docs: refresh 2 stale references in solutions docs
- refactoring-streamlit-data-loading.md: update test count 164/164 → 173/173 (7 new axis validation tests added) - svd-component-labels-mismatch.md: SVD_THEMES moved from explorer.py:434-611 → analysis/config.py:67+ per the refactoring that extracted constants to analysis/config.py
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@@ -0,0 +1,171 @@
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---
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module: explorer
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component: explorer.py
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problem_type: developer_experience
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component: development_workflow
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category: best-practices
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severity: medium
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tags:
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- streamlit
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- refactoring
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- horizontal-decomposition
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- duckdb
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- data-loading
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- constants
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title: "Refactoring Large Streamlit Apps: Extract Pure Data Logic"
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date: "2026-04-04"
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last_updated: "2026-04-05"
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applies_when:
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- Streamlit apps with large files (>1500 lines)
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- Mixed UI and business logic
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- Heavy data loading functions decorated with @st.cache_data
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- DuckDB connections scattered throughout
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---
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## Context
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The `explorer.py` Streamlit app (originally 3715 lines) had 39 functions mixing:
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- Streamlit UI rendering calls
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- DuckDB database queries
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- Business logic computations
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- Pure data transformations
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This pattern makes the code:
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- Hard to unit test (requires Streamlit runtime)
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- Difficult to understand (UI interwoven with logic)
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- Challenging to maintain (changes ripple unpredictably)
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## Guidance
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### Pattern: Horizontal Decomposition
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Extract pure functions by type, not just by moving code. The key insight is **separating concerns by computation type**, not just file size:
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**1. Data Loading Functions → `analysis/explorer_data.py`**
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```python
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# Before: Mixed in explorer.py with @st.cache_data
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@st.cache_data(show_spinner="Partijkaart laden…")
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def load_party_map(db_path: str) -> Dict[str, str]:
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con = duckdb.connect(database=db_path, read_only=True)
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try:
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rows = con.execute("SELECT mp_name, party FROM mp_metadata...").fetchall()
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return {mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party}
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finally:
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con.close()
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# After: Pure data access in analysis/explorer_data.py
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def load_party_map(db_path: str) -> Dict[str, str]:
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"""Return {mp_name: party} mapping, with party names normalised to abbreviations."""
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try:
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con = duckdb.connect(database=db_path, read_only=True)
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rows = con.execute("SELECT mp_name, party FROM mp_metadata WHERE party IS NOT NULL").fetchall()
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con.close()
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return {mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party}
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except Exception:
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logger.exception("Failed to load party map")
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return {}
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```
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**2. Pure Computation → `analysis/projections.py`**
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```python
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# Before: Mixed with UI
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def _should_swap_axes(axis_def: dict) -> bool:
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economic_labels = {"Verzorgingsstaat–Marktwerking", "Links–Rechts"}
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return axis_def.get("y_label") in economic_labels and axis_def.get("x_label") not in economic_labels
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# After: Pure function, no external dependencies
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def should_swap_axes(axis_def: dict) -> bool:
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"""Return True if the Y axis is economic left-right and the X axis is not."""
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economic_labels = {"Verzorgingsstaat–Marktwerking", "Links–Rechts"}
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return axis_def.get("y_label") in economic_labels and axis_def.get("x_label") not in economic_labels
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```
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**4. Constants → `analysis/config.py`**
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```python
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# Before: Defined inline in the data loading module
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_PARTY_NORMALIZE: Dict[str, str] = {
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"Nieuw Sociaal Contract": "NSC",
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"CU": "ChristenUnie",
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}
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# After: Import from config, define once
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from analysis.config import _PARTY_NORMALIZE
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def load_party_map(db_path: str) -> Dict[str, str]:
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"""Return {mp_name: party} mapping, with party names normalised to abbreviations."""
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try:
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con = duckdb.connect(database=db_path, read_only=True)
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rows = con.execute("SELECT mp_name, party FROM mp_metadata WHERE party IS NOT NULL").fetchall()
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con.close()
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return {mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party}
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except Exception:
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logger.exception("Failed to load party map")
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return {}
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```
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**Why this matters:** DRY enforcement. If `_PARTY_NORMALIZE` or `CURRENT_PARLIAMENT_PARTIES` are defined in multiple modules, changes must be made in all places. Importing from a single source of truth (`config.py`) prevents drift.
