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Replace matplotlib with Altair and add live charts to docs (#305)
* Replace matplotlib with Altair across all docs and add rendered Vega-Lite charts Switch all visualization examples from matplotlib to Altair for a more declarative, interactive charting experience. Add mkdocs-charts-plugin to render Vega-Lite specs directly in the docs so readers can see live, interactive charts with tooltips—not just code blocks. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Address PR review: add admonitions, DC data, and altair dep - Add `altair` to docs optional dependencies in pyproject.toml - Add DC (id: 11) to choropleth datasets in quickstart, decennial, and population-estimates pages that were missing it - Add `!!! example` admonitions above all rendered vegalite charts to clarify they show state-level sample data, not the exact output of the code above them - Update chart titles to remove "(sample data)" suffix now that admonitions explain the context Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Update uv.lock with mkdocs-charts-plugin and altair deps The lockfile was out of sync with pyproject.toml after adding mkdocs-charts-plugin and altair to the docs extras. ReadTheDocs uses `uv sync --frozen` which requires the lockfile to match. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Add CLAUDE.md with project context for Claude Code Documents project structure, common commands, dependency management (including the uv.lock gotcha with ReadTheDocs), docs conventions, CI pipeline, and test markers. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * Fix dot density map: extract geometry coords for Altair Altair cannot resolve nested attribute access like geometry.x in field references. Extract lon/lat into separate columns before passing to alt.Chart(). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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‎CLAUDE.md‎

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# CLAUDE.md
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Project-level context for Claude Code sessions working on PyPUMS.
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## Project overview
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PyPUMS is a Python interface to the US Census Bureau API. It provides functions for
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ACS, Decennial Census, PUMS microdata, population estimates, and migration flows.
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The package returns pandas DataFrames (or GeoDataFrames when `geometry=True`).
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- **Package source:** `pypums/`
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- **Tests:** `tests/`
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- **Docs:** `docs/` (MkDocs Material, hosted on ReadTheDocs)
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- **CLI:** `pypums/cli.py` (Typer)
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## Common commands
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```bash
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# Run tests (uses doctest + pytest)
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uv run pytest
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# Run a specific test file
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uv run pytest tests/test_get_acs.py
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# Lint and format
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uv run ruff check --fix .
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uv run ruff format .
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# Build docs locally
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uv run mkdocs build --strict
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# Serve docs locally (live reload)
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uv run mkdocs serve
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```
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## Dependencies and lockfile
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This project uses **uv** for dependency management.
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**Important:** When you add or change dependencies in `pyproject.toml`, you **must**
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run `uv lock` and commit the updated `uv.lock`. ReadTheDocs uses
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`uv sync --frozen --extra docs` which requires the lockfile to be in sync with
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`pyproject.toml`. Forgetting to update `uv.lock` will cause the docs build to fail
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on CI.
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Optional dependency groups:
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- `spatial` — geopandas (for `geometry=True` support)
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- `test` — pytest
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- `docs` — mkdocs-material, mkdocstrings, mkdocs-charts-plugin, altair, etc.
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## Code style
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- **Linter/formatter:** Ruff (config in `pyproject.toml`)
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- **Target:** Python 3.10+
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- **Pre-commit hooks:** trailing whitespace, end-of-file fixer, check-yaml, check-toml,
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ruff-check, ruff-format
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## Docs
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The documentation uses **MkDocs Material** with these notable plugins/extensions:
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- `mkdocs-charts-plugin` with `pymdownx.superfences` custom fences for rendering
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**Vega-Lite** charts inline. Use ` ```vegalite ` fenced code blocks with a JSON
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Vega-Lite spec to render interactive charts directly in the docs.
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- `mkdocstrings[python]` for API reference (numpy-style docstrings).
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- `mkdocs-typer` for CLI reference auto-generation.
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- Visualizations use **Altair** (not matplotlib). Code examples should use Altair, and
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rendered previews use Vega-Lite JSON specs in `vegalite` fenced blocks.
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Rendered Vega-Lite charts in docs should:
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- Use inline `"values"` data (not live API calls) so they render without a backend.
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- Include all 50 states + DC for choropleth maps.
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- Be wrapped in `!!! example "Interactive preview"` admonitions when the rendered chart
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shows a different geographic scale than the code example (e.g., state-level preview
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for a tract-level code example).
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## CI pipeline
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- **GitHub Actions:** lint (ruff), tests (Python 3.10-3.13)
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- **ReadTheDocs:** builds docs from `.readthedocs.yaml` using `uv sync --frozen --extra docs`
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- **Pre-commit hooks:** run on every commit (trailing whitespace, ruff, etc.)
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## Test markers
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```
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phase0 — Foundation (API infra, geography, FIPS, cache)
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phase1 — Core data functions (get_acs, get_decennial, load_variables)
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phase2 — MOE, spatial, enhanced PUMS
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phase3 — Estimates, flows, survey
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integration — Requires real Census API key and network
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spatial — Requires geopandas
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```

