WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WeatherPy\n",
    "----\n",
    "\n",
    "#### Note\n",
    "* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "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",
    "from scipy.stats import linregress\n",
    "\n",
    "# Import API key\n",
    "from api_keys import weather_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 = \"output_data.csv\"\n",
    "\n",
    "# Range of latitudes and longitudes\n",
    "lat_range = (-90, 90)\n",
    "lng_range = (-180, 180)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Cities List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'komsomolskiy'"
      ]
     },
     "execution_count": 3,
     "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(lat_range[0], lat_range[1], size=1500)\n",
    "lngs = np.random.uniform(lng_range[0], lng_range[1], 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)\n",
    "cities[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform API Calls\n",
    "* Perform a weather check on each city using a series of successive API calls.\n",
    "* Include a print log of each city as it'sbeing processed (with the city number and city name).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for 0, komsomolskiy\n",
      "Searching for 1, butaritari\n",
      "Searching for 2, goderich\n",
      "Searching for 3, mar del plata\n",
      "Searching for 4, sisophon\n",
      "Searching for 5, port alfred\n",
      "Searching for 6, kapaa\n",
      "Searching for 7, brindisi\n",
      "Searching for 8, port blair\n",
      "Searching for 9, methoni\n",
      "Searching for 10, ushuaia\n",
      "Searching for 11, jamestown\n",
      "Searching for 12, avarua\n",
      "Searching for 13, chatham\n",
      "Searching for 14, marienburg\n",
      "Searching for 15, saleaula\n",
      "City not found, skipping\n",
      "Searching for 16, hithadhoo\n",
      "Searching for 17, mataura\n",
      "Searching for 18, lasa\n",
      "Searching for 19, paamiut\n",
      "Searching for 20, souillac\n",
      "Searching for 21, upernavik\n",
      "Searching for 22, tongliao\n",
      "Searching for 23, khatanga\n",
      "Searching for 24, vestmanna\n",
      "Searching for 25, belushya guba\n",
      "City not found, skipping\n",
      "Searching for 26, leningradskiy\n",
      "Searching for 27, labuhan\n",
      "Searching for 28, yarada\n",
      "Searching for 29, yellowknife\n",
      "Searching for 30, geraldton\n",
      "Searching for 31, provideniya\n",
      "Searching for 32, barentsburg\n",
      "City not found, skipping\n",
      "Searching for 33, hermanus\n",
      "Searching for 34, nikolskoye\n",
      "Searching for 35, qaanaaq\n",
      "Searching for 36, tsihombe\n",
      "City not found, skipping\n",
      "Searching for 37, katsuura\n",
      "Searching for 38, aykhal\n",
      "Searching for 39, bambous virieux\n",
      "Searching for 40, laem sing\n",
      "Searching for 41, athabasca\n",
      "Searching for 42, port elizabeth\n",
      "Searching for 43, toliary\n",
      "City not found, skipping\n",
      "Searching for 44, ezequiel montes\n",
      "Searching for 45, barrow\n",
      "Searching for 46, albany\n",
      "Searching for 47, moyale\n",
      "Searching for 48, rikitea\n",
      "Searching for 49, punta arenas\n",
      "Searching for 50, saint-philippe\n",
      "Searching for 51, saldanha\n",
      "Searching for 52, cap malheureux\n",
      "Searching for 53, bluff\n",
      "Searching for 54, shenjiamen\n",
      "Searching for 55, kamenka\n",
      "Searching for 56, cockburn town\n",
      "Searching for 57, cidreira\n",
      "Searching for 58, vaini\n",
      "Searching for 59, maraba\n",
      "Searching for 60, nortelandia\n",
      "Searching for 61, ntungamo\n",
      "Searching for 62, dharchula\n",
      "Searching for 63, pacific grove\n",
      "Searching for 64, narsaq\n",
      "Searching for 65, saskylakh\n",
      "Searching for 66, mahibadhoo\n",
      "Searching for 67, taltal\n",
      "Searching for 68, acarau\n",
      "Searching for 69, hilo\n",
      "Searching for 70, marcona\n",
      "City not found, skipping\n",
      "Searching for 71, weyburn\n",
      "Searching for 72, samusu\n",
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      "Searching for 73, taburi\n",
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      "Searching for 74, vaitupu\n",
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      "Searching for 75, east london\n",
      "Searching for 76, hudiksvall\n",
      "Searching for 77, sadowara\n",
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      "Searching for 78, hami\n",
      "Searching for 79, chicama\n",
      "Searching for 80, fort nelson\n",
      "Searching for 81, north bend\n",
      "Searching for 82, new norfolk\n",
      "Searching for 83, teguise\n",
      "Searching for 84, tasiilaq\n",
      "Searching for 85, mahebourg\n",
      "Searching for 86, kharian\n",
      "Searching for 87, grand river south east\n",
      "City not found, skipping\n",
      "Searching for 88, hasaki\n",
      "Searching for 89, kangaatsiaq\n",
      "Searching for 90, kodiak\n",
      "Searching for 91, port hardy\n",
      "Searching for 92, najran\n",
      "Searching for 93, busselton\n",
      "Searching for 94, touros\n",
      "Searching for 95, santa isabel\n",
      "Searching for 96, tiksi\n",
      "Searching for 97, karamken\n",
      "City not found, skipping\n",
      "Searching for 98, castro\n",
      "Searching for 99, georgetown\n",
      "Searching for 100, airai\n",
      "Searching for 101, bredasdorp\n",
