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{
"cells": [
{
"cell_type": "code",
"execution_count": 50,
"id": "1f939e73",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"data = pd.read_csv('/home/xj/toolAugEnv/code/toolConstraint/database/restaurants/zomato.csv')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "876e4fff",
"metadata": {},
"outputs": [],
"source": [
"data_dict = data.to_dict(orient = 'split')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "dbaee06c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Restaurant ID',\n",
" 'Restaurant Name',\n",
" 'Country Code',\n",
" 'City',\n",
" 'Address',\n",
" 'Locality',\n",
" 'Locality Verbose',\n",
" 'Longitude',\n",
" 'Latitude',\n",
" 'Cuisines',\n",
" 'Average Cost for two',\n",
" 'Currency',\n",
" 'Has Table booking',\n",
" 'Has Online delivery',\n",
" 'Is delivering now',\n",
" 'Switch to order menu',\n",
" 'Price range',\n",
" 'Aggregate rating',\n",
" 'Rating color',\n",
" 'Rating text',\n",
" 'Votes']"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dict['columns']"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "cb540128",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9551"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data_dict['data'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ea9858c5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[6600970,\n",
" 'Pizza 礞 Bessa',\n",
" 30,\n",
" 'Bras韄lia',\n",
" 'SCS 214, Bloco C, Loja 40, Asa Sul, Bras韄lia',\n",
" 'Asa Sul',\n",
" 'Asa Sul, Bras韄lia',\n",
" -47.91566667,\n",
" -15.83116667,\n",
" 'Pizza',\n",
" 50,\n",
" 'Brazilian Real(R$)',\n",
" 'No',\n",
" 'No',\n",
" 'No',\n",
" 'No',\n",
" 2,\n",
" 3.2,\n",
" 'Orange',\n",
" 'Average',\n",
" 11]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dict['data'][26]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e21af5d1",
"metadata": {},
"outputs": [],
"source": [
"flight = pd.read_csv('/home/xj/toolAugEnv/code/toolConstraint/database/flights/clean_Flights_2022.csv')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "966feef9",
"metadata": {},
"outputs": [],
"source": [
"flight = flight.to_dict(orient = 'split')"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "c5f81f43",
"metadata": {},
"outputs": [],
"source": [
"city_set = open('/home/xj/toolAugEnv/code/toolConstraint/database/background/citySet.txt','r').read().strip().split('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "bfce5f56",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['San Diego',\n",
" 'Pellston',\n",
" 'Buffalo',\n",
" 'Charlotte Amalie',\n",
" 'Flagstaff',\n",
" 'Evansville',\n",
" 'Hilo',\n",
" 'Twin Falls',\n",
" 'Newark',\n",
" 'State College',\n",
" 'Johnstown',\n",
" 'Charleston',\n",
" 'Montgomery',\n",
" 'Redding',\n",
" 'Lynchburg',\n",
" 'South Bend',\n",
