{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a0dcf44b-c609-4701-8007-b270cf8c3d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tcin</th>\n",
       "      <th>gtin13</th>\n",
       "      <th>ingestion_time</th>\n",
       "      <th>primary_category</th>\n",
       "      <th>materials</th>\n",
       "      <th>packaging</th>\n",
       "      <th>origin</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>depth</th>\n",
       "      <th>ingestion_time</th>\n",
       "      <th>material_score</th>\n",
       "      <th>weight_score</th>\n",
       "      <th>packaging_score</th>\n",
       "      <th>origin_score</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81917300</td>\n",
       "      <td>840391145528</td>\n",
       "      <td>2023-06-25 20:31:00.725924</td>\n",
       "      <td>Toys</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>imported</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-25 20:31:00.725924</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84821007</td>\n",
       "      <td>9781801433983</td>\n",
       "      <td>2023-06-25 20:31:00.736690</td>\n",
       "      <td>School &amp; Office Supplies</td>\n",
       "      <td>[cardboard]</td>\n",
       "      <td>1</td>\n",
       "      <td>imported</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-25 20:31:00.736690</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15432753</td>\n",
       "      <td>883929408115</td>\n",
       "      <td>2023-06-25 20:31:00.742077</td>\n",
       "      <td>Movies, Music &amp; Books</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>usa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-25 20:31:00.742077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84199597</td>\n",
       "      <td>194425194489</td>\n",
       "      <td>2023-06-25 20:31:00.746501</td>\n",
       "      <td>Party Supplies</td>\n",
       "      <td>[cardboard]</td>\n",
       "      <td>24</td>\n",
       "      <td>imported</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-25 20:31:00.746501</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86345566</td>\n",
       "      <td>23271231140</td>\n",
       "      <td>2023-06-25 20:31:00.751118</td>\n",
       "      <td>Home</td>\n",
       "      <td>[metal]</td>\n",
       "      <td>1</td>\n",
       "      <td>imported</td>\n",
       "      <td>2109.20</td>\n",
       "      <td>58.42</td>\n",
       "      <td>2.54</td>\n",
       "      <td>58.42</td>\n",
       "      <td>2023-06-25 20:31:00.751118</td>\n",
       "      <td>0.353333</td>\n",
       "      <td>1581.9000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1582.853333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>83388852</td>\n",
       "      <td>4717592035292</td>\n",
       "      <td>2023-06-25 20:31:01.380622</td>\n",
       "      <td>Sports &amp; Outdoors</td>\n",
       "      <td>[plastic]</td>\n",
       "      <td>1</td>\n",
       "      <td>mixed</td>\n",
       "      <td>127.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.70</td>\n",
       "      <td>24.13</td>\n",
       "      <td>2023-06-25 20:31:01.380622</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>95.2575</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96.724167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>80836585</td>\n",
       "      <td>841821016982</td>\n",
       "      <td>2023-06-25 20:31:01.384865</td>\n",
       "      <td>Patio &amp; Garden</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>mixed</td>\n",
       "      <td>14514.94</td>\n",
       "      <td>30.48</td>\n",
       "      <td>30.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-25 20:31:01.384865</td>\n",
       "      <td>0.112500</td>\n",
       "      <td>10886.2050</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>10887.417500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>75477923</td>\n",
       "      <td>93422863070</td>\n",
       "      <td>2023-06-25 20:31:01.388505</td>\n",
       "      <td>Holiday Shop</td>\n",
       "      <td>[fabric]</td>\n",
       "      <td>1</td>\n",
       "      <td>mixed</td>\n",
       "      <td>78.64</td>\n",
       "      <td>12.06</td>\n",
       "      <td>5.71</td>\n",
       "      <td>5.71</td>\n",
       "      <td>2023-06-25 20:31:01.388505</td>\n",
       "      <td>0.403571</td>\n",
       "      <td>58.9800</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>60.483571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>85634544</td>\n",
       "      <td>194425213968</td>\n",
       "      <td>2023-06-25 20:31:01.391389</td>\n",
       "      <td>Household Essentials</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>imported</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-06-25 20:31:01.391389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>80239765</td>\n",
       "      <td>724235717129</td>\n",
       "      <td>2023-06-25 20:31:01.394481</td>\n",
       "      <td>Kitchen &amp; Dining</td>\n",
       "      <td>[stoneware]</td>\n",
       "      <td>1</td>\n",
       "      <td>imported</td>\n",
       "      <td>829.60</td>\n",
       "      <td>11.43</td>\n",
       "      <td>31.75</td>\n",
       "      <td>11.43</td>\n",
       "      <td>2023-06-25 20:31:01.394481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>622.2000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          tcin         gtin13             ingestion_time  \\\n",
       "0     81917300   840391145528 2023-06-25 20:31:00.725924   \n",
       "1     84821007  9781801433983 2023-06-25 20:31:00.736690   \n",
       "2     15432753   883929408115 2023-06-25 20:31:00.742077   \n",
       "3     84199597   194425194489 2023-06-25 20:31:00.746501   \n",
       "4     86345566    23271231140 2023-06-25 20:31:00.751118   \n",
       "..         ...            ...                        ...   \n",
       "162   83388852  4717592035292 2023-06-25 20:31:01.380622   \n",
       "163   80836585   841821016982 2023-06-25 20:31:01.384865   \n",