      "Searching for 102, egvekinot\n",
      "Searching for 103, sucua\n",
      "Searching for 104, tezu\n",
      "Searching for 105, baruun-urt\n",
      "Searching for 106, saint-pierre\n",
      "Searching for 107, nanortalik\n",
      "Searching for 108, nelson bay\n",
      "Searching for 109, umm lajj\n",
      "Searching for 110, gander\n",
      "Searching for 111, nago\n",
      "Searching for 112, fare\n",
      "Searching for 113, kavaratti\n",
      "Searching for 114, severo-kurilsk\n",
      "Searching for 115, illoqqortoormiut\n",
      "City not found, skipping\n",
      "Searching for 116, constitucion\n",
      "Searching for 117, chokurdakh\n",
      "Searching for 118, pevek\n",
      "Searching for 119, taolanaro\n",
      "City not found, skipping\n",
      "Searching for 120, hobart\n",
      "Searching for 121, cherskiy\n",
      "Searching for 122, bargal\n",
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      "Searching for 123, fairbanks\n",
      "Searching for 124, nemuro\n",
      "Searching for 125, san patricio\n",
      "Searching for 126, terrace\n",
      "Searching for 127, soyo\n",
      "Searching for 128, guerrero negro\n",
      "Searching for 129, esmeralda\n",
      "Searching for 130, cabo san lucas\n",
      "Searching for 131, arraial do cabo\n",
      "Searching for 132, kagoro\n",
      "Searching for 133, tidore\n",
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      "Searching for 134, cape town\n",
      "Searching for 135, attawapiskat\n",
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      "Searching for 136, vyazma\n",
      "Searching for 137, sitka\n",
      "Searching for 138, abiy adi\n",
      "City not found, skipping\n",
      "Searching for 139, tarudant\n",
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      "Searching for 140, los llanos de aridane\n",
      "Searching for 141, ciudad bolivar\n",
      "Searching for 142, atuona\n",
      "Searching for 143, sa kaeo\n",
      "Searching for 144, muros\n",
      "Searching for 145, torbay\n",
      "Searching for 146, coihaique\n",
      "Searching for 147, tuktoyaktuk\n",
      "Searching for 148, dudinka\n",
      "Searching for 149, lamu\n",
      "Searching for 150, dikson\n",
      "Searching for 151, marathon\n",
      "Searching for 152, revadanda\n",
      "Searching for 153, hamilton\n",
      "Searching for 154, kaitangata\n",
      "Searching for 155, puerto ayora\n",
      "Searching for 156, garowe\n",
      "Searching for 157, puerto del rosario\n",
      "Searching for 158, formoso do araguaia\n",
      "City not found, skipping\n",
      "Searching for 159, te anau\n",
      "Searching for 160, kamenskoye\n",
      "City not found, skipping\n",
      "Searching for 161, soure\n",
      "Searching for 162, kostomuksha\n",
      "Searching for 163, lebu\n",
      "Searching for 164, norman wells\n",
      "Searching for 165, acapulco\n",
      "Searching for 166, broome\n",
      "Searching for 167, klaksvik\n",
      "Searching for 168, bethanien\n",
      "Searching for 169, hobyo\n",
      "Searching for 170, quelimane\n",
      "Searching for 171, domodossola\n",
      "Searching for 172, tocopilla\n",
      "Searching for 173, mecca\n",
      "Searching for 174, parati\n",
      "Searching for 175, ribeira grande\n",
      "Searching for 176, khandyga\n",
      "Searching for 177, camacha\n",
      "Searching for 178, ponta do sol\n",
      "Searching for 179, qeshm\n",
      "Searching for 180, hualmay\n",
      "Searching for 181, krasnoselkup\n",
      "Searching for 182, srednekolymsk\n",
      "Searching for 183, chara\n",
      "Searching for 184, general roca\n",
      "Searching for 185, cortez\n",
      "Searching for 186, yirol\n",
      "City not found, skipping\n",
      "Searching for 187, fort saint john\n",
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      "Searching for 188, isangel\n",
      "Searching for 189, mirnyy\n",
      "Searching for 190, emilio carranza\n",
      "Searching for 191, mys shmidta\n",
      "City not found, skipping\n",
      "Searching for 192, shurugwi\n",
      "Searching for 193, yuanping\n",
      "Searching for 194, chongqing\n",
      "Searching for 195, borogontsy\n",
      "Searching for 196, vila velha\n",
      "Searching for 197, bethel\n",
      "Searching for 198, talas\n",
      "Searching for 199, quang ngai\n",
      "Searching for 200, angoche\n",
      "Searching for 201, navabad\n",
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      "Searching for 202, leshukonskoye\n",
      "Searching for 203, monrovia\n",
      "Searching for 204, makamba\n",
      "Searching for 205, novikovo\n",
      "Searching for 206, salalah\n",
      "Searching for 207, chimbote\n",
      "Searching for 208, baijiantan\n",
      "Searching for 209, nizhneyansk\n",
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      "Searching for 210, thompson\n",
      "Searching for 211, chuy\n",
      "Searching for 212, tawkar\n",
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      "Searching for 213, micheweni\n",
      "Searching for 214, ovre ardal\n",
      "Searching for 215, ilulissat\n",
      "Searching for 216, antofagasta\n",
      "Searching for 217, lubango\n",
      "Searching for 218, yarke pole\n",
      "Searching for 219, nara\n",
      "Searching for 220, temba\n",
      "Searching for 221, poum\n",
      "Searching for 222, san cristobal\n",
      "Searching for 223, kununurra\n",
      "Searching for 224, arlit\n",
      "Searching for 225, tautira\n",
      "Searching for 226, manakara\n",
      "Searching for 227, hattiesburg\n",
      "Searching for 228, faanui\n",
