removal
+ description: Removals and Deprecations
+ color: 9ae7ea
+- name: style
+ description: Style
+ color: c120e5
+- name: testing
+ description: Testing
+ color: b1fc6f
+- name: wontfix
+ description: This will not be worked on
+ color: ffffff
diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml
new file mode 100644
index 0000000..7a04410
--- /dev/null
+++ b/.github/release-drafter.yml
@@ -0,0 +1,29 @@
+categories:
+ - title: ":boom: Breaking Changes"
+ label: "breaking"
+ - title: ":rocket: Features"
+ label: "enhancement"
+ - title: ":fire: Removals and Deprecations"
+ label: "removal"
+ - title: ":beetle: Fixes"
+ label: "bug"
+ - title: ":racehorse: Performance"
+ label: "performance"
+ - title: ":rotating_light: Testing"
+ label: "testing"
+ - title: ":construction_worker: Continuous Integration"
+ label: "ci"
+ - title: ":books: Documentation"
+ label: "documentation"
+ - title: ":hammer: Refactoring"
+ label: "refactoring"
+ - title: ":lipstick: Style"
+ label: "style"
+ - title: ":package: Dependencies"
+ labels:
+ - "dependencies"
+ - "build"
+template: |
+ ## Changes
+
+ $CHANGES
diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml
new file mode 100644
index 0000000..465fa28
--- /dev/null
+++ b/.github/workflows/labeler.yml
@@ -0,0 +1,33 @@
+name: labeler
+
+on:
+ push:
+ branches:
+ - 'main'
+ paths:
+ - '.github/labels.yml'
+ - '.github/workflows/labels.yml'
+ pull_request:
+ paths:
+ - '.github/labels.yml'
+ - '.github/workflows/labels.yml'
+
+jobs:
+ labeler:
+ runs-on: ubuntu-latest
+ permissions:
+ contents: read
+ issues: write
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ - name: Run Labeler
+ uses: crazy-max/ghaction-github-labeler@v5
+ with:
+ skip-delete: true
+ github-token: ${{ secrets.GITHUB_TOKEN }}
+ yaml-file: .github/labels.yml
+ dry-run: ${{ github.event_name == 'pull_request' }}
+ exclude: |
+ help*
+ *issue
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..bf9bd54
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,70 @@
+name: Release
+
+permissions:
+ contents: write
+ pages: write
+
+on:
+ push:
+ branches:
+ - main
+
+jobs:
+ release:
+ name: Release
+ runs-on: ubuntu-latest
+ steps:
+ - name: Check out the repository
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 2
+
+ - name: Set up Python
+ uses: actions/setup-python@v5.3.0
+ with:
+ python-version: "3.10"
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
+ with:
+ # Install a specific version of uv.
+ version: "0.5.2"
+
+ - name: Check if there is a parent commit
+ id: check-parent-commit
+ run: |
+ echo "::set-output name=sha::$(git rev-parse --verify --quiet HEAD^)"
+
+ - name: Detect and tag new version
+ id: check-version
+ if: steps.check-parent-commit.outputs.sha
+ uses: salsify/action-detect-and-tag-new-version@v2.0.3
+ with:
+ version-command: |
+ uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version
+
+ - name: Bump version for developmental release
+ if: "! steps.check-version.outputs.tag"
+ run: |
+ uv run version_bumper.py &&
+ version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version) &&
+ uvx --from=toml-cli toml set --toml-path=pyproject.toml project.version $version.dev.$(date +%s)
+
+ - name: Install dependencies
+ run: |
+ uv sync
+
+ - name: Build book
+ run: uv run jupyter-book build . --verbose
+
+ - name: Publish
+ if: steps.check-version.outputs.tag
+ run: uv run ghp-import -n -p -f _build/html
+
+ - name: Publish the release notes
+ uses: release-drafter/release-drafter@v6.0.0
+ with:
+ publish: ${{ steps.check-version.outputs.tag != '' }}
+ tag: ${{ steps.check-version.outputs.tag }}
+ env:
+ GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
new file mode 100644
index 0000000..73d860e
--- /dev/null
+++ b/.github/workflows/tests.yml
@@ -0,0 +1,55 @@
+name: tests
+
+on:
+ - pull_request
+ - push
+
+jobs:
+ pre-commit:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
+ with:
+ python-version: '3.10'
+ - uses: pre-commit/action@v3.0.1
+
+ build:
+ runs-on: ubuntu-latest
+ steps:
+ - name: Check out the repository
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 2
+
+ - name: Set up Python
+ uses: actions/setup-python@v5.3.0
+ with:
+ python-version: "3.10"
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
+ with:
+ # Install a specific version of uv.
+ version: "0.5.2"
+
+ - name: set timezone
+ run: |
+ TZ="Europe/London" &&
+ sudo ln -snf /usr/share/zoneinfo/$TZ /etc/localtime
+
+ - name: install linux deps
+ 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
+
+ - name: success
+ run: |
+ echo "Success in building book without errors!"
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 115401e..ccf8257 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -13,26 +13,18 @@ repos:
entry: nbstripout
language: python
types: [jupyter]
- - repo: https://github.com/psf/black
- rev: 22.3.0
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ # Ruff version.
+ rev: v0.4.1
hooks:
- - id: black
- name: black
- description: 'applies black formatter to .py files'
- entry: black
- language: python
- types: [python]
- - id: black-jupyter
- name: black-jupyter
- description:
- "Black: The uncompromising Python code formatter (with Jupyter Notebook support)"
- entry: black
- language: python
- minimum_pre_commit_version: 2.9.2
- require_serial: true
+ # Run the linter.
+ - id: ruff
+ types_or: [python, pyi, jupyter]
+ args: [ --fix ]
+ - id: ruff
+ types_or: [python, pyi, jupyter]
+ name: sort imports with ruff
+ args: [--select, I, --fix]
+ # Run the formatter.
+ - id: ruff-format
types_or: [python, pyi, jupyter]
- additional_dependencies: [".[jupyter]"]
- - repo: https://github.com/pycqa/flake8
- rev: 3.9.2 # pick a git hash / tag to point to
- hooks:
- - id: flake8
diff --git a/Dockerfile b/Dockerfile
index 45bec85..7fb4283 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,38 +1,31 @@
-# DOCKERFILE for Coding for Economists
-# Please note that you will need a lot of RAM dedicated to Docker to build this image (15Gb+, change this in your docker settings)
+# DOCKERFILE for Python for Data Science
-# Set base image (this also loads the Alpine Linux operating system)
-FROM continuumio/miniconda3:4.10.3-alpine
+# Set base image
+FROM python:3.10-slim-bookworm
WORKDIR /app
# Update Linux package list and install some key libraries, including latex
-RUN apk update && apk add openssl graphviz \
- nano texlive alpine-sdk build-base graphviz-dev \
- bash
+RUN apt-get -y update && apt-get install -y openssl graphviz \
+ nano texlive graphviz-dev \
+ bash build-essential git
# change default shell from ash to bash
RUN sed -i -e "s/bin\/ash/bin\/bash/" /etc/passwd
-# Install mamba
-RUN conda install mamba -n base -c conda-forge
+# Create the environment:
+COPY uv.lock .
+COPY pyproject.toml .
