personal-projects/personal-portfolio
Template
1
1
Fork 0
Code Issues 3 Pull Requests Actions Packages Projects 1 Releases Wiki Activity

refactor: multi-dashboard structural migration
Some checks failed
CI / lint-and-test (pull_request) Has been cancelled
Details

Browse Source 
- Rename dbt project from toronto_housing to portfolio
- Restructure dbt models into domain subdirectories:
  - shared/ for cross-domain dimensions (dim_time)
  - staging/toronto/, intermediate/toronto/, marts/toronto/
- Update SQLAlchemy models for raw_toronto schema
- Add explicit cross-schema FK relationships for FactRentals
- Namespace figure factories under figures/toronto/
- Namespace notebooks under notebooks/toronto/
- Update Makefile with domain-specific targets and env loading
- Update all documentation for multi-dashboard structure

This enables adding new dashboard projects (e.g., /football, /energy)
without structural conflicts or naming collisions.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Leo Miranda
2026-02-01 19:08:20 -05:00
parent a5d6866d63
commit 62d1a52eed
73 changed files with 1114 additions and 623 deletions
Download Patch File Download Diff File Expand all files Collapse all files

187
notebooks/toronto/housing/affordability_choropleth.ipynb Normal file
View File

@@ -0,0 +1,187 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Affordability Index Choropleth Map\n",
"\n",
"Displays housing affordability across Toronto's 158 neighbourhoods. Index of 100 = city average."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_housing` | neighbourhood × year | affordability_index, rent_to_income_pct, avg_rent_2bed, geometry |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import pandas as pd\n",
"from dotenv import load_dotenv\n",
"from sqlalchemy import create_engine\n",
"\n",
"# Load .env from project root\n",
"load_dotenv(\"../../.env\")\n",
"\n",
"engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_id,\n",
" neighbourhood_name,\n",
" geometry,\n",
" year,\n",
" affordability_index,\n",
" rent_to_income_pct,\n",
" avg_rent_2bed,\n",
" median_household_income,\n",
" is_affordable\n",
"FROM public_marts.mart_neighbourhood_housing\n",
"WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n",
"ORDER BY affordability_index ASC\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} neighbourhoods\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Filter to most recent year\n",
"2. Convert geometry to GeoJSON\n",
"3. Lower index = more affordable (inverted for visualization clarity)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"import geopandas as gpd\n",
"\n",
"gdf = gpd.GeoDataFrame(\n",
" df, geometry=gpd.GeoSeries.from_wkb(df[\"geometry\"]), crs=\"EPSG:4326\"\n",
")\n",
"\n",
"geojson = json.loads(gdf.to_json())\n",
"data = df.drop(columns=[\"geometry\"]).to_dict(\"records\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[\n",
" [\n",
" \"neighbourhood_name\",\n",
" \"affordability_index\",\n",
" \"rent_to_income_pct\",\n",
" \"avg_rent_2bed\",\n",
" \"is_affordable\",\n",
" ]\n",
"].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_choropleth_figure` from `portfolio_app.figures.toronto.choropleth`.\n",
"\n",
"**Key Parameters:**\n",
"- `color_column`: 'affordability_index'\n",
"- `color_scale`: 'RdYlGn_r' (reversed: green=affordable, red=expensive)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.insert(0, \"../..\")\n",
"\n",
"from portfolio_app.figures.toronto.choropleth import create_choropleth_figure\n",
"\n",
"fig = create_choropleth_figure(\n",
" geojson=geojson,\n",
" data=data,\n",
" location_key=\"neighbourhood_id\",\n",
" color_column=\"affordability_index\",\n",
" hover_data=[\"neighbourhood_name\", \"rent_to_income_pct\", \"avg_rent_2bed\"],\n",
" color_scale=\"RdYlGn_r\", # Reversed: lower index (affordable) = green\n",
" title=\"Toronto Housing Affordability Index\",\n",
" zoom=10,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Index Interpretation\n",
"\n",
"| Index | Meaning |\n",
"|-------|--------|\n",
"| < 100 | More affordable than city average |\n",
"| = 100 | City average affordability |\n",
"| > 100 | Less affordable than city average |\n",
"\n",
"Affordability calculated as: `rent_to_income_pct / city_avg_rent_to_income * 100`"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