      "Searching for 229, san jose\n",
      "Searching for 230, doha\n",
      "Searching for 231, moctezuma\n",
      "Searching for 232, moree\n",
      "Searching for 233, belyy yar\n",
      "Searching for 234, alyangula\n",
      "Searching for 235, kavieng\n",
      "Searching for 236, bentiu\n",
      "Searching for 237, ruatoria\n",
      "City not found, skipping\n",
      "Searching for 238, esperance\n",
      "Searching for 239, mokokchung\n",
      "Searching for 240, bantou\n",
      "Searching for 241, snihurivka\n",
      "Searching for 242, saint george\n",
      "Searching for 243, two rivers\n",
      "Searching for 244, rafai\n",
      "Searching for 245, mezen\n",
      "Searching for 246, vilhena\n",
      "Searching for 247, harper\n",
      "Searching for 248, sur\n",
      "Searching for 249, petropavlovsk-kamchatskiy\n",
      "Searching for 250, ambodifototra\n",
      "City not found, skipping\n",
      "Searching for 251, vao\n",
      "Searching for 252, abbeyfeale\n",
      "Searching for 253, show low\n",
      "Searching for 254, ornskoldsvik\n",
      "Searching for 255, verkhnevilyuysk\n",
      "Searching for 256, kirovskiy\n",
      "Searching for 257, prachuap khiri khan\n",
      "Searching for 258, charlottesville\n",
      "Searching for 259, mogwase\n",
      "Searching for 260, portland\n",
      "Searching for 261, fortuna\n",
      "Searching for 262, parabel\n",
      "Searching for 263, pak phanang\n",
      "Searching for 264, tahta\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for 265, fukue\n",
      "Searching for 266, ancud\n",
      "Searching for 267, faya\n",
      "Searching for 268, amderma\n",
      "City not found, skipping\n",
      "Searching for 269, bataipora\n",
      "Searching for 270, ghanzi\n",
      "Searching for 271, avera\n",
      "Searching for 272, fonte boa\n",
      "Searching for 273, sabha\n",
      "Searching for 274, nicoya\n",
      "Searching for 275, mount isa\n",
      "Searching for 276, evensk\n",
      "Searching for 277, netrakona\n",
      "Searching for 278, yatou\n",
      "Searching for 279, jalu\n",
      "Searching for 280, naliya\n",
      "Searching for 281, lamar\n",
      "Searching for 282, hofn\n",
      "Searching for 283, salta\n",
      "Searching for 284, nome\n",
      "Searching for 285, eskasem\n",
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      "Searching for 286, kpandu\n",
      "Searching for 287, sangar\n",
      "Searching for 288, bukachacha\n",
      "Searching for 289, gat\n",
      "Searching for 290, hatillo\n",
      "Searching for 291, haines junction\n",
      "Searching for 292, zyryanka\n",
      "Searching for 293, julio de castilhos\n",
      "Searching for 294, palauig\n",
      "Searching for 295, tateyama\n",
      "Searching for 296, tonantins\n",
      "Searching for 297, san carlos de bariloche\n",
      "Searching for 298, zolotinka\n",
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      "Searching for 299, walvis bay\n",
      "Searching for 300, seoul\n",
      "Searching for 301, tatarsk\n",
      "Searching for 302, karasburg\n",
      "Searching for 303, manono\n",
      "Searching for 304, ozernovskiy\n",
      "Searching for 305, luderitz\n",
      "Searching for 306, laguna\n",
      "Searching for 307, hambantota\n",
      "Searching for 308, bathsheba\n",
      "Searching for 309, iqaluit\n",
      "Searching for 310, gaptsakh\n",
      "Searching for 311, carnarvon\n",
      "Searching for 312, victoria\n",
      "Searching for 313, ngukurr\n",
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      "Searching for 314, watsa\n",
      "Searching for 315, culleredo\n",
      "Searching for 316, beringovskiy\n",
      "Searching for 317, mandalgovi\n",
      "Searching for 318, neiafu\n",
      "Searching for 319, rodrigues alves\n",
      "Searching for 320, boguchany\n",
      "Searching for 321, soeng sang\n",
      "Searching for 322, bridlington\n",
      "Searching for 323, naftah\n",
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      "Searching for 324, umzimvubu\n",
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      "Searching for 325, bela pod bezdezem\n",
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      "Searching for 328, karoi\n",
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      "Searching for 339, mayo\n",
      "Searching for 340, bosconia\n",
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      "Searching for 342, tzucacab\n",
      "Searching for 343, high level\n",
      "Searching for 344, pisco\n",
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      "Searching for 347, gulu\n",
      "Searching for 348, road town\n",
      "Searching for 349, suzhou\n",
      "Searching for 350, san vicente\n",
      "Searching for 351, araguaina\n",
      "Searching for 352, hemsedal\n",
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      "Searching for 354, turtkul\n",
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      "Searching for 356, saint-georges\n",
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      "Searching for 359, solnechnyy\n",
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      "Searching for 362, husavik\n",
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      "Searching for 364, nouadhibou\n",
      "Searching for 365, bengkulu\n",
      "Searching for 366, takoradi\n",
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      "Searching for 374, burns lake\n",
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      "Searching for 376, saint-augustin\n",