" 'Sarasota',\n",
" 'Sioux Falls',\n",
" 'Paducah',\n",
" 'Kahului',\n",
" 'Atlantic City',\n",
" 'Bemidji',\n",
" 'Toledo',\n",
" 'Abilene',\n",
" 'Sacramento',\n",
" 'Amarillo',\n",
" 'Moline',\n",
" 'Hilton Head',\n",
" 'Manhattan',\n",
" 'Minneapolis',\n",
" 'Fort Myers',\n",
" 'Roswell',\n",
" 'Harlingen',\n",
" 'Seattle',\n",
" 'Manchester',\n",
" 'Gulfport',\n",
" 'Gainesville',\n",
" 'Pago Pago',\n",
" 'Wrangell',\n",
" 'Augusta',\n",
" 'Waterloo',\n",
" 'Yuma',\n",
" 'Saipan',\n",
" 'Christiansted',\n",
" 'North Bend',\n",
" 'Richmond',\n",
" 'Albuquerque',\n",
" 'Nashville',\n",
" 'Aberdeen',\n",
" 'Harrisburg',\n",
" 'Fort Wayne',\n",
" 'Green Bay',\n",
" 'Wenatchee',\n",
" 'Santa Fe',\n",
" 'St. Petersburg',\n",
" 'Belleville',\n",
" 'Greensboro',\n",
" 'Lake Charles',\n",
" 'Traverse City',\n",
" 'Erie',\n",
" 'Niagara Falls',\n",
" 'Pocatello',\n",
" 'Idaho Falls',\n",
" 'Alpena',\n",
" 'Wilmington',\n",
" 'Ontario',\n",
" 'Iron Mountain',\n",
" 'Lubbock',\n",
" 'Helena',\n",
" 'Kalamazoo',\n",
" 'Cleveland',\n",
" 'Grand Island',\n",
" 'Bishop',\n",
" 'New Bern',\n",
" 'Melbourne',\n",
" 'Bristol',\n",
" 'Orlando',\n",
" 'Bismarck',\n",
" 'Fresno',\n",
" 'Billings',\n",
" 'Jackson',\n",
" 'Daytona Beach',\n",
" 'College Station',\n",
" 'Jacksonville',\n",
" 'Salt Lake City',\n",
" 'Corpus Christi',\n",
" 'Florence',\n",
" 'Moab',\n",
" 'Grand Forks',\n",
" 'Las Vegas',\n",
" 'Fairbanks',\n",
" 'Petersburg',\n",
" 'Wichita',\n",
" 'Rhinelander',\n",
" 'Kansas City',\n",
" 'Dothan',\n",
" 'Alamosa',\n",
" 'Adak Island',\n",
" 'Islip',\n",
" 'Wichita Falls',\n",
" 'Presque Isle',\n",
" 'San Luis Obispo',\n",
" 'Dayton',\n",
" 'Brunswick',\n",
" 'Fort Smith',\n",
" \"Martha's Vineyard\",\n",
" 'Portland',\n",
" 'Waco',\n",
" 'New York',\n",
" 'Columbus',\n",
" 'Tampa',\n",
" 'Dallas',\n",
" 'Little Rock',\n",
" 'Kona',\n",
" 'Clarksburg',\n",
" 'San Angelo',\n",
" 'Saginaw',\n",
" 'Houston',\n",
" 'Duluth',\n",
" 'Valparaiso',\n",
" 'Phoenix',\n",
" 'Oakland',\n",
" 'Watertown',\n",
" 'Ogden',\n",
" 'Cedar Rapids',\n",
" 'Cape Girardeau',\n",
" 'Sun Valley',\n",
" 'Sault Ste. Marie',\n",
" 'Trenton',\n",
" 'Missoula',\n",
" 'Pasco',\n",
" 'Brainerd',\n",
" 'Newburgh',\n",
" 'Gustavus',\n",
" 'Branson',\n",
" 'Providence',\n",
" 'Minot',\n",
" 'Huntsville',\n",
" 'San Antonio',\n",
" 'Marquette',\n",
" 'Owensboro',\n",
" 'Del Rio',\n",
" 'Portsmouth',\n",
" 'Bloomington',\n",
" 'Lexington',\n",
" 'Santa Barbara',\n",
" 'Baltimore',\n",
" 'Panama City',\n",
" 'Kodiak',\n",
" 'Jacksonville',\n",
" 'Yakima',\n",
" 'Vernal',\n",
" 'Salisbury',\n",
" 'Mission',\n",
" 'Newport News',\n",
" 'Charlottesville',\n",
" 'Grand Junction',\n",
" 'Baton Rouge',\n",
" 'Beaumont',\n",
" 'Staunton',\n",
" 'Kalispell',\n",
" 'Key West',\n",
" 'Worcester',\n",
" 'West Palm Beach',\n",
