WeatherPy_HY.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import time\n",
    "import csv\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Import API key\n",
    "from api_keys import api_key\n",
    "\n",
    "# Incorporated citipy to determine city based on latitude and longitude\n",
    "from citipy import citipy\n",
    "\n",
    "# Output File (CSV)\n",
    "output_data_file = \"cities.csv\"\n",
    "\n",
    "# Range of latitudes and longitudes\n",
    "lat_range = (-90, 90)\n",
    "lng_range = (-180, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "616"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List for holding lat_lngs and cities\n",
    "lat_lngs = []\n",
    "cities = []\n",
    "\n",
    "# Create a set of random lat and lng combinations\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "\n",
    "# Identify nearest city for each lat, lng combination\n",
    "for lat_lng in lat_lngs:\n",
    "    city = citipy.nearest_city(lat_lng[0], lat_lng[1]).city_name\n",
    "    \n",
    "    # If the city is unique, then add it to a our cities list\n",
    "    if city not in cities:\n",
    "        cities.append(city)\n",
    "\n",
    "# Print the city count to confirm sufficient count\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output cities to a csv file\n",
    "with open(output_data_file, 'w',newline='') as csvfile:\n",
    "    csvwriter=csv.writer(csvfile,delimiter=',')\n",
    "    for city in cities:\n",
    "        csvwriter.writerow([city])  # make every element a list to output a csv file only containing one column\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval\n",
      "------------------------\n",
      "Processing city 1|cape town\n",
      "Processing city 2|yulara\n",
      "Processing city 3|mitu\n",
      "City not found. Skipping............\n",
      "Processing city 5|east london\n",
      "City not found. Skipping............\n",
      "Processing city 7|dikson\n",
      "Processing city 8|fortuna\n",
      "Processing city 9|traralgon\n",
      "Processing city 10|hilo\n",
      "Processing city 11|coro\n",
      "Processing city 12|miles city\n",
      "Processing city 13|petropavlovsk-kamchatskiy\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 16|vila franca do campo\n",
      "Processing city 17|ushuaia\n",
      "Processing city 18|guerrero negro\n",
      "Processing city 19|jamestown\n",
      "Processing city 20|cayenne\n",
      "Processing city 21|hermanus\n",
      "City not found. Skipping............\n",
      "Processing city 23|kodiak\n",
      "Processing city 24|coolum beach\n",
      "Processing city 25|itarema\n",
      "Processing city 26|saskylakh\n",
      "Processing city 27|yellowknife\n",
      "Processing city 28|bridgetown\n",
      "Processing city 29|saint-philippe\n",
      "Processing city 30|rikitea\n",
      "Processing city 31|alta floresta\n",
      "Processing city 32|naze\n",
      "Processing city 33|thunder bay\n",
      "Processing city 34|bethel\n",
      "Processing city 35|kutum\n",
      "Processing city 36|busselton\n",
      "Processing city 37|tuktoyaktuk\n",
      "City not found. Skipping............\n",
      "Processing city 39|chokurdakh\n",
      "Processing city 40|turukhansk\n",
      "Processing city 41|chaihe\n",
      "Processing city 42|kapaa\n",
      "Processing city 43|hobart\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 46|koslan\n",
      "City not found. Skipping............\n",
      "Processing city 48|biak\n",
      "City not found. Skipping............\n",
      "Processing city 50|clyde river\n",
      "Processing city 51|torbay\n",
      "Processing city 52|mataura\n",
      "Processing city 53|butaritari\n",
      "Processing city 54|redlands\n",
      "Processing city 55|lebu\n",
      "Processing city 56|ayan\n",
      "Processing city 57|pisco\n",
      "Processing city 58|nikolskoye\n",
      "Processing city 59|atuona\n",
      "Processing city 60|san quintin\n",
      "Processing city 61|port moresby\n",
      "Processing city 62|rimouski\n",
      "Processing city 63|nueva italia\n",
      "Processing city 64|tarpon springs\n",
      "Processing city 65|ust-kuyga\n",
      "Processing city 66|san cristobal\n",
      "Processing city 67|port alfred\n",
      "City not found. Skipping............\n",