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      "Searching for 378, sao jose da coroa grande\n",
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      "Searching for 380, monkey bay\n",
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      "Searching for 382, trairi\n",
      "Searching for 383, abu kamal\n",
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      "Searching for 386, gayny\n",
      "Searching for 387, pandan\n",
      "Searching for 388, paita\n",
      "Searching for 389, dukat\n",
      "Searching for 390, moerai\n",
      "Searching for 391, shakawe\n",
      "Searching for 392, taiyuan\n",
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      "Searching for 394, jieshi\n",
      "Searching for 395, chernyshevskiy\n",
      "Searching for 396, sawakin\n",
      "Searching for 397, ampanihy\n",
      "Searching for 398, flinders\n",
      "Searching for 399, lagoa\n",
      "Searching for 400, port lincoln\n",
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      "Searching for 402, vardo\n",
      "Searching for 403, bolshoy tsaryn\n",
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      "Searching for 410, shimoda\n",
      "Searching for 411, borujerd\n",
      "Searching for 412, eureka\n",
      "Searching for 413, polunochnoye\n",
      "Searching for 414, contai\n",
      "Searching for 415, clyde river\n",
      "Searching for 416, labutta\n",
      "City not found, skipping\n",
      "Searching for 417, khorixas\n",
      "Searching for 418, kahului\n",
      "Searching for 419, traralgon\n",
      "Searching for 420, polis\n",
      "Searching for 421, mangrol\n",
      "Searching for 422, ust-nera\n",
      "Searching for 423, sorland\n",
      "Searching for 424, marakkanam\n",
      "Searching for 425, itarema\n",
      "Searching for 426, troy\n",
      "Searching for 427, jamame\n",
      "Searching for 428, inhambane\n",
      "Searching for 429, bosaso\n",
      "Searching for 430, perehonivka\n",
      "Searching for 431, sinjar\n",
      "Searching for 432, solovetskiy\n",
      "City not found, skipping\n",
      "Searching for 433, westport\n",
      "Searching for 434, zaysan\n",
      "Searching for 435, ylivieska\n",
      "Searching for 436, san policarpo\n",
      "Searching for 437, metamorfosis\n",
      "City not found, skipping\n",
      "Searching for 438, spassk-dalniy\n",
      "Searching for 439, menongue\n",
      "Searching for 440, jacmel\n",
      "Searching for 441, mto wa mbu\n",
      "Searching for 442, sao filipe\n",
      "Searching for 443, benjamin constant\n",
      "Searching for 444, anadyr\n",
      "Searching for 445, tevaitoa\n",
      "Searching for 446, la rioja\n",
      "Searching for 447, agen\n",
      "Searching for 448, penticton\n",
      "Searching for 449, kashi\n",
      "Searching for 450, otradnoye\n",
      "Searching for 451, maraa\n",
      "Searching for 452, bismil\n",
      "Searching for 453, grindavik\n",
      "Searching for 454, tawang\n",
      "Searching for 455, banda aceh\n",
      "Searching for 456, tobe\n",
      "Searching for 457, meulaboh\n",
      "Searching for 458, zhumadian\n",
      "Searching for 459, san julian\n",
      "Searching for 460, bonavista\n",
      "Searching for 461, soe\n",
      "Searching for 462, puerto el triunfo\n",
      "Searching for 463, tumannyy\n",
      "City not found, skipping\n",
      "Searching for 464, teruel\n",
      "Searching for 465, tabiauea\n",
      "City not found, skipping\n",
      "Searching for 466, morwell\n",
      "Searching for 467, omboue\n",
      "Searching for 468, rungata\n",
      "City not found, skipping\n",
      "Searching for 469, vostok\n",
      "Searching for 470, allende\n",
      "Searching for 471, calabozo\n",
      "Searching for 472, coquimbo\n",
      "Searching for 473, georgiyevskoye\n",
      "Searching for 474, namibe\n",
      "Searching for 475, odweyne\n",
      "Searching for 476, palu\n",
      "Searching for 477, karratha\n",
      "Searching for 478, lubumbashi\n",
      "Searching for 479, warner robins\n",
      "Searching for 480, benguela\n",
      "Searching for 481, aguada de pasajeros\n",
      "Searching for 482, cairns\n",
      "Searching for 483, mergui\n",
      "City not found, skipping\n",
      "Searching for 484, taybad\n",
      "Searching for 485, warqla\n",
      "City not found, skipping\n",
      "Searching for 486, morondava\n",
      "Searching for 487, cueramaro\n",
      "Searching for 488, goure\n",
      "Searching for 489, zhenlai\n",
      "Searching for 490, haibowan\n",
      "City not found, skipping\n",
      "Searching for 491, saint-francois\n",
      "Searching for 492, cabedelo\n",
      "Searching for 493, mukhen\n",
      "Searching for 494, peniche\n",
      "Searching for 495, port-de-paix\n",
      "City not found, skipping\n",
      "Searching for 496, pasighat\n",
      "Searching for 497, novaya malykla\n",
      "Searching for 498, tilichiki\n",
      "Searching for 499, padang\n",
      "Searching for 500, lakatoro\n",
      "Searching for 501, wysokie mazowieckie\n",
      "Searching for 502, slave lake\n",
      "Searching for 503, barawe\n",
      "City not found, skipping\n",
      "Searching for 504, loding\n",
      "Searching for 505, rock sound\n",
      "Searching for 506, tambura\n",
      "Searching for 507, port augusta\n",
      "Searching for 508, raudeberg\n",
      "Searching for 509, demba\n",
      "Searching for 510, sorvag\n",
      "City not found, skipping\n",
      "Searching for 511, hovd\n",
      "Searching for 512, visby\n",
      "Searching for 513, mount gambier\n",
      "Searching for 514, kumluca\n",