" 'Boise',\n",
" 'Grand Rapids',\n",
" 'Salina',\n",
" 'Fort Leonard Wood',\n",
" 'Walla Walla',\n",
" 'Everett',\n",
" 'Dillingham',\n",
" 'Bellingham',\n",
" 'Lansing',\n",
" 'Madison',\n",
" 'Victoria',\n",
" 'Sioux City',\n",
" 'Hattiesburg',\n",
" 'Stockton',\n",
" 'Anchorage',\n",
" 'Charlotte',\n",
" 'Jamestown',\n",
" 'Laramie',\n",
" 'Decatur',\n",
" 'Durango',\n",
" 'Longview',\n",
" 'Syracuse',\n",
" 'St. Cloud',\n",
" 'Santa Rosa',\n",
" 'Bakersfield',\n",
" 'North Platte',\n",
" 'La Crosse',\n",
" 'Plattsburgh',\n",
" 'Concord',\n",
" 'Atlanta',\n",
" 'Provo',\n",
" 'Ogdensburg',\n",
" 'Ithaca',\n",
" 'Colorado Springs',\n",
" 'Washington',\n",
" 'Williston',\n",
" 'Tulsa',\n",
" 'Midland',\n",
" 'Champaign',\n",
" 'Devils Lake',\n",
" 'Greer',\n",
" 'Muskegon',\n",
" 'Hibbing',\n",
" 'Santa Ana',\n",
" 'Ponce',\n",
" 'Prescott',\n",
" 'Indianapolis',\n",
" 'International Falls',\n",
" 'Rapid City',\n",
" 'Ketchikan',\n",
" 'St. Louis',\n",
" 'Santa Maria',\n",
" 'Elmira',\n",
" 'Alexandria',\n",
" 'San Jose',\n",
" 'Tucson',\n",
" 'San Juan',\n",
" 'Dubuque',\n",
" 'Burbank',\n",
" 'Gunnison',\n",
" 'Cedar City',\n",
" 'Hyannis',\n",
" 'Raleigh',\n",
" 'Norfolk',\n",
" 'New Orleans',\n",
" 'Medford',\n",
" 'White Plains',\n",
" 'Oklahoma City',\n",
" 'Chicago',\n",
" 'El Paso',\n",
" 'Rockford',\n",
" 'Aguadilla',\n",
" 'Omaha',\n",
" 'Scottsbluff',\n",
" 'Yakutat',\n",
" 'Arcata',\n",
" 'Spokane',\n",
" 'Brownsville',\n",
" 'Bend',\n",
" 'Hagerstown',\n",
" 'Peoria',\n",
" 'Appleton',\n",
" 'Roanoke',\n",
" 'Eugene',\n",
" 'Rock Springs',\n",
" 'Dodge City',\n",
" 'Austin',\n",
" 'Miami',\n",
" 'Dallas',\n",
" 'Mosinee',\n",
" 'Killeen',\n",
" 'Lihue',\n",
" 'Pittsburgh',\n",
" 'Tallahassee',\n",
" 'Butte',\n",
" 'Lawton',\n",
" 'Honolulu',\n",
" 'Greenville',\n",
" 'Juneau',\n",
" 'Myrtle Beach',\n",
" 'Boston',\n",
" 'Charleston',\n",
" 'Latrobe',\n",
" 'Knoxville',\n",
" 'Denver',\n",
" 'Bangor',\n",
" 'Albany',\n",
" 'Punta Gorda',\n",
" 'Fort Lauderdale',\n",
" 'Philadelphia',\n",
" 'Binghamton',\n",
" 'Great Falls',\n",
" 'Shreveport',\n",
" 'Asheville',\n",
" 'Cheyenne',\n",
" 'Milwaukee',\n",
" 'Nome',\n",
" 'Laredo',\n",
" 'Des Moines',\n",
" 'Fayetteville',\n",
" 'Lewisburg',\n",
" 'Fort Dodge',\n",
" 'Cody',\n",
" 'Chattanooga',\n",
" 'Deadhorse',\n",
" 'Kotzebue',\n",
" 'Sitka',\n",
" 'Bozeman',\n",
" 'Palm Springs',\n",
" 'Memphis',\n",
" 'Nantucket',\n",
" 'Texarkana',\n",
" 'Lewiston',\n",
" 'Valdosta',\n",
" 'Birmingham',\n",
" 'Scranton',\n",
" 'Pensacola',\n",
" 'Hancock',\n",
" 'Los Angeles',\n",
" 'Mason City',\n",
" 'Savannah',\n",
" 'West Yellowstone',\n",
" 'Long Beach',\n",
" 'Reno',\n",
" 'Akron',\n",
" 'Louisville',\n",
" 'Hartford',\n",
" 'Cincinnati',\n",
" 'Rochester',\n",
" 'San Francisco',\n",
" 'Detroit',\n",
