diff --git a/.ipynb_checkpoints/cheese-checkpoint.ipynb b/.ipynb_checkpoints/cheese-checkpoint.ipynb new file mode 100644 index 0000000..88c8de1 --- /dev/null +++ b/.ipynb_checkpoints/cheese-checkpoint.ipynb @@ -0,0 +1,3846 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5f7c9658-c285-4854-96c0-e899fc55421b", + "metadata": {}, + "source": [ + "# DM project: cheese" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7f4f2b89-8257-468c-9f5e-a77e11b8b8ff", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from mlxtend.preprocessing import TransactionEncoder\n", + "from mlxtend.frequent_patterns import apriori\n", + "from geopy.geocoders import Nominatim\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "import tqdm.notebook as tqdm\n", + "import random\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "1a0afba8-692b-4377-a2ce-5114983e3bbb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cheeseurlmilkcountryregionfamilytypefat_contentcalcium_contenttexturerindcolorflavoraromavegetarianvegansynonymsalt_spellingsproducers
0Aarewasserhttps://www.cheese.com/aarewasser/cowSwitzerlandNaNNaNsemi-softNaNNaNbutterywashedyellowsweetbutteryFalseFalseNaNNaNJumi
1Abbaye de Bellochttps://www.cheese.com/abbaye-de-belloc/sheepFrancePays BasqueNaNsemi-hard, artisanNaNNaNcreamy, dense, firmnaturalyellowburnt caramellanolineTrueFalseAbbaye Notre-Dame de BellocNaNNaN
2Abbaye de Belvalhttps://www.cheese.com/abbaye-de-belval/cowFranceNaNNaNsemi-hard40-46%NaNelasticwashedivoryNaNaromaticFalseFalseNaNNaNNaN
3Abbaye de Citeauxhttps://www.cheese.com/abbaye-de-citeaux/cowFranceBurgundyNaNsemi-soft, artisan, brinedNaNNaNcreamy, dense, smoothwashedwhiteacidic, milky, smoothbarnyardy, earthyFalseFalseNaNNaNNaN
4Abbaye de Tamiéhttps://www.cheese.com/tamie/cowFranceSavoieNaNsoft, artisanNaNNaNcreamy, open, smoothwashedwhitefruity, nuttyperfumed, pungentFalseFalseNaNTamié, Trappiste de Tamie, Abbey of TamieNaN
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1182Sveciaosthttps://www.cheese.com/sveciaost/cowSwedenLow-laying regionsNaNsemi-hard, brined45%NaNcreamy, supplerindlesspale yellowacidicNaNFalseFalseNaNNaNNaN
1183Swaghttps://www.cheese.com/swag/goatAustraliaSouth AustraliaNaNfresh firm, artisanNaNNaNcreamy, crumblyash coatedwhiteacidic, creamyfreshTrueFalseNaNNaNWoodside Cheese Wrights
1184Swaledalehttps://www.cheese.com/swaledale/sheepEnglandSwaledale, North YorkshireNaNhardNaNNaNsemi firmNaNyellowsmooth, sweetfloralTrueFalseSwaledale Sheep CheeseNaNNaN
1185Sweet Style Swisshttps://www.cheese.com/sweet-style-swiss/NaNSwitzerlandNaNNaNsemi-hard, artisanNaNNaNfirm, supplewaxedNaNnuttynutty, sweetFalseFalseNaNNaNNaN
1186Swiss cheesehttps://www.cheese.com/swiss/cowUnited StatesNaNSwiss Cheesehard, artisan, processed7.8 g/100gNaNfirmrindlesspale yellownutty, sweetNaNTrueFalseAmerican Swiss CheeseNaNVarious
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1187 rows × 19 columns

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" + ], + "text/plain": [ + " cheese url milk \\\n", + "0 Aarewasser https://www.cheese.com/aarewasser/ cow \n", + "1 Abbaye de Belloc https://www.cheese.com/abbaye-de-belloc/ sheep \n", + "2 Abbaye de Belval https://www.cheese.com/abbaye-de-belval/ cow \n", + "3 Abbaye de Citeaux https://www.cheese.com/abbaye-de-citeaux/ cow \n", + "4 Abbaye de Tamié https://www.cheese.com/tamie/ cow \n", + "... ... ... ... \n", + "1182 Sveciaost https://www.cheese.com/sveciaost/ cow \n", + "1183 Swag https://www.cheese.com/swag/ goat \n", + "1184 Swaledale https://www.cheese.com/swaledale/ sheep \n", + "1185 Sweet Style Swiss https://www.cheese.com/sweet-style-swiss/ NaN \n", + "1186 Swiss cheese https://www.cheese.com/swiss/ cow \n", + "\n", + " country region family \\\n", + "0 Switzerland NaN NaN \n", + "1 France Pays Basque NaN \n", + "2 France NaN NaN \n", + "3 France Burgundy NaN \n", + "4 France Savoie NaN \n", + "... ... ... ... \n", + "1182 Sweden Low-laying regions NaN \n", + "1183 Australia South Australia NaN \n", + "1184 England Swaledale, North Yorkshire NaN \n", + "1185 Switzerland NaN NaN \n", + "1186 United States NaN Swiss Cheese \n", + "\n", + " type fat_content calcium_content \\\n", + "0 semi-soft NaN NaN \n", + "1 semi-hard, artisan NaN NaN \n", + "2 semi-hard 40-46% NaN \n", + "3 semi-soft, artisan, brined NaN NaN \n", + "4 soft, artisan NaN NaN \n", + "... ... ... ... \n", + "1182 semi-hard, brined 45% NaN \n", + "1183 fresh firm, artisan NaN NaN \n", + "1184 hard NaN NaN \n", + "1185 semi-hard, artisan NaN NaN \n", + "1186 hard, artisan, processed 7.8 g/100g NaN \n", + "\n", + " texture rind color flavor \\\n", + "0 buttery washed yellow sweet \n", + "1 creamy, dense, firm natural yellow burnt caramel \n", + "2 elastic washed ivory NaN \n", + "3 creamy, dense, smooth washed white acidic, milky, smooth \n", + "4 creamy, open, smooth washed white fruity, nutty \n", + "... ... ... ... ... \n", + "1182 creamy, supple rindless pale yellow acidic \n", + "1183 creamy, crumbly ash coated white acidic, creamy \n", + "1184 semi firm NaN yellow smooth, sweet \n", + "1185 firm, supple waxed NaN nutty \n", + "1186 firm rindless pale yellow nutty, sweet \n", + "\n", + " aroma vegetarian vegan synonyms \\\n", + "0 buttery False False NaN \n", + "1 lanoline True False Abbaye Notre-Dame de Belloc \n", + "2 aromatic False False NaN \n", + "3 barnyardy, earthy False False NaN \n", + "4 perfumed, pungent False False NaN \n", + "... ... ... ... ... \n", + "1182 NaN False False NaN \n", + "1183 fresh True False NaN \n", + "1184 floral True False Swaledale Sheep Cheese \n", + "1185 nutty, sweet False False NaN \n", + "1186 NaN True False American Swiss Cheese \n", + "\n", + " alt_spellings producers \n", + "0 NaN Jumi \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 Tamié, Trappiste de Tamie, Abbey of Tamie NaN \n", + "... ... ... \n", + "1182 NaN NaN \n", + "1183 NaN Woodside Cheese Wrights \n", + "1184 NaN NaN \n", + "1185 NaN NaN \n", + "1186 NaN Various \n", + "\n", + "[1187 rows x 19 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data=pd.read_csv(\"cheeses.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "id": "bf3b548c-5ac4-4126-9ae9-5578ad158015", + "metadata": {}, + "source": [ + "## Cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2018aac2-6f3d-489a-b5d0-90b7c7793076", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'pale yellow', 'blue', 'brownish yellow', 'ivory', 'orange', 'brown', 'straw', 'blue-grey', 'cream', 'pale white', 'red', 'green', nan, 'golden yellow', 'white', 'golden orange', 'pink and white', 'yellow'}\n" + ] + }, + { + "data": { + "text/html": [ + "
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cheeseurlmilkcountryregionfamilytypefat_contentcalcium_contenttexturerindcolorflavoraromavegetarianvegansynonymsalt_spellingsproducers
10Acapellahttps://www.cheese.com/acapella/goatUnited StatesCaliforniaNaNsoft, soft-ripenedNaNNaNNaNNaNNaNbutteryfresh, herbalFalseFalseNaNNaNNaN
13Acornhttps://www.cheese.com/acorn/sheepUnited KingdomBethaniaNaNhard, artisan52%NaNcrumbly, firmNaNNaNburnt caramel, citrusy, herbaceousfruityTrueFalseNaNNaNNaN
19Afuega'l Pituhttps://www.cheese.com/afuegal-pitu/cowSpainAsturiasNaNsoft, artisanNaNNaNsmoothcloth wrappedNaNspicy, strongNaNFalseFalseNaNNaNNaN
48Alpe di Frabosahttps://www.cheese.com/alpe-di-frabosa/cowItalyNaNNaNsemi-softNaNNaNNaNNaNNaNbittermilky, mushroomFalseFalseNaNNaNNaN
50Alpicrèmehttps://www.cheese.com/alpicreme/goatFranceNaNNaNsoftNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN
............................................................
1172Strathdon Bluehttps://www.cheese.com/strathdon-blue/cowScotlandTainBluesemi-softNaNNaNcreamyNaNNaNcreamy, spicyaromatic, richTrueFalseNaNNaNHighland Fine Cheeses Limited
1175String Cheesehttps://www.cheese.com/string/NaNNaNNaNNaNsemi-hardNaNNaNchewy, firm, stringyNaNNaNNaNNaNNaNNaNNaNNaNNaN
1177Sulgunihttps://www.cheese.com/sulguni/buffalo, cowGeorgiaSvaneti, SamegreloNaNsemi-firmNaNNaNdense, elasticNaNNaNsalty, smokey , sourNaNNaNNaNGeorgian Pickle CheeseMegruli Sulguni, Shebolili Megruli SulguniNaN
1181Sussex Slipcotehttps://www.cheese.com/sussex-slipcote/sheepEnglandNaNNaNsoftNaNNaNNaNNaNNaNsharpNaNTrueFalseNaNNaNHigh Weald Dairy
1185Sweet Style Swisshttps://www.cheese.com/sweet-style-swiss/NaNSwitzerlandNaNNaNsemi-hard, artisanNaNNaNfirm, supplewaxedNaNnuttynutty, sweetFalseFalseNaNNaNNaN
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142 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " cheese url \\\n", + "10 Acapella https://www.cheese.com/acapella/ \n", + "13 Acorn https://www.cheese.com/acorn/ \n", + "19 Afuega'l Pitu https://www.cheese.com/afuegal-pitu/ \n", + "48 Alpe di Frabosa https://www.cheese.com/alpe-di-frabosa/ \n", + "50 Alpicrème https://www.cheese.com/alpicreme/ \n", + "... ... ... \n", + "1172 Strathdon Blue https://www.cheese.com/strathdon-blue/ \n", + "1175 String Cheese https://www.cheese.com/string/ \n", + "1177 Sulguni https://www.cheese.com/sulguni/ \n", + "1181 Sussex Slipcote https://www.cheese.com/sussex-slipcote/ \n", + "1185 Sweet Style Swiss https://www.cheese.com/sweet-style-swiss/ \n", + "\n", + " milk country region family \\\n", + "10 goat United States California NaN \n", + "13 sheep United Kingdom Bethania NaN \n", + "19 cow Spain Asturias NaN \n", + "48 cow Italy NaN NaN \n", + "50 goat France NaN NaN \n", + "... ... ... ... ... \n", + "1172 cow Scotland Tain Blue \n", + "1175 NaN NaN NaN NaN \n", + "1177 buffalo, cow Georgia Svaneti, Samegrelo NaN \n", + "1181 sheep England NaN NaN \n", + "1185 NaN Switzerland NaN NaN \n", + "\n", + " type fat_content calcium_content texture \\\n", + "10 soft, soft-ripened NaN NaN NaN \n", + "13 hard, artisan 52% NaN crumbly, firm \n", + "19 soft, artisan NaN NaN smooth \n", + "48 semi-soft NaN NaN NaN \n", + "50 soft NaN NaN NaN \n", + "... ... ... ... ... \n", + "1172 semi-soft NaN NaN creamy \n", + "1175 semi-hard NaN NaN chewy, firm, stringy \n", + "1177 semi-firm NaN NaN dense, elastic \n", + "1181 soft NaN NaN NaN \n", + "1185 semi-hard, artisan NaN NaN firm, supple \n", + "\n", + " rind color flavor \\\n", + "10 NaN NaN buttery \n", + "13 NaN NaN burnt caramel, citrusy, herbaceous \n", + "19 cloth wrapped NaN spicy, strong \n", + "48 NaN NaN bitter \n", + "50 NaN NaN NaN \n", + "... ... ... ... \n", + "1172 NaN NaN creamy, spicy \n", + "1175 NaN NaN NaN \n", + "1177 NaN NaN salty, smokey , sour \n", + "1181 NaN NaN sharp \n", + "1185 waxed NaN nutty \n", + "\n", + " aroma vegetarian vegan synonyms \\\n", + "10 fresh, herbal False False NaN \n", + "13 fruity True False NaN \n", + "19 NaN False False NaN \n", + "48 milky, mushroom False False NaN \n", + "50 NaN False False NaN \n", + "... ... ... ... ... \n", + "1172 aromatic, rich True False NaN \n", + "1175 NaN NaN NaN NaN \n", + "1177 NaN NaN NaN Georgian Pickle Cheese \n", + "1181 NaN True False NaN \n", + "1185 nutty, sweet False False NaN \n", + "\n", + " alt_spellings \\\n", + "10 NaN \n", + "13 NaN \n", + "19 NaN \n", + "48 NaN \n", + "50 NaN \n", + "... ... \n", + "1172 NaN \n", + "1175 NaN \n", + "1177 Megruli Sulguni, Shebolili Megruli Sulguni \n", + "1181 NaN \n", + "1185 NaN \n", + "\n", + " producers \n", + "10 NaN \n", + "13 NaN \n", + "19 NaN \n", + "48 NaN \n", + "50 NaN \n", + "... ... \n", + "1172 Highland Fine Cheeses Limited \n", + "1175 NaN \n", + "1177 NaN \n", + "1181 High Weald Dairy \n", + "1185 NaN \n", + "\n", + "[142 rows x 19 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(set(data[\"color\"]))\n", + "data[pd.isnull(data[\"color\"])]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a0a77563-518e-4808-b744-9fc0c76763fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1162\n", + "939\n" + ] + } + ], + "source": [ + "print(len(data[pd.isnull(data[\"calcium_content\"])]))\n", + "print(len(data[pd.isnull(data[\"fat_content\"])]))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "c8489ffa-1067-4eb7-b65a-2fa18fdb4b04", + "metadata": {}, + "outputs": [], + "source": [ + "del data[\"alt_spellings\"]\n", + "del data[\"producers\"]\n", + "del data[\"calcium_content\"]\n", + "del data[\"url\"]\n", + "del data[\"fat_content\"]\n", + "del data[\"synonyms\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "5379265a-cd49-41fa-845c-bfae33bb8f5a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cheesemilkcountryregionfamilytypetexturerindcolorflavoraromavegetarianvegan
0AarewassercowSwitzerlandNaNNaNsemi-softbutterywashedyellowsweetbutteryFalseFalse
1Abbaye de BellocsheepFrancePays BasqueNaNsemi-hard, artisancreamy, dense, firmnaturalyellowburnt caramellanolineTrueFalse
2Abbaye de BelvalcowFranceNaNNaNsemi-hardelasticwashedivoryNaNaromaticFalseFalse
3Abbaye de CiteauxcowFranceBurgundyNaNsemi-soft, artisan, brinedcreamy, dense, smoothwashedwhiteacidic, milky, smoothbarnyardy, earthyFalseFalse
4Abbaye de TamiécowFranceSavoieNaNsoft, artisancreamy, open, smoothwashedwhitefruity, nuttyperfumed, pungentFalseFalse
..........................................