      "Searching for 515, matara\n",
      "Searching for 516, altona\n",
      "Searching for 517, koumac\n",
      "Searching for 518, craig\n",
      "Searching for 519, kelo\n",
      "Searching for 520, cangucu\n",
      "Searching for 521, harnosand\n",
      "Searching for 522, el salto\n",
      "Searching for 523, gizo\n",
      "Searching for 524, brae\n",
      "Searching for 525, pinega\n",
      "Searching for 526, daxian\n",
      "City not found, skipping\n",
      "Searching for 527, palabuhanratu\n",
      "City not found, skipping\n",
      "Searching for 528, khor\n",
      "Searching for 529, mehamn\n",
      "Searching for 530, campanha\n",
      "Searching for 531, simao\n",
      "Searching for 532, port macquarie\n",
      "Searching for 533, ketchikan\n",
      "Searching for 534, gebre guracha\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for 535, ishigaki\n",
      "Searching for 536, nongan\n",
      "Searching for 537, pangnirtung\n",
      "Searching for 538, scarborough\n",
      "Searching for 539, ayan\n",
      "Searching for 540, tucuma\n",
      "Searching for 541, aripuana\n",
      "Searching for 542, kertosono\n",
      "Searching for 543, cassilandia\n",
      "Searching for 544, kenai\n",
      "Searching for 545, verkh-usugli\n",
      "Searching for 546, la canada\n",
      "Searching for 547, pakhtakoron\n",
      "Searching for 548, lompoc\n",
      "Searching for 549, ariquemes\n",
      "Searching for 550, pauini\n",
      "Searching for 551, madang\n",
      "Searching for 552, deputatskiy\n",
      "Searching for 553, vila do maio\n",
      "Searching for 554, gawler\n",
      "Searching for 555, ust-kuyga\n",
      "Searching for 556, olinda\n",
      "Searching for 557, tavda\n",
      "Searching for 558, vagur\n",
      "Searching for 559, grandola\n",
      "Searching for 560, akureyri\n",
      "Searching for 561, utiroa\n",
      "City not found, skipping\n",
      "Searching for 562, omsukchan\n",
      "Searching for 563, beidao\n",
      "Searching for 564, nadym\n",
      "Searching for 565, necochea\n",
      "Searching for 566, chiali\n",
      "City not found, skipping\n",
      "Searching for 567, tubruq\n",
      "City not found, skipping\n",
      "Searching for 568, muisne\n",
      "Searching for 569, kaeo\n",
      "Searching for 570, belaya gora\n",
      "Searching for 571, turukhansk\n",
      "Searching for 572, narrabri\n",
      "Searching for 573, tuchkovo\n",
      "Searching for 574, grand gaube\n",
      "Searching for 575, okhotsk\n",
      "Searching for 576, puerto quijarro\n",
      "Searching for 577, iguape\n",
      "Searching for 578, abohar\n",
      "Searching for 579, notse\n",
      "Searching for 580, solhan\n",
      "Searching for 581, nishihara\n",
      "Searching for 582, oranjestad\n"
     ]
    }
   ],
   "source": [
    "base_url = \"http://api.openweathermap.org/data/2.5/weather?q=\"\n",
    "max_temp = []\n",
    "humidity = []\n",
    "cloudiness = []\n",
    "wind_speed = []\n",
    "country = []\n",
    "date = []\n",
    "latitutde = []\n",
    "longitude = []\n",
    "\n",
    "for city in cities:\n",
    "    # 1 Second sleep time to prevent issues with API timing out\n",
    "    time.sleep(1)\n",
    "    try:\n",
    "        #Add info for each api request to lists\n",
    "        print(f\"Searching for {cities.index(city)}, {city}\")\n",
    "        full_url = f\"{base_url}{city}&appid={weather_api_key}&units=metric\"\n",
    "        result = requests.get(full_url).json()\n",
    "        max_temp.append(result['main']['temp'])\n",
    "        humidity.append(result['main']['humidity'])\n",
    "        cloudiness.append(result['clouds']['all'])\n",
    "        wind_speed.append(result['wind']['speed'])\n",
    "        country.append(result['sys']['country'])\n",
    "        date.append(result['dt'])\n",
    "        latitutde.append(result['coord']['lat'])\n",
    "        longitude.append(result['coord']['lon'])\n",
    "    except:\n",
    "        #If city not found, create Null entires in lists, to drop in DF later\n",
    "        print(\"City not found, skipping\")\n",
    "        max_temp.append(None)\n",
    "        humidity.append(None)\n",
    "        cloudiness.append(None)\n",
    "        wind_speed.append(None)\n",
    "        country.append(None)\n",
    "        date.append(None)\n",
    "        latitutde.append(None)\n",
    "        longitude.append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_df = pd.DataFrame({\"City\": cities,\n",
    "                        \"Lat\" : latitutde,\n",
    "                        \"Lng\" : longitude,\n",
    "                        \"Max Temp\": max_temp,\n",
    "                        \"Humidity\": humidity,\n",
    "                        \"Cloudiness\": cloudiness,\n",
    "                        \"Wind Speed\": wind_speed,\n",
    "                        \"Country\": country,\n",
    "                        \"Date\": date\n",
    "                       })\n",
    "city_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Raw Data to DataFrame\n",
    "* Export the city data into a .csv.\n",
    "* Display the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 526 entries, 0 to 582\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   City        526 non-null    object \n",
      " 1   Lat         526 non-null    float64\n",
      " 2   Lng         526 non-null    float64\n",
      " 3   Max Temp    526 non-null    float64\n",
      " 4   Humidity    526 non-null    float64\n",
      " 5   Cloudiness  526 non-null    float64\n",
      " 6   Wind Speed  526 non-null    float64\n",