" 'Monterey',\n",
" 'Escanaba',\n",
" 'Eau Claire']"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"city_set"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "cd0f41fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 Restaurant Name\n",
"3 City\n",
"9 Cuisines\n",
"10 Average Cost for two\n",
"11 Currency\n",
"17 Aggregate rating\n"
]
}
],
"source": [
"for idx, unit in enumerate(data_dict['columns']):\n",
" if unit in ['Restaurant Name', 'City', 'Cuisines', 'Average Cost for two','Aggregate rating','Currency']:\n",
" print(idx,unit)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "04fe71b7",
"metadata": {},
"outputs": [],
"source": [
"currency_set = set()\n",
"for unit in data_dict['data']:\n",
" currency_set.add(unit[11])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3988186d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Botswana Pula(P)',\n",
" 'Brazilian Real(R$)',\n",
" 'Dollar($)',\n",
" 'Emirati Diram(AED)',\n",
" 'Indian Rupees(Rs.)',\n",
" 'Indonesian Rupiah(IDR)',\n",
" 'NewZealand($)',\n",
" 'Pounds(專)',\n",
" 'Qatari Rial(QR)',\n",
" 'Rand(R)',\n",
" 'Sri Lankan Rupee(LKR)',\n",
" 'Turkish Lira(TL)'}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"currency_set"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "257e6a76",
"metadata": {},
"outputs": [],
"source": [
"exchange_rate = {\"Botswana Pula(P)\":0.074,\n",
" \"Brazilian Real(R$)\":0.21, \n",
" 'Dollar($)':1, \n",
" 'Emirati Diram(AED)':0.27,\n",
" \"Indian Rupees(Rs.)\":0.012087,\n",
" \"Indonesian Rupiah(IDR)\":0.000066,\n",
" 'NewZealand($)':0.61,\n",
" \"Pounds(專)\":1.28,\n",
" \"Qatari Rial(QR)\":0.27,\n",
" 'Rand(R)': 0.054,\n",
" \"Sri Lankan Rupee(LKR)\":0.0031,\n",
" 'Turkish Lira(TL)':0.037\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "c6b2691e",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3984855550f54090b3264d7adc859433",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm.autonotebook import tqdm\n",
"import random\n",
"new_data = []\n",
"\n",
"for idx, unit in tqdm(enumerate(data_dict['data'])):\n",
" tmp_dict = {k:\"\" for k in ['Name', 'City', 'Cuisines', 'Average Cost','Aggregate Rating']}\n",
" tmp_dict[\"Name\"] = unit[1]\n",
" tmp_dict[\"City\"] = random.sample(city_set,1)[0]\n",
" tmp_dict[\"Cuisines\"] = unit[9]\n",
" tmp_dict[\"Average Cost\"] = max(random.randint(10,100),int(unit[10] / 2 * exchange_rate[unit[11]]))\n",
" tmp_dict[\"Aggregate Rating\"] = unit[17]\n",
" new_data.append(tmp_dict)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"id": "f27aaff1",
"metadata": {},
"outputs": [],
"source": [
"countries = [\"Chinese\", \"American\", \"Italian\", \"Mexican\", \"Indian\",\"Mediterranean\",\"French\"]\n",
"cuisine = [\"Tea\",\"Seafood\",\"Bakery\",\"Desserts\",\"BBQ\",\"Fast Food\",\"Cafe\",\"Pizza\"]\n",