       "164   75477923    93422863070 2023-06-25 20:31:01.388505   \n",
       "165   85634544   194425213968 2023-06-25 20:31:01.391389   \n",
       "166   80239765   724235717129 2023-06-25 20:31:01.394481   \n",
       "\n",
       "             primary_category    materials  packaging    origin    weight  \\\n",
       "0                        Toys         None          1  imported       NaN   \n",
       "1    School & Office Supplies  [cardboard]          1  imported       NaN   \n",
       "2       Movies, Music & Books         None          1       usa       NaN   \n",
       "3              Party Supplies  [cardboard]         24  imported       NaN   \n",
       "4                        Home      [metal]          1  imported   2109.20   \n",
       "..                        ...          ...        ...       ...       ...   \n",
       "162         Sports & Outdoors    [plastic]          1     mixed    127.01   \n",
       "163            Patio & Garden         None          1     mixed  14514.94   \n",
       "164              Holiday Shop     [fabric]          1     mixed     78.64   \n",
       "165      Household Essentials         None          1  imported       NaN   \n",
       "166          Kitchen & Dining  [stoneware]          1  imported    829.60   \n",
       "\n",
       "     height  width  depth             ingestion_time  material_score  \\\n",
       "0       NaN    NaN    NaN 2023-06-25 20:31:00.725924        0.625000   \n",
       "1       NaN  30.23    NaN 2023-06-25 20:31:00.736690        0.253333   \n",
       "2       NaN    NaN    NaN 2023-06-25 20:31:00.742077             NaN   \n",
       "3       NaN    NaN    NaN 2023-06-25 20:31:00.746501        0.625000   \n",
       "4     58.42   2.54  58.42 2023-06-25 20:31:00.751118        0.353333   \n",
       "..      ...    ...    ...                        ...             ...   \n",
       "162     NaN  12.70  24.13 2023-06-25 20:31:01.380622        0.366667   \n",
       "163   30.48  30.48    NaN 2023-06-25 20:31:01.384865        0.112500   \n",
       "164   12.06   5.71   5.71 2023-06-25 20:31:01.388505        0.403571   \n",
       "165     NaN    NaN    NaN 2023-06-25 20:31:01.391389             NaN   \n",
       "166   11.43  31.75  11.43 2023-06-25 20:31:01.394481             NaN   \n",
       "\n",
       "     weight_score  packaging_score  origin_score         score  \n",
       "0             NaN              0.6           0.0           NaN  \n",
       "1             NaN              0.6           0.0           NaN  \n",
       "2             NaN              0.6           1.0           NaN  \n",
       "3             NaN             14.4           0.0           NaN  \n",
       "4       1581.9000              0.6           0.0   1582.853333  \n",
       "..            ...              ...           ...           ...  \n",
       "162       95.2575              0.6           0.5     96.724167  \n",
       "163    10886.2050              0.6           0.5  10887.417500  \n",
       "164       58.9800              0.6           0.5     60.483571  \n",
       "165           NaN              0.6           0.0           NaN  \n",
       "166      622.2000              0.6           0.0           NaN  \n",
       "\n",
       "[167 rows x 17 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = [20, 15]\n",
    "\n",
    "engine = create_engine('postgresql://sustainability_score:sustainability_score@postgres:5432/sustainability_score')\n",
    "\n",
    "query = \"\"\"\n",
    "    SELECT *\n",
    "    FROM sustainability_score.products AS products\n",
    "    JOIN sustainability_score.scored_products AS scores\n",
    "    USING (tcin);\n",
    "\"\"\"\n",
    "\n",
    "products = pd.read_sql_query(query, engine)\n",
    "products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0f00acc1-4dec-45f9-9e38-dcae2b7a271d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(products[\"score\"], color='blue', edgecolor='black', bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "87ed5f21-e0bf-4af2-855a-09994118d13c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scored: 55.688622754491014 %\n",
      "unscored: 44.31137724550898 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = dict((~products[\"score\"].isnull()).value_counts())\n",
    "total = len(products.index)\n",
    "scored = counts[True]\n",
    "unscored = counts[False]\n",
    "\n",
    "print(f\"scored: {scored/total * 100} %\")\n",
    "print(f\"unscored: {unscored/total * 100} %\")\n",
    "\n",
    "plt.bar([\"scored\", \"unscored\"], [scored/total, unscored/total])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "04e2b9c5-4eff-405f-a3c3-44e1bdfd483f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(2, 2, 1)\n",
    "plt.hist(products[\"material_score\"], color='blue', edgecolor='black', bins=50)\n",
    "ax.set_title(\"material score\")\n",
    "ax = plt.subplot(2, 2, 2)\n",
    "plt.hist(products[\"weight_score\"], color='blue', edgecolor='black', bins=50)\n",
    "ax.set_title(\"weight score\")\n",
    "ax = plt.subplot(2, 2, 3)\n",
    "plt.hist(products[\"packaging_score\"], color='blue', edgecolor='black', bins=50)\n",
    "ax.set_title(\"packaging score\")\n",
    "ax = plt.subplot(2, 2, 4)\n",
    "plt.hist(products[\"origin_score\"], color='blue', edgecolor='black', bins=50)\n",
    "ax.set_title(\"origin score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "34009b25-9ed1-4cdb-987a-bd6315a6f15d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 19 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "categories = set(products[\"primary_category\"])\n",
    "n_categories = 19  # len(categories)\n",
    "for i, category in enumerate(categories):\n",
    "    ax = plt.subplot(5, 4, i+1)\n",
    "    sel = products[\"primary_category\"] == category\n",
    "    plt.xlim(0, 1)\n",
    "    plt.hist(products.loc[sel, \"origin_score\"], color='blue', edgecolor='black', bins=50)\n",
    "plt.show()\n",
    "print(len(categories))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}