{
 "cells": [
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Setup Django environment\n",
    "import os\n",
    "import sys\n",
    "import django\n",
    "\n",
    "# Set up Django environment\n",
    "os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'car_inventory.settings')\n",
    "django.setup()\n",
    "\n",
    "# Import models\n",
    "from inventory.models import CarMake, CarModel, CarSerie, CarTrim"
   ],
   "id": "64ce4f8ed9e90534"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Get all car makes\n",
    "all_makes = CarMake.objects.all()\n",
    "total_makes = all_makes.count()\n",
    "\n",
    "# Find makes without models using annotations\n",
    "from django.db.models import Count\n",
    "makes_without_models = CarMake.objects.annotate(model_count=Count('carmodel')).filter(model_count=0)\n",
    "makes_without_models_count = makes_without_models.count()\n",
    "\n",
    "print(f\"Total car makes: {total_makes}\")\n",
    "print(f\"Car makes without models: {makes_without_models_count} ({makes_without_models_count/total_makes*100:.2f}%)\")\n",
    "\n",
    "# Display the first 10 makes without models\n",
    "if makes_without_models:\n",
    "    print(\"\\nSample of car makes without models:\")\n",
    "    for make in makes_without_models[:10]:\n",
    "        print(f"
   ],
   "id": "7807a14272fbf44b"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Get all car models\n",
    "all_models = CarModel.objects.all()\n",
    "total_models = all_models.count()\n",
    "\n",
    "# Find models without series using annotations\n",
    "models_without_series = CarModel.objects.annotate(serie_count=Count('carserie')).filter(serie_count=0)\n",
    "models_without_series_count = models_without_series.count()\n",
    "\n",
    "print(f\"\\nTotal car models: {total_models}\")\n",
    "print(f\"Car models without series: {models_without_series_count} ({models_without_series_count/total_models*100:.2f}%)\")\n",
    "\n",
    "# Display the first 10 models without series\n",
    "if models_without_series:\n",
    "    print(\"\\nSample of car models without series:\")\n",
    "    for model in models_without_series[:10]:\n",
    "        print(f\"- {model.name} (Make: {model.id_car_make."
   ],
   "id": "318b5426d54f0705"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Get all car series\n",
    "all_series = CarSerie.objects.all()\n",
    "total_series = all_series.count()\n",
    "\n",
    "# Find series without trims using annotations\n",
    "series_without_trims = CarSerie.objects.annotate(trim_count=Count('cartrim')).filter(trim_count=0)\n",
    "series_without_trims_count = series_without_trims.count()\n",
    "\n",
    "print(f\"\\nTotal car series: {total_series}\")\n",
    "print(f\"Car series without trims: {series_without_trims_count} ({series_without_trims_count/total_series*100:.2f}%)\")\n",
    "\n",
    "# Display the first 10 series without trims\n",
    "if series_without_trims:\n",
    "    print(\"\\nSample of car series without trims:\")\n",
    "    for serie in series_without_trims[:10]:\n",
    "        print(f\"- {serie.name} (Model: {serie.id_car_model.name}, Make: {serie.id_car_model.id_car_make."
   ],
   "id": "30eaf3383d2b34e1"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for the chart\n",
    "categories = ['Makes', 'Models', 'Series']\n",
    "total_counts = [total_makes, total_models, total_series]\n",
    "empty_counts = [makes_without_models_count, models_without_series_count, series_without_trims_count]\n",
    "with_children_counts = [total_makes - makes_without_models_count,\n",
    "                        total_models - models_without_series_count,\n",
    "                        total_series - series_without_trims_count]\n",
    "\n",
    "# Create a figure with two subplots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Bar chart showing total vs. without children\n",
    "x = np.arange(len(categories))\n",
    "width = 0.35\n",
    "\n",
    "ax1.bar(x - width/2, total_counts, width, label='Total')\n",
    "ax1.bar(x + width/2, empty_counts, width, label='Without children')\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(categories)\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.set_title('Car Hierarchy Analysis')\n",
    "ax1.legend()\n",
    "\n",
    "# Pie charts showing percentage with/without children\n",
    "labels = ['With children', 'Without children']\n",
    "sizes = [[with_children_counts[i], empty_counts[i]] for i in range(3)]\n",