      "Processing city 69|bemidji\n",
      "Processing city 70|namatanai\n",
      "Processing city 71|avarua\n",
      "Processing city 72|arraial do cabo\n",
      "Processing city 73|fairbanks\n",
      "Processing city 74|manggar\n",
      "Processing city 75|coihaique\n",
      "Processing city 76|san juan\n",
      "Processing city 77|la ronge\n",
      "Processing city 78|port elizabeth\n",
      "City not found. Skipping............\n",
      "Processing city 80|ipixuna\n",
      "Processing city 81|punta arenas\n",
      "Processing city 82|vao\n",
      "Processing city 83|kunming\n",
      "Processing city 84|hambantota\n",
      "Processing city 85|souillac\n",
      "Processing city 86|haapiti\n",
      "Processing city 87|imeni zhelyabova\n",
      "Processing city 88|okhotsk\n",
      "Processing city 89|ambilobe\n",
      "Processing city 90|bluff\n",
      "Processing city 91|leh\n",
      "Processing city 92|baker city\n",
      "City not found. Skipping............\n",
      "Processing city 94|wagga wagga\n",
      "Processing city 95|tawang\n",
      "Processing city 96|chuy\n",
      "Processing city 97|nyzhni sirohozy\n",
      "Processing city 98|rapar\n",
      "Processing city 99|priekule\n",
      "Processing city 100|vangaindrano\n",
      "Processing city 101|arlit\n",
      "Processing city 102|kurmanayevka\n",
      "Processing city 103|bredasdorp\n",
      "Processing city 104|nouadhibou\n",
      "Processing city 105|anapolis\n",
      "Processing city 106|rocha\n",
      "Processing city 107|lichtenburg\n",
      "Processing city 108|beloha\n",
      "Processing city 109|vaini\n",
      "Processing city 110|faanui\n",
      "Processing city 111|albany\n",
      "Processing city 112|batemans bay\n",
      "Processing city 113|saurimo\n",
      "Processing city 114|kirakira\n",
      "City not found. Skipping............\n",
      "Processing city 116|tiksi\n",
      "Processing city 117|carupano\n",
      "Processing city 118|morondava\n",
      "Processing city 119|gazanjyk\n",
      "Processing city 120|ust-kut\n",
      "Processing city 121|westport\n",
      "Processing city 122|hanyang\n",
      "Processing city 123|acapulco\n",
      "Processing city 124|bulgan\n",
      "Processing city 125|ponta delgada\n",
      "Processing city 126|esperance\n",
      "Processing city 127|mackay\n",
      "Processing city 128|henties bay\n",
      "Processing city 129|sao filipe\n",
      "Processing city 130|waingapu\n",
      "Processing city 131|hovd\n",
      "Processing city 132|kozhva\n",
      "City not found. Skipping............\n",
      "Processing city 134|longyearbyen\n",
      "Processing city 135|victoria\n",
      "Processing city 136|paamiut\n",
      "Processing city 137|cidreira\n",
      "City not found. Skipping............\n",
      "Processing city 139|carbondale\n",
      "Processing city 140|mersing\n",
      "Processing city 141|hamilton\n",
      "Processing city 142|sao geraldo do araguaia\n",
      "Processing city 143|broome\n",
      "Processing city 144|cherskiy\n",
      "Processing city 145|san patricio\n",
      "Processing city 146|ronfe\n",
      "Processing city 147|ajdabiya\n",
      "Processing city 148|desaguadero\n",
      "Processing city 149|narsaq\n",
      "Processing city 150|grindavik\n",
      "Processing city 151|new norfolk\n",
      "Processing city 152|puerto ayora\n",
      "Processing city 153|kuching\n",
      "Processing city 154|lora del rio\n",
      "Processing city 155|carnarvon\n",
      "Processing city 156|ouallam\n",
      "Processing city 157|georgetown\n",
      "Processing city 158|port-gentil\n",
      "Processing city 159|tocopilla\n",
      "Processing city 160|berlevag\n",
      "Processing city 161|imeni poliny osipenko\n",
      "Processing city 162|appenzell\n",
      "Processing city 163|chilliwack\n",
      "Processing city 164|kaitangata\n",
      "Processing city 165|klyuchi\n",
      "Processing city 166|praya\n",
      "Processing city 167|armizonskoye\n",
      "Processing city 168|burgeo\n",
      "Processing city 169|dingle\n",
      "Processing city 170|brae\n",
      "Processing city 171|coquimbo\n",
      "Processing city 172|upernavik\n",
      "City not found. Skipping............\n",
      "Processing city 174|saint stephen\n",