1182SveciaostcowSwedenLow-laying regionsNaNsemi-hard, brinedcreamy, supplerindlesspale yellowacidicNaNFalseFalse
1183SwaggoatAustraliaSouth AustraliaNaNfresh firm, artisancreamy, crumblyash coatedwhiteacidic, creamyfreshTrueFalse
1184SwaledalesheepEnglandSwaledale, North YorkshireNaNhardsemi firmNaNyellowsmooth, sweetfloralTrueFalse
1185Sweet Style SwissNaNSwitzerlandNaNNaNsemi-hard, artisanfirm, supplewaxedNaNnuttynutty, sweetFalseFalse
1186Swiss cheesecowUnited StatesNaNSwiss Cheesehard, artisan, processedfirmrindlesspale yellownutty, sweetNaNTrueFalse
\n", + "

1187 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " cheese milk country region \\\n", + "0 Aarewasser cow Switzerland NaN \n", + "1 Abbaye de Belloc sheep France Pays Basque \n", + "2 Abbaye de Belval cow France NaN \n", + "3 Abbaye de Citeaux cow France Burgundy \n", + "4 Abbaye de Tamié cow France Savoie \n", + "... ... ... ... ... \n", + "1182 Sveciaost cow Sweden Low-laying regions \n", + "1183 Swag goat Australia South Australia \n", + "1184 Swaledale sheep England Swaledale, North Yorkshire \n", + "1185 Sweet Style Swiss NaN Switzerland NaN \n", + "1186 Swiss cheese cow United States NaN \n", + "\n", + " family type texture \\\n", + "0 NaN semi-soft buttery \n", + "1 NaN semi-hard, artisan creamy, dense, firm \n", + "2 NaN semi-hard elastic \n", + "3 NaN semi-soft, artisan, brined creamy, dense, smooth \n", + "4 NaN soft, artisan creamy, open, smooth \n", + "... ... ... ... \n", + "1182 NaN semi-hard, brined creamy, supple \n", + "1183 NaN fresh firm, artisan creamy, crumbly \n", + "1184 NaN hard semi firm \n", + "1185 NaN semi-hard, artisan firm, supple \n", + "1186 Swiss Cheese hard, artisan, processed firm \n", + "\n", + " rind color flavor aroma \\\n", + "0 washed yellow sweet buttery \n", + "1 natural yellow burnt caramel lanoline \n", + "2 washed ivory NaN aromatic \n", + "3 washed white acidic, milky, smooth barnyardy, earthy \n", + "4 washed white fruity, nutty perfumed, pungent \n", + "... ... ... ... ... \n", + "1182 rindless pale yellow acidic NaN \n", + "1183 ash coated white acidic, creamy fresh \n", + "1184 NaN yellow smooth, sweet floral \n", + "1185 waxed NaN nutty nutty, sweet \n", + "1186 rindless pale yellow nutty, sweet NaN \n", + "\n", + " vegetarian vegan \n", + "0 False False \n", + "1 True False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "1182 False False \n", + "1183 True False \n", + "1184 True False \n", + "1185 False False \n", + "1186 True False \n", + "\n", + "[1187 rows x 13 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "633ed80e-e416-41f6-ae58-b86ce4c132af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1181 rows remaining\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13743/3522053431.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"country\"]=data[\"country\"].fillna(\"\")\n", + "/tmp/ipykernel_13743/3522053431.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"region\"]=data[\"region\"].fillna(\"\")\n" + ] + } + ], + "source": [ + "data=data.dropna(subset=[\"country\",\"region\"], how=\"all\")\n", + "data[\"country\"]=data[\"country\"].fillna(\"\")\n", + "data[\"region\"]=data[\"region\"].fillna(\"\")\n", + "print(f\"{len(data)} rows remaining\")" + ] + }, + { + "cell_type": "markdown", + "id": "fd66568f-78d4-4e1a-a91c-8ec483b4b03c", + "metadata": {}, + "source": [ + "We removed 6 rows for which we could not find a suitable location. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a4c0e30-8535-498b-9a9e-0d7d232d4eb7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "7ef7494b-ff08-40a5-890f-e0f718cf2842", + "metadata": {}, + "outputs": [], + "source": [ + "data.loc[data.country.str.contains(\"England, Great Britain, United Kingdom\")|data.country.str.contains(\"England, United Kingdom\"),\"country\"]=\"England\"\n", + "data.loc[data.country.str.contains(\"Scotland\"),\"country\"]=\"Scotland\"\n", + "data.loc[data.country.str.contains(\"Great Britain, United Kingdom, Wales\")|data.country.str.contains(\"United Kingdom, Wales\"),\"country\"]=\"Wales\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "fb044984-c33c-492c-91a2-4e9fff29ceb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39\n" + ] + }, + { + "data": { + "text/html": [ + "
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cheesemilkcountryregionfamilytypetexturerindcolorflavoraromavegetarianvegan
12Ackawicow, goat, sheepCyprus, Egypt, Israel, Jordan, Lebanon, Middle...+Fetasoft, brinedelastic, smooth, springynaturalwhitemild, milky, saltymild, milkyFalseFalse
116Baladicow, goat, sheepLebanon, Middle EastNaNfresh soft, artisancreamy, dense, smoothrindlesswhitebuttery, mild, salty, sweetfreshFalseFalse
160Beemster 2% MilkcowCanada, Denmark, France, Germany, Netherlands,...NaNsemi-softsmoothNaNNaNnuttyaromatic, floral, fruityFalseFalse
212Blissful BlocksNaNCanada, United StatesCheddarhardcreamy, crumblyplasticyellowcreamy, savory, sharp, spicyNaNTrueFalse
213Blissful ToppingsNaNCanada, United StatesParmesansoftcrumblyartificialyellowsavory, sharpNaNTrueFalse
243Bootleggercow, sheepCanada, ItalyLombardyNaNhard, artisancrumbly, firmnaturalpale yellowfruity, full-flavored, strongfloralNaNNaN
262Brebis d'AzuresheepCanada, ItalyLombardyBluesemi-hard, artisan, blue-veinedsoftnaturalpale yellowsharparomaticNaNNaN
297Brunostcow, goatDenmark, Finland, Germany, Iceland, Norway, Sw...NaNsemi-soft, wheydensenaturalbrowncaramel, sweetNaNNaNNaN
300BryndzasheepHungary, Poland, SlovakiaNaNsoft, artisanspreadablerindlesswhitemild, saltyNaNNaNNaN
311Burratawater buffaloItaly, United StatesApuliaMozzarellafresh soft, artisancreamy, stringyleaf wrappedwhitebuttery, milkyfresh, milkyFalseFalse
316ButterkasecowAustria, GermanyNaNsemi-softcreamy, smooth, spreadablenaturalpale yellowbuttery, mildNaNFalseFalse
367Cap CressygoatCanada, ItalyLombardyNaNsemi-hard, artisan, smear-ripenedcompact, densewashedpale yellowmellow, savory, sweetlacticNaNNaN
375Capri BlugoatCanada, ItalyLombardyBluesoft, blue-veinedcreamy, softnaturalpale yellowcreamy, subtle, sweetgoatyNaNNaN
377CapricegoatCanada, ItalyLombardyNaNsoftcreamy, smoothnaturalwhitesubtlegoatyNaNNaN
407Casu marzusheepFrance, ItalySardinia (Italy), Southern Corsica (France)NaNsoft, soft-ripenedsoft-ripenednaturalNaNNaNNaNNaNNaN
437Cheese CurdsNaNCanada, India, United StatesCheddarfresh firmfirm, springynaturalwhitemild, milkyfreshNaNNaN
445Chhurpicow, yakChina, Nepal, TibetCottagesoft, hard, artisandensenaturalpale yellowtangyNaNNaNNaN
455Chura KampoyakChina, TibetTibetNaNhard, artisandense, dry, firmnaturalNaNNaNaromaticNaNNaN
508Cottage CheesecowUnited Kingdom, United StatesCottagesoft, artisan, processedcreamy, crumblyrindlesswhitesweetNaNTrueFalse
512Counting Sheep…and Goats...cow, goat, sheepCanada, ItalyLombardyNaNsoft, soft-ripenedcreamy, softNaNwhitecreamy, subtle, sweetnuttyNaNNaN
524Cream Cheesy BlissNaNCanada, United StatesNaNsoftcreamy, spreadableartificialwhitecreamy, garlicky, herbaceous, sweetrichTrueFalse
537Cressy BlucowCanada, ItalyLombardyBluesemi-hard, blue-veinedcreamy, crumbly, firmnaturalpale yellowcreamy, savory, sweetNaNNaNNaN
569La Couronne - Fort Aged ComtécowFrance, SwitzerlandFranche ComtéNaNsemi-harddenseNaNpale yellowcaramel, nutty, sweetrichNaNNaN
601Lamb ChoppersheepNetherlands, United StatesCaliforniaGoudaharddense, firm, smoothwaxedivorybuttery, caramel, creamy, nutty, sweetmild, sweetTrueFalse
621Le Conquerant Demi Pont L'evequecowAustralia, FranceNaNsoft, artisanchalky, creamy, softwashedNaNcreamy, mildpungentNaNNaN
646LimburgercowBelgium, Germany, NetherlandsDuchy of LimburgNaNsemi-soft, smear-ripenedcrumbly, firm, smoothwashedstrawgrassy, mild, mushroomystinkyFalseFalse
675LunettacowCanada, ItalyLombardyNaNfresh soft, artisancreamynaturalwhitecreamy, saltyaromatic, freshNaNNaN
701MamirollecowCanada, FrancePlessisville, QuebecNaNsemi-soft, artisanchewy, supplewashedivorybuttery, fruity, salty, sweetearthy, pungentFalseFalse
776Monastery CheesescowBelgium, Canada, France, Switzerland, United S...NaNsoft, semi-soft, brinedchalky, creamy, firm, grainywashedgolden yellowmild, pungentpungent, strongFalseFalse
789Monterey JackcowMexico, United StatesMonterey, CaliforniaMonterey Jacksemi-hardcompact, creamy, firm, open, suppleNaNpale yellowbuttery, mildaromaticNaNNaN
852Paneercow, water buffaloBangladesh, IndiaCottagefresh firmcrumbly, firmrindlesswhitemilkyfresh, milkyTrueFalse
975President Fat Free FetacowFrance, United StatesNew YorkFetafirm, artisan, brinedcrumblynaturalwhiteherbaceous, salty, tangyfreshNaNNaN
1001Purple's a Mustcow, goatCanada, ItalyLombardyBluesemi-hard, artisan, blue-veinedcreamy, crumblymold ripenedpale yellowfull-flavored, strongrichNaNNaN
1082Shanklishcow, sheepEgypt, Lebanon, SyriaFetafresh firm, hard, artisancreamy, crumbly, firmmold ripenedwhitesharp, spicy, strongpungent, strongFalseFalse
1099Shredded BlissNaNCanada, United StatesMozzarellasemi-softelastic, smooth, springy, stringyplasticpale yellowmild, milkyfresh, mildTrueFalse
1106Sirenecow, goat, sheepAlbania, Bulgaria, Croatia, Greece, Israel, Ma...TrakiaFetafresh soft, brinedcrumbly, grainy, smoothnaturalwhitelemony, salty, sharp, tangystrongFalseFalse
1108Slices Of BlissNaNCanada, United StatesCheddarsoftcreamyplasticyellowcreamy, savory, sharp, spicyNaNTrueFalse
1123Soshagoat, yakChina, Nepal, TibetTibetNaNsoft, artisancreamynaturalwhitepungent, strongpungent, strongNaNNaN
1173Strawberry MooncowCanada, ItalyLombardyNaNsemi-hard, artisan, smear-ripenedcompact, creamy, densewashedstrawsubtle, sweetstrongNaNNaN
\n", + "
