unsprawl.reporter

Reporting module for filtering and presenting valuation results.

This module handles filtering, ranking, and formatting of valuation results for display and export.

Classes

ReportGenerator

Filter, rank, and render a clean buy list table to console.

Module Contents

class ReportGenerator(schema=None)[source]

Filter, rank, and render a clean buy list table to console.

Filtering

  • Optional exact/partial town filter.

  • Optional budget filter for maximum resale price.

  • Extended filters: flat_model, flat_type (exact or partial), storey_min/max, area_min/max, lease_min/max.

Ranking

  • Sort by valuation_score descending (highest implies most undervalued), with ties broken by lowest price_efficiency and then lowest resale_price.

  • Display the top N results (default: 10).

Rendering

  • Human-friendly table using pandas’ built-in formatting.

schema
logger
static _parse_storey_range(sr)[source]

Parse HDB storey range strings like “07 TO 09” into (min, max).

Non-parsable inputs return (None, None).

_apply_filters(df, town=None, town_like=None, budget=None, flat_type=None, flat_type_like=None, flat_model=None, flat_model_like=None, storey_min=None, storey_max=None, area_min=None, area_max=None, lease_min=None, lease_max=None)[source]

Apply user-specified filters to DataFrame.

Parameters:

  • df (pd.DataFrame) – The scored dataset.

  • town (Optional[str]) – Town name for exact case-insensitive filtering.

  • town_like (Optional[str]) – Substring case-insensitive match for town.

  • budget (Optional[float]) – Maximum resale price.

  • flat_type (Optional[str]) – Exact match filter for flat_type (case-insensitive).

  • flat_type_like (Optional[str]) – Substring match for flat_type.

  • flat_model (Optional[str]) – Exact match filter for flat_model.

  • flat_model_like (Optional[str]) – Substring match for flat_model.

  • storey_min, storey_max (Optional[int]) – Min/max storey number filter (overlap with storey_range).

  • area_min, area_max (Optional[float]) – Floor area filters.

  • lease_min, lease_max (Optional[float]) – Remaining lease (years) filters.

generate_dataframe(df, town=None, town_like=None, budget=None, flat_type=None, flat_type_like=None, flat_model=None, flat_model_like=None, storey_min=None, storey_max=None, area_min=None, area_max=None, lease_min=None, lease_max=None, top_n=10, full=False)[source]

Produce the filtered, sorted DataFrame for display/export.

If full is True, returns all rows after sorting; otherwise, returns the top_n rows.

render(df, town=None, town_like=None, budget=None, flat_type=None, flat_type_like=None, flat_model=None, flat_model_like=None, storey_min=None, storey_max=None, area_min=None, area_max=None, lease_min=None, lease_max=None, top_n=10)[source]

Generate the formatted table for the buy list.

The table prioritizes the most undervalued units by sorting on valuation_score desc, breaking ties by price_efficiency asc and resale_price asc.