      "City not found. Skipping............\n",
      "Processing city 176|almenara\n",
      "Processing city 177|codrington\n",
      "Processing city 178|byron bay\n",
      "Processing city 179|broken hill\n",
      "Processing city 180|eldorado\n",
      "Processing city 181|hithadhoo\n",
      "Processing city 182|severo-kurilsk\n",
      "Processing city 183|totness\n",
      "Processing city 184|nador\n",
      "Processing city 185|saint george\n",
      "Processing city 186|khatanga\n",
      "Processing city 187|abrau-dyurso\n",
      "City not found. Skipping............\n",
      "Processing city 189|vakhrushev\n",
      "Processing city 190|san fernando\n",
      "Processing city 191|marzuq\n",
      "Processing city 192|barrow\n",
      "City not found. Skipping............\n",
      "Processing city 194|susanville\n",
      "Processing city 195|sibolga\n",
      "Processing city 196|kamenka\n",
      "City not found. Skipping............\n",
      "Processing city 198|dali\n",
      "Processing city 199|hobyo\n",
      "Processing city 200|verkhnevilyuysk\n",
      "Processing city 201|llanes\n",
      "Processing city 202|saint-pierre\n",
      "Processing city 203|luderitz\n",
      "Processing city 204|mar del plata\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 207|kavali\n",
      "Processing city 208|pathein\n",
      "Processing city 209|pitimbu\n",
      "City not found. Skipping............\n",
      "Processing city 211|tara\n",
      "Processing city 212|salinas\n",
      "Processing city 213|giyani\n",
      "Processing city 214|airai\n",
      "Processing city 215|oranjestad\n",
      "Processing city 216|nemuro\n",
      "City not found. Skipping............\n",
      "Processing city 218|anjangaon\n",
      "Processing city 219|leningradskiy\n",
      "Processing city 220|erzincan\n",
      "Processing city 221|rawson\n",
      "Processing city 222|san vicente\n",
      "Processing city 223|pedernales\n",
      "City not found. Skipping............\n",
      "Processing city 225|ngunguru\n",
      "Processing city 226|yar-sale\n",
      "Processing city 227|banjar\n",
      "Processing city 228|constitucion\n",
      "Processing city 229|barcelos\n",
      "Processing city 230|klaksvik\n",
      "Processing city 231|najran\n",
      "Processing city 232|katherine\n",
      "Processing city 233|khuzhir\n",
      "Processing city 234|linden\n",
      "Processing city 235|acari\n",
      "Processing city 236|solnechnyy\n",
      "Processing city 237|palmer\n",
      "Processing city 238|kununurra\n",
      "City not found. Skipping............\n",
      "Processing city 240|santa rosa\n",
      "Processing city 241|tasiilaq\n",
      "Processing city 242|sao joao da barra\n",
      "Processing city 243|prachuap khiri khan\n",
      "Processing city 244|kidal\n",
      "Processing city 245|maragogi\n",
      "Processing city 246|beausejour\n",
      "Processing city 247|vila velha\n",
      "Processing city 248|cam ranh\n",
      "Processing city 249|port alberni\n",
      "Processing city 250|hofn\n",
      "Processing city 251|turtas\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 254|hearst\n",
      "Processing city 255|saldanha\n",
      "Processing city 256|medicine hat\n",
      "Processing city 257|dondo\n",
      "Processing city 258|qaanaaq\n",
      "Processing city 259|xining\n",
      "Processing city 260|kruisfontein\n",
      "Processing city 261|ponta do sol\n",
      "Processing city 262|dunedin\n",
      "Processing city 263|nawa\n",
      "Processing city 264|cabedelo\n",
      "Processing city 265|ribeira grande\n",
      "Processing city 266|west wendover\n",
      "Processing city 267|follonica\n",
      "Processing city 268|bonthe\n",
      "Processing city 269|ixtapa\n",
      "Processing city 270|castro\n",
      "Processing city 271|springbok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing city 272|chernyshevskiy\n",
      "Processing city 273|winchester\n",
      "Processing city 274|zhezkazgan\n",
      "Processing city 275|anchorage\n",
      "Processing city 276|kijang\n",
      "Processing city 277|campobello di mazara\n",
      "Processing city 278|jackson\n",
      "Processing city 279|ilulissat\n",
      "Processing city 280|alofi\n",