      " 7   Country     526 non-null    object \n",
      " 8   Date        526 non-null    float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 41.1+ KB\n"
     ]
    }
   ],
   "source": [
    "city_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_df.Humidity.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the data and remove the cities where the humidity > 100%.\n",
    "----\n",
    "Skip this step if there are no cities that have humidity > 100%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>5.500000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>19.973545</td>\n",
       "      <td>17.124400</td>\n",
       "      <td>58.331400</td>\n",
       "      <td>67.890909</td>\n",
       "      <td>52.141818</td>\n",
       "      <td>8.544800</td>\n",
       "      <td>1.585764e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.284840</td>\n",
       "      <td>91.595451</td>\n",
       "      <td>25.795297</td>\n",
       "      <td>20.864881</td>\n",
       "      <td>35.766469</td>\n",
       "      <td>6.078869</td>\n",
       "      <td>5.539674e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-54.800000</td>\n",
       "      <td>-179.170000</td>\n",
       "      <td>-11.340000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>1.585764e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.077500</td>\n",
       "      <td>-64.627500</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.525000</td>\n",
       "      <td>1.585764e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.630000</td>\n",
       "      <td>19.635000</td>\n",
       "      <td>64.940000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>7.325000</td>\n",
       "      <td>1.585764e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.672500</td>\n",
       "      <td>97.350000</td>\n",
       "      <td>78.800000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>11.410000</td>\n",
       "      <td>1.585764e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.220000</td>\n",
       "      <td>179.320000</td>\n",
       "      <td>102.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>46.080000</td>\n",
       "      <td>1.585764e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Lat         Lng    Max Temp    Humidity  Cloudiness  Wind Speed  \\\n",
       "count  550.000000  550.000000  550.000000  550.000000  550.000000  550.000000   \n",
       "mean    19.973545   17.124400   58.331400   67.890909   52.141818    8.544800   \n",
       "std     33.284840   91.595451   25.795297   20.864881   35.766469    6.078869   \n",
       "min    -54.800000 -179.170000  -11.340000    9.000000    0.000000    0.160000   \n",
       "25%     -8.077500  -64.627500   42.800000   55.000000   20.000000    4.525000   \n",
       "50%     23.630000   19.635000   64.940000   72.000000   57.000000    7.325000   \n",
       "75%     48.672500   97.350000   78.800000   83.000000   86.750000   11.410000   \n",
       "max     78.220000  179.320000  102.200000  100.000000  100.000000   46.080000   \n",
       "\n",
       "               Date  \n",
       "count  5.500000e+02  \n",
       "mean   1.585764e+09  \n",
       "std    5.539674e+01  \n",
       "min    1.585764e+09  \n",
       "25%    1.585764e+09  \n",
       "50%    1.585764e+09  \n",
       "75%    1.585764e+09  \n",
       "max    1.585764e+09  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Skipping, no cities with >100%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([], dtype='int64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Get the indices of cities that have humidity over 100%.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>butaritari</td>\n",
       "      <td>3.07</td>\n",
       "      <td>172.79</td>\n",
       "      <td>83.32</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>10.42</td>\n",
       "      <td>KI</td>\n",
       "      <td>1593605283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>yerbogachen</td>\n",
       "      <td>61.28</td>\n",
       "      <td>108.01</td>\n",
       "      <td>79.52</td>\n",
       "      <td>58</td>\n",
       "      <td>20</td>\n",
       "      <td>7.70</td>\n",
       "      <td>RU</td>\n",
       "      <td>1593605121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>cape town</td>\n",
       "      <td>-33.93</td>\n",
       "      <td>18.42</td>\n",
       "      <td>73.00</td>\n",
       "      <td>77</td>\n",
       "      <td>27</td>\n",
       "      <td>4.70</td>\n",
       "      <td>ZA</td>\n",
       "      <td>1593605039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>touba</td>\n",
       "      <td>8.28</td>\n",
       "      <td>-7.68</td>\n",
       "      <td>79.97</td>\n",
       "      <td>75</td>\n",
       "      <td>82</td>\n",
       "      <td>3.58</td>\n",
       "      <td>CI</td>\n",
       "      <td>1593605284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>jamestown</td>\n",
       "      <td>42.10</td>\n",
       "      <td>-79.24</td>\n",
       "      <td>64.00</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>6.93</td>\n",
       "      <td>US</td>\n",
       "      <td>1593605284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City    Lat     Lng  Max Temp  Humidity  Cloudiness  Wind Speed  \\\n",
       "0   butaritari   3.07  172.79     83.32        74          84       10.42   \n",
       "1  yerbogachen  61.28  108.01     79.52        58          20        7.70   \n",
       "2    cape town -33.93   18.42     73.00        77          27        4.70   \n",
       "3        touba   8.28   -7.68     79.97        75          82        3.58   \n",