-# Ensure pip's setuptools is latest
-RUN pip install --upgrade setuptools wheel
+# Install uv
+COPY --from=ghcr.io/astral-sh/uv:0.5.14 /uv /bin/uv
-# Create the environment:
-COPY environment.yml .
# Install everything at once:
-RUN mamba env create -f environment.yml
-# Do a debug or incremental env install (builds quickly):
-# RUN mamba create -n python4DS -c conda-forge python=3.9 numpy pandas -y
-
-# Make RUN commands use the new environment:
-SHELL ["conda", "run", "-n", "py4ds2e", "/bin/bash", "-c"]
+RUN uv sync --frozen
-RUN mamba list
+RUN uv pip list
# Copy the current directory contents into the container at /app
COPY . /app
-RUN echo "Success building the python4DS container!"
+RUN echo "Success building the Python4DS container!"
diff --git a/_config.yml b/_config.yml
index cf1820e..5e927d1 100644
--- a/_config.yml
+++ b/_config.yml
@@ -4,13 +4,16 @@
title: Python for Data Science
author: The Py4DS Community
logo: logo.png
-
+exclude_patterns: [_build, Thumbs.db, .DS_Store, "**.ipynb_checkpoints", ".venv"]
# 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]
+ exclude_patterns: ["README.md"]
+ allow_errors: false
+ nb_output_stderr: show
+
# Define the name of the latex output file for PDF builds
latex:
@@ -32,9 +35,19 @@ html:
use_issues_button: true
use_repository_button: true
favicon: "favicon.ico" # A path to a favicon image
- google_analytics_id: "G-LXJC37BJVX" # A GA id that can be used to track book views.
+ 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"
\ No newline at end of file
+ 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/categorical-data.ipynb b/categorical-data.ipynb
index 7714e58..7a670c9 100644
--- a/categorical-data.ipynb
+++ b/categorical-data.ipynb
@@ -25,8 +25,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
diff --git a/communicate-plots.ipynb b/communicate-plots.ipynb
index a2041c1..01b7435 100644
--- a/communicate-plots.ipynb
+++ b/communicate-plots.ipynb
@@ -37,8 +37,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -52,10 +52,9 @@
"metadata": {},
"outputs": [],
"source": [
- "from lets_plot import *\n",
- "from lets_plot.mapping import as_discrete\n",
- "import pandas as pd\n",
"import numpy as np\n",
+ "import pandas as pd\n",
+ "from lets_plot import *\n",
"\n",
"LetsPlot.setup_html()"
]
@@ -1326,9 +1325,6 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
- },
"jupytext": {
"cell_metadata_filter": "-all",
"encoding": "# -*- coding: utf-8 -*-",
@@ -1336,7 +1332,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -1350,7 +1346,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.13"
+ "version": "3.10.0"
},
"toc-showtags": true
},
diff --git a/data-import.ipynb b/data-import.ipynb
index c6e0fdd..6b2d13b 100644
--- a/data-import.ipynb
+++ b/data-import.ipynb
@@ -181,8 +181,6 @@
"metadata": {},
"outputs": [],
"source": [
- "import numpy as np\n",
- "\n",
"students[\"age\"] = students[\"age\"].replace(\"five\", 5)\n",
"students[\"age\"]"
]
diff --git a/data-tidy.ipynb b/data-tidy.ipynb
index b66ee93..28456c1 100644
--- a/data-tidy.ipynb
+++ b/data-tidy.ipynb
@@ -29,8 +29,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
diff --git a/data-visualise.ipynb b/data-visualise.ipynb
index 50a8909..b00e19e 100644
--- a/data-visualise.ipynb
+++ b/data-visualise.ipynb
@@ -68,9 +68,8 @@
"metadata": {},
"outputs": [],
"source": [
- "import pandas as pd\n",
- "from palmerpenguins import load_penguins\n",
"from lets_plot import *\n",
+ "from palmerpenguins import load_penguins\n",
"\n",
"LetsPlot.setup_html()"
]
diff --git a/databases.ipynb b/databases.ipynb
index ddc50af..4250f82 100644
--- a/databases.ipynb
+++ b/databases.ipynb
@@ -453,47 +453,39 @@
"import ibis\n",
"\n",
"ibis.options.interactive = True\n",
- "connection = ibis.sqlite.connect(Path(\"data/Chinook.sqlite\"))\n",
+ "connection = ibis.sqlite.connect(\"data/Chinook.sqlite\")\n",
"track = connection.table(\"track\")\n",
"track.head()"
]
},
{
"cell_type": "markdown",
- "id": "965cc983",
+ "id": "6dcd7d71",
"metadata": {},
"source": [
- "As an aside, you can list the available tables using"
+ "Okay, now let's reproduce the following query: \"SELECT albumid, AVG(milliseconds)/1e3/60 FROM track GROUP BY albumid ORDER BY AVG(milliseconds) ASC LIMIT 5;\". We'll use a groupby, a mutate (which you can think of like **pandas**' assign statement), a sort, and then `limit()` to only show the first five entries."
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "58369c80",
+ "id": "5e5a482b",
"metadata": {},
"outputs": [],
"source": [
- "connection.list_tables()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6dcd7d71",
- "metadata": {},
- "source": [
- "Okay, now let's reproduce the following query: \"SELECT albumid, AVG(milliseconds)/1e3/60 FROM track GROUP BY albumid ORDER BY AVG(milliseconds) ASC LIMIT 5;\". We'll use a groupby, a mutate (which you can think of like **pandas**' assign statement), a sort, and then `limit()` to only show the first five entries."