      "Processing city 281|oistins\n",
      "Processing city 282|tambo\n",
      "Processing city 283|mercedes\n",
      "Processing city 284|cururupu\n",
      "Processing city 285|abu samrah\n",
      "Processing city 286|amapa\n",
      "Processing city 287|isilkul\n",
      "Processing city 288|pervomayskoye\n",
      "Processing city 289|shache\n",
      "Processing city 290|powell river\n",
      "Processing city 291|basoko\n",
      "Processing city 292|platanos\n",
      "Processing city 293|khandyga\n",
      "Processing city 294|dawei\n",
      "Processing city 295|senador jose porfirio\n",
      "Processing city 296|lai\n",
      "Processing city 297|cabo san lucas\n",
      "Processing city 298|margate\n",
      "Processing city 299|axim\n",
      "Processing city 300|magalia\n",
      "Processing city 301|balsas\n",
      "Processing city 302|dehui\n",
      "Processing city 303|grand-lahou\n",
      "Processing city 304|pangnirtung\n",
      "Processing city 305|port blair\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 308|moroto\n",
      "Processing city 309|belaya gora\n",
      "Processing city 310|markapur\n",
      "City not found. Skipping............\n",
      "Processing city 312|srednekolymsk\n",
      "Processing city 313|ketchikan\n",
      "Processing city 314|provideniya\n",
      "City not found. Skipping............\n",
      "Processing city 316|lompoc\n",
      "Processing city 317|mitsamiouli\n",
      "Processing city 318|ostrovnoy\n",
      "Processing city 319|micomeseng\n",
      "Processing city 320|knysna\n",
      "Processing city 321|pevek\n",
      "Processing city 322|tuy hoa\n",
      "Processing city 323|adrar\n",
      "Processing city 324|sakaiminato\n",
      "Processing city 325|inta\n",
      "Processing city 326|tongliao\n",
      "Processing city 327|port hardy\n",
      "Processing city 328|kalia\n",
      "Processing city 329|bambous virieux\n",
      "Processing city 330|saint anthony\n",
      "Processing city 331|flinders\n",
      "City not found. Skipping............\n",
      "Processing city 333|rockport\n",
      "City not found. Skipping............\n",
      "Processing city 335|burnie\n",
      "Processing city 336|piacabucu\n",
      "Processing city 337|bud\n",
      "Processing city 338|vestmannaeyjar\n",
      "Processing city 339|tessalit\n",
      "Processing city 340|zainsk\n",
      "Processing city 341|shirokiy\n",
      "Processing city 342|varhaug\n",
      "Processing city 343|salalah\n",
      "Processing city 344|thompson\n",
      "Processing city 345|thinadhoo\n",
      "Processing city 346|sistranda\n",
      "Processing city 347|buala\n",
      "Processing city 348|purpe\n",
      "City not found. Skipping............\n",
      "Processing city 350|znamenskoye\n",
      "Processing city 351|bukama\n",
      "Processing city 352|lumphat\n",
      "Processing city 353|chicama\n",
      "Processing city 354|homer\n",
      "Processing city 355|worthington\n",
      "Processing city 356|nizwa\n",
      "Processing city 357|jieshi\n",
      "Processing city 358|marienburg\n",
      "Processing city 359|casablanca\n",
      "Processing city 360|ancud\n",
      "Processing city 361|carutapera\n",
      "Processing city 362|cubara\n",
      "Processing city 363|birganj\n",
      "Processing city 364|makakilo city\n",
      "City not found. Skipping............\n",
      "Processing city 366|shimoda\n",
      "Processing city 367|shubarshi\n",
      "Processing city 368|neubrandenburg\n",
      "Processing city 369|richards bay\n",
      "City not found. Skipping............\n",
      "Processing city 371|brookhaven\n",
      "Processing city 372|marsa matruh\n",
      "Processing city 373|lavrentiya\n",
      "Processing city 374|manta\n",
      "Processing city 375|paramonga\n",
      "Processing city 376|lira\n",
      "Processing city 377|rozivka\n",
      "Processing city 378|kumluca\n",
      "Processing city 379|acurenam\n",
      "Processing city 380|palana\n",
      "Processing city 381|villa carlos paz\n",
      "Processing city 382|el carmen\n",
      "Processing city 383|stawell\n",
      "City not found. Skipping............\n",
      "Processing city 385|tamasopo\n",
      "Processing city 386|polunochnoye\n",
      "Processing city 387|walvis bay\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 390|troitskoye\n",