    "colors = [['#66b3ff', '#ff9999'], ['#99ff99', '#ffcc99'], ['#c2c2f0', '#ffb3e6']]\n",
    "\n",
    "for i, category in enumerate(categories):\n",
    "    if i == 0:\n",
    "        ax2.pie(sizes[i], labels=labels, colors=colors[i], autopct='%1.1f%%',\n",
    "                startangle=90, wedgeprops={'alpha':0.7})\n",
    "        centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
    "        ax2.add_patch(centre_circle)\n",
    "        ax2.text(0, 0, category, horizontalalignment='center', verticalalignment='center')\n",
    "    else:\n",
    "        # Calculate position for smaller pie charts\n",
    "        x_offset = 1.2 * (i-1)\n",
    "        ax2.pie(sizes[i], colors=colors[i], autopct='%1.1f%%',\n",
    "                startangle=90, wedgeprops={'alpha':0.7},\n",
    "                center=(x_offset, 0), radius=0.5)\n",
    "        centre_circle = plt.Circle((x_offset,0),0.35,fc='white')\n",
    "        ax2.add_patch(centre_circle)\n",
    "        ax2.text(x_offset, 0, category, horizontalalignment='center', verticalalignment='center')\n",
    "\n",
    "ax2.axis('equal')\n",
    "ax2.set_title('Percentage Distribution')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "d23f0edf1ff334f7"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Find all trims and go up the hierarchy to find complete chains\n",
    "complete_hierarchies = []\n",
    "\n",
    "for trim in CarTrim.objects.select_related('id_car_serie__id_car_model__id_car_make')[:20]:\n",
    "    serie = trim.id_car_serie\n",
    "    model = serie.id_car_model\n",
    "    make = model.id_car_make\n",
    "\n",
    "    complete_hierarchies.append({\n",
    "        'make': make.name,\n",
    "        'model': model.name,\n",
    "        'serie': serie.name,\n",
    "        'trim': trim.name\n",
    "    })\n",
    "\n",
    "# Display as a formatted table\n",
    "from IPython.display import display, HTML\n",
    "import pandas as pd\n",
    "\n",
    "if complete_hierarchies:\n",
    "    print(\"\\nSample of complete car hierarchies:\")\n",
    "    df = pd.DataFrame(complete_hierarchies)\n",
    "    display(HTML(df.to_html(index=False"
   ],
   "id": "3cd9822a8ac1a880"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Create DataFrames for export\n",
    "import pandas as pd\n",
    "\n",
    "# Makes without models\n",
    "makes_without_models_df = pd.DataFrame({\n",
    "    'make_id': [make.id_car_make for make in makes_without_models],\n",
    "    'make_name': [make.name for make in makes_without_models],\n",
    "    'is_sa_import': [make.is_sa_import for make in makes_without_models]\n",
    "})\n",
    "\n",
    "# Models without series\n",
    "models_without_series_df = pd.DataFrame({\n",
    "    'model_id': [model.id_car_model for model in models_without_series],\n",
    "    'model_name': [model.name for model in models_without_series],\n",
    "    'make_id': [model.id_car_make.id_car_make for model in models_without_series],\n",
    "    'make_name': [model.id_car_make.name for model in models_without_series]\n",
    "})\n",
    "\n",
    "# Series without trims\n",
    "series_without_trims_df = pd.DataFrame({\n",
    "    'serie_id': [serie.id_car_serie for serie in series_without_trims],\n",
    "    'serie_name': [serie.name for serie in series_without_trims],\n",
    "    'model_id': [serie.id_car_model.id_car_model for serie in series_without_trims],\n",
    "    'model_name': [serie.id_car_model.name for serie in series_without_trims],\n",
    "    'make_id': [serie.id_car_model.id_car_make.id_car_make for serie in series_without_trims],\n",
    "    'make_name': [serie.id_car_model.id_car_make.name for serie in series_without_trims]\n",
    "})\n",
    "\n",
    "# Save to CSV files (uncomment to use)\n",
    "# makes_without_models_df.to_csv('makes_without_models.csv', index=False)\n",
    "# models_without_series_df.to_csv('models_without_series.csv', index=False)\n",
    "# series_without_trims_df.to_csv('series_without_trims.csv', index=False)\n",
    "\n",
    "# Display the first few rows of each DataFrame\n",
    "print(\"\\nMakes without models (first 5):\")\n",
    "display(makes_without_models_df.head())\n",
    "\n",
    "print(\"\\nModels without series (first 5):\")\n",
    "display(models_without_series_df.head())\n",
    "\n",
    "print(\"\\nSeries without trims (first 5):\")\n",
    "display(series_without_trims_df.head())"
   ],
   "id": "6edeb052b327a48c"
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}