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**Anti-pattern to avoid:** Creating new constant definitions in extracted modules without checking if they already exist in `config.py`. Before adding a constant to any `analysis/*.py` module, grep for existing definitions of that constant name across all `analysis/` modules.
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When tests import from the original module, preserve wrappers:
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```python
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# In explorer.py - preserves test compatibility
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def _should_swap_axes(axis_def: dict) -> bool:
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"""Return True if the Y axis is economic left-right and the X axis is not."""
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return projections.should_swap_axes(axis_def)
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```
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### Import Direction Rule
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```
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explorer.py → analysis/*.py (one-way)
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analysis/*.py ↛ explorer.py (never)
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```
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This prevents circular imports and clarifies module boundaries.
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### Preserve Function Signatures
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Refactoring shouldn't change public APIs:
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```python
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# Keep these unchanged:
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@st.cache_data(show_spinner="...")
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def load_party_map(db_path: str) -> Dict[str, str]:
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return explorer_data.load_party_map(db_path)
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```
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## Why This Matters
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1. **Testability**: Pure functions in `analysis/` can be unit tested without Streamlit runtime
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2. **Reusability**: Data loading functions can be imported by other modules
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3. **Maintainability**: Changes to business logic are isolated from UI changes
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4. **Readability**: Single responsibility - each function does one thing
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## Prevention
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1. **Audit existing constants before creating new ones**: Before adding a constant to any `analysis/*.py` module, grep across `analysis/` for existing definitions. DRY violations cause maintenance burden and drift.
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2. **Define `__all__` in every analysis module**: Without `__all__`, it's hard to know what's public API. Add it when creating the module, not as an afterthought. All 6 public functions in `analysis/trajectory.py` (compute_trajectories, compute_2d_trajectories, top_drifters, compute_party_discipline, window_to_dates, choose_trajectory_title) should be exported.
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3. **Test assertions may need updates**: When extracting constants, grep for all test references to those constants and update assertions. See `docs/solutions/test-failures/svd-label-tests-after-refactoring.md` for an example.
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4. **Verify tests pass after extraction**: Run the full test suite to catch any broken import chains or assertion mismatches.
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## Results
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| Metric | Before | After |
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|--------|--------|-------|
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| explorer.py lines | 3715 | 3069 |
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| analysis/ modules | 0 | 12 modules |
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| Functions extracted | 0 | 15+ |
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| Test pass rate | 164/164 | 173/173 |
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**Practical minimum reached at ~3000 lines.** Tab functions (`build_*_tab()`) and their `_render_*` helpers are inherently Streamlit-coupled — they mix UI rendering with business logic. Further reduction requires decoupling data preparation from rendering (prepare data in `analysis/`, render in `explorer.py`), which is a significant architectural refactor.
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## When to Apply
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Apply this pattern when:
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- A Streamlit file exceeds 1500 lines
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- Functions mix `@st.cache_data` with DuckDB queries and business logic
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- Tests require Streamlit runtime to run
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- Multiple tabs share similar data loading patterns
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## Examples
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See `analysis/config.py` for constants (PARTY_COLOURS, SVD_THEMES, KNOWN_MAJOR_PARTIES, CURRENT_PARLIAMENT_PARTIES, CANONICAL_RIGHT, CANONICAL_LEFT, _PARTY_NORMALIZE), `analysis/explorer_data.py` for extracted data loading functions, and `analysis/projections.py` for pure computation utilities.
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@@ -93,4 +93,4 @@ The fix works because it aligns labels with actual voting data:
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## Related Issues
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- Analysis: `thoughts/explorer/top_svd_top_motions_report.md`
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- JSON generator: `scripts/generate_svd_json.py`
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- Labels source: `explorer.py:434-611` (SVD_THEMES dictionary)
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- Labels source: `analysis/config.py:67+` (SVD_THEMES dictionary)
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