      "Processing city 391|aykhal\n",
      "Processing city 392|san rafael\n",
      "Processing city 393|pirojpur\n",
      "Processing city 394|high level\n",
      "City not found. Skipping............\n",
      "Processing city 396|mbini\n",
      "Processing city 397|naples\n",
      "Processing city 398|biloela\n",
      "Processing city 399|sweetwater\n",
      "Processing city 400|geraldton\n",
      "Processing city 401|caiaponia\n",
      "Processing city 402|havoysund\n",
      "Processing city 403|juneau\n",
      "Processing city 404|dabakala\n",
      "Processing city 405|tandil\n",
      "City not found. Skipping............\n",
      "Processing city 407|yumen\n",
      "Processing city 408|caravelas\n",
      "Processing city 409|husavik\n",
      "Processing city 410|inhambane\n",
      "Processing city 411|moron\n",
      "Processing city 412|kieta\n",
      "Processing city 413|kenora\n",
      "Processing city 414|mahebourg\n",
      "Processing city 415|ocos\n",
      "Processing city 416|nanortalik\n",
      "Processing city 417|monterey\n",
      "Processing city 418|guadalajara\n",
      "Processing city 419|pasni\n",
      "Processing city 420|passos\n",
      "Processing city 421|ndioum\n",
      "Processing city 422|bay roberts\n",
      "City not found. Skipping............\n",
      "Processing city 424|beachwood\n",
      "Processing city 425|forestville\n",
      "Processing city 426|chinsali\n",
      "Processing city 427|maningrida\n",
      "Processing city 428|trincomalee\n",
      "Processing city 429|baykit\n",
      "Processing city 430|canela\n",
      "Processing city 431|seydi\n",
      "Processing city 432|phayao\n",
      "Processing city 433|san policarpo\n",
      "Processing city 434|nome\n",
      "Processing city 435|le port\n",
      "Processing city 436|tynda\n",
      "Processing city 437|saint-augustin\n",
      "Processing city 438|iqaluit\n",
      "Processing city 439|kati\n",
      "Processing city 440|pontianak\n",
      "City not found. Skipping............\n",
      "Processing city 442|plouzane\n",
      "Processing city 443|imbituba\n",
      "Processing city 444|gorodishche\n",
      "Processing city 445|pamekasan\n",
      "Processing city 446|la roda\n",
      "Processing city 447|chihuahua\n",
      "Processing city 448|benjamin aceval\n",
      "Processing city 449|omsukchan\n",
      "Processing city 450|quang ngai\n",
      "Processing city 451|karratha\n",
      "Processing city 452|hastings\n",
      "City not found. Skipping............\n",
      "Processing city 454|plettenberg bay\n",
      "Processing city 455|qasigiannguit\n",
      "Processing city 456|sumbe\n",
      "Processing city 457|abha\n",
      "Processing city 458|upata\n",
      "Processing city 459|lamu\n",
      "Processing city 460|havre-saint-pierre\n",
      "Processing city 461|ayagoz\n",
      "Processing city 462|lorengau\n",
      "Processing city 463|novyy urengoy\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 466|sinnamary\n",
      "Processing city 467|vardo\n",
      "City not found. Skipping............\n",
      "Processing city 469|goderich\n",
      "Processing city 470|ayios nikolaos\n",
      "Processing city 471|cockburn town\n",
      "Processing city 472|warwick\n",
      "Processing city 473|albertville\n",
      "Processing city 474|shenzhen\n",
      "Processing city 475|vostochnyy\n",
      "Processing city 476|wheeling\n",
      "Processing city 477|fukue\n",
      "City not found. Skipping............\n",
      "Processing city 479|tosya\n",
      "Processing city 480|calbuco\n",
      "City not found. Skipping............\n",
      "Processing city 482|grand gaube\n",
      "Processing city 483|padang\n",
      "Processing city 484|sovetskaya gavan\n",
      "Processing city 485|caratinga\n",
      "Processing city 486|buchanan\n",
      "Processing city 487|laguna\n",
      "Processing city 488|guangyuan\n",
      "Processing city 489|ust-abakan\n",
      "Processing city 490|erenhot\n",
      "Processing city 491|miri\n",
      "Processing city 492|manicore\n",
      "Processing city 493|atar\n",
      "Processing city 494|ulladulla\n",
      "Processing city 495|bambanglipuro\n",
      "Processing city 496|lagoa\n",