" + ], + "text/plain": [ + " cheese milk \\\n", + "12 Ackawi cow, goat, sheep \n", + "116 Baladi cow, goat, sheep \n", + "160 Beemster 2% Milk cow \n", + "212 Blissful Blocks NaN \n", + "213 Blissful Toppings NaN \n", + "243 Bootlegger cow, sheep \n", + "262 Brebis d'Azure sheep \n", + "297 Brunost cow, goat \n", + "300 Bryndza sheep \n", + "311 Burrata water buffalo \n", + "316 Butterkase cow \n", + "367 Cap Cressy goat \n", + "375 Capri Blu goat \n", + "377 Caprice goat \n", + "407 Casu marzu sheep \n", + "437 Cheese Curds NaN \n", + "445 Chhurpi cow, yak \n", + "455 Chura Kampo yak \n", + "508 Cottage Cheese cow \n", + "512 Counting Sheep…and Goats... cow, goat, sheep \n", + "524 Cream Cheesy Bliss NaN \n", + "537 Cressy Blu cow \n", + "569 La Couronne - Fort Aged Comté cow \n", + "601 Lamb Chopper sheep \n", + "621 Le Conquerant Demi Pont L'eveque cow \n", + "646 Limburger cow \n", + "675 Lunetta cow \n", + "701 Mamirolle cow \n", + "776 Monastery Cheeses cow \n", + "789 Monterey Jack cow \n", + "852 Paneer cow, water buffalo \n", + "975 President Fat Free Feta cow \n", + "1001 Purple's a Must cow, goat \n", + "1082 Shanklish cow, sheep \n", + "1099 Shredded Bliss NaN \n", + "1106 Sirene cow, goat, sheep \n", + "1108 Slices Of Bliss NaN \n", + "1123 Sosha goat, yak \n", + "1173 Strawberry Moon cow \n", + "\n", + " country \\\n", + "12 Cyprus, Egypt, Israel, Jordan, Lebanon, Middle... \n", + "116 Lebanon, Middle East \n", + "160 Canada, Denmark, France, Germany, Netherlands,... \n", + "212 Canada, United States \n", + "213 Canada, United States \n", + "243 Canada, Italy \n", + "262 Canada, Italy \n", + "297 Denmark, Finland, Germany, Iceland, Norway, Sw... \n", + "300 Hungary, Poland, Slovakia \n", + "311 Italy, United States \n", + "316 Austria, Germany \n", + "367 Canada, Italy \n", + "375 Canada, Italy \n", + "377 Canada, Italy \n", + "407 France, Italy \n", + "437 Canada, India, United States \n", + "445 China, Nepal, Tibet \n", + "455 China, Tibet \n", + "508 United Kingdom, United States \n", + "512 Canada, Italy \n", + "524 Canada, United States \n", + "537 Canada, Italy \n", + "569 France, Switzerland \n", + "601 Netherlands, United States \n", + "621 Australia, France \n", + "646 Belgium, Germany, Netherlands \n", + "675 Canada, Italy \n", + "701 Canada, France \n", + "776 Belgium, Canada, France, Switzerland, United S... \n", + "789 Mexico, United States \n", + "852 Bangladesh, India \n", + "975 France, United States \n", + "1001 Canada, Italy \n", + "1082 Egypt, Lebanon, Syria \n", + "1099 Canada, United States \n", + "1106 Albania, Bulgaria, Croatia, Greece, Israel, Ma... \n", + "1108 Canada, United States \n", + "1123 China, Nepal, Tibet \n", + "1173 Canada, Italy \n", + "\n", + " region family \\\n", + "12 + Feta \n", + "116 NaN \n", + "160 NaN \n", + "212 Cheddar \n", + "213 Parmesan \n", + "243 Lombardy NaN \n", + "262 Lombardy Blue \n", + "297 NaN \n", + "300 NaN \n", + "311 Apulia Mozzarella \n", + "316 NaN \n", + "367 Lombardy NaN \n", + "375 Lombardy Blue \n", + "377 Lombardy NaN \n", + "407 Sardinia (Italy), Southern Corsica (France) NaN \n", + "437 Cheddar \n", + "445 Cottage \n", + "455 Tibet NaN \n", + "508 Cottage \n", + "512 Lombardy NaN \n", + "524 NaN \n", + "537 Lombardy Blue \n", + "569 Franche Comté NaN \n", + "601 California Gouda \n", + "621 NaN \n", + "646 Duchy of Limburg NaN \n", + "675 Lombardy NaN \n", + "701 Plessisville, Quebec NaN \n", + "776 NaN \n", + "789 Monterey, California Monterey Jack \n", + "852 Cottage \n", + "975 New York Feta \n", + "1001 Lombardy Blue \n", + "1082 Feta \n", + "1099 Mozzarella \n", + "1106 Trakia Feta \n", + "1108 Cheddar \n", + "1123 Tibet NaN \n", + "1173 Lombardy NaN \n", + "\n", + " type texture \\\n", + "12 soft, brined elastic, smooth, springy \n", + "116 fresh soft, artisan creamy, dense, smooth \n", + "160 semi-soft smooth \n", + "212 hard creamy, crumbly \n", + "213 soft crumbly \n", + "243 hard, artisan crumbly, firm \n", + "262 semi-hard, artisan, blue-veined soft \n", + "297 semi-soft, whey dense \n", + "300 soft, artisan spreadable \n", + "311 fresh soft, artisan creamy, stringy \n", + "316 semi-soft creamy, smooth, spreadable \n", + "367 semi-hard, artisan, smear-ripened compact, dense \n", + "375 soft, blue-veined creamy, soft \n", + "377 soft creamy, smooth \n", + "407 soft, soft-ripened soft-ripened \n", + "437 fresh firm firm, springy \n", + "445 soft, hard, artisan dense \n", + "455 hard, artisan dense, dry, firm \n", + "508 soft, artisan, processed creamy, crumbly \n", + "512 soft, soft-ripened creamy, soft \n", + "524 soft creamy, spreadable \n", + "537 semi-hard, blue-veined creamy, crumbly, firm \n", + "569 semi-hard dense \n", + "601 hard dense, firm, smooth \n", + "621 soft, artisan chalky, creamy, soft \n", + "646 semi-soft, smear-ripened crumbly, firm, smooth \n", + "675 fresh soft, artisan creamy \n", + "701 semi-soft, artisan chewy, supple \n", + "776 soft, semi-soft, brined chalky, creamy, firm, grainy \n", + "789 semi-hard compact, creamy, firm, open, supple \n", + "852 fresh firm crumbly, firm \n", + "975 firm, artisan, brined crumbly \n", + "1001 semi-hard, artisan, blue-veined creamy, crumbly \n", + "1082 fresh firm, hard, artisan creamy, crumbly, firm \n", + "1099 semi-soft elastic, smooth, springy, stringy \n", + "1106 fresh soft, brined crumbly, grainy, smooth \n", + "1108 soft creamy \n", + "1123 soft, artisan creamy \n", + "1173 semi-hard, artisan, smear-ripened compact, creamy, dense \n", + "\n", + " rind color flavor \\\n", + "12 natural white mild, milky, salty \n", + "116 rindless white buttery, mild, salty, sweet \n", + "160 NaN NaN nutty \n", + "212 plastic yellow creamy, savory, sharp, spicy \n", + "213 artificial yellow savory, sharp \n", + "243 natural pale yellow fruity, full-flavored, strong \n", + "262 natural pale yellow sharp \n", + "297 natural brown caramel, sweet \n", + "300 rindless white mild, salty \n", + "311 leaf wrapped white buttery, milky \n", + "316 natural pale yellow buttery, mild \n", + "367 washed pale yellow mellow, savory, sweet \n", + "375 natural pale yellow creamy, subtle, sweet \n", + "377 natural white subtle \n", + "407 natural NaN NaN \n", + "437 natural white mild, milky \n", + "445 natural pale yellow tangy \n", + "455 natural NaN NaN \n", + "508 rindless white sweet \n", + "512 NaN white creamy, subtle, sweet \n", + "524 artificial white creamy, garlicky, herbaceous, sweet \n", + "537 natural pale yellow creamy, savory, sweet \n", + "569 NaN pale yellow caramel, nutty, sweet \n", + "601 waxed ivory buttery, caramel, creamy, nutty, sweet \n", + "621 washed NaN creamy, mild \n", + "646 washed straw grassy, mild, mushroomy \n", + "675 natural white creamy, salty \n", + "701 washed ivory buttery, fruity, salty, sweet \n", + "776 washed golden yellow mild, pungent \n", + "789 NaN pale yellow buttery, mild \n", + "852 rindless white milky \n", + "975 natural white herbaceous, salty, tangy \n", + "1001 mold ripened pale yellow full-flavored, strong \n", + "1082 mold ripened white sharp, spicy, strong \n", + "1099 plastic pale yellow mild, milky \n", + "1106 natural white lemony, salty, sharp, tangy \n", + "1108 plastic yellow creamy, savory, sharp, spicy \n", + "1123 natural white pungent, strong \n", + "1173 washed straw subtle, sweet \n", + "\n", + " aroma vegetarian vegan \n", + "12 mild, milky False False \n", + "116 fresh False False \n", + "160 aromatic, floral, fruity False False \n", + "212 NaN True False \n", + "213 NaN True False \n", + "243 floral NaN NaN \n", + "262 aromatic NaN NaN \n", + "297 NaN NaN NaN \n", + "300 NaN NaN NaN \n", + "311 fresh, milky False False \n", + "316 NaN False False \n", + "367 lactic NaN NaN \n", + "375 goaty NaN NaN \n", + "377 goaty NaN NaN \n", + "407 NaN NaN NaN \n", + "437 fresh NaN NaN \n", + "445 NaN NaN NaN \n", + "455 aromatic NaN NaN \n", + "508 NaN True False \n", + "512 nutty NaN NaN \n", + "524 rich True False \n", + "537 NaN NaN NaN \n", + "569 rich NaN NaN \n", + "601 mild, sweet True False \n", + "621 pungent NaN NaN \n", + "646 stinky False False \n", + "675 aromatic, fresh NaN NaN \n", + "701 earthy, pungent False False \n", + "776 pungent, strong False False \n", + "789 aromatic NaN NaN \n", + "852 fresh, milky True False \n", + "975 fresh NaN NaN \n", + "1001 rich NaN NaN \n", + "1082 pungent, strong False False \n", + "1099 fresh, mild True False \n", + "1106 strong False False \n", + "1108 NaN True False \n", + "1123 pungent, strong NaN NaN \n", + "1173 strong NaN NaN " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(len(data[data[\"country\"].str.contains(\",\")]))\n", + "data[data[\"country\"].str.contains(\",\")]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "43058589-f931-46ad-99a7-44be63f962cc", + "metadata": {}, + "outputs": [], + "source": [ + "data=data.drop(index=data[data[\"country\"].str.contains(\",\")].index)" + ] + }, + { + "cell_type": "markdown", + "id": "2f42c973-247a-4f51-947e-fbd76f8f12fc", + "metadata": {}, + "source": [ + "We removed 39 cheeses because they can come froms several countries. " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "59c4e6e7-d624-45a5-a9ea-eb375102b771", + "metadata": {}, + "outputs": [], + "source": [ + "data[\"location\"]=data[\"region\"]+\", \"+data[\"country\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "0dee0f25-4699-4e46-97d0-21bb36d9c603", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cheesemilkcountryregionfamilytypetexturerindcolorflavoraromavegetarianveganlocation
0AarewassercowSwitzerlandNaNsemi-softbutterywashedyellowsweetbutteryFalseFalse, Switzerland
1Abbaye de BellocsheepFrancePays BasqueNaNsemi-hard, artisancreamy, dense, firmnaturalyellowburnt caramellanolineTrueFalsePays Basque, France
2Abbaye de BelvalcowFranceNaNsemi-hardelasticwashedivoryNaNaromaticFalseFalse, France
3Abbaye de CiteauxcowFranceBurgundyNaNsemi-soft, artisan, brinedcreamy, dense, smoothwashedwhiteacidic, milky, smoothbarnyardy, earthyFalseFalseBurgundy, France
4Abbaye de TamiécowFranceSavoieNaNsoft, artisancreamy, open, smoothwashedwhitefruity, nuttyperfumed, pungentFalseFalseSavoie, France
.............................................