+ "track.group_by(\"AlbumId\").mutate(\n",
+ " mean_mins_track=track.Milliseconds.mean() / 1e3 / 60\n",
+ ").order_by(\"mean_mins_track\").limit(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
- "id": "5e5a482b",
+ "id": "39c80365",
"metadata": {},
"outputs": [],
"source": [
- "track.group_by(\"AlbumId\").mutate(\n",
- " mean_mins_track=track.Milliseconds.mean() / 1e3 / 60\n",
- ").order_by(\"mean_mins_track\").limit(5)"
+ "track.group_by(\"AlbumId\").mutate(mean_mins_track=track.Milliseconds.mean() / 1e3 / 60)"
]
},
{
@@ -788,7 +780,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": "Python 3.9.12 ('python4DS')",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -802,14 +794,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.10.0"
},
- "toc-showtags": true,
- "vscode": {
- "interpreter": {
- "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
- }
- }
+ "toc-showtags": true
},
"nbformat": 4,
"nbformat_minor": 5
diff --git a/dates-and-times.ipynb b/dates-and-times.ipynb
index ca502af..3f423d7 100644
--- a/dates-and-times.ipynb
+++ b/dates-and-times.ipynb
@@ -37,8 +37,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -513,7 +513,7 @@
"metadata": {},
"outputs": [],
"source": [
- "pd.date_range(\"2018-01-01\", periods=3, freq=\"H\")"
+ "pd.date_range(\"2018-01-01\", periods=3, freq=\"h\")"
]
},
{
@@ -531,7 +531,7 @@
"metadata": {},
"outputs": [],
"source": [
- "dti = pd.date_range(\"2018-01-01\", periods=3, freq=\"H\").tz_localize(\"UTC\")\n",
+ "dti = pd.date_range(\"2018-01-01\", periods=3, freq=\"h\").tz_localize(\"UTC\")\n",
"dti.tz_convert(\"US/Pacific\")"
]
},
@@ -552,10 +552,11 @@
"source": [
"import requests\n",
"\n",
- "url = \"https://api.ons.gov.uk/timeseries/JP9P/dataset/UNEM/data\"\n",
+ "url = \"https://api.beta.ons.gov.uk/v1/data?uri=/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/timeseries/jp9z/lms/previous/v108\"\n",
"\n",
"# Get the data from the ONS API:\n",
- "df = pd.DataFrame(pd.json_normalize(requests.get(url).json()[\"months\"]))\n",
+ "json_data = requests.get(url).json()\n",
+ "df = pd.DataFrame(pd.json_normalize(json_data[\"months\"]))\n",
"df[\"value\"] = pd.to_numeric(df[\"value\"])\n",
"df = df[[\"date\", \"value\"]]\n",
"df = df.rename(columns={\"value\": \"Vacancies (ICT), thousands\"})\n",
@@ -841,7 +842,7 @@
"metadata": {},
"outputs": [],
"source": [
- "df.resample(\"5A\").agg([\"mean\", \"std\"]).head()"
+ "df.resample(\"5YE\").agg([\"mean\", \"std\"]).head()"
]
},
{
@@ -902,6 +903,16 @@
"xf = xf.sort_index()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab1efae3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.rcParams[\"axes.prop_cycle\"]"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -909,16 +920,18 @@
"metadata": {},
"outputs": [],
"source": [
+ "from itertools import cycle\n",
+ "\n",
"fig, ax = plt.subplots()\n",
"data = xf.iloc[:10, 3]\n",
- "cycle = ax._get_lines.prop_cycler\n",
+ "colour_wheel = cycle(plt.rcParams[\"axes.prop_cycle\"])\n",
"\n",
"data.asfreq(\"D\").plot(ax=ax, marker=\"o\", linestyle=\"None\", zorder=3)\n",
"data.asfreq(\"D\", method=\"bfill\").plot(\n",
- " ax=ax, style=\"-.o\", lw=1, color=next(cycle)[\"color\"]\n",
+ " ax=ax, style=\"-.o\", lw=1, color=next(colour_wheel)[\"color\"]\n",
")\n",
"data.asfreq(\"D\", method=\"ffill\").plot(\n",
- " ax=ax, style=\"--o\", lw=1, color=next(cycle)[\"color\"]\n",
+ " ax=ax, style=\"--o\", lw=1, color=next(colour_wheel)[\"color\"]\n",
")\n",
"ax.set_ylabel(\"Close ($)\")\n",
"ax.legend([\"original\", \"back-fill\", \"forward-fill\"]);"
@@ -1069,9 +1082,6 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
- },
"jupytext": {
"cell_metadata_filter": "-all",
"encoding": "# -*- coding: utf-8 -*-",
@@ -1079,7 +1089,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -1093,7 +1103,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.10.0"
},
"toc-showtags": true
},
diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index 52d5278..0000000
--- a/environment.yml
+++ /dev/null
@@ -1,42 +0,0 @@
-
-name: py4ds2e
-channels:
- - conda-forge
-dependencies:
- - jupyter
- - numpy
- - pandas
- - pip
- - python=3.10
- - pyyaml
- - scipy
- - statsmodels
- - yaml
- - pycodestyle
- - autopep8
- - pandas-datareader
- - pandasdmx
- - jupyter-book
- - pytest
- - pre-commit
- - jupyterlab
- - nbstripout
- - ghp-import
- - pip
- - black
- - black-jupyter
- - beautifulsoup4
- - geopandas
- - pip:
- - skimpy
- - pyarrow
- - watermark
- - graphviz
- - openpyxl
- - sqlmodel
- - ibis-framework
- - lets-plot
- - polars
- - palmerpenguins
- - pandas-profiling
- - rich
\ No newline at end of file
diff --git a/exploratory-data-analysis.ipynb b/exploratory-data-analysis.ipynb
index 2694bdd..9745396 100644
--- a/exploratory-data-analysis.ipynb
+++ b/exploratory-data-analysis.ipynb
@@ -34,11 +34,11 @@
"metadata": {},
"outputs": [],
"source": [
- "from skimpy import skim\n",
"import pandas as pd\n",
- "from pandas.api.types import CategoricalDtype\n",
"from lets_plot import *\n",
"from lets_plot.mapping import as_discrete\n",
+ "from pandas.api.types import CategoricalDtype\n",
+ "from skimpy import skim\n",
"\n",
"LetsPlot.setup_html()"
]
@@ -54,8 +54,8 @@
},
"outputs": [],
"source": [
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
diff --git a/functions.ipynb b/functions.ipynb
index d3c0b25..4e06889 100644
--- a/functions.ipynb
+++ b/functions.ipynb
@@ -167,8 +167,8 @@
"metadata": {},
"outputs": [],
"source": [
- "import pandas as pd\n",
"import numpy as np\n",
+ "import pandas as pd\n",
"\n",
"df = pd.DataFrame(np.random.normal(size=(10, 4)), columns=[\"a\", \"b\", \"c\", \"d\"])\n",
"\n",
@@ -210,14 +210,14 @@
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"id": "a16d6dd0",
"metadata": {},
- "outputs": [],
"source": [
+ "```python\n",
"def rescale(x):\n",
- " return (x - x.min()) / (x.max() - x.min())"
+ " return (x - x.min()) / (x.max() - x.min())\n",
+ "```"
]
},
{
diff --git a/introduction.ipynb b/introduction.ipynb
index 0dad97b..c4c686a 100644
--- a/introduction.ipynb
+++ b/introduction.ipynb
@@ -167,9 +167,6 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
- },
"jupytext": {
"cell_metadata_filter": "-all",
"encoding": "# -*- coding: utf-8 -*-",
@@ -177,7 +174,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -191,7 +188,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.10.0"
},
"toc-showtags": true
},
diff --git a/iteration.ipynb b/iteration.ipynb
index 72227f0..defbd71 100644
--- a/iteration.ipynb
+++ b/iteration.ipynb
@@ -37,8 +37,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -480,8 +480,8 @@
"metadata": {},
"outputs": [],
"source": [
- "import pandas as pd\n",
"import numpy as np\n",
+ "import pandas as pd\n",
"\n",
"df = pd.DataFrame(np.random.normal(size=(6, 4)), columns=[\"a\", \"b\", \"c\", \"d\"])\n",
"df"
diff --git a/joins.ipynb b/joins.ipynb
index 5e29990..2bb4f52 100644
--- a/joins.ipynb
+++ b/joins.ipynb
@@ -27,8 +27,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -72,16 +72,18 @@
"source": [
"import pandas as pd\n",
"\n",
- "base_url = \"http://www.stata-press.com/data/r14/\"\n",
+ "base_url = (\n",
+ " \"https://github.com/aeturrell/coding-for-economists/raw/refs/heads/main/data/\"\n",
+ ")\n",
"state_codes = [\"ca\", \"il\"]\n",
"end_url = \"pop.dta\"\n",
"\n",
- "# This grabs the two data frames, one for each state\n",
+ "# This grabs the two dataframes, one for each state\n",
"list_of_state_dfs = [pd.read_stata(base_url + state + end_url) for state in state_codes]\n",
- "# Show example of first entry in list of data frames\n",
+ "# Show example of first entry in list of dataframes\n",
"print(list_of_state_dfs[0])\n",
"\n",
- "# Concatenate the list of data frames\n",
+ "# Concatenate the list of dataframes\n",
"df = pd.concat(list_of_state_dfs, keys=state_codes, axis=0)\n",
"df"
]
diff --git a/logo.py b/logo.py
index 84aaf19..5e0bdf3 100644
--- a/logo.py
+++ b/logo.py
@@ -3,7 +3,6 @@