      "Processing city 497|dehloran\n",
      "Processing city 498|samarai\n",
      "Processing city 499|port macquarie\n",
      "Processing city 500|russellville\n",
      "Processing city 501|vila\n",
      "Processing city 502|tura\n",
      "City not found. Skipping............\n",
      "Processing city 504|gamba\n",
      "Processing city 505|te anau\n",
      "Processing city 506|sitka\n",
      "Processing city 507|tommot\n",
      "Processing city 508|nalut\n",
      "Processing city 509|poum\n",
      "Processing city 510|port augusta\n",
      "Processing city 511|norman wells\n",
      "Processing city 512|vershino-shakhtaminskiy\n",
      "Processing city 513|faya\n",
      "Processing city 514|tigil\n",
      "Processing city 515|saint-francois\n",
      "Processing city 516|bontang\n",
      "Processing city 517|sorong\n",
      "Processing city 518|stromness\n",
      "Processing city 519|mogadishu\n",
      "Processing city 520|urengoy\n",
      "Processing city 521|aksu\n",
      "Processing city 522|marawi\n",
      "Processing city 523|melo\n",
      "Processing city 524|tezu\n",
      "Processing city 525|payson\n",
      "Processing city 526|nacala\n",
      "Processing city 527|bom jardim\n",
      "Processing city 528|ust-nera\n",
      "Processing city 529|chumphon\n",
      "Processing city 530|jalu\n",
      "Processing city 531|manzhouli\n",
      "Processing city 532|sawakin\n",
      "Processing city 533|kalabo\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 536|bathsheba\n",
      "Processing city 537|nelson bay\n",
      "Processing city 538|aklavik\n",
      "Processing city 539|xingyi\n",
      "Processing city 540|murchison\n",
      "Processing city 541|conde\n",
      "Processing city 542|estelle\n",
      "Processing city 543|jaru\n",
      "Processing city 544|aasiaat\n",
      "Processing city 545|kavieng\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing city 546|elizabeth city\n",
      "Processing city 547|tawau\n",
      "Processing city 548|kahului\n",
      "City not found. Skipping............\n",
      "Processing city 550|san matias\n",
      "Processing city 551|el alto\n",
      "Processing city 552|dryden\n",
      "Processing city 553|quesnel\n",
      "Processing city 554|mandera\n",
      "Processing city 555|reriutaba\n",
      "Processing city 556|kushiro\n",
      "City not found. Skipping............\n",
      "Processing city 558|morgan city\n",
      "Processing city 559|pedasi\n",
      "Processing city 560|labuhan\n",
      "Processing city 561|lensk\n",
      "Processing city 562|aiquile\n",
      "Processing city 563|puri\n",
      "Processing city 564|dzhebariki-khaya\n",
      "Processing city 565|lumajang\n",
      "Processing city 566|mehamn\n",
      "Processing city 567|remedios\n",
      "Processing city 568|hinton\n",
      "Processing city 569|olbia\n",
      "Processing city 570|lata\n",
      "Processing city 571|karpathos\n",
      "City not found. Skipping............\n",
      "Processing city 573|sri aman\n",
      "Processing city 574|dudinka\n",
      "Processing city 575|mendi\n",
      "Processing city 576|boden\n",
      "Processing city 577|tansen\n",
      "Processing city 578|ouargaye\n",
      "Processing city 579|quelimane\n",
      "Processing city 580|quatre cocos\n",
      "Processing city 581|isangel\n",
      "City not found. Skipping............\n",
      "Processing city 583|canico\n",
      "Processing city 584|aksarka\n",
      "Processing city 585|ballina\n",
      "Processing city 586|jipijapa\n",
      "Processing city 587|tarnogskiy gorodok\n",
      "Processing city 588|churapcha\n",
      "Processing city 589|bang len\n",
      "Processing city 590|duluth\n",
      "Processing city 591|moose factory\n",
      "Processing city 592|willmar\n",
      "Processing city 593|pinhao\n",
      "Processing city 594|deogarh\n",
      "Processing city 595|itaueira\n",
      "Processing city 596|vuktyl\n",
      "Processing city 597|iracoubo\n",
      "Processing city 598|bayan\n",
      "Processing city 599|dwarka\n",
      "Processing city 600|desbiens\n",
      "Processing city 601|luancheng\n",
      "City not found. Skipping............\n",
      "City not found. Skipping............\n",
      "Processing city 604|tautira\n",