1182SveciaostcowSwedenLow-laying regionsNaNsemi-hard, brinedcreamy, supplerindlesspale yellowacidicNaNFalseFalseLow-laying regions, Sweden
1183SwaggoatAustraliaSouth AustraliaNaNfresh firm, artisancreamy, crumblyash coatedwhiteacidic, creamyfreshTrueFalseSouth Australia, Australia
1184SwaledalesheepEnglandSwaledale, North YorkshireNaNhardsemi firmNaNyellowsmooth, sweetfloralTrueFalseSwaledale, North Yorkshire, England
1185Sweet Style SwissNaNSwitzerlandNaNsemi-hard, artisanfirm, supplewaxedNaNnuttynutty, sweetFalseFalse, Switzerland
1186Swiss cheesecowUnited StatesSwiss Cheesehard, artisan, processedfirmrindlesspale yellownutty, sweetNaNTrueFalse, United States
\n", + "

1142 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " cheese milk country region \\\n", + "0 Aarewasser cow Switzerland \n", + "1 Abbaye de Belloc sheep France Pays Basque \n", + "2 Abbaye de Belval cow France \n", + "3 Abbaye de Citeaux cow France Burgundy \n", + "4 Abbaye de Tamié cow France Savoie \n", + "... ... ... ... ... \n", + "1182 Sveciaost cow Sweden Low-laying regions \n", + "1183 Swag goat Australia South Australia \n", + "1184 Swaledale sheep England Swaledale, North Yorkshire \n", + "1185 Sweet Style Swiss NaN Switzerland \n", + "1186 Swiss cheese cow United States \n", + "\n", + " family type texture \\\n", + "0 NaN semi-soft buttery \n", + "1 NaN semi-hard, artisan creamy, dense, firm \n", + "2 NaN semi-hard elastic \n", + "3 NaN semi-soft, artisan, brined creamy, dense, smooth \n", + "4 NaN soft, artisan creamy, open, smooth \n", + "... ... ... ... \n", + "1182 NaN semi-hard, brined creamy, supple \n", + "1183 NaN fresh firm, artisan creamy, crumbly \n", + "1184 NaN hard semi firm \n", + "1185 NaN semi-hard, artisan firm, supple \n", + "1186 Swiss Cheese hard, artisan, processed firm \n", + "\n", + " rind color flavor aroma \\\n", + "0 washed yellow sweet buttery \n", + "1 natural yellow burnt caramel lanoline \n", + "2 washed ivory NaN aromatic \n", + "3 washed white acidic, milky, smooth barnyardy, earthy \n", + "4 washed white fruity, nutty perfumed, pungent \n", + "... ... ... ... ... \n", + "1182 rindless pale yellow acidic NaN \n", + "1183 ash coated white acidic, creamy fresh \n", + "1184 NaN yellow smooth, sweet floral \n", + "1185 waxed NaN nutty nutty, sweet \n", + "1186 rindless pale yellow nutty, sweet NaN \n", + "\n", + " vegetarian vegan location \n", + "0 False False , Switzerland \n", + "1 True False Pays Basque, France \n", + "2 False False , France \n", + "3 False False Burgundy, France \n", + "4 False False Savoie, France \n", + "... ... ... ... \n", + "1182 False False Low-laying regions, Sweden \n", + "1183 True False South Australia, Australia \n", + "1184 True False Swaledale, North Yorkshire, England \n", + "1185 False False , Switzerland \n", + "1186 True False , United States \n", + "\n", + "[1142 rows x 14 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ef7351c-f117-403b-bc6e-f9f30a98c9d2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "353724eb-8d64-4b64-84c6-f06be36acd8b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54d56bd4-c83a-4e8c-8751-b4b2f7830a9e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd68f1bb-c9f6-4c57-951b-8ac1f3192f09", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeff487e-9b66-4c4b-b4f6-dc5352fb2144", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdb0d04c-e0f2-4553-8906-e9282f4942d2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a551e0f4-3f99-4dae-9b31-6205b772ebf5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c02cc29-fe07-4ff9-8c6b-8638d37830cd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de579631-a29c-4620-9bbf-7085b83d16b7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "e2e868d4-33a1-4602-af97-afb1d29e612f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{', Afghanistan',\n", + " ', Argentina',\n", + " ', Armenia',\n", + " ', Australia',\n", + " ', Austria',\n", + " ', Belgium',\n", + " ', Brazil',\n", + " ', Canada',\n", + " ', Cyprus',\n", + " ', Denmark',\n", + " ', England',\n", + " ', France',\n", + " ', Germany',\n", + " ', Greece',\n", + " ', Holland',\n", + " ', Hungary',\n", + " ', Iceland',\n", + " ', Iraq',\n", + " ', Ireland',\n", + " ', Israel',\n", + " ', Italy',\n", + " ', Mauritania',\n", + " ', Mexico',\n", + " ', Mexico and Caribbean',\n", + " ', Middle East',\n", + " ', Mongolia',\n", + " ', Netherlands',\n", + " ', New Zealand',\n", + " ', Poland',\n", + " ', Portugal',\n", + " ', Scotland',\n", + " ', Serbia',\n", + " ', Spain',\n", + " ', Sweden',\n", + " ', Switzerland',\n", + " ', Turkey',\n", + " ', United Kingdom',\n", + " ', United States',\n", + " ', Wales',\n", + " 'Aberdeenshire, Scotland',\n", + " 'Aconcagua, Chile',\n", + " 'Adamstown, Co Wexford, Ireland',\n", + " 'Airedale farming district, New Zealand',\n", + " 'Alba, Italy',\n", + " 'All Holland, Netherlands',\n", + " 'Allagau, Bavarian Alps, Germany',\n", + " 'Allgaeu Alps, Germany',\n", + " 'Allgau, Germany',\n", + " 'Allgäu, Germany',\n", + " 'Amou, Gascony, France',\n", + " 'Anjou, France',\n", + " 'Ann Arbor, MI, United States',\n", + " 'Ann Arbor, Michigan, United States',\n", + " 'Aquitaine, France',\n", + " 'Asiago, Italy',\n", + " 'Asturias, Spain',\n", + " 'Auvergne, France',\n", + " 'Auvergne, Salers, France',\n", + " 'Averyon, France',\n", + " 'Avesnes, France',\n", + " 'Aveyron, France',\n", + " 'Aveyron, Laguiole, France',\n", + " 'Avila, Spain',\n", + " 'Azores, Portugal',\n", + " 'Ballarat, Victoria, Australia',\n", + " 'Banks Peninsular in Canterbury, New Zealand',\n", + " 'Banon, France',\n", + " 'Barcelona, Spain',\n", + " 'Bas-Languedoc, Comtat Venaissin, France',\n", + " 'Basilicata, Italy',\n", + " 'Basque, Pyrenees Mountains, France',\n", + " 'Bavaria, Germany',\n", + " 'Beara Peninsula, Co. Cork, Ireland',\n", + " 'Beira Baixa Province, Portugal',\n", + " 'Belvederis, Lithuania',\n", + " 'Bergues, France',\n", + " 'Bermondsey, London, England',\n", + " 'Berry, France',\n", + " 'Bethania, United Kingdom',\n", + " 'Bjurholm, Sweden',\n", + " 'Blarney, Ireland',\n", + " 'Bloomdale, United States',\n", + " 'Bornholm, Denmark',\n", + " 'Bourgogne, France',\n", + " 'Bregenzerwald, Kleinwalsertal, Großwalsertal, Laiblachtal (Pfänderstock) and Rheintal, Austria',\n", + " 'Brickhill, Co. Clare, Ireland',\n", + " 'Brisbane, Australia',\n", + " 'British Columbia, Canada',\n", + " 'Brittany, France',\n", + " 'Brooklyn NY, United States',\n", + " 'Burgund, France',\n", + " 'Burgundy, France',\n", + " 'Bursa, Turkey',\n", + " 'Buxton, Derbyshire, England',\n", + " 'Béarnaise in Pyrénées-Atlantique, France',\n", + " 'Calabria, Italy',\n", + " 'California, United States',\n", + " 'Campania, Italy',\n", + " 'Campania, Paestum, Foggia, Italy',\n", + " 'Canary Islands, Spain',\n", + " 'Canton of Glarus, Switzerland',\n", + " 'Carmarthenshire, Wales',\n", + " 'Carneros, Sonoma, California, United States',\n", + " 'Carnia, Italy',\n", + " 'Carrigtwohill, ',\n", + " 'Carrigtwohill, Ireland',\n", + " 'Castelo Branco, Fundão and Idanha-a-Nova, Portugal',\n", + " 'Castile-Leon, Spain',\n", + " 'Castilla Leon, Spain',\n", + " 'Castille-Leon, Spain',\n", + " 'Central Balkan Mountains, Bulgaria',\n", + " 'Central and Western Macedonia, Thessalia, Greece',\n", + " 'Central and Western Macedonia, Thessaly, Greece',\n", + " 'Centre , the department of Loiret, France',\n", + " 'Centre-Val de Loire, France',\n", + " 'Ceredigion, United Kingdom',\n", + " 'Cevenes, France',\n", + " 'Charentes, France',\n", + " 'Charentes-Poitou, France',\n", + " 'Charm, Ohio, United States',\n", + " 'Chelmarsh, Bridgnorth, Shropshire, England',\n", + " 'Cheshire, England',\n", + " 'Chirac, France',\n", + " 'Co Clare, Ireland',\n", + " 'Co Limerick, Ireland',\n", + " 'Co. Carlow, Ireland',\n", + " 'Co. Cork, Ireland',\n", + " 'Co. Mayo, Ireland',\n", + " 'Co. Offaly, Ireland',\n", + " 'Coast of Oregon, United States',\n", + " 'Colby, Wisconsin, United States',\n", + " 'Colorado, United States',\n", + " 'Comox Valley, Vancouver Island, Canada',\n", + " 'Coquet, England',\n", + " 'Cornwall, ',\n", + " 'Cornwall, England',\n", + " 'Corsica, France',\n", + " 'Cotherstone, England',\n", + " 'Cotswolds, England',\n", + " 'County Antrim, Ireland',\n", + " 'County Carlow, Ireland',\n", + " 'County Cavan, Ireland',\n", + " 'County Tipperary, Clogheen, Ireland',\n", + " 'County Wexford, Ireland',\n", + " 'Croisy-sur-Eure, France',\n", + " 'Crotone, Italy',\n", + " 'Cumbrian, United Kingdom',\n", + " 'Dalmatia, Croatia',\n", + " 'Derbyshire, Leicestershire, Nottinghamshire, England',\n", + " 'Devon, England',\n", + " 'Dorset, England',\n", + " 'Duhallow, Ireland',\n", + " 'Dumfries, Scotland',\n", + " 'Dumfriesshire, Scotland',\n", + " 'East Midlands, England',\n", + " 'East Sussex, United Kingdom',\n", + " 'Emilia Romagna, Italy',\n", + " 'Emilia-Romagna, Italy',\n", + " 'Extremadura, Spain',\n", + " 'Fairview, United States',\n", + " 'Fethard, Co Tipperary, Ireland',\n", + " 'Fife, Scotland',\n", + " 'Flanders, Belgium',\n", + " 'Fornells de la Selva, Gironès, Spain',\n", + " 'Franche Comté, France',\n", + " 'French Basque Country, Midi-Pyrénées, France',\n", + " 'Friuli Venezia Giulia and Veneto, Italy',\n", + " 'Friuli-Venezia Giulia and the Veneto, Italy',\n", + " 'Friuli-Venezia Giulia, Italy',\n", + " 'Galax, Virginia, United States',\n", + " 'Galicia, Spain',\n", + " 'Georgia, United States',\n", + " 'Gevrey-Chambertin, Burgundy, France',\n", + " 'Gippsland, Victoria, Australia',\n", + " 'Gloucestershire County, England',\n", + " 'Gloucestershire, England',\n", + " 'Gravina in Puglia, Murgia, Italy',\n", + " 'Greensboro, VT, United States',\n", + " 'Greenville, Indiana, United States',\n", + " 'Gujarat, India',\n", + " 'Gâtinais, France',\n", + " 'Hamilton, New Zealand',\n", + " 'Haute Vienne, France',\n", + " 'Haute-Savoie / Upper Savoy, France',\n", + " 'Herault, France',\n", + " 'Herefordshire, West Midlands, United Kingdom',\n", + " 'Het Groene Hart, Netherlands',\n", + " 'Huizen, Netherlands',\n", + " 'Hunter Valley, Australia',\n", + " 'Ile de France, France',\n", + " 'Ile-de-France/Champagne, France',\n", + " 'Illinois, United States',\n", + " 'Illoud (Haute-Marne), France',\n", + " 'Inagh, Co Clare, ',\n", + " 'Inagh, Co Clare, Ireland',\n", + " 'Indiana, United States',\n", + " 'Iowa, United States',\n", + " 'Isere, France',\n", + " 'Island of Pag, Croatia',\n", + " 'Jura, Switzerland',\n", + " 'Karlovy Vary, Czech Republic',\n", + " 'Kent, United Kingdom',\n", + " 'Kilmallock County Limerick, Ireland',\n", + " 'Kimball, United States',\n", + " 'Kinfauns, Perthshire, Scotland',\n", + " 'La Velle, Wisconsin, United States',\n", + " 'Lanarkshire, Scotland',\n", + " 'Landford, England',\n", + " 'Landshut, Germany',\n", + " 'Languedoc, France',\n", + " 'Languedoc-Roussillon, France',\n", + " 'Lapland, Finland',\n", + " 'Laqueuille, France',\n", + " 'Laruns, France',\n", + " 'Larzac, France',\n", + " 'Lazio, Sardinia, Italy',\n", + " 'Lebanon, CT, United States',\n", + " 'Leiden, Netherlands',\n", + " 'Lincolnshire, England',\n", + " 'Lodi, Italy',\n", + " 'Loire Valley, France',\n", + " 'Loire, France',\n", + " 'Lombardy, Italy',\n", + " 'Low-laying regions, Sweden',\n", + " 'Lower Normandy, France',\n", + " \"Lucerne, Schwyz, Unterwald, and Zoug, and the following additional places: Muri district in d'Argovi, Switzerland\",\n", + " 'Macedonia, Thrace, Thessalia, Peloponissos, Ionian Islands, Aegean islands, Crete Island and Epirus, Greece',\n", + " 'Maine, United States',\n", + " 'Manitoba, Canada',\n", + " 'Mankato, MN, United States',\n", + " 'Marathon, NY, United States',\n", + " 'Maribo, Denmark',\n", + " 'Massachusetts, United States',\n", + " 'Menorca, Balearic Islands, Spain',\n", + " 'Midi-Pyrenees, France',\n", + " 'Midi-Pyrénées, France',\n", + " 'Milford, NJ, United States',\n", + " 'Minas Gerais, Brazil',\n", + " 'Minnesota, United