{ "cells": [ { "cell_type": "code", "execution_count": 15, "id": "a0dcf44b-c609-4701-8007-b270cf8c3d35", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tcingtin13ingestion_timeprimary_categorymaterialspackagingoriginweightheightwidthdepthingestion_timematerial_scoreweight_scorepackaging_scoreorigin_scorescore
0819173008403911455282023-06-25 20:31:00.725924ToysNone1importedNaNNaNNaNNaN2023-06-25 20:31:00.7259240.625000NaN0.60.0NaN
18482100797818014339832023-06-25 20:31:00.736690School & Office Supplies[cardboard]1importedNaNNaN30.23NaN2023-06-25 20:31:00.7366900.253333NaN0.60.0NaN
2154327538839294081152023-06-25 20:31:00.742077Movies, Music & BooksNone1usaNaNNaNNaNNaN2023-06-25 20:31:00.742077NaNNaN0.61.0NaN
3841995971944251944892023-06-25 20:31:00.746501Party Supplies[cardboard]24importedNaNNaNNaNNaN2023-06-25 20:31:00.7465010.625000NaN14.40.0NaN
486345566232712311402023-06-25 20:31:00.751118Home[metal]1imported2109.2058.422.5458.422023-06-25 20:31:00.7511180.3533331581.90000.60.01582.853333
......................................................
1628338885247175920352922023-06-25 20:31:01.380622Sports & Outdoors[plastic]1mixed127.01NaN12.7024.132023-06-25 20:31:01.3806220.36666795.25750.60.596.724167
163808365858418210169822023-06-25 20:31:01.384865Patio & GardenNone1mixed14514.9430.4830.48NaN2023-06-25 20:31:01.3848650.11250010886.20500.60.510887.417500
16475477923934228630702023-06-25 20:31:01.388505Holiday Shop[fabric]1mixed78.6412.065.715.712023-06-25 20:31:01.3885050.40357158.98000.60.560.483571
165856345441944252139682023-06-25 20:31:01.391389Household EssentialsNone1importedNaNNaNNaNNaN2023-06-25 20:31:01.391389NaNNaN0.60.0NaN
166802397657242357171292023-06-25 20:31:01.394481Kitchen & Dining[stoneware]1imported829.6011.4331.7511.432023-06-25 20:31:01.394481NaN622.20000.60.0NaN
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167 rows × 17 columns

\n", "
" ], "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": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sqlalchemy import create_engine\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\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": null, "id": "0f00acc1-4dec-45f9-9e38-dcae2b7a271d", "metadata": {}, "outputs": [], "source": [ "ax = plt.subplot(1, 2, 1)\n", "plt.hist(weight, color='blue', edgecolor='black', bins=50)\n", "ax = plt.subplot(1, 2, 2)\n", "plt.hist(weight[weight <= 1], color='blue', edgecolor='black', bins=50)\n", "plt.show()" ] } ], "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 }