      "City not found. Skipping............\n",
      "Processing city 606|cedar city\n",
      "Processing city 607|portree\n",
      "Processing city 608|deniliquin\n",
      "Processing city 609|ukiah\n",
      "Processing city 610|eastbourne\n",
      "Processing city 611|emmett\n",
      "Processing city 612|smidovich\n",
      "Processing city 613|verkhnyaya toyma\n",
      "Processing city 614|avera\n",
      "Processing city 615|ivanivka\n",
      "City not found. Skipping............\n"
     ]
    }
   ],
   "source": [
    "# send api requests\n",
    "latitude=[]\n",
    "humidity=[]\n",
    "temperature_max=[]\n",
    "cloudiness=[]\n",
    "wind_speed=[]\n",
    "city_found=[]\n",
    "print('Beginning Data Retrieval')\n",
    "print('------------------------')\n",
    "for index,city_name in enumerate(cities):\n",
    "    try:\n",
    "        url='https://api.openweathermap.org/data/2.5/weather?'\n",
    "        city=city_name\n",
    "        units='imperial'\n",
    "        query_url=f'{url}appid={api_key}&q={city}&units={units}'\n",
    "        response=requests.get(query_url).json()\n",
    "        latitude.append(response['coord']['lat'])\n",
    "        humidity.append(response['main']['humidity'])\n",
    "        temperature_max.append(response['main']['temp_max'])\n",
    "        cloudiness.append(response['clouds']['all'])\n",
    "        wind_speed.append(response['wind']['speed'])\n",
    "        print(f'Processing city {index+1}|{city_name}')\n",
    "        city_found.append(city_name)\n",
    "    except KeyError:\n",
    "        print(f'City not found. Skipping............')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Humidity (%)</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Max Temperature (F)</th>\n",
       "      <th>Cloudiness (%)</th>\n",
       "      <th>Wind Speed (mph)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cape town</td>\n",
       "      <td>93</td>\n",
       "      <td>-33.93</td>\n",
       "      <td>46.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yulara</td>\n",
       "      <td>29</td>\n",
       "      <td>-25.24</td>\n",
       "      <td>64.40</td>\n",
       "      <td>0</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitu</td>\n",
       "      <td>46</td>\n",
       "      <td>36.20</td>\n",
       "      <td>68.27</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>east london</td>\n",
       "      <td>70</td>\n",
       "      <td>-33.02</td>\n",
       "      <td>46.40</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dikson</td>\n",
       "      <td>100</td>\n",
       "      <td>73.51</td>\n",
       "      <td>40.55</td>\n",
       "      <td>100</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Humidity (%)  Latitude  Max Temperature (F)  Cloudiness (%)  \\\n",
       "0    cape town            93    -33.93                46.00               0   \n",
       "1       yulara            29    -25.24                64.40               0   \n",
       "2         mitu            46     36.20                68.27               0   \n",
       "3  east london            70    -33.02                46.40               0   \n",
       "4       dikson           100     73.51                40.55             100   \n",
       "\n",
       "   Wind Speed (mph)  \n",
       "0              2.24  \n",
       "1              9.17  \n",
       "2              4.00  \n",
       "3              8.05  \n",
       "4              6.31  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store response in a datafame\n",
    "cities_df=pd.DataFrame({'City':city_found,\n",
    "                        'Humidity (%)':humidity,\n",
    "                        'Latitude':latitude,\n",
    "                        'Max Temperature (F)':temperature_max,\n",
    "                        'Cloudiness (%)':cloudiness,\n",
    "                        'Wind Speed (mph)':wind_speed})\n",
    "cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities_df.to_csv('city_info.csv',index=False,header=True) #output results to a csv file. don't have to run api requests again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5576345763185341\n",
      "0.02496449233972211\n"
     ]
    }
   ],
   "source": [
    "#City Latitude vs Max Temperature\n",