‎docs/getting-started/quickstart.md‎

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@@ -84,22 +84,124 @@ print(type(la_poverty)) # <class 'geopandas.geodataframe.GeoDataFrame'>
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4. **county** -- `"037"` is the FIPS code for Los Angeles County. Use `pypums.datasets.fips.lookup_fips(state="California", county="Los Angeles County")` to look up codes.
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5. **geometry** -- When `True`, PyPUMS fetches TIGER/Line cartographic boundary shapefiles and merges them with the data. The result is a `GeoDataFrame` with a `geometry` column.
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Now plot it:
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Now plot it with [Altair](https://altair-viz.github.io/):
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```python
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la_poverty.plot(
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column="estimate",
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cmap="YlOrRd",
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legend=True,
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figsize=(12, 10),
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edgecolor="0.8",
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linewidth=0.3,
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missing_kwds={"color": "lightgrey"},
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import altair as alt
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alt.Chart(la_poverty).mark_geoshape(
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stroke="white", strokeWidth=0.3,
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).encode(
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color=alt.Color(
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"estimate:Q",
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scale=alt.Scale(scheme="yelloworangered"),
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legend=alt.Legend(title="Below Poverty Level"),
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),
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tooltip=["NAME:N", alt.Tooltip("estimate:Q", format=",")],
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).project("albersUsa").properties(
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width=600, height=500,
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title="Poverty by Census Tract, Los Angeles County",
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)
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```
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The resulting map shows poverty counts by Census tract across Los Angeles County, with darker shades indicating higher counts.
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!!! example "Interactive preview — state-level choropleth with ACS population data"
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The chart below shows a state-level choropleth to demonstrate what
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`geometry=True` output looks like when plotted with Altair. Your actual
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tract-level map will have much finer geographic detail.
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```vegalite
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{
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"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
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"width": 600,
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"height": 400,
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"title": "ACS Total Population by State",
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"data": {
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"url": "https://cdn.jsdelivr.net/npm/vega-datasets@v2.7.0/data/us-10m.json",
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"format": {"type": "topojson", "feature": "states"}
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},
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"transform": [
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{
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"lookup": "id",
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"from": {
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"data": {
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"values": [
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{"id": 1, "estimate": 5074296, "name": "Alabama"},
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{"id": 2, "estimate": 733583, "name": "Alaska"},
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{"id": 4, "estimate": 7359197, "name": "Arizona"},
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{"id": 5, "estimate": 3045637, "name": "Arkansas"},
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{"id": 6, "estimate": 39029342, "name": "California"},
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{"id": 8, "estimate": 5839926, "name": "Colorado"},
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{"id": 9, "estimate": 3626205, "name": "Connecticut"},
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{"id": 10, "estimate": 1018396, "name": "Delaware"},
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{"id": 11, "estimate": 671803, "name": "District of Columbia"},
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{"id": 12, "estimate": 22244823, "name": "Florida"},
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{"id": 13, "estimate": 10912876, "name": "Georgia"},
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{"id": 15, "estimate": 1440196, "name": "Hawaii"},
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{"id": 16, "estimate": 1939033, "name": "Idaho"},
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{"id": 17, "estimate": 12582032, "name": "Illinois"},