200
notebooks/toronto/housing/rent_trend_line.ipynb Normal file
View File

@@ -0,0 +1,200 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rent Trend Line Chart\n",
"\n",
"Shows 5-year rental price trends across Toronto neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_housing` | neighbourhood × year | year, avg_rent_2bed, rent_yoy_change_pct |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import pandas as pd\n",
"from dotenv import load_dotenv\n",
"from sqlalchemy import create_engine\n",
"\n",
"# Load .env from project root\n",
"load_dotenv(\"../../.env\")\n",
"\n",
"engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
"\n",
"# City-wide average rent by year\n",
"query = \"\"\"\n",
"SELECT\n",
" year,\n",
" AVG(avg_rent_bachelor) as avg_rent_bachelor,\n",
" AVG(avg_rent_1bed) as avg_rent_1bed,\n",
" AVG(avg_rent_2bed) as avg_rent_2bed,\n",
" AVG(avg_rent_3bed) as avg_rent_3bed,\n",
" AVG(rent_yoy_change_pct) as avg_yoy_change\n",
"FROM public_marts.mart_neighbourhood_housing\n",
"WHERE year >= (SELECT MAX(year) - 5 FROM public_marts.mart_neighbourhood_housing)\n",
"GROUP BY year\n",
"ORDER BY year\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} years of rent data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Aggregate rent by year (city-wide average)\n",
"2. Convert year to datetime for proper x-axis\n",
"3. Reshape for multi-line chart by bedroom type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create date column from year\n",
"df[\"date\"] = pd.to_datetime(df[\"year\"].astype(str) + \"-01-01\")\n",
"\n",
"# Melt for multi-line chart\n",
"df_melted = df.melt(\n",
" id_vars=[\"year\", \"date\"],\n",
" value_vars=[\"avg_rent_bachelor\", \"avg_rent_1bed\", \"avg_rent_2bed\", \"avg_rent_3bed\"],\n",
" var_name=\"bedroom_type\",\n",
" value_name=\"avg_rent\",\n",
")\n",
"\n",
"# Clean labels\n",
"df_melted[\"bedroom_type\"] = df_melted[\"bedroom_type\"].map(\n",
" {\n",
" \"avg_rent_bachelor\": \"Bachelor\",\n",
" \"avg_rent_1bed\": \"1 Bedroom\",\n",
" \"avg_rent_2bed\": \"2 Bedroom\",\n",
" \"avg_rent_3bed\": \"3 Bedroom\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[\n",
" [\n",
" \"year\",\n",
" \"avg_rent_bachelor\",\n",
" \"avg_rent_1bed\",\n",
" \"avg_rent_2bed\",\n",
" \"avg_rent_3bed\",\n",
" \"avg_yoy_change\",\n",
" ]\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_price_time_series` from `portfolio_app.figures.toronto.time_series`.\n",
"\n",
"**Key Parameters:**\n",
"- `date_column`: 'date'\n",
"- `price_column`: 'avg_rent'\n",
"- `group_column`: 'bedroom_type' (for multi-line)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.insert(0, \"../..\")\n",
"\n",
"from portfolio_app.figures.toronto.time_series import create_price_time_series\n",
"\n",
"data = df_melted.to_dict(\"records\")\n",
"\n",
"fig = create_price_time_series(\n",
" data=data,\n",
" date_column=\"date\",\n",
" price_column=\"avg_rent\",\n",
" group_column=\"bedroom_type\",\n",
" title=\"Toronto Average Rent Trend (5 Years)\",\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### YoY Change Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show year-over-year changes\n",
"print(\"Year-over-Year Rent Change (%)\")\n",
"df[[\"year\", \"avg_yoy_change\"]].dropna()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