from matplotlib.font_manager import FontProperties
from matplotlib.patheffects import Stroke
-
text = "Python4DS"
cmap = plt.cm.cividis
diff --git a/missing-values.ipynb b/missing-values.ipynb
index 4b5c084..c39b798 100644
--- a/missing-values.ipynb
+++ b/missing-values.ipynb
@@ -26,8 +26,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -67,8 +67,8 @@
"metadata": {},
"outputs": [],
"source": [
- "import pandas as pd\n",
"import numpy as np\n",
+ "import pandas as pd\n",
"\n",
"df = pd.DataFrame([5, 27.3, np.nan, -16], columns=[\"numbers\"])\n",
"df"
diff --git a/numbers.ipynb b/numbers.ipynb
index 461ed14..5fccfef 100644
--- a/numbers.ipynb
+++ b/numbers.ipynb
@@ -30,8 +30,8 @@
},
"outputs": [],
"source": [
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
diff --git a/prerequisites.ipynb b/prerequisites.ipynb
index c89ffc5..71e9913 100644
--- a/prerequisites.ipynb
+++ b/prerequisites.ipynb
@@ -41,11 +41,11 @@
"\n",
"### A Python interpreter\n",
"\n",
- "Python is both a programming language that you can read, and a language that computers can read, interpret, and then carry out instructions based on. For your computer to be able to read and execute Python code, you will need to get Python installed on your computer. There are lots of ways to install a Python \"interpreter\" on your computer, but this book recommends the *Anaconda distribution* of Python for its flexibility and simplicity.\n",
+ "Python is both a programming language that you can read, and a language that computers can read, interpret, and then carry out instructions based on. For your computer to be able to read and execute Python code, you will need to get Python installed on your computer. There are lots of ways to install a Python \"interpreter\" on your computer, but this book recommends the *uv distribution* of Python for its flexibility and simplicity.\n",
"\n",
"### Packages\n",
"\n",
- "A Python **package** is a collection of functions, data, and documentation that extends the capabilities of an installed version of Python. Using packages is key to most data science because most of the functionality we'll need comes from extra packages. You'll see statetments like `import numpy as np` at the start of many Python code scripts—these are instructions to use an installed package (here one called `numpy`) and to give it a shortened name (`np`, for convenience) in the rest of the script. The functions in the `numpy` package are then accessed through syntax like `np.`; for example, you can take logs with `np.log(x)` where `x` is a variable containing a number. You need only install packages once. \n",
+ "A Python **package** is a collection of functions, data, and documentation that extends the capabilities of an installed version of Python. Using packages is key to most data science because most of the functionality we'll need comes from extra packages. You'll see statetments like `import numpy as np` at the start of many Python code scripts—these are instructions to use an installed package (here one called `numpy`) and to give it a shortened name (`np`, for convenience) in the rest of the script. The functions in the `numpy` package are then accessed through syntax like `np.`; for example, you can take logs with `np.log(x)` where `x` is a variable containing a number. You need only install packages once.\n",
"\n",
"### Typical workflow\n",
"\n",
@@ -76,16 +76,28 @@
"\n",
"### Installing Python\n",
"\n",
- "To download and install Python, we'll use the Anaconda \"distribution\" of Python, which is available on all major operating systems. To install it, follow the instructions below or watch this video on *[how to install Python using the Anaconda distribution of Python](https://www.youtube.com/watch?v=ZWQwGR5ppnk)*.\n",
+ "### Installing Python\n",
"\n",
- "VIDEO \n",
+ "To download and install Python, we'll use the uv \"distribution\" of Python, which is available on all major operating systems. To install it, follow the instructions at [this website](https://docs.astral.sh/uv/getting-started/installation/#installation-methods). Unlike installing normal programmes, we're going to use the *command line* to install Python. Linux, Mac, and Windows all have built-in command lines: search for *Terminal* on Mac or Linux, and *Powershell* on Windows. These apps will bring up boxes that you can type commands in. As of the time of writing, the commands are:\n",
"\n",
- "Download the individual edition of the [Anaconda distribution](https://www.anaconda.com/) of Python for your operating system and install it. This will provide you with a Python installation and a host of the most useful libraries. If you get stuck, there are more detailed instructions available for installing the Anaconda distribution of Python [on Windows](https://docs.anaconda.com/anaconda/install/windows/), [on Mac](https://docs.anaconda.com/anaconda/install/mac-os/), and [on Linux](https://docs.anaconda.com/anaconda/install/linux/).\n",
+ "```bash\n",
+ "curl -LsSf https://astral.sh/uv/install.sh | sh\n",
+ "```\n",
"\n",
- "You can confirm that you've set up Anaconda correctly by following the [verify installation instructions](https://docs.anaconda.com/free/anaconda/install/verify-install/#conda) on the Anaconda website.\n",
+ "for Linux and Mac, and\n",
"\n",
- "```{note}\n",
- "If you're using Windows, you can check if Anaconda has installed properly by opening the 'Anaconda prompt' (a special text-based way to issue commands to your computer) and type `where python`. You should see a path rendered as text in the prompt that includes \"Anaconda3\", for example something like `C:\\Users\\\\Anaconda3\\...`. On Mac and Linux you may need to run `conda init` on your command line to activate your Anaconda Python environment. You can check you've got the right Python with `which python`, which should result in a message back saying `/Users//opt/anaconda3/bin/python`.\n",
+ "```powershell\n",
+ "powershell -ExecutionPolicy ByPass -c \"irm https://astral.sh/uv/install.ps1 | iex\"\n",
+ "```\n",
+ "\n",
+ "for Windows. Hit return to execute the commands.\n",
+ "\n",
+ "Once you have installed uv, you can check it's installed properly by running `uv --version`. You should see a message pop up that says \"uv\" and then the latest version number.\n",
+ "\n",
+ "We didn't actually download Python yet--we downloaded **uv**, which is going to manage our Python environment. So next we need to run\n",
+ "\n",
+ "```bash\n",
+ "uv python install\n",
"```"
]
},
@@ -101,6 +113,8 @@
"\n",
"Download and install Visual Studio Code. If you need some help, there is a video below that will walk you through downloading and installing Visual Studio Code, and then using it to run Python code in both scripts and in notebooks. We'll go through these instructions in detail in the rest of this chapter.\n",
"\n",
+ "Note that if you are testing out notebooks and the interactive window, you will need to create a new folder and work in it in Visual Studio Code. Once you've opened a folder explicitly in VS Code, open the command line within Visual Studio Code then run `uv init` to create a Python installation, and `uv add jupyter` to provide this nascent Python environment with the means to create interactive windows.\n",
+ "\n",
"VIDEO \n",
"\n",
"*[How to install Visual Studio Code and use it to run Python code](https://www.youtube.com/watch?v=1kKTYsQdaPw)*"
@@ -123,7 +137,9 @@
"\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",
- "You will find that a new page loads with several panels in. This is an online version of Visual Studio Code that works much like if you had installed it on your own computer. It will already have a version of Python installed—you can check which one by running `python --version` in the terminal. The terminal is usually found in the lowest panel of Visual Studio Code, and, in Codespaces, will typically display a welcome message."