States',\n", + " 'Missouri, United States',\n", + " 'Modena, Italy',\n", + " 'Moliterno, Italy',\n", + " 'Mols, Denmark',\n", + " 'Monterey, California, United States',\n", + " 'Mornington Peninsula, Melbourne, Australia',\n", + " 'Murazzano, Italy',\n", + " 'Murcia, Spain',\n", + " 'NY, United States',\n", + " 'Naples, Italy',\n", + " 'New Hampshire, United States',\n", + " 'New Jersey, United States',\n", + " 'New South Wales, Australia',\n", + " 'New York, France',\n", + " 'New York, United States',\n", + " 'Nicasio, United States',\n", + " 'Nord-Pas-de-Calais, France',\n", + " 'Normandy, Auvilliers, France',\n", + " 'Normandy, France',\n", + " 'North Carolina, United States',\n", + " 'North Cornwall, England',\n", + " 'North East Victoria, ',\n", + " 'North East Victoria, Australia',\n", + " 'North Wootton, England',\n", + " 'North Yorkshire, England',\n", + " 'Northeastern Brazil, Brazil',\n", + " 'Northern Holland, Netherlands',\n", + " 'Northern Wisconsin, United States',\n", + " 'Northwest, United States',\n", + " 'Nottinghamshire, England',\n", + " 'Odell, Bedfordshire, England',\n", + " 'Ontario, Canada',\n", + " 'Oregon Coast Range, United States',\n", + " 'Oregon, United States',\n", + " 'Oristano, Italy',\n", + " 'Orkney Islands, Scotland',\n", + " 'Orkney Isles, Scotland',\n", + " 'Oviken, Sweden',\n", + " 'Oxfordshire, Great Britain',\n", + " 'Passendale, Belgium',\n", + " 'Pays Basque, France',\n", + " 'Pays d’Auge, Normandy, France',\n", + " 'Peekskill, United States',\n", + " 'Pembrokeshire, United Kingdom',\n", + " 'Pembrokeshire, Wales',\n", + " 'Pennsylvania, United States',\n", + " 'Pesaro-Urbino, Italy',\n", + " 'Petaluma, California, United States',\n", + " 'Piave Valley, Italy, Italy',\n", + " 'Piedmont, Italy',\n", + " 'Piemonte, Italy',\n", + " 'Pienza, Italy',\n", + " 'Pinconning, Michigan, United States',\n", + " 'Piora Valley, Switzerland',\n", + " 'Po valley region, Italy',\n", + " 'Poitou-Charentes, France',\n", + " 'Pokolbin, Hunter Valley, Australia',\n", + " 'Port Townsend, United States',\n", + " 'Postel, Belgium',\n", + " 'Prince Edward County, Ontario, Canada',\n", + " 'Prince Edward Island, Canada',\n", + " 'Provencale, France',\n", + " 'Provence, France',\n", + " 'Puimichel in Provence Alpes, France',\n", + " 'Pullman, Washington, United States',\n", + " 'Pyrenees, France',\n", + " 'Pyrenees-Atlantiques, France',\n", + " 'Pyrénées, France',\n", + " 'Pyrénées-Atlantiques, France',\n", + " 'Póvoa de Lanhoso, Portugal',\n", + " 'Quebec, Canada',\n", + " 'Queenstown, New Zealand',\n", + " 'Québec, Canada',\n", + " 'Rhone Valley, France',\n", + " 'Rhone-Alps, France',\n", + " 'Rhône-Alpes, France',\n", + " 'Richfield, Wisconsin, United States',\n", + " 'Rio Grande do Sul, Brazil',\n", + " 'Romanian Carpathians, Romania',\n", + " 'Roncq, France',\n", + " 'Roxburghshire, Scotland',\n", + " 'Sardegna, Italy',\n", + " 'Sardinia & Campania, Italy',\n", + " 'Savoie, France',\n", + " 'Schoonrewoerd, Leerdam, Netherlands',\n", + " 'Seattle, Washington, United States',\n", + " 'Sebastopol, California, United States',\n", + " 'Serra da Canastra, Minas Gerais state, Brazil',\n", + " 'Serra da Estrela, Portugal',\n", + " 'Setubal, Palmela and Sesimbra, Portugal',\n", + " 'Severn Valley, England',\n", + " 'Shelburne Farms, United States',\n", + " 'Somerset, England',\n", + " 'Sonoma, California, United States',\n", + " 'South Australia, Australia',\n", + " 'South East England, United Kingdom',\n", + " 'South West England, England',\n", + " 'South West England, United Kingdom',\n", + " 'Southern California, United States',\n", + " 'Southwestern Wisconsin, United States',\n", + " 'St Antoine, France',\n", + " 'St. Gallen (canton), Tufertschwil, Switzerland',\n", + " 'St. Louis, Missouri, United States',\n", + " 'Staffordshire, England',\n", + " 'Stawley, near Wellington, Somerset, England',\n", + " 'Stewarton, Scotland',\n", + " 'Stonegate, East Sussex, England',\n", + " 'Stoneyford, Ireland',\n", + " 'Stranraer, Scotland',\n", + " 'Sulzberg, Austria',\n", + " 'Svaneti, Samegrelo, Georgia',\n", + " 'Swabia, Germany',\n", + " 'Swaledale, North Yorkshire, England',\n", + " 'Tain, Scotland',\n", + " 'Tasmania, Australia',\n", + " 'Taxco, Mexico',\n", + " 'Tieton, Washington, United States',\n", + " 'Timsbury, Somerset, England',\n", + " 'Timsbury, Somerset, Scotland',\n", + " 'Tipperary, Ireland',\n", + " 'Tomales, California, United States',\n", + " 'Treviso, Veneto, Italy',\n", + " 'Troyes , Aube, France',\n", + " 'Tuscany, Italy',\n", + " 'Umbria, Lazio, Italy',\n", + " 'Upper Corsica, France',\n", + " 'Utah, United States',\n", + " 'Valencia, Spain',\n", + " 'Valpadana, Italy',\n", + " 'Veneto, ',\n", + " 'Veneto, Italy',\n", + " 'Veneto, Trentino, Italy',\n", + " 'Vermont, United States',\n", + " 'Victoria, Australia',\n", + " 'Virginia, United States',\n", + " 'Vorarlberg, Austria',\n", + " 'Västra Götaland, Sweden',\n", + " 'Wales, Great Britain',\n", + " 'Wales, London, Wales',\n", + " 'Wallonia, Belgium',\n", + " 'Websterville, VT, United States',\n", + " 'West Bengal, India',\n", + " 'West Pawlet, VT, United States',\n", + " 'Wigtownshire, Scotland',\n", + " 'Wisconsin, United States',\n", + " 'Zasavica, Serbia',\n", + " 'island wide, Cyprus',\n", + " 'massif des Causses, France',\n", + " 'old Liburnia (Dalmatia), Croatia',\n", + " 'province of Brittany, France',\n", + " 'Äänekoski, Finland'}" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "locs=set(data[\"location\"])\n", + "locs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3bb9a47-56fa-49c4-8761-0db015944446", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "debb780e-ec13-4502-ac44-6001335e507d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eed3ac7b-5283-4d8e-bc26-61e1d821ccaf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "0043fe0d-e2d2-48f0-8953-ffc3dee52ba6", + "metadata": {}, + "outputs": [], + "source": [ + "def str_to_gps(loc):\n", + " l=loc.split(\",\")\n", + " loc=\",\".join([l[0],l[-1]])# removing details gives less errors while fetching the GPS coordinates\n", + " try:\n", + " res=Nominatim(user_agent=\"dmProject\").geocode(loc) \n", + " return (res.latitude, res.longitude)\n", + " except AttributeError:\n", + " loc=l[-1]\n", + " res=Nominatim(user_agent=\"dmProject\").geocode(loc) \n", + " return (res.latitude, res.longitude)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "710341db-408f-4a4a-a849-65b963582ebc", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": 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cheeseurlmilkcountryregionfamilytypefat_contentcalcium_contenttexturerindcolorflavoraromavegetarianvegansynonymsalt_spellingsproducers
10Acapellahttps://www.cheese.com/acapella/goatUnited StatesCaliforniaNaNsoft, soft-ripenedNaNNaNNaNNaNNaNbutteryfresh, herbalFalseFalseNaNNaNNaN
13Acornhttps://www.cheese.com/acorn/sheepUnited KingdomBethaniaNaNhard, artisan52%NaNcrumbly, firmNaNNaNburnt caramel, citrusy, herbaceousfruityTrueFalseNaNNaNNaN
19Afuega'l Pituhttps://www.cheese.com/afuegal-pitu/cowSpainAsturiasNaNsoft, artisanNaNNaNsmoothcloth wrappedNaNspicy, strongNaNFalseFalseNaNNaNNaN
48Alpe di Frabosahttps://www.cheese.com/alpe-di-frabosa/cowItalyNaNNaNsemi-softNaNNaNNaNNaNNaNbittermilky, mushroomFalseFalseNaNNaNNaN
50Alpicrèmehttps://www.cheese.com/alpicreme/goatFranceNaNNaNsoftNaNNaNNaNNaNNaNNaNNaNFalseFalseNaNNaNNaN
............................................................
1172Strathdon Bluehttps://www.cheese.com/strathdon-blue/cowScotlandTainBluesemi-softNaNNaNcreamyNaNNaNcreamy, spicyaromatic, richTrueFalseNaNNaNHighland Fine Cheeses Limited
1175String Cheesehttps://www.cheese.com/string/NaNNaNNaNNaNsemi-hardNaNNaNchewy, firm, stringyNaNNaNNaNNaNNaNNaNNaNNaNNaN
1177Sulgunihttps://www.cheese.com/sulguni/buffalo, cowGeorgiaSvaneti, SamegreloNaNsemi-firmNaNNaNdense, elasticNaNNaNsalty, smokey , sourNaNNaNNaNGeorgian Pickle CheeseMegruli Sulguni, Shebolili Megruli SulguniNaN
1181Sussex Slipcotehttps://www.cheese.com/sussex-slipcote/sheepEnglandNaNNaNsoftNaNNaNNaNNaNNaNsharpNaNTrueFalseNaNNaNHigh Weald Dairy
1185Sweet Style Swisshttps://www.cheese.com/sweet-style-swiss/NaNSwitzerlandNaNNaNsemi-hard, artisanNaNNaNfirm, supplewaxedNaNnuttynutty, sweetFalseFalseNaNNaNNaN
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142 rows × 19 columns

\n", - "" - ], - "text/plain": [ - " cheese url \\\n", - "10 Acapella https://www.cheese.com/acapella/ \n", - "13 Acorn https://www.cheese.com/acorn/ \n", - "19 Afuega'l Pitu https://www.cheese.com/afuegal-pitu/ \n", - "48 Alpe di Frabosa https://www.cheese.com/alpe-di-frabosa/ \n", - "50 Alpicrème https://www.cheese.com/alpicreme/ \n", - "... ... ... \n", - "1172 Strathdon Blue https://www.cheese.com/strathdon-blue/ \n", - "1175 String Cheese https://www.cheese.com/string/ \n", - "1177 Sulguni https://www.cheese.com/sulguni/ \n", - "1181 Sussex Slipcote https://www.cheese.com/sussex-slipcote/ \n", - "1185 Sweet Style Swiss https://www.cheese.com/sweet-style-swiss/ \n", - "\n", - " milk country region family \\\n", - "10 goat United States California NaN \n", - "13 sheep United Kingdom Bethania NaN \n", - "19 cow Spain Asturias NaN \n", - "48 cow Italy NaN NaN \n", - "50 goat France NaN NaN \n", - "... ... ... ... ... \n", - "1172 cow Scotland Tain Blue \n", - "1175 NaN NaN NaN NaN \n", - "1177 buffalo, cow Georgia Svaneti, Samegrelo NaN \n", - "1181 sheep England NaN NaN \n", - "1185 NaN Switzerland NaN NaN \n", - "\n", - " type fat_content calcium_content texture \\\n", - "10 soft, soft-ripened NaN NaN NaN \n", - "13 hard, artisan 52% NaN crumbly, firm \n", - "19 soft, artisan NaN NaN smooth \n", - "48 semi-soft NaN NaN NaN \n", - "50 soft NaN NaN NaN \n", - "... ... ... ... ... \n", - "1172 semi-soft NaN NaN creamy \n", - "1175 semi-hard NaN NaN chewy, firm, stringy \n", - "1177 semi-firm NaN NaN dense, elastic \n", - "1181 soft NaN NaN NaN \n", - "1185 semi-hard, artisan NaN NaN firm, supple \n", - "\n", - " rind color flavor \\\n", - "10 NaN NaN buttery \n", - "13 NaN NaN burnt caramel, citrusy, herbaceous \n", - "19 cloth wrapped NaN spicy, strong \n", - "48 NaN NaN bitter \n", - "50 NaN NaN NaN \n", - "... ... ... ... \n", - "1172 NaN NaN creamy, spicy \n", - "1175 NaN NaN NaN \n", - "1177 NaN NaN salty, smokey , sour \n", - "1181 NaN NaN sharp \n", - "1185 waxed NaN nutty \n", - "\n", - " aroma vegetarian vegan synonyms \\\n", - "10 fresh, herbal False False NaN \n", - "13 fruity True False NaN \n", - "19 NaN False False NaN \n", - "48 milky, mushroom False False NaN \n", - "50 NaN False False NaN \n", - "... ... ... ... ... \n", - "1172 aromatic, rich True False NaN \n", - "1175 NaN NaN NaN NaN \n", - "1177 NaN NaN NaN Georgian Pickle Cheese \n", - "1181 NaN True False NaN \n", - "1185 nutty, sweet False False NaN \n", - "\n", - " alt_spellings \\\n", - "10 NaN \n", - "13 NaN \n", - "19 NaN \n", - "48 NaN \n", - "50 NaN \n", - "... ... \n", - "1172 NaN \n", - "1175 NaN \n", - "1177 Megruli Sulguni, Shebolili Megruli Sulguni \n", - "1181 NaN \n", - "1185 NaN \n", - "\n", - " producers \n", - "10 NaN \n", - "13 NaN \n", - "19 NaN \n", - "48 NaN \n", - "50 NaN \n", - "... ... \n", - "1172 Highland Fine Cheeses Limited \n", - "1175 NaN \n", - "1177 NaN \n", - "1181 High Weald Dairy \n", - "1185 NaN \n", - "\n", - "[142 rows x 19 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data[pd.isnull(data[\"color\"])]" - ] - }, { "cell_type": "markdown", "id": "bf3b548c-5ac4-4126-9ae9-5578ad158015", @@ -883,10 +423,17 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "fb5ddb0a-8551-4e7c-971e-00a819ebb4b3", + "execution_count": 45, + "id": "2018aac2-6f3d-489a-b5d0-90b7c7793076", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'pale yellow', 'blue', 'brownish yellow', 'ivory', 'orange', 'brown', 'straw', 'blue-grey', 'cream', 'pale white', 'red', 'green', nan, 'golden yellow', 'white', 'golden orange', 'pink and white', 'yellow'}\n" + ] + }, { "data": { "text/html": [ @@ -1272,18 +819,19 @@ "[142 rows x 19 columns]" ] }, - "execution_count": 6, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data[data[\"color\"].isnull()]" + "print(set(data[\"color\"]))\n", + "data[pd.isnull(data[\"color\"])]" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 46, "id": "a0a77563-518e-4808-b744-9fc0c76763fe", "metadata": {}, "outputs": [ @@ -1303,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 47, "id": "c8489ffa-1067-4eb7-b65a-2fa18fdb4b04", "metadata": {}, "outputs": [], @@ -1318,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "id": "5379265a-cd49-41fa-845c-bfae33bb8f5a", "metadata": {}, "outputs": [ @@ -1596,7 +1144,7 @@ "[1187 rows x 13 columns]" ] }, - "execution_count": 9, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1607,10 +1155,84 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 49, "id": "633ed80e-e416-41f6-ae58-b86ce4c132af", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1181 rows remaining\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_13743/3522053431.