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{"id": 18, "estimate": 6833037, "name": "Indiana"},
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{"id": 19, "estimate": 3200517, "name": "Iowa"},
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{"id": 20, "estimate": 2937150, "name": "Kansas"},
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{"id": 21, "estimate": 4512310, "name": "Kentucky"},
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{"id": 22, "estimate": 4590241, "name": "Louisiana"},
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{"id": 23, "estimate": 1385340, "name": "Maine"},
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{"id": 24, "estimate": 6164660, "name": "Maryland"},
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{"id": 25, "estimate": 6981974, "name": "Massachusetts"},
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{"id": 26, "estimate": 10034113, "name": "Michigan"},
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{"id": 27, "estimate": 5717184, "name": "Minnesota"},
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{"id": 28, "estimate": 2940057, "name": "Mississippi"},
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{"id": 29, "estimate": 6177957, "name": "Missouri"},
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{"id": 30, "estimate": 1122867, "name": "Montana"},
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{"id": 31, "estimate": 1967923, "name": "Nebraska"},
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{"id": 32, "estimate": 3177772, "name": "Nevada"},
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{"id": 33, "estimate": 1395231, "name": "New Hampshire"},
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{"id": 34, "estimate": 9261699, "name": "New Jersey"},
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{"id": 35, "estimate": 2113344, "name": "New Mexico"},
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{"id": 36, "estimate": 19677151, "name": "New York"},
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{"id": 37, "estimate": 10698973, "name": "North Carolina"},
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{"id": 38, "estimate": 779261, "name": "North Dakota"},
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{"id": 39, "estimate": 11756058, "name": "Ohio"},
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{"id": 40, "estimate": 4019800, "name": "Oklahoma"},
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{"id": 41, "estimate": 4240137, "name": "Oregon"},
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{"id": 42, "estimate": 12972008, "name": "Pennsylvania"},
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{"id": 44, "estimate": 1093734, "name": "Rhode Island"},
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{"id": 45, "estimate": 5282634, "name": "South Carolina"},
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{"id": 46, "estimate": 909824, "name": "South Dakota"},
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{"id": 47, "estimate": 7051339, "name": "Tennessee"},
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{"id": 48, "estimate": 30029572, "name": "Texas"},
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{"id": 49, "estimate": 3380800, "name": "Utah"},
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{"id": 50, "estimate": 647064, "name": "Vermont"},
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{"id": 51, "estimate": 8642274, "name": "Virginia"},
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{"id": 53, "estimate": 7785786, "name": "Washington"},
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{"id": 54, "estimate": 1775156, "name": "West Virginia"},
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{"id": 55, "estimate": 5892539, "name": "Wisconsin"},
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{"id": 56, "estimate": 576851, "name": "Wyoming"}
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]
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},
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"key": "id",
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"fields": ["estimate", "name"]
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}
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}
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],
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"projection": {"type": "albersUsa"},
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"mark": {"type": "geoshape", "stroke": "white", "strokeWidth": 0.5},
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"encoding": {
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"color": {
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"field": "estimate",
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"type": "quantitative",
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"scale": {"scheme": "yelloworangered"},
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"legend": {"title": "Population", "format": ","}
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},
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"tooltip": [
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{"field": "name", "type": "nominal", "title": "State"},
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{"field": "estimate", "type": "quantitative", "title": "Population", "format": ","}
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]
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}
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}
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```
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!!! info "What is a TIGER/Line shapefile?"
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The Census Bureau publishes free geographic boundary files called
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TIGER/Line shapefiles. When you set `geometry=True`, PyPUMS automatically