    "plt.plot(cities_df['Latitude'], cities_df['Max Temperature (F)'], 'bo')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Max Temperature (F)')\n",
    "plt.title('City Latitude vs Max Temperature')\n",
    "t_slope, t_intercept, t_r_value, t_p_value, t_std_err=linregress(cities_df['Latitude'], cities_df['Max Temperature (F)'])\n",
    "t_fit=t_slope*cities_df['Latitude']+ t_intercept\n",
    "plt.plot(cities_df['Latitude'],t_fit,'r-')\n",
    "plt.savefig('l_mf.png')\n",
    "plt.show()\n",
    "print(t_p_value)\n",
    "print(t_r_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5749241472904856\n",
      "-0.02387724939211677\n"
     ]
    }
   ],
   "source": [
    "#City Latitude vs Humidity\n",
    "plt.plot(cities_df['Latitude'], cities_df['Humidity (%)'], 'bo')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Humidity (%)')\n",
    "plt.title('City Latitude vs Humidity')\n",
    "h_slope, h_intercept, h_r_value, h_p_value, h_std_err=linregress(cities_df['Latitude'], cities_df['Humidity (%)'])\n",
    "h_fit=h_slope*cities_df['Latitude']+ h_intercept\n",
    "plt.plot(cities_df['Latitude'],h_fit,'r-')\n",
    "plt.savefig('l_h.png')\n",
    "plt.show()\n",
    "print(h_p_value)\n",
    "print(h_r_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8501447206674122\n",
      "-0.008045068177875182\n"
     ]
    }
   ],
   "source": [
    "#City Latitude vs Cloudiness\n",
    "plt.plot(cities_df['Latitude'], cities_df['Cloudiness (%)'], 'bo')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Cloudiness (%)')\n",
    "plt.title('City Latitude vs Cloudiness')\n",
    "c_slope, c_intercept, c_r_value, c_p_value, c_std_err=linregress(cities_df['Latitude'], cities_df['Cloudiness (%)'])\n",
    "c_fit=c_slope*cities_df['Latitude']+ c_intercept\n",
    "plt.plot(cities_df['Latitude'],c_fit,'r-')\n",
    "plt.savefig('l_c.png')\n",
    "plt.show()\n",
    "print(c_p_value)\n",
    "print(c_r_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0355142375356166\n",
      "-0.08934745887584637\n"
     ]
    }
   ],
   "source": [
    "#City Latitude vs Wind Speed\n",
    "plt.plot(cities_df['Latitude'], cities_df['Wind Speed (mph)'], 'bo')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Wind Speed (mph)')\n",
    "plt.title('City Latitude vs Wind Speed')\n",
    "w_slope, w_intercept, w_r_value, w_p_value, w_std_err=linregress(cities_df['Latitude'], cities_df['Wind Speed (mph)'])\n",
    "w_fit=w_slope*cities_df['Latitude']+ w_intercept\n",
    "plt.plot(cities_df['Latitude'],w_fit,'r-')\n",
    "plt.savefig('l_w.png')\n",
    "plt.show()\n",
    "print(w_p_value)\n",
    "print(w_r_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P Value</th>\n",
       "      <th>Correlation Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>City Latitude vs Max Temperature</th>\n",
       "      <td>0.557635</td>\n",
       "      <td>0.024964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City Latitude vs Humidity</th>\n",
       "      <td>0.574924</td>\n",
       "      <td>-0.023877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City Latitude vs Cloudiness</th>\n",
       "      <td>0.850145</td>\n",
       "      <td>-0.008045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City Latitude vs Wind Speed</th>\n",
       "      <td>0.035514</td>\n",
       "      <td>-0.089347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   P Value  Correlation Coefficient\n",
       "City Latitude vs Max Temperature  0.557635                 0.024964\n",
       "City Latitude vs Humidity         0.574924                -0.023877\n",
       "City Latitude vs Cloudiness       0.850145                -0.008045\n",
       "City Latitude vs Wind Speed       0.035514                -0.089347"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# linear regession results in a dataframe\n",
    "analysis_df=pd.DataFrame({'P Value':[t_p_value,h_p_value,c_p_value,w_p_value],\n",
    "                         'Correlation Coefficient':[t_r_value,h_r_value,c_r_value,w_r_value]},\n",
    "                         index=['City Latitude vs Max Temperature','City Latitude vs Humidity','City Latitude vs Cloudiness','City Latitude vs Wind Speed'])\n",
    "analysis_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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