202
notebooks/toronto/housing/tenure_breakdown_bar.ipynb Normal file
View File

@@ -0,0 +1,202 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Housing Tenure Breakdown Bar Chart\n",
"\n",
"Shows the distribution of owner-occupied vs renter-occupied dwellings across neighbourhoods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Reference\n",
"\n",
"### Source Tables\n",
"\n",
"| Table | Grain | Key Columns |\n",
"|-------|-------|-------------|\n",
"| `mart_neighbourhood_housing` | neighbourhood × year | pct_owner_occupied, pct_renter_occupied, income_quintile |\n",
"\n",
"### SQL Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import pandas as pd\n",
"from dotenv import load_dotenv\n",
"from sqlalchemy import create_engine\n",
"\n",
"# Load .env from project root\n",
"load_dotenv(\"../../.env\")\n",
"\n",
"engine = create_engine(os.environ[\"DATABASE_URL\"])\n",
"\n",
"query = \"\"\"\n",
"SELECT\n",
" neighbourhood_name,\n",
" pct_owner_occupied,\n",
" pct_renter_occupied,\n",
" income_quintile,\n",
" total_rental_units,\n",
" average_dwelling_value\n",
"FROM public_marts.mart_neighbourhood_housing\n",
"WHERE year = (SELECT MAX(year) FROM public_marts.mart_neighbourhood_housing)\n",
" AND pct_owner_occupied IS NOT NULL\n",
"ORDER BY pct_renter_occupied DESC\n",
"\"\"\"\n",
"\n",
"df = pd.read_sql(query, engine)\n",
"print(f\"Loaded {len(df)} neighbourhoods with tenure data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation Steps\n",
"\n",
"1. Filter to most recent year with tenure data\n",
"2. Melt owner/renter columns for stacked bar\n",
"3. Sort by renter percentage (highest first)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prepare for stacked bar\n",
"df_stacked = df.melt(\n",
" id_vars=[\"neighbourhood_name\", \"income_quintile\"],\n",
" value_vars=[\"pct_owner_occupied\", \"pct_renter_occupied\"],\n",
" var_name=\"tenure_type\",\n",
" value_name=\"percentage\",\n",
")\n",
"\n",
"df_stacked[\"tenure_type\"] = df_stacked[\"tenure_type\"].map(\n",
" {\"pct_owner_occupied\": \"Owner\", \"pct_renter_occupied\": \"Renter\"}\n",
")\n",
"\n",
"data = df_stacked.to_dict(\"records\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sample Output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Highest Renter Neighbourhoods:\")\n",
"df[\n",
" [\n",
" \"neighbourhood_name\",\n",
" \"pct_renter_occupied\",\n",
" \"pct_owner_occupied\",\n",
" \"income_quintile\",\n",
" ]\n",
"].head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Visualization\n",
"\n",
"### Figure Factory\n",
"\n",
"Uses `create_stacked_bar` from `portfolio_app.figures.toronto.bar_charts`.\n",
"\n",
"**Key Parameters:**\n",
"- `x_column`: 'neighbourhood_name'\n",
"- `value_column`: 'percentage'\n",
"- `category_column`: 'tenure_type'\n",
"- `show_percentages`: True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"sys.path.insert(0, \"../..\")\n",
"\n",
"from portfolio_app.figures.toronto.bar_charts import create_stacked_bar\n",
"\n",
"# Show top 20 by renter percentage\n",
"top_20_names = df.head(20)[\"neighbourhood_name\"].tolist()\n",
"data_filtered = [d for d in data if d[\"neighbourhood_name\"] in top_20_names]\n",
"\n",
"fig = create_stacked_bar(\n",
" data=data_filtered,\n",
" x_column=\"neighbourhood_name\",\n",
" value_column=\"percentage\",\n",
" category_column=\"tenure_type\",\n",
" title=\"Housing Tenure Mix - Top 20 Renter Neighbourhoods\",\n",
" color_map={\"Owner\": \"#4CAF50\", \"Renter\": \"#2196F3\"},\n",
" show_percentages=True,\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### City-Wide Distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# City-wide averages\n",
"print(f\"City Average Owner-Occupied: {df['pct_owner_occupied'].mean():.1f}%\")\n",
"print(f\"City Average Renter-Occupied: {df['pct_renter_occupied'].mean():.1f}%\")\n",
"\n",
"# By income quintile\n",
"print(\"\\nTenure by Income Quintile:\")\n",
"df.groupby(\"income_quintile\")[\n",
" [\"pct_owner_occupied\", \"pct_renter_occupied\"]\n",
"].mean().round(1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}