"total_cuisine = countries + cuisine\n",
"for unit in new_data:\n",
" flag = False\n",
" final_cuisine = set()\n",
"# for c in total_cuisine:\n",
"# if c in str(unit['Cuisines']):\n",
"# final_cuisine.add(c)\n",
" choice_number = random.choices([1,1,2])[0]\n",
" for x in random.sample(countries,choice_number):\n",
" final_cuisine.add(x)\n",
" choice_number = random.choices([2,3,4])[0]\n",
" for x in random.sample(cuisine,choice_number):\n",
" final_cuisine.add(x)\n",
" unit['Cuisines'] = \", \".join(x for x in final_cuisine)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"id": "9e3afb30",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Name': 'Karak韄y G韄ll韄o埕lu',\n",
" 'City': 'Phoenix',\n",
" 'Cuisines': 'Bakery, Indian, Desserts, Seafood',\n",
" 'Average Cost': 75,\n",
" 'Aggregate Rating': 4.7}"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_data[-7]"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "bfb243c0",
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(new_data)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"id": "af7e3411",
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('/home/xj/toolAugEnv/code/toolConstraint/database/restaurants/clean_restaurant_2022.csv')"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "dad9bf9f",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>City</th>\n",
" <th>Cuisines</th>\n",
" <th>Average Cost</th>\n",
" <th>Aggregate Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Le Petit Souffle</td>\n",
" <td>Eagle</td>\n",
" <td>Desserts, French, Fast Food, Chinese, Indian</td>\n",
" <td>40</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Izakaya Kikufuji</td>\n",
" <td>Hilton Head</td>\n",
" <td>Mexican, BBQ, Mediterranean, Pizza</td>\n",
" <td>95</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Heat - Edsa Shangri-La</td>\n",
" <td>Trenton</td>\n",
" <td>Tea, Pizza, French, Indian, Mediterranean, Sea...</td>\n",
" <td>148</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Ooma</td>\n",
" <td>Portland</td>\n",
" <td>Tea, Pizza, French, BBQ, Cafe</td>\n",
" <td>89</td>\n",
" <td>4.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Sambo Kojin</td>\n",
" <td>Milwaukee</td>\n",
" <td>Desserts, Tea, Italian, Cafe, Mediterranean</td>\n",
" <td>60</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9546</th>\n",
" <td>Naml郾 Gurme</td>\n",
" <td>Worcester</td>\n",
" <td>Tea, BBQ, Fast Food, Chinese, American, Medite...</td>\n",
" <td>72</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9547</th>\n",
" <td>Ceviz A埕ac郾</td>\n",
" <td>San Francisco</td>\n",
" <td>Cafe, Mexican, Pizza, Bakery</td>\n",
" <td>14</td>\n",
" <td>4.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9548</th>\n",
" <td>Huqqa</td>\n",
" <td>Guam</td>\n",
" <td>Pizza, Italian, French, Mexican, BBQ, Chinese,...</td>\n",