‎docs/guides/acs-data.md‎

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=== "Tidy (default)"
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Each row is one geography-variable combination. This is ideal for
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plotting with libraries like Altair, Plotly, or seaborn.
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plotting with [Altair](https://altair-viz.github.io/).
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```python
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df_tidy = pypums.get_acs(
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print(type(ca_counties_geo))
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# <class 'geopandas.geodataframe.GeoDataFrame'>
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ca_counties_geo.plot(column="estimate", legend=True)
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import altair as alt
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alt.Chart(ca_counties_geo).mark_geoshape(stroke="white", strokeWidth=0.5).encode(
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color=alt.Color("estimate:Q", legend=alt.Legend(title="Population")),
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tooltip=["NAME:N", alt.Tooltip("estimate:Q", format=",")],
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).project("albersUsa").properties(width=500, height=400)
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```
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!!! note "Optional dependency"

‎docs/guides/decennial-data.md‎

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year=2020,
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geometry=True,
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)
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county_geo.plot(column="value", legend=True)
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import altair as alt
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alt.Chart(county_geo).mark_geoshape(stroke="white", strokeWidth=0.5).encode(
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color=alt.Color("value:Q", legend=alt.Legend(title="Population")),
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tooltip=["NAME:N", alt.Tooltip("value:Q", format=",")],
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).project("albersUsa").properties(width=500, height=400)
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```
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!!! example "Interactive preview — state-level 2020 Census population"
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The code above fetches county-level data for Illinois. The chart below
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uses state-level data to demonstrate what `geometry=True` + Altair
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looks like at a broader geographic scale.
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```vegalite
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{
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"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
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"width": 500,
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"height": 350,
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"title": "2020 Census Population by State",
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"data": {
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"url": "https://cdn.jsdelivr.net/npm/vega-datasets@v2.7.0/data/us-10m.json",
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"format": {"type": "topojson", "feature": "states"}
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},
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"transform": [
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{
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"lookup": "id",
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"from": {
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"data": {
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"values": [
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{"id": 1, "value": 5024279, "name": "Alabama"},
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{"id": 2, "value": 733391, "name": "Alaska"},
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{"id": 4, "value": 7151502, "name": "Arizona"},
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{"id": 5, "value": 3011524, "name": "Arkansas"},
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{"id": 6, "value": 39538223, "name": "California"},
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{"id": 8, "value": 5773714, "name": "Colorado"},
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{"id": 9, "value": 3605944, "name": "Connecticut"},
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{"id": 10, "value": 989948, "name": "Delaware"},
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{"id": 11, "value": 689545, "name": "District of Columbia"},
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{"id": 12, "value": 21538187, "name": "Florida"},
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{"id": 13, "value": 10711908, "name": "Georgia"},
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{"id": 15, "value": 1455271, "name": "Hawaii"},
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{"id": 16, "value": 1839106, "name": "Idaho"},
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{"id": 17, "value": 12812508, "name": "Illinois"},
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{"id": 18, "value": 6785528, "name": "Indiana"},
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{"id": 19, "value": 3190369, "name": "Iowa"},
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{"id": 20, "value": 2937880, "name": "Kansas"},
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{"id": 21, "value": 4505836, "name": "Kentucky"},
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{"id": 22, "value": 4657757, "name": "Louisiana"},
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{"id": 23, "value": 1362359, "name": "Maine"},
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{"id": 24, "value": 6177224, "name": "Maryland"},
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{"id": 25, "value": 7029917, "name": "Massachusetts"},
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{"id": 26, "value": 10077331, "name": "Michigan"},
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{"id": 27, "value": 5706494, "name": "Minnesota"},
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{"id": 28, "value": 2961279, "name": "Mississippi"},
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{"id": 29, "value": 6154913, "name": "Missouri"},
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{"id": 30, "value": 1084225, "name": "Montana"},
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{"id": 31, "value": 1961504, "name": "Nebraska"},
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{"id": 32, "value": 3104614, "name": "Nevada"},
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{"id": 33, "value": 1377529, "name": "New Hampshire"},
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{"id": 34, "value": 9288994, "name": "New Jersey"},
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{"id": 35, "value": 2117522, "name": "New Mexico"},
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{"id": 36, "value": 20201249, "name": "New York"},
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{"id": 37, "value": 10439388, "name": "North Carolina"},
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{"id": 38, "value": 779094, "name": "North Dakota"},
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{"id": 39, "value": 11799448, "name": "Ohio"},
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{"id": 40, "value": 3959353, "name": "Oklahoma"},
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{"id": 41, "value": 4237256, "name": "Oregon"},
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{"id": 42, "value": 13002700, "name": "Pennsylvania"},
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{"id": 44, "value": 1097379, "name": "Rhode Island"},
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{"id": 45, "value": 5118425, "name": "South Carolina"},
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{"id": 46, "value": 886667, "name": "South Dakota"},
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{"id": 47, "value": 6910840, "name": "Tennessee"},
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{"id": 48, "value": 29145505, "name": "Texas"},
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{"id": 49, "value": 3271616, "name": "Utah"},
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{"id": 50, "value": 643077, "name": "Vermont"},
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{"id": 51, "value": 8631393, "name": "Virginia"},
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{"id": 53, "value": 7705281, "name": "Washington"},
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{"id": 54, "value": 1793716, "name": "West Virginia"},
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{"id": 55, "value": 5893718, "name": "Wisconsin"},
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{"id": 56, "value": 576851, "name": "Wyoming"}
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]
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},
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"key": "id",
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"fields": ["value", "name"]
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}
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}
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],
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"projection": {"type": "albersUsa"},
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"mark": {"type": "geoshape", "stroke": "white", "strokeWidth": 0.5},
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"encoding": {
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"color": {
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"field": "value",
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"type": "quantitative",
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"scale": {"scheme": "blues"},
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"legend": {"title": "Population", "format": ","}
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},
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"tooltip": [
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{"field": "name", "type": "nominal", "title": "State"},
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{"field": "value", "type": "quantitative", "title": "Population", "format": ","}
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]
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}
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}
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```
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!!! note "Optional dependency"
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year=2020,
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geometry=True,
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)
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pop_map.plot(column="value", legend=True, cmap="YlOrRd")
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import altair as alt
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alt.Chart(pop_map).mark_geoshape(stroke="white", strokeWidth=0.3).encode(
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color=alt.Color("value:Q", scale=alt.Scale(scheme="yelloworangered")),
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tooltip=["NAME:N", alt.Tooltip("value:Q", format=",")],
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).project("albersUsa").properties(width=500, height=400)
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```
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### Comparing 2010 and 2020

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