+ "You will find that a new page loads with several panels in. This is an online version of Visual Studio Code that works much like if you had installed it on your own computer. It will already have a version of Python installed—you can check which one by running `python --version` in the terminal. The terminal is usually found in the lowest panel of Visual Studio Code, and, in Codespaces, will typically display a welcome message.\n",
+ "\n",
+ "However, you should note that the distribution of Python on Codespaces might be different to the **uv** one we're using in the book. But you can install **uv** in your codespaces machine and follow the other instructions from the \"Installing Python\" section above."
]
},
{
@@ -170,7 +186,9 @@
"\n",
"In Visual Studio Code, click on the \"Explorer\" symbol (some files on the left-hand side of the screen) to bring up a file explorer. Check you're in a good location on your computer to try things out and, if not, change the folder you're in using File -> Open Folder until you're happy.\n",
"\n",
- "Now, still with the explorer panel open, click on the symbol that looks like a blank piece of paper with a \"+\" sign on it. This will create a new file, and your cursor should move to name it. Name it `hello_world.py`. The file extension, `.py`, is very important as it implicitly tells Visual Studio Code that this is a Python script.\n",
+ "Now open up a terminal *within* Visual Studio Code. Make sure it's the same type of terminal that you installed uv in, eg use Powershell on Windows (you can select to use different types of terminals.) The shortcut to open the terminal panel is Ctrl + \\` on Linux and Windows, and Cmd + \\` on Mac. The terminal panel appears along the bottom of VS Code. Run `uv init` in the terminal to initiate an installation of Python. **This Python installation only lives in your current folder**. Then, to install the package we need for this section run `uv add jupyer`. (We will cover the terminal and installing packages in much more detail shortly.)\n",
+ "\n",
+ "Now, still with the explorer panel open on the left hand side, click on the symbol that looks like a blank piece of paper with a \"+\" sign on it. This will create a new file, and your cursor should move to name it. Name it `hello_world.py`. The file extension, `.py`, is very important as it implicitly tells Visual Studio Code that this is a Python script.\n",
"\n",
"In the Visual Studio Code editor, add a single line to the file:\n",
"\n",
@@ -189,6 +207,7 @@
"```python\n",
"print(\"Hello world!\")\n",
"```\n",
+ "\n",
"```text\n",
"Hello world!\n",
"```\n",
@@ -204,7 +223,6 @@
"\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",
- "\n",
"```{admonition} Running code in the terminal instead\n",
":class: dropdown\n",
"\n",
@@ -236,7 +254,15 @@
"\n",
"We use the *terminal* or *command line* within Visual Studio Code to install additional Python packages. In the figure earlier in the Chapter, this is labelled as panel number 4.\n",
"\n",
- "To install an extra package, run `pip install **packagename**` in the command line. You could try installing the data analysispackage **polars** this way, by running `pip install polars`. We'll see how to use **polars** in later chapters, but if you want to know if it installed correctly, just look for the message saying \"Successfully installed polars\" followed by the version number.\n",
+ "To install any packages within the Python environment in the folder you're using in Visual Studio Code, enter the following in Visual Studio Code's terminal (the same place where you just ran `uv run python --version`).\n",
+ "\n",
+ "```bash\n",
+ "uv add packagename\n",
+ "```\n",
+ "\n",
+ "and hit return. In the above, `packagename` might be `pandas`, for example. 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. You could try installing the data analysispackage **polars** this way, by running `uv add polars`. We'll see how to use **polars** in later chapters, but if you want to know if it installed correctly, just look for the message saying \"Successfully installed polars\" followed by the version number.\n",
+ "\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",
"\n",
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..9798d78
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,43 @@
+[project]
+name = "python4ds"
+version = "1.0.1"
+description = "The online book that teaches you how to use Python for data science."
+readme = "README.md"
+requires-python = ">=3.10.0"
+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",
+ "nbstripout>=0.8.1",
+ "numpy>=2.2.1",
+ "openpyxl>=3.1.5",
+ "palmerpenguins>=0.1.4",
+ "pandas>=2.2.3",
+ "pandas-datareader>=0.10.0",
+ "pandas-profiling>=3.2.0",
+ "pandasdmx>=1.10.0",
+ "pip>=24.3.1",
+ "polars>=1.19.0",
+ "pre-commit>=4.0.1",
+ "pytest>=8.3.4",
+ "pyyaml>=6.0.2",
+ "rich>=13.9.4",
+ "scipy>=1.15.0",
+ "skimpy>=0.0.17",
+ "sqlalchemy>=1.4.39",
+ "sqlmodel>=0.0.22",
+ "statsmodels>=0.14.4",
+ "toml>=0.10.2",
+ "watermark>=2.5.0",
+]
+
+
+[tool.ruff.lint]
+ignore = ["F405", "F403"]
diff --git a/rectangling.ipynb b/rectangling.ipynb
index 86b34e1..afd529e 100644
--- a/rectangling.ipynb
+++ b/rectangling.ipynb
@@ -27,8 +27,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -391,12 +391,10 @@
"source": [
"import requests\n",
"\n",
- "url = \"https://api.ons.gov.uk/timeseries/MGSX/dataset/LMS/data\"\n",
+ "url = \"https://api.beta.ons.gov.uk/v1/data?uri=employmentandlabourmarket/peoplenotinwork/unemployment/timeseries/mgsx/lms/previous/v106\"\n",
"\n",
"# Get the data from the ONS API:\n",
- "json_data = requests.get(url).json()\n",
- "# There's too much data to show it all here, so just show the first 2 entries\n",
- "dict(list(json_data.items())[:2])"
+ "json_data = requests.get(url).json()"
]
},
{
@@ -404,7 +402,7 @@
"id": "051d3b4a",
"metadata": {},
"source": [
- "There's a lot here, and it's not terribly easy to read! Let's check what type we got:"
+ "Let's check what type we got:"
]
},
{
@@ -424,6 +422,24 @@
"source": [
"As expected, the JSON data has automatically been read in as a dictionary—but be wary that the fields have been read in as text rather than numbers, datetimes, and other specific data types.\n",
"\n",
+ "We could print the whole object out but that would take up a lot of space; instead let's look at a couple of entries under the \"months\" key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8cc4f408",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "json_data[\"months\"][:2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce713def",
+ "metadata": {},
+ "source": [
"#### From a File or Stream\n",
"\n",
"For this exercise, you'll need to download the JSON file 'cakes.json' from the [data folder of the repository](https://github.com/aeturrell/python4DS/tree/main/data) associated with this book and save it in a sub-folder called \"data\". We can take a peek at the data using the terminal (which is what the preceeding exclamation mark means):"
@@ -591,9 +607,6 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
- },
"jupytext": {
"cell_metadata_filter": "-all",
"encoding": "# -*- coding: utf-8 -*-",
@@ -601,7 +614,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -615,7 +628,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.10.0"
},
"toc-showtags": true
},
diff --git a/spreadsheets.ipynb b/spreadsheets.ipynb
index d18239b..9bca0db 100644
--- a/spreadsheets.ipynb
+++ b/spreadsheets.ipynb
@@ -27,8 +27,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -75,7 +75,6 @@
"outputs": [],
"source": [
"import pandas as pd\n",
- "import numpy as np\n",
"\n",
"students = pd.read_excel(\n",
" \"https://github.com/aeturrell/python4DS/raw/main/data/students.xlsx\"\n",
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+ { url = "https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 },
+]
diff --git a/version_bumper.py b/version_bumper.py
new file mode 100644
index 0000000..c5f2732
--- /dev/null
+++ b/version_bumper.py
@@ -0,0 +1,50 @@
+# /// script
+# dependencies = [
+# "toml>=0.10.2"
+# ]
+# ///
+import subprocess
+from typing import Literal
+
+import toml
+
+
+def bump_version(part: Literal["major", "minor", "patch"] = "patch") -> None:
+ file_path = "pyproject.toml"
+
+ with open(file_path, "r") as f:
+ pyproject = toml.load(f)
+
+ version = pyproject["project"]["version"]
+ major, minor, patch = map(int, version.split("."))