" <td>25</td>\n",
" <td>3.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9549</th>\n",
" <td>A侓侓k Kahve</td>\n",
" <td>Louisville</td>\n",
" <td>Chinese, Tea, Mexican, Cafe, Indian</td>\n",
" <td>96</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9550</th>\n",
" <td>Walter's Coffee Roastery</td>\n",
" <td>Monroe</td>\n",
" <td>Pizza, Italian, Cafe, Indian, Mediterranean</td>\n",
" <td>79</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9551 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Name City \\\n",
"0 Le Petit Souffle Eagle \n",
"1 Izakaya Kikufuji Hilton Head \n",
"2 Heat - Edsa Shangri-La Trenton \n",
"3 Ooma Portland \n",
"4 Sambo Kojin Milwaukee \n",
"... ... ... \n",
"9546 Naml郾 Gurme Worcester \n",
"9547 Ceviz A埕ac郾 San Francisco \n",
"9548 Huqqa Guam \n",
"9549 A侓侓k Kahve Louisville \n",
"9550 Walter's Coffee Roastery Monroe \n",
"\n",
" Cuisines Average Cost \\\n",
"0 Desserts, French, Fast Food, Chinese, Indian 40 \n",
"1 Mexican, BBQ, Mediterranean, Pizza 95 \n",
"2 Tea, Pizza, French, Indian, Mediterranean, Sea... 148 \n",
"3 Tea, Pizza, French, BBQ, Cafe 89 \n",
"4 Desserts, Tea, Italian, Cafe, Mediterranean 60 \n",
"... ... ... \n",
"9546 Tea, BBQ, Fast Food, Chinese, American, Medite... 72 \n",
"9547 Cafe, Mexican, Pizza, Bakery 14 \n",
"9548 Pizza, Italian, French, Mexican, BBQ, Chinese,... 25 \n",
"9549 Chinese, Tea, Mexican, Cafe, Indian 96 \n",
"9550 Pizza, Italian, Cafe, Indian, Mediterranean 79 \n",
"\n",
" Aggregate Rating \n",
"0 4.8 \n",
"1 4.5 \n",
"2 4.4 \n",
"3 4.9 \n",
"4 4.8 \n",
"... ... \n",
"9546 4.1 \n",
"9547 4.2 \n",
"9548 3.7 \n",
"9549 4.0 \n",
"9550 4.0 \n",
"\n",
"[9551 rows x 5 columns]"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "e168b1c5",
"metadata": {},
"outputs": [],
"source": [
"cuisine_dict = {}\n",
"for unit in new_data:\n",
" for x in str(unit['Cuisines']).split(', '):\n",
" if x not in cuisine_dict:\n",
" cuisine_dict[x] = 1\n",
" else:\n",
" cuisine_dict[x] += 1"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "564d4bda",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"French 29\n",
"Japanese 135\n",
"Desserts 653\n",
"Seafood 174\n",
"Asian 233\n",
"Filipino 10\n",
"Indian 70\n",
"Sushi 75\n",
"Korean 21\n",
"Chinese 2735\n",
"European 148\n",
"Mexican 181\n",
"American 390\n",
"Ice Cream 226\n",
"Cafe 703\n",
"Italian 764\n",
"Pizza 381\n",
"Bakery 745\n",
"Mediterranean 112\n",
"Fast Food 1986\n",
"Brazilian 28\n",
"Arabian 28\n",
"Bar Food 39\n",
"Grill 21\n",
"International 21\n",
"Peruvian 1\n",
"Latin American 11\n",
"Burger 251\n",
"Juices 29\n",
"Healthy Food 150\n",
"Beverages 229\n",
"Lebanese 69\n",
"Sandwich 53\n",
"Steak 62\n",
"BBQ 33\n",
"Gourmet Fast Food 1\n",
"Mineira 1\n",
"North Eastern 9\n",
"nan 9\n",
"Coffee and Tea 19\n",
"Vegetarian 23\n",