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"country\"]=data[\"country\"].fillna(\"\")\n", + "/tmp/ipykernel_13743/3522053431.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data[\"region\"]=data[\"region\"].fillna(\"\")\n" + ] + } + ], + "source": [ + "data=data.dropna(subset=[\"country\",\"region\"], how=\"all\")\n", + "data[\"country\"]=data[\"country\"].fillna(\"\")\n", + "data[\"region\"]=data[\"region\"].fillna(\"\")\n", + "print(f\"{len(data)} rows remaining\")" + ] + }, + { + "cell_type": "markdown", + "id": "fd66568f-78d4-4e1a-a91c-8ec483b4b03c", + "metadata": {}, + "source": [ + "We removed 6 rows for which we could not find a suitable location. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a4c0e30-8535-498b-9a9e-0d7d232d4eb7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "7ef7494b-ff08-40a5-890f-e0f718cf2842", + "metadata": {}, + "outputs": [], + "source": [ + "data.loc[data.country.str.contains(\"England, Great Britain, United Kingdom\")|data.country.str.contains(\"England, United Kingdom\"),\"country\"]=\"England\"\n", + "data.loc[data.country.str.contains(\"Scotland\"),\"country\"]=\"Scotland\"\n", + "data.loc[data.country.str.contains(\"Great Britain, United Kingdom, Wales\")|data.country.str.contains(\"United Kingdom, Wales\"),\"country\"]=\"Wales\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "fb044984-c33c-492c-91a2-4e9fff29ceb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39\n" + ] + }, { "data": { "text/html": [ @@ -1649,318 +1271,923 @@ 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France\n", - " Pays Basque\n", + " smooth\n", " NaN\n", - " semi-hard, artisan\n", - " creamy, dense, firm\n", - " natural\n", - " yellow\n", - " burnt caramel\n", - " lanoline\n", - " True\n", - " False\n", - " \n", - " \n", - " 2\n", - " Abbaye de Belval\n", - " cow\n", - " France\n", - " NaN\n", - " NaN\n", - " semi-hard\n", - " elastic\n", - " washed\n", - " ivory\n", - " NaN\n", - " aromatic\n", - " False\n", - " False\n", - " \n", - " \n", - " 3\n", - " Abbaye de Citeaux\n", - " cow\n", - " France\n", - " Burgundy\n", - " NaN\n", - " semi-soft, artisan, brined\n", - " creamy, dense, smooth\n", - " washed\n", - " white\n", - " acidic, milky, smooth\n", - " barnyardy, earthy\n", - " False\n", - " False\n", - " \n", - " \n", - " 4\n", - " Abbaye de Tamié\n", - " cow\n", - " France\n", - " Savoie\n", - " NaN\n", - " soft, artisan\n", - " creamy, open, smooth\n", - " washed\n", - " white\n", - " fruity, nutty\n", - " perfumed, pungent\n", - " False\n", - " False\n", - " \n", - " \n", 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creamy, subtle, sweet\n", + " goaty\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 377\n", + " Caprice\n", + " goat\n", + " Canada, Italy\n", + " Lombardy\n", + " NaN\n", + " soft\n", + " creamy, smooth\n", + " natural\n", + " white\n", + " subtle\n", + " goaty\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 407\n", + " Casu marzu\n", + " sheep\n", + " France, Italy\n", + " Sardinia (Italy), Southern Corsica (France)\n", + " NaN\n", + " soft, soft-ripened\n", + " soft-ripened\n", + " natural\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 437\n", + " Cheese Curds\n", + " NaN\n", + " Canada, India, United States\n", + " \n", + " Cheddar\n", + " fresh firm\n", + " firm, springy\n", + " natural\n", + " white\n", + " mild, milky\n", + " fresh\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 445\n", + " Chhurpi\n", + " cow, yak\n", + " China, Nepal, Tibet\n", + " \n", + " Cottage\n", + " soft, hard, artisan\n", + " dense\n", + " natural\n", + " pale yellow\n", + " tangy\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 455\n", + " Chura Kampo\n", + " yak\n", + " China, Tibet\n", + " Tibet\n", + " NaN\n", + " hard, artisan\n", + " dense, dry, firm\n", + " natural\n", + " NaN\n", + " NaN\n", + " aromatic\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 508\n", + " Cottage Cheese\n", + " cow\n", + " United Kingdom, United States\n", + " \n", + " Cottage\n", + " soft, artisan, processed\n", + " creamy, crumbly\n", + " rindless\n", + " white\n", + " sweet\n", + " NaN\n", + " True\n", + " False\n", + " \n", + " \n", + " 512\n", + " Counting Sheep…and Goats...\n", + " cow, goat, sheep\n", + " Canada, Italy\n", + " Lombardy\n", + " NaN\n", + " soft, soft-ripened\n", + " creamy, soft\n", + " NaN\n", + " white\n", + " creamy, subtle, sweet\n", + " nutty\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 524\n", + " Cream Cheesy Bliss\n", + " NaN\n", + " Canada, United States\n", + " \n", + " NaN\n", + " soft\n", + " creamy, spreadable\n", + " artificial\n", + " white\n", + " creamy, garlicky, herbaceous, sweet\n", + " rich\n", + " True\n", + " False\n", + " \n", + " \n", + " 537\n", + " Cressy Blu\n", + " cow\n", + " Canada, Italy\n", + " Lombardy\n", + " Blue\n", + " semi-hard, blue-veined\n", + " creamy, crumbly, firm\n", + " natural\n", + " pale yellow\n", + " creamy, savory, sweet\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 569\n", + " La Couronne - Fort Aged Comté\n", + " cow\n", + " France, Switzerland\n", + " Franche Comté\n", + " NaN\n", + " semi-hard\n", + " dense\n", + " NaN\n", + " pale yellow\n", + " caramel, nutty, sweet\n", + " rich\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 601\n", + " Lamb Chopper\n", + " sheep\n", + " Netherlands, United States\n", + " California\n", + " Gouda\n", + " hard\n", + " dense, firm, smooth\n", + " waxed\n", + " ivory\n", + " buttery, caramel, creamy, nutty, sweet\n", + " mild, sweet\n", + " True\n", + " False\n", + " \n", + " \n", + " 621\n", + " Le Conquerant Demi Pont L'eveque\n", + " cow\n", + " Australia, France\n", + " \n", + " NaN\n", + " soft, artisan\n", + " chalky, creamy, soft\n", + " washed\n", + " NaN\n", + " creamy, mild\n", + " pungent\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 646\n", + " Limburger\n", + " cow\n", + " Belgium, Germany, Netherlands\n", + " Duchy of Limburg\n", + " NaN\n", + " semi-soft, smear-ripened\n", + " crumbly, firm, smooth\n", + " washed\n", + " straw\n", + " grassy, mild, mushroomy\n", + " stinky\n", + " False\n", + " False\n", + " \n", + " \n", + " 675\n", + " Lunetta\n", + " cow\n", + " Canada, Italy\n", + " Lombardy\n", + " NaN\n", + " fresh soft, artisan\n", + " creamy\n", + " natural\n", + " white\n", + " creamy, salty\n", + " aromatic, fresh\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 701\n", + " Mamirolle\n", + " cow\n", + " Canada, France\n", + " Plessisville, Quebec\n", + " NaN\n", + " semi-soft, artisan\n", + " chewy, supple\n", + " washed\n", + " ivory\n", + " buttery, fruity, salty, sweet\n", + " earthy, pungent\n", + " False\n", + " False\n", + " \n", + " \n", + " 776\n", + " Monastery Cheeses\n", + " cow\n", + " Belgium, Canada, France, Switzerland, United S...\n", + " \n", + " NaN\n", + " soft, semi-soft, brined\n", + " chalky, creamy, firm, grainy\n", + " washed\n", + " golden yellow\n", + " mild, pungent\n", + " pungent, strong\n", + " False\n", + " False\n", + " \n", + " \n", + " 789\n", + " Monterey Jack\n", + " cow\n", + " Mexico, United States\n", + " Monterey, California\n", + " Monterey Jack\n", + " semi-hard\n", + " compact, creamy, firm, open, supple\n", + " NaN\n", + " pale yellow\n", + " buttery, mild\n", + " aromatic\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 852\n", + " Paneer\n", + " cow, water buffalo\n", + " Bangladesh, India\n", + " \n", + " Cottage\n", + " fresh firm\n", + " crumbly, firm\n", + " rindless\n", + " white\n", + " milky\n", + " fresh, milky\n", + " True\n", + " False\n", + " \n", + " \n", + " 975\n", + " President Fat Free Feta\n", + " cow\n", + " France, United States\n", + " New York\n", + " Feta\n", + " firm, artisan, brined\n", + " crumbly\n", + " natural\n", + " white\n", + " herbaceous, salty, tangy\n", + " fresh\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1001\n", + " Purple's a Must\n", + " cow, goat\n", + " Canada, Italy\n", + " Lombardy\n", + " Blue\n", + " semi-hard, artisan, blue-veined\n", + " creamy, crumbly\n", + " mold ripened\n", + " pale yellow\n", + " full-flavored, strong\n", + " rich\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1082\n", + " Shanklish\n", + " cow, sheep\n", + " Egypt, Lebanon, Syria\n", + " \n", + " Feta\n", + " fresh firm, hard, artisan\n", + " creamy, crumbly, firm\n", + " mold ripened\n", + " white\n", + " sharp, spicy, strong\n", + " pungent, strong\n", + " False\n", + " False\n", + " \n", + " \n", + " 1099\n", + " Shredded Bliss\n", + " NaN\n", + " Canada, United States\n", + " \n", + " Mozzarella\n", + " semi-soft\n", + " elastic, smooth, springy, stringy\n", + " plastic\n", + " pale yellow\n", + " mild, milky\n", + " fresh, mild\n", + " True\n", + " False\n", + " \n", + " \n", + " 1106\n", + " Sirene\n", + " cow, goat, sheep\n", + " Albania, Bulgaria, Croatia, Greece, Israel, Ma...\n", + " Trakia\n", + " Feta\n", + " fresh soft, brined\n", + " crumbly, grainy, smooth\n", + " natural\n", + " white\n", + " lemony, salty, sharp, tangy\n", + " strong\n", + " False\n", + " False\n", + " \n", + " \n", + " 1108\n", + " Slices Of Bliss\n", + " NaN\n", + " Canada, United States\n", + " \n", + " Cheddar\n", + " soft\n", + " creamy\n", + " plastic\n", + " yellow\n", + " creamy, savory, sharp, spicy\n", + " NaN\n", + " True\n", + " False\n", + " \n", + " \n", + " 1123\n", + " Sosha\n", + " goat, yak\n", + " China, Nepal, Tibet\n", + " Tibet\n", + " NaN\n", + " soft, artisan\n", + " creamy\n", + " natural\n", + " white\n", + " pungent, strong\n", + " pungent, strong\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 1173\n", + " Strawberry Moon\n", + " cow\n", + " Canada, Italy\n", + " Lombardy\n", + " NaN\n", + " semi-hard, artisan, smear-ripened\n", + " compact, creamy, dense\n", + " washed\n", + " straw\n", + " subtle, sweet\n", + " strong\n", + " NaN\n", + " NaN\n", + " \n", " \n", "\n", - "

1181 rows × 13 columns