       "4    jamestown  42.10  -79.24     64.00        88           1        6.93   \n",
       "\n",
       "  Country        Date  \n",
       "0      KI  1593605283  \n",
       "1      RU  1593605121  \n",
       "2      ZA  1593605039  \n",
       "3      CI  1593605284  \n",
       "4      US  1593605284  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new DataFrame equal to the city data to drop all humidity outliers by index.\n",
    "# Passing \"inplace=False\" will make a copy of the city_data DataFrame, which we call \"clean_city_data\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>komsomolskiy</td>\n",
       "      <td>40.4272</td>\n",
       "      <td>71.7189</td>\n",
       "      <td>16.38</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>UZ</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>butaritari</td>\n",
       "      <td>3.0707</td>\n",
       "      <td>172.7902</td>\n",
       "      <td>28.18</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>KI</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>goderich</td>\n",
       "      <td>43.7501</td>\n",
       "      <td>-81.7165</td>\n",
       "      <td>6.21</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.20</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mar del plata</td>\n",
       "      <td>-38.0023</td>\n",
       "      <td>-57.5575</td>\n",
       "      <td>13.20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.34</td>\n",
       "      <td>AR</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sisophon</td>\n",
       "      <td>13.5859</td>\n",
       "      <td>102.9737</td>\n",
       "      <td>28.57</td>\n",
       "      <td>63.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>KH</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>abohar</td>\n",
       "      <td>30.1500</td>\n",
       "      <td>74.1833</td>\n",
       "      <td>24.54</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.27</td>\n",
       "      <td>IN</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>notse</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>1.1667</td>\n",
       "      <td>23.44</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>TG</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>solhan</td>\n",
       "      <td>38.9634</td>\n",
       "      <td>41.0293</td>\n",
       "      <td>3.79</td>\n",
       "      <td>47.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.84</td>\n",
       "      <td>TR</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>nishihara</td>\n",
       "      <td>26.1842</td>\n",
       "      <td>127.7558</td>\n",
       "      <td>26.28</td>\n",
       "      <td>52.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.63</td>\n",
       "      <td>JP</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>oranjestad</td>\n",
       "      <td>12.5240</td>\n",
       "      <td>-70.0270</td>\n",
       "      <td>27.89</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>AW</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>526 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0     komsomolskiy  40.4272   71.7189     16.38      25.0        70.0   \n",
       "1       butaritari   3.0707  172.7902     28.18      75.0         9.0   \n",
       "2         goderich  43.7501  -81.7165      6.21      81.0       100.0   \n",
       "3    mar del plata -38.0023  -57.5575     13.20      80.0         0.0   \n",
       "4         sisophon  13.5859  102.9737     28.57      63.0        83.0   \n",
       "..             ...      ...       ...       ...       ...         ...   \n",
       "578         abohar  30.1500   74.1833     24.54      37.0         0.0   \n",
       "579          notse   6.9500    1.1667     23.44      94.0       100.0   \n",
       "580         solhan  38.9634   41.0293      3.79      47.0        43.0   \n",
       "581      nishihara  26.1842  127.7558     26.28      52.0        20.0   \n",
       "582     oranjestad  12.5240  -70.0270     27.89      80.0         7.0   \n",
       "\n",
       "     Wind Speed Country          Date  \n",
       "0          0.00      UZ  1.635224e+09  \n",
       "1          3.61      KI  1.635224e+09  \n",
       "2          7.20      CA  1.635224e+09  \n",
       "3          1.34      AR  1.635224e+09  \n",
       "4          3.37      KH  1.635224e+09  \n",
       "..          ...     ...           ...  \n",
       "578        2.27      IN  1.635225e+09  \n",
       "579        1.60      TG  1.635225e+09  \n",
       "580        1.84      TR  1.635225e+09  \n",
       "581        4.63      JP  1.635225e+09  \n",
       "582        9.96      AW  1.635225e+09  \n",
       "\n",
       "[526 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_df.to_csv(output_data_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Data\n",
    "* Use proper labeling of the plots using plot titles (including date of analysis) and axes labels.\n",
    "* Save the plotted figures as .pngs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latitude vs. Temperature Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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": [
      "This scatter shows that max temperature is highly correlated with the city's Latitude\n"
     ]
    }
   ],
   "source": [
    "city_df.plot.scatter(x='Lat', y='Max Temp', grid=True,edgecolors='black')\n",
    "plt.title(\"City Latitude vs. Max Temperature\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.show()\n",
    "print(\"This scatter shows that max temperature is highly correlated with the city's Latitude\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latitude vs. Humidity Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