"Tapas 19\n",
"Breakfast 41\n",
"Diner 6\n",
"Southern 24\n",
"Southwestern 7\n",
"Spanish 16\n",
"Argentine 2\n",
"Caribbean 7\n",
"German 10\n",
"Vietnamese 21\n",
"Thai 234\n",
"Modern Australian 11\n",
"Teriyaki 2\n",
"Cajun 10\n",
"Canadian 1\n",
"Tex-Mex 19\n",
"Middle Eastern 22\n",
"Greek 15\n",
"Bubble Tea 1\n",
"Tea 48\n",
"Australian 5\n",
"Fusion 4\n",
"Cuban 2\n",
"Hawaiian 8\n",
"Salad 93\n",
"Irish 1\n",
"New American 2\n",
"Soul Food 1\n",
"Turkish 15\n",
"Pub Food 2\n",
"Persian 2\n",
"Continental 736\n",
"Singaporean 4\n",
"Malay 1\n",
"Cantonese 2\n",
"Dim Sum 3\n",
"Western 10\n",
"Finger Food 114\n",
"British 16\n",
"Deli 3\n",
"Indonesian 14\n",
"North Indian 3960\n",
"Mughlai 995\n",
"Biryani 177\n",
"South Indian 636\n",
"Pakistani 12\n",
"Afghani 14\n",
"Hyderabadi 26\n",
"Rajasthani 21\n",
"Street Food 562\n",
"Goan 20\n",
"African 8\n",
"Portuguese 7\n",
"Gujarati 11\n",
"Armenian 3\n",
"Mithai 380\n",
"Maharashtrian 10\n",
"Modern Indian 16\n",
"Charcoal Grill 4\n",
"Malaysian 22\n",
"Burmese 10\n",
"Chettinad 11\n",
"Parsi 8\n",
"Tibetan 44\n",
"Raw Meats 114\n",
"Kerala 23\n",
"Belgian 2\n",
"Kashmiri 20\n",
"South American 2\n",
"Bengali 29\n",
"Iranian 3\n",
"Lucknowi 13\n",
"Awadhi 11\n",
"Nepalese 9\n",
"Drinks Only 2\n",
"Oriya 2\n",
"Bihari 6\n",
"Assamese 4\n",
"Andhra 10\n",
"Mangalorean 4\n",
"Malwani 1\n",
"Cuisine Varies 1\n",
"Moroccan 5\n",
"Naga 8\n",
"Sri Lankan 5\n",
"Peranakan 1\n",
"Sunda 3\n",
"Ramen 2\n",
"Kiwi 6\n",
"Asian Fusion 2\n",
"Taiwanese 2\n",
"Fish and Chips 1\n",
"Contemporary 9\n",
"Scottish 3\n",
"Curry 6\n",
"Patisserie 4\n",
"South African 6\n",
"Durban 1\n",
"Kebab 10\n",
"Turkish Pizza 8\n",
"Izgara 2\n",
"World Cuisine 4\n",
"D韄ner 1\n",
"Restaurant Cafe 4\n",
"B韄rek 1\n"
]
}
],
"source": [
"for unit in cuisine_dict:\n",
" print(unit,cuisine_dict[unit])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "967426f0",
"metadata": {},
"outputs": [],
"source": [
"cuisine = [\"Chinese\", \"American\", \"Italian\", \"Mexican\", \"Indian\",\"Mediterranean\",\"Middle Eastern\",\"Breakfast\",\"Korean\",\"Asian\",\"French\",\"Tea\",\"Seafood\",\"Bakery\",\"Street Food\"]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "880dd6bf",
"metadata": {},
"outputs": [],
"source": [
"countries = [\"Chinese\", \"American\", \"Italian\", \"Mexican\", \"Indian\",\"Mediterranean\",\"Middle Eastern\",,\"Korean\",\"Asian\",\"French\"]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "89d9aba9",
"metadata": {},
"outputs": [],
"source": [
"cuisine = [\"Tea\",\"Seafood\",\"Bakery\",\"Street Food\",\"Desserts\",\"BBQ\",\"Street Food\",\"Fast Food\",\"Cafe\",\"Pizza\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff103725",
"metadata": {},
"outputs": [],
"source": []
}
],
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|