---
module: explorer
component: explorer.py
problem_type: developer_experience
component: development_workflow
category: best-practices
severity: medium
tags:
  - streamlit
  - refactoring
  - horizontal-decomposition
  - duckdb
  - data-loading
  - constants
title: "Refactoring Large Streamlit Apps: Extract Pure Data Logic"
date: "2026-04-04"
last_updated: "2026-04-05"
applies_when:
  - Streamlit apps with large files (>1500 lines)
  - Mixed UI and business logic
  - Heavy data loading functions decorated with @st.cache_data
  - DuckDB connections scattered throughout
---

## Context

The `explorer.py` Streamlit app (originally 3715 lines) had 39 functions mixing:
- Streamlit UI rendering calls
- DuckDB database queries  
- Business logic computations
- Pure data transformations

This pattern makes the code:
- Hard to unit test (requires Streamlit runtime)
- Difficult to understand (UI interwoven with logic)
- Challenging to maintain (changes ripple unpredictably)

## Guidance

### Pattern: Horizontal Decomposition

Extract pure functions by type, not just by moving code. The key insight is **separating concerns by computation type**, not just file size:

**1. Data Loading Functions → `analysis/explorer_data.py`**
```python
# Before: Mixed in explorer.py with @st.cache_data
@st.cache_data(show_spinner="Partijkaart laden…")
def load_party_map(db_path: str) -> Dict[str, str]:
    con = duckdb.connect(database=db_path, read_only=True)
    try:
        rows = con.execute("SELECT mp_name, party FROM mp_metadata...").fetchall()
        return {mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party}
    finally:
        con.close()

# After: Pure data access in analysis/explorer_data.py
def load_party_map(db_path: str) -> Dict[str, str]:
    """Return {mp_name: party} mapping, with party names normalised to abbreviations."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute("SELECT mp_name, party FROM mp_metadata WHERE party IS NOT NULL").fetchall()
        con.close()
        return {mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party}
    except Exception:
        logger.exception("Failed to load party map")
        return {}
```

**2. Pure Computation → `analysis/projections.py`**
```python
# Before: Mixed with UI
def _should_swap_axes(axis_def: dict) -> bool:
    economic_labels = {"Verzorgingsstaat–Marktwerking", "Links–Rechts"}
    return axis_def.get("y_label") in economic_labels and axis_def.get("x_label") not in economic_labels

# After: Pure function, no external dependencies
def should_swap_axes(axis_def: dict) -> bool:
    """Return True if the Y axis is economic left-right and the X axis is not."""
    economic_labels = {"Verzorgingsstaat–Marktwerking", "Links–Rechts"}
    return axis_def.get("y_label") in economic_labels and axis_def.get("x_label") not in economic_labels
```

**4. Constants → `analysis/config.py`**
```python
# Before: Defined inline in the data loading module
_PARTY_NORMALIZE: Dict[str, str] = {
    "Nieuw Sociaal Contract": "NSC",
    "CU": "ChristenUnie",
}

# After: Import from config, define once
from analysis.config import _PARTY_NORMALIZE

def load_party_map(db_path: str) -> Dict[str, str]:
    """Return {mp_name: party} mapping, with party names normalised to abbreviations."""
    try:
        con = duckdb.connect(database=db_path, read_only=True)
        rows = con.execute("SELECT mp_name, party FROM mp_metadata WHERE party IS NOT NULL").fetchall()
        con.close()
        return {mp: _PARTY_NORMALIZE.get(party, party) for mp, party in rows if mp and party}
    except Exception:
        logger.exception("Failed to load party map")
        return {}
```

**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.

**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.
When tests import from the original module, preserve wrappers:
```python
# In explorer.py - preserves test compatibility
def _should_swap_axes(axis_def: dict) -> bool:
    """Return True if the Y axis is economic left-right and the X axis is not."""
    return projections.should_swap_axes(axis_def)
```

### Import Direction Rule

```
explorer.py → analysis/*.py  (one-way)
analysis/*.py ↛ explorer.py  (never)
```

This prevents circular imports and clarifies module boundaries.

### Preserve Function Signatures

Refactoring shouldn't change public APIs:
```python
# Keep these unchanged:
@st.cache_data(show_spinner="...")
def load_party_map(db_path: str) -> Dict[str, str]:
    return explorer_data.load_party_map(db_path)
```

## Why This Matters

1. **Testability**: Pure functions in `analysis/` can be unit tested without Streamlit runtime
2. **Reusability**: Data loading functions can be imported by other modules
3. **Maintainability**: Changes to business logic are isolated from UI changes
4. **Readability**: Single responsibility - each function does one thing

## Prevention

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.
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.
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.
4. **Verify tests pass after extraction**: Run the full test suite to catch any broken import chains or assertion mismatches.

## Results

| Metric | Before | After |
|--------|--------|-------|
| explorer.py lines | 3715 | 3069 |
| analysis/ modules | 0 | 12 modules |
| Functions extracted | 0 | 15+ |
| Test pass rate | 164/164 | 173/173 |

**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.

## When to Apply

Apply this pattern when:
- A Streamlit file exceeds 1500 lines
- Functions mix `@st.cache_data` with DuckDB queries and business logic
- Tests require Streamlit runtime to run
- Multiple tabs share similar data loading patterns

## Examples

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.