+
+ if part == "major":
+ major += 1
+ minor = 0
+ patch = 0
+ elif part == "minor":
+ minor += 1
+ patch = 0
+ elif part == "patch":
+ patch += 1
+ else:
+ raise ValueError("Invalid part value. Choose 'major', 'minor', or 'patch'.")
+
+ new_version = f"{major}.{minor}.{patch}"
+ subprocess.run(
+ [
+ "uvx",
+ "--from=toml-cli",
+ "toml",
+ "set",
+ "--toml-path=pyproject.toml",
+ "project.version",
+ new_version,
+ ]
+ )
+
+ print(f"Version bumped to {major}.{minor}.{patch}")
+
+
+if __name__ == "__main__":
+ bump_version()
diff --git a/vis-layers.ipynb b/vis-layers.ipynb
index e3c3303..e98fd0c 100644
--- a/vis-layers.ipynb
+++ b/vis-layers.ipynb
@@ -50,8 +50,6 @@
"source": [
"import pandas as pd\n",
"from lets_plot import *\n",
- "from lets_plot.geo_data import *\n",
- "from lets_plot.mapping import as_discrete\n",
"\n",
"LetsPlot.setup_html()"
]
@@ -920,90 +918,6 @@
" Create a visualisation of the `mpg` dataset that demonstrates it."
]
},
- {
- "cell_type": "markdown",
- "id": "353de15d",
- "metadata": {},
- "source": [
- "### Co-ordinate Systems\n",
- "\n",
- "Note that, to create maps, **lets-plot** requires **geopandas**, and we strongly recommend you install **geopandas** using `conda install geopandas`, as it has a lot of complicated dependencies.\n",
- "\n",
- "Co-ordinate systems can certainly get complicated! They're very important in mathematics and physics but, more obviously, for maps. Most of the time, we're working in *cartesian* co-ordinates where everything is mapped out by x, y, and sometimes z (usually height or colour on continuous scales). But because the Earth is not always well-represented by cartesian co-ordinates (especially as you \"zoom out\"), you often need a different co-ordinate system to make visualisations of it. We don't want to spend too long on this, because it's a big topic, but what's important for you to know here is that **lets-plot** does have the capability work with map-based co-ordinate systems.\n",
- "\n",
- "The first element of plotting any map is retrieving the geographic information. Let's grab info on the UK"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a96d3d89",
- "metadata": {},
- "outputs": [],
- "source": [
- "uk = geocode(\"state\").scope(\"United Kingdom\").get_boundaries(6)\n",
- "uk"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4b0add0f",
- "metadata": {},
- "source": [
- "Now let's pass this to `geom_map()`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "44102ffc",
- "metadata": {},
- "outputs": [],
- "source": [
- "(\n",
- " ggplot()\n",
- " + geom_map(map=uk, fill=\"gray\", color=\"white\")\n",
- " + coord_map(xlim=(-10, 6), ylim=(50, 59))\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "003be0d6",
- "metadata": {},
- "source": [
- "We can spruce this up with some colours, and with some cities!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "487eda0c",
- "metadata": {},
- "outputs": [],
- "source": [
- "names = [\"London\", \"Edinburgh\", \"Belfast\", \"Cardiff\"]\n",
- "states = [\"England\", \"Scotland\", \"Northern Ireland\", \"Wales\"]\n",
- "cities = geocode(names=names, states=states).ignore_not_found().get_centroids()\n",
- "cities"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b3cf5712",
- "metadata": {},
- "outputs": [],
- "source": [
- "(\n",
- " ggplot()\n",
- " + geom_map(aes(fill=\"found name\"), map=uk, color=\"white\")\n",
- " + coord_map(xlim=(-10, 6), ylim=(50, 59))\n",
- " + geom_point(data=cities, size=5)\n",
- " + theme(legend_position=\"none\")\n",
- ")"
- ]
- },
{
"cell_type": "markdown",
"id": "7d302c31",
diff --git a/visualise.md b/visualise.md
index 3e2f59f..fefe0f0 100644
--- a/visualise.md
+++ b/visualise.md
@@ -8,7 +8,7 @@ jupytext:
format_version: '0.8'
jupytext_version: 1.5.0
kernelspec:
- display_name: 'Python 3.10.12 64-bit (''py4ds2e'': conda)'
+ display_name: python3
language: python
name: python3
---
diff --git a/webscraping-and-apis.ipynb b/webscraping-and-apis.ipynb
index ca3e5fa..c5cae45 100644
--- a/webscraping-and-apis.ipynb
+++ b/webscraping-and-apis.ipynb
@@ -26,8 +26,8 @@
"outputs": [],
"source": [
"# remove cell\n",
- "import matplotlib_inline.backend_inline\n",
"import matplotlib.pyplot as plt\n",
+ "import matplotlib_inline.backend_inline\n",
"\n",
"# Plot settings\n",
"plt.style.use(\"https://github.com/aeturrell/python4DS/raw/main/plot_style.txt\")\n",
@@ -80,9 +80,10 @@
"metadata": {},
"outputs": [],
"source": [
- "import requests\n",
"import textwrap\n",
+ "\n",
"import pandas as pd\n",
+ "import requests\n",
"from bs4 import BeautifulSoup\n",
"from lets_plot import *\n",
"\n",
@@ -130,7 +131,7 @@
"\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",
- "An API has an 'endpoint', the base url, and then a URL that encodes the question. Let's see an example with the ONS API for which the endpoint is \"https://api.ons.gov.uk/\". The rest of the API has the form 'key/value', for example we'll ask for timeseries data 'timeseries' followed by 'JP9Z' for the vacancies in the UK services sector. We then ask for 'dataset' followed by 'UNEM' to specify which overarching dataset the series we want is in. The last part asks for the data with 'data'. Often you won't need to know all of these details, but it's useful to see a detailed example.\n",
+ "An API has an 'endpoint', the base url, and then a URL that encodes the question. Let's see an example with the ONS API for which the endpoint is \"https://api.beta.ons.gov.uk/v1/\". The rest of the API has the form 'data?uri=' and then the long ID of both the timeseries (jp9z) and then the dataset (LMS), which is vacancies in the UK services sector.\n",
"\n",
"The data that are returned by APIs are typically in JSON format, which looks a lot like a nested Python dictionary and its entries can be accessed in the same way--this is what is happening when getting the series' title in the example below. JSON is not good for analysis, so we'll use **pandas** to put the data into shape."
