File size: 4,520 Bytes
ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 ec070a7 710ccb9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import json\n",
"from pathlib import Path\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"BASE_DIR = Path('.')\n",
"ART_DIR = BASE_DIR / 'artifacts' / 'py'\n",
"FIG_DIR = ART_DIR / 'figures'\n",
"TAB_DIR = ART_DIR / 'tables'\n",
"\n",
"FIG_DIR.mkdir(parents=True, exist_ok=True)\n",
"TAB_DIR.mkdir(parents=True, exist_ok=True)\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"df_food = pd.read_csv('clean_food_products.csv')\n",
"df_reviews = pd.read_csv('synthetic_food_reviews.csv')\n",
"\n",
"print(df_food.shape)\n",
"print(df_reviews.shape)\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# KPI calculation\n",
"kpis = {\n",
" 'n_products': int(len(df_food)),\n",
" 'avg_calories_per_100g': round(float(df_food['energy-kcal_100g'].mean()), 2),\n",
" 'healthy_count': int((df_food['health_label'] == 'healthy').sum()),\n",
" 'unhealthy_count': int((df_food['health_label'] == 'unhealthy').sum())\n",
"}\n",
"\n",
"with open(TAB_DIR / 'kpis.json', 'w') as f:\n",
" json.dump(kpis, f, indent=2)\n",
"\n",
"kpis\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Save main dashboard table\n",
"df_food.to_csv(TAB_DIR / 'food_dashboard.csv', index=False)\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Health label counts\n",
"health_counts = df_food['health_label'].value_counts().reset_index()\n",
"health_counts.columns = ['health_label', 'count']\n",
"health_counts.to_csv(TAB_DIR / 'health_label_counts.csv', index=False)\n",
"health_counts\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Nutrition by health label\n",
"nutrition = df_food.groupby('health_label')[[\n",
" 'energy-kcal_100g',\n",
" 'sugars_100g',\n",
" 'fat_100g',\n",
" 'salt_100g',\n",
" 'proteins_100g',\n",
" 'fiber_100g'\n",
"]].mean().reset_index()\n",
"\n",
"nutrition.to_csv(TAB_DIR / 'nutrition_by_health_label.csv', index=False)\n",
"nutrition\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Nutri-score vs health label\n",
"df_compare = df_food[~df_food['nutriscore_grade'].isin(['unknown','not-applicable'])]\n",
"\n",
"nutri = pd.crosstab(df_compare['nutriscore_grade'], df_compare['health_label']).reset_index()\n",
"nutri_long = nutri.melt(id_vars='nutriscore_grade', var_name='health_label', value_name='count')\n",
"\n",
"nutri_long.to_csv(TAB_DIR / 'nutriscore_vs_health.csv', index=False)\n",
"nutri_long\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Recommendations\n",
"df_food[['product_name','health_label','nutriscore_grade']].to_csv(\n",
" TAB_DIR / 'recommendations.csv', index=False\n",
")\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Charts\n",
"plt.figure(figsize=(6,4))\n",
"df_food['health_label'].value_counts().plot(kind='bar')\n",
"plt.title('Health Label Distribution')\n",
"plt.tight_layout()\n",
"plt.savefig(FIG_DIR / 'health_label_distribution.png')\n",
"plt.close()\n",
"\n",
"plt.figure(figsize=(8,5))\n",
"pd.crosstab(df_compare['nutriscore_grade'], df_compare['health_label']).plot(kind='bar', stacked=True)\n",
"plt.title('Nutri-Score vs Health Label')\n",
"plt.tight_layout()\n",
"plt.savefig(FIG_DIR / 'nutriscore_vs_health.png')\n",
"plt.close()\n"
]
}
]
} |