\n", "" ], "text/plain": [ - " cheese milk country region \\\n", - "0 Aarewasser cow Switzerland NaN \n", - "1 Abbaye de Belloc sheep France Pays Basque \n", - "2 Abbaye de Belval cow France NaN \n", - "3 Abbaye de Citeaux cow France Burgundy \n", - "4 Abbaye de Tamié cow France Savoie \n", - "... ... ... ... ... \n", - "1182 Sveciaost cow Sweden Low-laying regions \n", - "1183 Swag goat Australia South Australia \n", - "1184 Swaledale sheep England Swaledale, North Yorkshire \n", - "1185 Sweet Style Swiss NaN Switzerland NaN \n", - "1186 Swiss cheese cow United States NaN \n", + " cheese milk \\\n", + "12 Ackawi cow, goat, sheep \n", + "116 Baladi cow, goat, sheep \n", + "160 Beemster 2% Milk cow \n", + "212 Blissful Blocks NaN \n", + "213 Blissful Toppings NaN \n", + "243 Bootlegger cow, sheep \n", + "262 Brebis d'Azure sheep \n", + "297 Brunost cow, goat \n", + "300 Bryndza sheep \n", + "311 Burrata water buffalo \n", + "316 Butterkase cow \n", + "367 Cap Cressy goat \n", + "375 Capri Blu goat \n", + "377 Caprice goat \n", + "407 Casu marzu sheep \n", + "437 Cheese Curds NaN \n", + "445 Chhurpi cow, yak \n", + "455 Chura Kampo yak \n", + "508 Cottage Cheese cow \n", + "512 Counting Sheep…and Goats... cow, goat, sheep \n", + "524 Cream Cheesy Bliss NaN \n", + "537 Cressy Blu cow \n", + "569 La Couronne - Fort Aged Comté cow \n", + "601 Lamb Chopper sheep \n", + "621 Le Conquerant Demi Pont L'eveque cow \n", + "646 Limburger cow \n", + "675 Lunetta cow \n", + "701 Mamirolle cow \n", + "776 Monastery Cheeses cow \n", + "789 Monterey Jack cow \n", + "852 Paneer cow, water buffalo \n", + "975 President Fat Free Feta cow \n", + "1001 Purple's a Must cow, goat \n", + "1082 Shanklish cow, sheep \n", + "1099 Shredded Bliss NaN \n", + "1106 Sirene cow, goat, sheep \n", + "1108 Slices Of Bliss NaN \n", + "1123 Sosha goat, yak \n", + "1173 Strawberry Moon cow \n", "\n", - " family type texture \\\n", - "0 NaN semi-soft buttery \n", - "1 NaN semi-hard, artisan creamy, dense, firm \n", - "2 NaN semi-hard elastic \n", - "3 NaN semi-soft, artisan, brined creamy, dense, smooth \n", - "4 NaN soft, artisan creamy, open, smooth \n", - "... ... ... ... \n", - "1182 NaN semi-hard, brined creamy, supple \n", - "1183 NaN fresh firm, artisan creamy, crumbly \n", - "1184 NaN hard semi firm \n", - "1185 NaN semi-hard, artisan firm, supple \n", - "1186 Swiss Cheese hard, artisan, processed firm \n", + " country \\\n", + "12 Cyprus, Egypt, Israel, Jordan, Lebanon, Middle... \n", + "116 Lebanon, Middle East \n", + "160 Canada, Denmark, France, Germany, Netherlands,... \n", + "212 Canada, United States \n", + "213 Canada, United States \n", + "243 Canada, Italy \n", + "262 Canada, Italy \n", + "297 Denmark, Finland, Germany, Iceland, Norway, Sw... \n", + "300 Hungary, Poland, Slovakia \n", + "311 Italy, United States \n", + "316 Austria, Germany \n", + "367 Canada, Italy \n", + "375 Canada, Italy \n", + "377 Canada, Italy \n", + "407 France, Italy \n", + "437 Canada, India, United States \n", + "445 China, Nepal, Tibet \n", + "455 China, Tibet \n", + "508 United Kingdom, United States \n", + "512 Canada, Italy \n", + "524 Canada, United States \n", + "537 Canada, Italy \n", + "569 France, Switzerland \n", + "601 Netherlands, United States \n", + "621 Australia, France \n", + "646 Belgium, Germany, Netherlands \n", + "675 Canada, Italy \n", + "701 Canada, France \n", + "776 Belgium, Canada, France, Switzerland, United S... \n", + "789 Mexico, United States \n", + "852 Bangladesh, India \n", + "975 France, United States \n", + "1001 Canada, Italy \n", + "1082 Egypt, Lebanon, Syria \n", + "1099 Canada, United States \n", + "1106 Albania, Bulgaria, Croatia, Greece, Israel, Ma... \n", + "1108 Canada, United States \n", + "1123 China, Nepal, Tibet \n", + "1173 Canada, Italy \n", "\n", - " rind color flavor aroma \\\n", - "0 washed yellow sweet buttery \n", - "1 natural yellow burnt caramel lanoline \n", - "2 washed ivory NaN aromatic \n", - "3 washed white acidic, milky, smooth barnyardy, earthy \n", - "4 washed white fruity, nutty perfumed, pungent \n", - "... ... ... ... ... \n", - "1182 rindless pale yellow acidic NaN \n", - "1183 ash coated white acidic, creamy fresh \n", - "1184 NaN yellow smooth, sweet floral \n", - "1185 waxed NaN nutty nutty, sweet \n", - "1186 rindless pale yellow nutty, sweet NaN \n", + " region family \\\n", + "12 + Feta \n", + "116 NaN \n", + "160 NaN \n", + "212 Cheddar \n", + "213 Parmesan \n", + "243 Lombardy NaN \n", + "262 Lombardy Blue \n", + "297 NaN \n", + "300 NaN \n", + "311 Apulia Mozzarella \n", + "316 NaN \n", + "367 Lombardy NaN \n", + "375 Lombardy Blue \n", + "377 Lombardy NaN \n", + "407 Sardinia (Italy), Southern Corsica (France) NaN \n", + "437 Cheddar \n", + "445 Cottage \n", + "455 Tibet NaN \n", + "508 Cottage \n", + "512 Lombardy NaN \n", + "524 NaN \n", + "537 Lombardy Blue \n", + "569 Franche Comté NaN \n", + "601 California Gouda \n", + "621 NaN \n", + "646 Duchy of Limburg NaN \n", + "675 Lombardy NaN \n", + "701 Plessisville, Quebec NaN \n", + "776 NaN \n", + "789 Monterey, California Monterey Jack \n", + "852 Cottage \n", + "975 New York Feta \n", + "1001 Lombardy Blue \n", + "1082 Feta \n", + "1099 Mozzarella \n", + "1106 Trakia Feta \n", + "1108 Cheddar \n", + "1123 Tibet NaN \n", + "1173 Lombardy NaN \n", "\n", - " vegetarian vegan \n", - "0 False False \n", - "1 True False \n", - "2 False False \n", - "3 False False \n", - "4 False False \n", - "... ... ... \n", - "1182 False False \n", - "1183 True False \n", - "1184 True False \n", - "1185 False False \n", - "1186 True False \n", + " type texture \\\n", + "12 soft, brined elastic, smooth, springy \n", + "116 fresh soft, artisan creamy, dense, smooth \n", + "160 semi-soft smooth \n", + "212 hard creamy, crumbly \n", + "213 soft crumbly \n", + "243 hard, artisan crumbly, firm \n", + "262 semi-hard, artisan, blue-veined soft \n", + "297 semi-soft, whey dense \n", + "300 soft, artisan spreadable \n", + "311 fresh soft, artisan creamy, stringy \n", + "316 semi-soft creamy, smooth, spreadable \n", + "367 semi-hard, artisan, smear-ripened compact, dense \n", + "375 soft, blue-veined creamy, soft \n", + "377 soft creamy, smooth \n", + "407 soft, soft-ripened soft-ripened \n", + "437 fresh firm firm, springy \n", + "445 soft, hard, artisan dense \n", + "455 hard, artisan dense, dry, firm \n", + "508 soft, artisan, processed creamy, crumbly \n", + "512 soft, soft-ripened creamy, soft \n", + "524 soft creamy, spreadable \n", + "537 semi-hard, blue-veined creamy, crumbly, firm \n", + "569 semi-hard dense \n", + "601 hard dense, firm, smooth \n", + "621 soft, artisan chalky, creamy, soft \n", + "646 semi-soft, smear-ripened crumbly, firm, smooth \n", + "675 fresh soft, artisan creamy \n", + "701 semi-soft, artisan chewy, supple \n", + "776 soft, semi-soft, brined chalky, creamy, firm, grainy \n", + "789 semi-hard compact, creamy, firm, open, supple \n", + "852 fresh firm crumbly, firm \n", + "975 firm, artisan, brined crumbly \n", + "1001 semi-hard, artisan, blue-veined creamy, crumbly \n", + "1082 fresh firm, hard, artisan creamy, crumbly, firm \n", + "1099 semi-soft elastic, smooth, springy, stringy \n", + "1106 fresh soft, brined crumbly, grainy, smooth \n", + "1108 soft creamy \n", + "1123 soft, artisan creamy \n", + "1173 semi-hard, artisan, smear-ripened compact, creamy, dense \n", "\n", - "[1181 rows x 13 columns]" + " rind color flavor \\\n", + "12 natural white mild, milky, salty \n", + "116 rindless white buttery, mild, salty, sweet \n", + "160 NaN NaN nutty \n", + "212 plastic yellow creamy, savory, sharp, spicy \n", + "213 artificial yellow savory, sharp \n", + "243 natural pale yellow fruity, full-flavored, strong \n", + "262 natural pale yellow sharp \n", + "297 natural brown caramel, sweet \n", + "300 rindless white mild, salty \n", + "311 leaf wrapped white buttery, milky \n", + "316 natural pale yellow buttery, mild \n", + "367 washed pale yellow mellow, savory, sweet \n", + "375 natural pale yellow creamy, subtle, sweet \n", + "377 natural white subtle \n", + "407 natural NaN NaN \n", + "437 natural white mild, milky \n", + "445 natural pale yellow tangy \n", + "455 natural NaN NaN \n", + "508 rindless white sweet \n", + "512 NaN white creamy, subtle, sweet \n", + "524 artificial white creamy, garlicky, herbaceous, sweet \n", + "537 natural pale yellow creamy, savory, sweet \n", + "569 NaN pale yellow caramel, nutty, sweet \n", + "601 waxed ivory buttery, caramel, creamy, nutty, sweet \n", + "621 washed NaN creamy, mild \n", + "646 washed straw grassy, mild, mushroomy \n", + "675 natural white creamy, salty \n", + "701 washed ivory buttery, fruity, salty, sweet \n", + "776 washed golden yellow mild, pungent \n", + "789 NaN pale yellow buttery, mild \n", + "852 rindless white milky \n", + "975 natural white herbaceous, salty, tangy \n", + "1001 mold ripened pale yellow full-flavored, strong \n", + "1082 mold ripened white sharp, spicy, strong \n", + "1099 plastic pale yellow mild, milky \n", + "1106 natural white lemony, salty, sharp, tangy \n", + "1108 plastic yellow creamy, savory, sharp, spicy \n", + "1123 natural white pungent, strong \n", + "1173 washed straw subtle, sweet \n", + "\n", + " aroma vegetarian vegan \n", + "12 mild, milky False False \n", + "116 fresh False False \n", + "160 aromatic, floral, fruity False False \n", + "212 NaN True False \n", + "213 NaN True False \n", + "243 floral NaN NaN \n", + "262 aromatic NaN NaN \n", + "297 NaN NaN NaN \n", + "300 NaN NaN NaN \n", + "311 fresh, milky False False \n", + "316 NaN False False \n", + "367 lactic NaN NaN \n", + "375 goaty NaN NaN \n", + "377 goaty NaN NaN \n", + "407 NaN NaN NaN \n", + "437 fresh NaN NaN \n", + "445 NaN NaN NaN \n", + "455 aromatic NaN NaN \n", + "508 NaN True False \n", + "512 nutty NaN NaN \n", + "524 rich True False \n", + "537 NaN NaN NaN \n", + "569 rich NaN NaN \n", + "601 mild, sweet True False \n", + "621 pungent NaN NaN \n", + "646 stinky False False \n", + "675 aromatic, fresh NaN NaN \n", + "701 earthy, pungent False False \n", + "776 pungent, strong False False \n", + "789 aromatic NaN NaN \n", + "852 fresh, milky True False \n", + "975 fresh NaN NaN \n", + "1001 rich NaN NaN \n", + "1082 pungent, strong False False \n", + "1099 fresh, mild True False \n", + "1106 strong False False \n", + "1108 NaN True False \n", + "1123 pungent, strong NaN NaN \n", + "1173 strong NaN NaN " ] }, - "execution_count": 10, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data=data.dropna(subset=[\"country\",\"region\"], how=\"all\")\n", - "data" + "print(len(data[data[\"country\"].str.contains(\",\")]))\n", + "data[data[\"country\"].str.contains(\",\")]\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e28eb2ee-1bed-4dd7-8133-c99b1f7ea26b", + "execution_count": 52, + "id": "43058589-f931-46ad-99a7-44be63f962cc", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "data=data.drop(index=data[data[\"country\"].str.contains(\",\")].index)" + ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "5a4c0e30-8535-498b-9a9e-0d7d232d4eb7", + "cell_type": "markdown", + "id": "2f42c973-247a-4f51-947e-fbd76f8f12fc", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_39592/2649664722.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " data[\"country\"]=data[\"country\"].fillna(\"\")\n", - "/tmp/ipykernel_39592/2649664722.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " data[\"region\"]=data[\"region\"].fillna(\"\")\n" - ] - } - ], "source": [ - "data[[\"country\",\"region\"]]\n", - "data[\"country\"]=data[\"country\"].fillna(\"\")\n", - "data[\"region\"]=data[\"region\"].fillna(\"\")" + "We removed 39 cheeses because they can come froms several countries. " ] }, { "cell_type": "code", - "execution_count": 35, - "id": "0edcee6f-a2c3-4804-a665-507b77f2651b", + "execution_count": 53, + "id": "59c4e6e7-d624-45a5-a9ea-eb375102b771", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_39592/3582726305.py:1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " data[\"location\"]=data[\"region\"]+\", \"+data[\"country\"]\n" - ] - } - ], + "outputs": [], "source": [ "data[\"location\"]=data[\"region\"]+\", \"+data[\"country\"]" ] }, { "cell_type": "code", - "execution_count": 36, - "id": "017e30da-4f60-4d16-b6d4-56fb76acb740", + "execution_count": 54, + "id": "0dee0f25-4699-4e46-97d0-21bb36d9c603", "metadata": {}, "outputs": [ { @@ -2190,7 +2417,7 @@ " \n", " \n", "\n", - "

1181 rows × 14 columns

\n", + "

1142 rows × 14 columns