]
@@ -142,7 +143,7 @@
"metadata": {},
"outputs": [],
"source": [
- "url = \"https://api.ons.gov.uk/timeseries/JP9Z/dataset/UNEM/data\"\n",
+ "url = \"https://api.beta.ons.gov.uk/v1/data?uri=/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/timeseries/jp9z/lms/previous/v108\"\n",
"\n",
"# Get the data from the ONS API:\n",
"json_data = requests.get(url).json()\n",
@@ -218,26 +219,20 @@
},
"outputs": [],
"source": [
- "# World Bank CO2 emissions (metric tons per capita)\n",
- "# https://data.worldbank.org/indicator/EN.ATM.CO2E.PC\n",
- "# World Bank pop\n",
- "# https://data.worldbank.org/indicator/SP.POP.TOTL\n",
+ "# World Bank total greenhouse emissions (CO2e 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",
"\n",
- "df = wb.download(\n",
- " indicator=\"EN.ATM.CO2E.PC\",\n",
- " country=[\"US\", \"CHN\", \"IND\", \"Z4\", \"Z7\"],\n",
- " start=2017,\n",
- " end=2017,\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",
")\n",
- "# remove country as index for ease of plotting with seaborn\n",
- "df = df.reset_index()\n",
- "# wrap long country names\n",
- "df[\"country\"] = df[\"country\"].apply(lambda x: textwrap.fill(x, 10))\n",
- "# order based on size\n",
- "df = df.sort_values(\"EN.ATM.CO2E.PC\")\n",
- "df.head()"
+ "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"
]
},
{
@@ -523,9 +518,6 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "9d7534ecd9fbc7d385378f8400cf4d6cb9c6175408a574f1c99c5269f08771cc"
- },
"jupytext": {
"cell_metadata_filter": "-all",
"encoding": "# -*- coding: utf-8 -*-",
@@ -533,7 +525,7 @@
"main_language": "python"
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv",
"language": "python",
"name": "python3"
},
@@ -547,7 +539,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.13"
+ "version": "3.10.0"
},
"toc-showtags": true
},
diff --git a/workflow-packages-and-environments.md b/workflow-packages-and-environments.md
index d316d1d..a898bfa 100644
--- a/workflow-packages-and-environments.md
+++ b/workflow-packages-and-environments.md
@@ -5,7 +5,7 @@ jupytext:
extension: .md
format_name: myst
kernelspec:
- display_name: py4ds2e
+ display_name: python3
language: python
name: python3
---
@@ -18,7 +18,7 @@ In this chapter, you're going to learn about packages and how to install them pl
### Introduction
-Packages (also called libraries) are key to extending the functionality of Python. The default installation of Anaconda comes with many (around 250) of the packages you'll need, but it won't be long before you'll need to install some extra ones. 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.
+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.
@@ -26,7 +26,7 @@ The three Python packages **numpy**, **pandas**, and **maplotlib**, which respec
There are typically two steps to using a new Python package:
-1. *install* the package on the command line (aka the terminal), eg using `pip install pandas`
+1. *install* the package on the command line (aka the terminal), eg using `uv install pandas`
2. *import* the package into your Python session, eg using `import pandas as pd`
@@ -39,52 +39,45 @@ The *terminal* or *command line* or sometimes the *command prompt* was labelled
```{note}
To open up the command line within Visual Studio Code, use the ⌃ + \` keyboard shortcut (Mac) or ctrl + \` (Windows/Linux), or click "View > Terminal".
-Windows users may find it easiest to use the Anaconda Prompt as their terminal, at least for installing Python packages.
-
-If you want to open up the command line independently of Visual Studio Code, search for "Terminal" on Mac and Linux, and "Anaconda Prompt" on Windows.
+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}
-The command line interacts with your operating system and is used to create, activate, or change python installations.
+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 the Anaconda distribution of Python, your terminal should look something like this as your 'command prompt':
+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':
```bash
-(base) your-username@your-computer current-directory %
+your-username@your-computer current-directory %
```
-on Mac, and the same but with '%' replaced by '$' on linux, and (using the Anaconda Prompt)
+on Mac, and the same but with '%' replaced by '$' on linux, and (using Powershell)
-```bash
-(base) C:\Users\YourUsername>
+```powershell
+PS C:\Windows\System32>
```
-on Windows. If you don't see the word `(base)` at the start of the line, you may need to type `conda activate` first.
+on Windows.
-The `(base)` part is saying that your current Python environment is the base one (later, we'll see how to add others for reproducibility and to isolate projects). Unfortunately, and confusingly, the commands that you can use in the terminal on Mac and Linux, on the one hand, and Windows, on the other, are different but many of the principles are the same.
+You can check that uv has successfully installed Python in your current project's folder by running
-For now, to at least try out the command line, let's use something that works across all three of the major operating systems. Type `python` on the command prompt that came up in your new terminal window. You should see information about your installation of Python appear, including the version, followed by a Python prompt that looks like `>>>`. This is a kind of interactive Python session, in the terminal. It's much less rich than the one available in Visual Studio Code (it can't run scripts line-by-line, for example) but you can try `print('Hello World!')` and it will run, printing your message. To exit the terminal-based Python session, type `exit()` to go back to the regular command line.
-
-### Installing Packages
-
-To install extra Python packages, the default and easiest way is to use `pip install **packagename**`. In true programming-humour style, pip is a recursive acronym that stands for 'pip install packages'. There are over 330,000 Python packages on PyPI (the Python Package Index)! You can see what packages you have installed already by running `conda list` into the command line.
+```bash
+uv run python --version
+```
-`pip install ...` will install packages into your default Anaconda environment, usually called "base". You'll need to have Anaconda "activated" before installing a package in the terminal--if you don't see the name of an environment, eg `(base)`, at the start of your terminal's line, use the `conda activate` command first. On Windows, this is usually the command prompt (available in the integrated Visual Studio Code terminal) or the Anaconda Command Prompt (available in the start menu).
+For now, to at least try out the command line, let's use something that works across all three of the major operating systems. Type `uv run python` on the command prompt that came up in your new terminal window. You should see information about your installation of Python appear, including the version, followed by a Python prompt that looks like `>>>`. This is a kind of interactive Python session, in the terminal. It's much less rich than the one available in Visual Studio Code (it can't run scripts line-by-line, for example) but you can try `print('Hello World!')` and it will run, printing your message. To exit the terminal-based Python session, type `exit()` to go back to the regular command line.
-There is a second way to install packages that directly uses `conda` instead of `pip`, which we'll come to shortly in the context of different Python environments.
+### Installing Packages
-Here's a full example of the commands used to install the **pandas** package into the base environment (you may not need the first one):
+To install extra Python packages, the default and easiest way is to use `uv add **packagename**`. There are over 330,000 Python packages on PyPI (the Python Package Index)! You can see what packages you have installed already by running `uv pip list` into the command line.
-```bash
-your-username@your-computer current-directory % conda activate
-(base) your-username@your-computer current-directory % pip install pandas
-```
+`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
-Try installing the **matplotlib**, **pandas**, **statsmodels**, and **skimpy** packages using `pip install`.
+Try installing the **matplotlib**, **pandas**, **statsmodels**, and **skimpy** packages using `uv add`.
```
### Using Packages
@@ -105,122 +98,31 @@ Virtual code environments allow you to isolate all of the packages that you're u
To be more concrete, let's say you're using Python 3.9, **statsmodels**, and **pandas** for one project, project A. And, for project B, you need to use Python 3.10 with **numpy** and **scikit-learn**. Even with the same version of Python, best practice would be to have two separate virtual Python environments: environment A, with everything needed for project A, and environment B, with everything needed for project B. For the case where you're using different versions of Python, this isn't just best practice, it's essential.
-Many programming languages now come with an option to install packages and a version of the language in isolated environments. In Python, there are multiple tools for managing different environments. And, of those, the easiest to work with is probably [**Anaconda**](https://docs.conda.io/projects/conda/en/latest/index.html) (conda for short).
-
-### Conda as a package manager
-
-To learn how to use virtual code environments, we need to make a brief detour into `conda` as a package manager.
-
-Conda does more than just provide a Python interpreter: it can also manage packages and *different* Python installations, aka Python environments. So you can also install packages with
-
-```bash
-conda install package-name -c conda-forge
-```
-
-This will try to install a version of the package that is already optimised for your type of computer, and will automatically come with any dependencies (packages the package you're installing needs to run). The pre-built packages that are provided by Anaconda are convenient for a host of reasons. Anaconda provide pre-built versions of around 7,500 of the most popular packages (including the statistical programming language R). This is far less than PyPI but what you tend to find in practice is that Anaconda's `conda-forge` channel (`-c` selects the `conda-forge` "channel") has most of what you need.
-
-Okay, so how does this help with creating virtual environments? Because `conda install ...` is able to select versions of packages that work well with your computer, it's good at finding a combination of packages that will work well together without issue. With so many packages on PyPI, not all versions of all packages work together! Conda aims to solve that problem, and it makes working with virtual environments much nicer. But you can still `pip install ...` in a specific conda environment when you need to (eg because that particular package isn't available on `conda-forge`).
+Many programming languages now come with an option to install packages and a version of the language in isolated environments. In Python, there are multiple tools for managing different environments. And, of those, the easiest to work with is probably [**uv**](https://docs.astral.sh/uv/).
-You can see all of the packages in your (currently activated) conda environment by running `conda list` on the command line. This book uses a conda environment, and here's an example of looking at the installed packages within it, filtering them just to the ones beginning with "s". You can see which packages are from `conda-forge` and which are from PyPI.
+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
-conda list | grep ^s
-```
-
-### Using Anaconda to Manage Python Environments
-
-Okay, we're now ready to look at using conda to manager Python environments. Much of these two subsections is covered by the Anaconda documentation on [managing virtual environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
-
-If you're using Anaconda, you manage and change environments on the command line (remember, there's much more on the command line in {ref}`command-line`). Before following these instructions, check that you have Anaconda installed and activated. You should see something like `(base) username@computername:~$` on the command line (base is the default conda environment).
-
-To create a new environment called "myenv" with a specific version of Python (but no extra packages installed), it's
-
-```bash
-conda create -n myenv python=3.8
-```
-
-where you can of course specify other versions of Python by changing the number. To throw in a package or two, just add them to the end, for example
-
-```bash
-conda create -n myenv python=3.8 pandas jupyter
-```
-
-You can see a list of the currently installed environments by running
-
-```bash
-conda env list
-```
-
-When you install Anaconda, you will begin with a "base" environment. As noted, it's best practice not to use this for projects but to instead to create a new environment for each project.
-
-There are two downsides to installing environments directly from the command line. One is that you may have lots of packages. The second is that you may wish to keep a record of the environment you created! This is really good practice, because it helps you to make your work more reproducible. For both of these reasons, you can specify a conda environment using a file.
-
-A very simple environment that just had pandas and an interactive console would look like this in a file:
-
-```yaml
-name: myenv
-channels:
- - conda-forge
-dependencies:
- - python=3.9
- - pandas
- - jupyter
-
-```
-
-The environment is given by `name`, the channel (where to look for the packages) by `channels`, the specific packages by `dependencies`. Not all packages are available on conda's channels, so sometimes extra ones are needed. By specifying `conda-forge` we get the widest possible selection of packages. But, as we noted before, some packages are only available on PyPI (`pip`); these can be specified with a sub-section of the file like so for the **skimpy** package:
-
-```yaml
-name: myenv
-channels:
- - conda-forge
-dependencies:
- - python=3.8
- - pandas
- - jupyter
- - pip:
- - skimpy
-
+uv run pip list | grep ^s
```
-This goes into a file called `environment.yml`, which can be installed by running
-
-```bash
-conda env create -f environment.yml
-```
-
-This book is put together using an isolated *conda* environment specified in a file. It's an unusually big one because there are a lot of packages featured in the book! Here they are:
-
-```{code-cell} visualisation
-:tags: ["hide-input"]
-from rich import print
-
-with open("environment.yml", 'r') as stream:
- data_loaded = stream.read()
+### The pyproject.toml file in Python Environments
-print(data_loaded)
-```
-
-Of course, you can install packages as you go too, you don't have to specify them when you create the environment. With the relevant environment activated, use `conda install packagename` to do this.
+You may have noticed that a file called `pyproject.toml` has been created.
-Finally, to remove an environment, it's
+```{code-cell} ipython
+import toml
+from rich import print_json
-```bash
-conda remove --name myenv --all
+print_json(data=toml.load("pyproject.toml"))
```
-### Using and Switching Between Conda Environments
-
-To switch between conda environments on the command line, for example from the base environment to an environment called "myenv", use
-
-```bash
-conda activate myenv
-```
+This lists all of the dependencies, and the version, of a **uv** Python project. There are lots of benefits to tracking what versions of packages you're using like this. One of the most important is that you can *share* projects with other people, and they can install them from these files too.
-on the command line. However, this only switches the environment if you plan to run code on the command line!
+As you install or remove packages, the `pyproject.toml` file changes in lockstep.
-Fortunately, Visual Studio Code has you covered and makes it very easy to switch Python environments for a project at the click of a button.
+Noe that Visual Studio Code shows which Python environment you are using when you open a Python script or Jupyter Notebook.

-In the screenshot above, you can see the project-environment in two places: on the blue bar at the bottom of the screen, and (in 5), at the top right hand side of the interactive window. Click on either to change the Python environment that will be used to execute code. A similar top right selector is present for Jupyter Notebooks too.
+In the screenshot above, you can see the project-environment in two places: on the blue bar at the bottom of the screen, and (in 5), at the top right hand side of the interactive window. A similar top right indicator is present when you have a Jupyter Notebook open too.