\n", "" ], "text/plain": [ @@ -2246,10 +2473,10 @@ "1185 False False , Switzerland \n", "1186 True False , United States \n", "\n", - "[1181 rows x 14 columns]" + "[1142 rows x 14 columns]" ] }, - "execution_count": 36, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -2260,530 +2487,545 @@ }, { "cell_type": "code", - "execution_count": 37, - "id": "bf9d5b2a-bd47-4c4c-85c4-5ad7769a3f31", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "430" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(set(data[\"location\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "e7ae8f76-b33b-42ce-9dea-9fab9e33069e", + "execution_count": null, + "id": "2ef7351c-f117-403b-bc6e-f9f30a98c9d2", "metadata": {}, "outputs": [], - "source": [ - "locs=list(set(loc for loc in data[\"location\"]))" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 49, - "id": "fb044984-c33c-492c-91a2-4e9fff29ceb3", + "execution_count": null, + "id": "353724eb-8d64-4b64-84c6-f06be36acd8b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54d56bd4-c83a-4e8c-8751-b4b2f7830a9e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd68f1bb-c9f6-4c57-951b-8ac1f3192f09", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeff487e-9b66-4c4b-b4f6-dc5352fb2144", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdb0d04c-e0f2-4553-8906-e9282f4942d2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a551e0f4-3f99-4dae-9b31-6205b772ebf5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c02cc29-fe07-4ff9-8c6b-8638d37830cd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de579631-a29c-4620-9bbf-7085b83d16b7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "e2e868d4-33a1-4602-af97-afb1d29e612f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - 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'Emilia-Romagna, Italy',\n", + " 'Extremadura, Spain',\n", + " 'Fairview, United States',\n", + " 'Fethard, Co Tipperary, Ireland',\n", + " 'Fife, Scotland',\n", + " 'Flanders, Belgium',\n", + " 'Fornells de la Selva, Gironès, Spain',\n", + " 'Franche Comté, France',\n", + " 'French Basque Country, Midi-Pyrénées, France',\n", + " 'Friuli Venezia Giulia and Veneto, Italy',\n", + " 'Friuli-Venezia Giulia and the Veneto, Italy',\n", + " 'Friuli-Venezia Giulia, Italy',\n", + " 'Galax, Virginia, United States',\n", + " 'Galicia, Spain',\n", + " 'Georgia, United States',\n", + " 'Gevrey-Chambertin, Burgundy, France',\n", + " 'Gippsland, Victoria, Australia',\n", + " 'Gloucestershire County, England',\n", + " 'Gloucestershire, England',\n", + " 'Gravina in Puglia, Murgia, Italy',\n", + " 'Greensboro, VT, United States',\n", + " 'Greenville, Indiana, United States',\n", + " 'Gujarat, India',\n", + " 'Gâtinais, France',\n", + " 'Hamilton, New Zealand',\n", + " 'Haute Vienne, France',\n", + " 'Haute-Savoie / Upper Savoy, France',\n", + " 'Herault, France',\n", + " 'Herefordshire, West Midlands, United Kingdom',\n", + " 'Het Groene Hart, Netherlands',\n", + " 'Huizen, Netherlands',\n", + " 'Hunter Valley, Australia',\n", + " 'Ile de France, France',\n", + " 'Ile-de-France/Champagne, France',\n", + " 'Illinois, United States',\n", + " 'Illoud (Haute-Marne), France',\n", + " 'Inagh, Co Clare, ',\n", + " 'Inagh, Co Clare, Ireland',\n", + " 'Indiana, United States',\n", + " 'Iowa, United States',\n", + " 'Isere, France',\n", + " 'Island of Pag, Croatia',\n", + " 'Jura, Switzerland',\n", + " 'Karlovy Vary, Czech Republic',\n", + " 'Kent, United Kingdom',\n", + " 'Kilmallock County Limerick, Ireland',\n", + " 'Kimball, United States',\n", + " 'Kinfauns, Perthshire, Scotland',\n", + " 'La Velle, Wisconsin, United States',\n", + " 'Lanarkshire, Scotland',\n", + " 'Landford, England',\n", + " 'Landshut, Germany',\n", + " 'Languedoc, France',\n", + " 'Languedoc-Roussillon, France',\n", + " 'Lapland, Finland',\n", + " 'Laqueuille, France',\n", + " 'Laruns, France',\n", + " 'Larzac, France',\n", + " 'Lazio, Sardinia, Italy',\n", + " 'Lebanon, CT, United States',\n", + " 'Leiden, Netherlands',\n", + " 'Lincolnshire, England',\n", + " 'Lodi, Italy',\n", + " 'Loire Valley, France',\n", + " 'Loire, France',\n", + " 'Lombardy, Italy',\n", + " 'Low-laying regions, Sweden',\n", + " 'Lower Normandy, France',\n", + " \"Lucerne, Schwyz, Unterwald, and Zoug, and the following additional places: Muri district in d'Argovi, Switzerland\",\n", + " 'Macedonia, Thrace, Thessalia, Peloponissos, Ionian Islands, Aegean islands, Crete Island and Epirus, Greece',\n", + " 'Maine, United States',\n", + " 'Manitoba, Canada',\n", + " 'Mankato, MN, United States',\n", + " 'Marathon, NY, United States',\n", + " 'Maribo, Denmark',\n", + " 'Massachusetts, United States',\n", + " 'Menorca, Balearic Islands, Spain',\n", + " 'Midi-Pyrenees, France',\n", + " 'Midi-Pyrénées, France',\n", + " 'Milford, NJ, United States',\n", + " 'Minas Gerais, Brazil',\n", + " 'Minnesota, United States',\n", + " 'Missouri, United States',\n", + " 'Modena, Italy',\n", + " 'Moliterno, Italy',\n", + " 'Mols, Denmark',\n", + " 'Monterey, California, United States',\n", + " 'Mornington Peninsula, Melbourne, Australia',\n", + " 'Murazzano, Italy',\n", + " 'Murcia, Spain',\n", + " 'NY, United States',\n", + " 'Naples, Italy',\n", + " 'New Hampshire, United States',\n", + " 'New Jersey, United States',\n", + " 'New South Wales, Australia',\n", + " 'New York, France',\n", + " 'New York, United States',\n", + " 'Nicasio, United States',\n", + " 'Nord-Pas-de-Calais, France',\n", + " 'Normandy, Auvilliers, France',\n", + " 'Normandy, France',\n", + " 'North Carolina, United States',\n", + " 'North Cornwall, England',\n", + " 'North East Victoria, ',\n", + " 'North East Victoria, Australia',\n", + " 'North Wootton, England',\n", + " 'North Yorkshire, England',\n", + " 'Northeastern Brazil, Brazil',\n", + " 'Northern Holland, Netherlands',\n", + " 'Northern Wisconsin, United States',\n", + " 'Northwest, United States',\n", + " 'Nottinghamshire, England',\n", + " 'Odell, Bedfordshire, England',\n", + " 'Ontario, Canada',\n", + " 'Oregon Coast Range, United States',\n", + " 'Oregon, United States',\n", + " 'Oristano, Italy',\n", + " 'Orkney Islands, Scotland',\n", + " 'Orkney Isles, Scotland',\n", + " 'Oviken, Sweden',\n", + " 'Oxfordshire, Great Britain',\n", + " 'Passendale, Belgium',\n", + " 'Pays Basque, France',\n", + " 'Pays d’Auge, Normandy, France',\n", + " 'Peekskill, United States',\n", + " 'Pembrokeshire, United Kingdom',\n", + " 'Pembrokeshire, Wales',\n", + " 'Pennsylvania, United States',\n", + " 'Pesaro-Urbino, Italy',\n", + " 'Petaluma, California, United States',\n", + " 'Piave Valley, Italy, Italy',\n", + " 'Piedmont, Italy',\n", + " 'Piemonte, Italy',\n", + " 'Pienza, Italy',\n", + " 'Pinconning, Michigan, United States',\n", + " 'Piora Valley, Switzerland',\n", + " 'Po valley region, Italy',\n", + " 'Poitou-Charentes, France',\n", + " 'Pokolbin, Hunter Valley, Australia',\n", + " 'Port Townsend, United States',\n", + " 'Postel, Belgium',\n", + " 'Prince Edward County, Ontario, Canada',\n", + " 'Prince Edward Island, Canada',\n", + " 'Provencale, France',\n", + " 'Provence, France',\n", + " 'Puimichel in Provence Alpes, France',\n", + " 'Pullman, Washington, United States',\n", + " 'Pyrenees, France',\n", + " 'Pyrenees-Atlantiques, France',\n", + " 'Pyrénées, France',\n", + " 'Pyrénées-Atlantiques, France',\n", + " 'Póvoa de Lanhoso, Portugal',\n", + " 'Quebec, Canada',\n", + " 'Queenstown, New Zealand',\n", + " 'Québec, Canada',\n", + " 'Rhone Valley, France',\n", + " 'Rhone-Alps, France',\n", + " 'Rhône-Alpes, France',\n", + " 'Richfield, Wisconsin, United States',\n", + " 'Rio Grande do Sul, Brazil',\n", + " 'Romanian Carpathians, Romania',\n", + " 'Roncq, France',\n", + " 'Roxburghshire, Scotland',\n", + " 'Sardegna, Italy',\n", + " 'Sardinia & Campania, Italy',\n", + " 'Savoie, France',\n", + " 'Schoonrewoerd, Leerdam, Netherlands',\n", + " 'Seattle, Washington, United States',\n", + " 'Sebastopol, California, United States',\n", + " 'Serra da Canastra, Minas Gerais state, Brazil',\n", + " 'Serra da Estrela, Portugal',\n", + " 'Setubal, Palmela and Sesimbra, Portugal',\n", + " 'Severn Valley, England',\n", + " 'Shelburne Farms, United States',\n", + " 'Somerset, England',\n", + " 'Sonoma, California, United States',\n", + " 'South Australia, Australia',\n", + " 'South East England, United Kingdom',\n", + " 'South West England, England',\n", + " 'South West England, United Kingdom',\n", + " 'Southern California, United States',\n", + " 'Southwestern Wisconsin, United States',\n", + " 'St Antoine, France',\n", + " 'St. Gallen (canton), Tufertschwil, Switzerland',\n", + " 'St. Louis, Missouri, United States',\n", + " 'Staffordshire, England',\n", + " 'Stawley, near Wellington, Somerset, England',\n", + " 'Stewarton, Scotland',\n", + " 'Stonegate, East Sussex, England',\n", + " 'Stoneyford, Ireland',\n", + " 'Stranraer, Scotland',\n", + " 'Sulzberg, Austria',\n", + " 'Svaneti, Samegrelo, Georgia',\n", + " 'Swabia, Germany',\n", + " 'Swaledale, North Yorkshire, England',\n", + " 'Tain, Scotland',\n", + " 'Tasmania, Australia',\n", + " 'Taxco, Mexico',\n", + " 'Tieton, Washington, United States',\n", + " 'Timsbury, Somerset, England',\n", + " 'Timsbury, Somerset, Scotland',\n", + " 'Tipperary, Ireland',\n", + " 'Tomales, California, United States',\n", + " 'Treviso, Veneto, Italy',\n", + " 'Troyes , Aube, France',\n", + " 'Tuscany, Italy',\n", + " 'Umbria, Lazio, Italy',\n", + " 'Upper Corsica, France',\n", + " 'Utah, United States',\n", + " 'Valencia, Spain',\n", + " 'Valpadana, Italy',\n", + " 'Veneto, ',\n", + " 'Veneto, Italy',\n", + " 'Veneto, Trentino, Italy',\n", + " 'Vermont, United States',\n", + " 'Victoria, Australia',\n", + " 'Virginia, United States',\n", + " 'Vorarlberg, Austria',\n", + " 'Västra Götaland, Sweden',\n", + " 'Wales, Great Britain',\n", + " 'Wales, London, Wales',\n", + " 'Wallonia, Belgium',\n", + " 'Websterville, VT, United States',\n", + " 'West Bengal, India',\n", + " 'West Pawlet, VT, United States',\n", + " 'Wigtownshire, Scotland',\n", + " 'Wisconsin, United States',\n", + " 'Zasavica, Serbia',\n", + " 'island wide, Cyprus',\n", + " 'massif des Causses, France',\n", + " 'old Liburnia (Dalmatia), Croatia',\n", + " 'province of Brittany, France',\n", + " 'Äänekoski, Finland'}" ] }, - "execution_count": 49, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "locs=set(data[\"location\"])\n", "locs" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "debb780e-ec13-4502-ac44-6001335e507d", + "execution_count": null, + "id": "f3bb9a47-56fa-49c4-8761-0db015944446", "metadata": {}, "outputs": [], - "source": [ - "from geopy.geocoders import Nominatim" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "eed3ac7b-5283-4d8e-bc26-61e1d821ccaf", - "metadata": {}, - "outputs": [], - "source": [ - "geolocator=Nominatim(user_agent=\"toto\")" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "0043fe0d-e2d2-48f0-8953-ffc3dee52ba6", - "metadata": {}, - "outputs": [], - "source": [ - "location=geolocator.geocode(locs[1])" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, + "id": "debb780e-ec13-4502-ac44-6001335e507d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eed3ac7b-5283-4d8e-bc26-61e1d821ccaf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "0043fe0d-e2d2-48f0-8953-ffc3dee52ba6", + "metadata": {}, + "outputs": [], + "source": [ + "def str_to_gps(loc):\n", + " l=loc.split(\",\")\n", + " loc=\",\".join([l[0],l[-1]])# removing details gives less errors while fetching the GPS coordinates\n", + " try:\n", + " res=Nominatim(user_agent=\"dmProject\").geocode(loc) \n", + " return (res.latitude, res.longitude)\n", + " except AttributeError:\n", + " loc=l[-1]\n", + " res=Nominatim(user_agent=\"dmProject\").geocode(loc) \n", + " return (res.latitude, res.longitude)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, "id": "710341db-408f-4a4a-a849-65b963582ebc", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2bbdb40ac6c43b0bfe08d014970db40", + "model_id": "b78765c7ef7a4fad8cb4520512a198c8", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/430 [00:00]" + "1" ] }, - "execution_count": 75, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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