      "This scatter is showing the relationship between Latitude and Humidity. In general, cities will have higher humididty with higher latitude, but there are plenty of outliers as well\n"
     ]
    }
   ],
   "source": [
    "city_df.plot.scatter(x='Lat', y='Humidity', grid=True,edgecolors='black')\n",
    "plt.title(\"City Latitude vs. Humidity\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.show()\n",
    "print(\"This scatter is showing the relationship between Latitude and Humidity. In general, cities will have higher humididty with higher latitude, but there are plenty of outliers as well\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latitude vs. Cloudiness Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
      "This scatter shows that cloudiness of a city is very independant of Latitude, there is no realistic correclation\n"
     ]
    }
   ],
   "source": [
    "city_df.plot.scatter(x='Lat', y='Cloudiness', grid=True,edgecolors='black')\n",
    "plt.title(\"City Latitude vs. Cloudiness\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.show()\n",
    "print(\"This scatter shows that cloudiness of a city is very independant of Latitude, there is no realistic correclation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Latitude vs. Wind Speed Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IDB2U4k8CqZzGKxQKRSBK8SeJVE7jFQqFwhel+JOImkorFIp0QGXgUigUiiGGUvwKhUIxxFCKX6FQKIYYSvErFArFEEMpfoVCoRhiKK+eNMdmsyXVBdRzvcOHD7Njxw7Gjx+f8GsOFpJ9r9IFh8PBjh074lruoVqXyUKN+NOYZMdI91zvP7/1Pf7re5NYtOIR/u8f/+Dib307odcdDAzVePb19Zv5+0cfxbXcQ7Uuk4lS/GmKb4x0y+QVFF2+iKnTZyQspLPneqba79HX3UV22Ujo7cGQX8Lrb/wPb7/9dkKuOxhI9r1KFzzlNpSMiFu5h2pdJhul+NOUZMdIb2pqIqewnKPvbqVyymKGX/8YVdesxHmsg6z8Yl577bWEXHcwkG7x7JOV98FTbmE0AfEpd7rV5WBFKf40Jdkx0mtqauhub8ZQWO7X6YTBiNPexkUXXZSQ6w4G0imefTLNJJ5yS0cvEJ9yp1NdDmbU4m6akuwY6VarlUdWrmTGLTP9oojKij7OP+88vvrVrybkuoOBdIlnn+wUip5yNx8+iL1+dVzKnS51GQuZtCCtFH8ak+wY6dOm1YGA22fNwVRUTk/7YYY/uY43/+eNhF53MJAO8ezDmUmikScaBTZlymS2b9/O77dsjFu506Euo6W+frPrYVVSRU97M+tXP8GUKZNTLVZIlOJPc5Id2G1aXR2XTZrk7XQff/xx0q6d6aQ6CJ/HTGIaQN6HWBSY0Whk3LhxA5Ten1TXZTRkYrJ6pfgVQWRSp1OcZKBmkkxUYOlAvGZayUQpfoViEDEQM0kmKrB0IB4zrWSjFL9CMciIdcaWiQosHcjEBWml+BUKBZCZCixdyLQF6ZQofiHET4CpgAT+DlwvpTyeClkUQ49McrtLNpmmwNKJTFobS/oGLiHECOBW4Bwp5emAAUhfvyfFoCLT48AkY1eu1Wpl3LhxGaPEFNGTqp27RiBPCGEEzMCnKZJDMYTI9Dgwmf7QGmwkKzRGIhBSSu0vhHgMlylGEynlrTFfVIjbgAeBHuA1KeUPNH5TB9QBVFZWnr15s6uR2+12LBZLrJdOGUru5KIld3d3N//81x6ySqu9n/W3HeCzp4zBbDYnW8QgwtW1w+Hg7x99hKFkBMJoQjp6cbYf5Aunn47RGF+LrcPhoLe3F5PJpOvcg6mN6KWtrZ29+/aSZcim39nH6FGjKS0tibOE2kQj94QJE/4mpTwn6AsppeYL+JH7tRb4MzDT/foj8FCo4yK9gBLgfwArkA38GvhhuGPOPvts6eHNN9+UmUg6y93S0iIbGhpkS0tL0HfpLHc4tORuaWmR5sJiOez6x+Toeb+Tw65/TJoLizXLnQrC1XVDQ4MsG/15OXre77yvslGfkw0NDXGVYdOmemkuLJZloz8vzYXFctOm+ojHDKY2oodUt6No5Abekxo6NeTjXEr5SwAhxHXABClln/v9amAgoRovBPZIKW3u870IfAV4ZgDnVMRIpm01HwiZ7LWSDFfLRGzgGowL6YNhv4OeOeJwoABoc7+3uD+LlX3AuUIIMy5TzwXAewM4nyJGhuJOzUz1WknGQyveCm2wDioGw34HPYr/Z0CjEOJN9/uvA/fFekEp5btCiC3A+4ADaMRlTlIkmcEwcomFTHC70xopJ/qhFU+FNpgHFZk8c/QQUfFLKZ8WQvwe+JL7o/lSyuaBXFRKeS9w70DOoRg4g2Hk4ouvssxkwo2UE/nQiqdCG+yDikydOXqIqPiFEAKXXf4zUsr7hRCjhBDjpZQNiRdPkUgGw8jFQ6Cy3Pj0U6kWKSZSPVKOl0IbbIMKLTJh5hgKPaaeXwD9wDeA+4EuYCsQ3zisipSQ6SMX0FaWe/ftxWazZVx50mGkHA+FNpgGFYMRPYr/S1LKs4QQjQBSynYhhCnBcimSSCaPXEBbWWYZmjLSrDCYRsqDYVAxWNGj+PuEEAbcm7mEEFZcMwCFIi3QUpb9VY6MVJaDbaSc6YOKwYoexf8o8BJQKYR4ELgCWJhQqRRAYnygB4tfdWA5ApXl6EueytjyDbaR8mBpc4OJiLF6pJTPAnOBxcAh4HtSyhcSLdhQJxFxWQZLrBetckyZMpmm3Z/w+y0badr9SdK2zyeKwRIoLR5tLpNj4qQreoN8lAPdbtdOqxBijJRyTyIFG8okagflYPCrjlSOTCrLYCcebW6wbgJLNRFH/EKIe4F5wAL3R9mo8AoJJZxnRzqdM1GEG+FlUjmGOgO9V5keTTWd0ROWeRJwKXAMQEr5Ka4QDooE4Vms7G1xTari4dmRiHMmgkimgUwph2Lg9yraB4cyCelHj6mnV0ophRAer578BMs05LFaraxavpTbZ80hr7SKE50tA/bsCOUtArBjx460WHjTYxoYbF4vg5mBtrloXFv1mITUIvNJ9Cj+54UQa4BiIcRNwA3AusSKNfgJ1wjr6zcza8488koq6G77lEdWroyLXTPQW2T79jeoGXtq2thP9W5eGmxeL4OZgbQ5vQ95PQMGrQfDsGFVCS9/uqInVs8KIcQ3gaPAqcA9UsrXEy7ZICbc6CSwEee27GHW3HlcdtmkuCg4zwJoqhd7tR58g2nzkuIkA2lzeh7ykQYMoa778tah65yoN/Xi34E/4UrC8vfEiTP4ibRgNZAFsWhsnKlcJA1lx/eM8Dq33o29fjadW+/WHOENFrfURJKO9u5Y21wk19ZIawmhrtvb2xufgmUgerx6pgINwGW4Nm+9I4S4IdGCDVYiNf5QjdhisYTtyNEqw1QtkkZ68AX642vZaVPt6ZGOStWXdH0wJqrNWa1Wbrj2Wpo3zubTtXU0b5zNDddeGzSTDLyuyTR0I8/osfHPAWqllK0AQogy4C9AZoY/TDGRzBlads0brr2Wc879Ski7aCxT6FQtkuqx44fzx091ELN09ytPtQkvHIlqczabjad+9SusV95HVnYu/X3HeepXy7jnnoXetqR13XjnKs4k9JT8AK6InB66gP2JEWfwo6fx+9o1LRYL55z7lbAdOVZlmIpF0oHa8VO5DpDOStVDqh+MkUhEm/OUOW/UGd7PnAFl1rruW2+9NeBrZyp6FP9B4F0hxMu4ArV9F2gQQswCkFKuSqB8gxI9jd8zUtmxY0fEjjwQZZjs3a4DHfWl0p0z3ZUqpN8CudYifrg2F4vLpd4yq53dJ9Gj+P/lfnl42f1XbeIaAHoboZ5GnWm+7QMd9aXKnTPdlKoW6dQWojWLRfJ2C3W/06nMGYOUUvcLKAFENMfE43X22WdLD2+++abMRAYi96ZN9dJcWCzLRn1OmguL5aZN9Zq/a2lpkQ0NDbKlpSXmawWe75VXXonb+ZJJotqJ3nsRC/GUOd5tIRxacre0tEhzYbEcdv1jcvS838lh1z8mzYXFIeUJ93tvnY/+fFzbf6p0yUDvTTRyA+9JDZ0a0qtHCHGPEOLz7v9zhBD/g2vkf1gIcWEyHkoKF5E8XTzEI6Kjx2NlzZq11Iw9lX/+a09aeYakGr33ItWkOrpntK6boX7f2Nio24sr2jI7HI6ke2eli8dVOHfO7wP/cP//I/dvrcDXcYVoViSRgXZkPS6InkZ58WVXM+OWmRRdvois0moVHCuAeCnVdHcLHQjRum6G+j2QkP0m9fWb+ftHHyVVAaeDK7KHcIq/1z1VALgYqJdSOqWUu9AfzlkRA/FWCHpGGb6NMucbP8ZYOkJFwEwg6TLyg8Q8gPRuxov0+9ra2rj7/nvauqFkRFIVcDpFlg2nwE8IIU4HDgMTgDt8vjMP5KJCiGJgPXA6Lk+hG6SUfx3IOQcL8fYT1+uC6Nsond2dOI8ecXe2kWm5iJnJpFMIgVjbm+9iayiiXYQP9ft4L9x62rowujZwJcs7K52cA8Ip/tuALbjMOw9Jd+IVIcREoHGA130E+G8p5RXuxO0DepAMFhLhJ67XBTGwURZ+6XKaN87GWbsq4mhNER3pEkIg1vYW+LDY+HTovZzRulBq/T7eXlyeti4dvYAxaQo4nbyPQip+KeW7wOc1Pn8VeDXWCwohCoHzgOvc5+sFhm7QDB8S4ScejY+zb6Ps7TjMEz9/jJEjq2na/YlS+nEk1D2JJoRAPEIMx9LetB4We/ftxWazJbSNxNMH39PWmw8fxF6/OqkKWO9DLNEhpMVJM35yEEKcCawFdgJfBP4G3CalPBbwuzqgDqCysvLszZtdNlC73Y7FYkmmyHFBj9wOh4O/f/QRhpIRCKMJ6ejF2X6QL5x++oC2l7e1tbN3316yDNn0O/sYPWp0yJy0DoeD3t5eTCYTRqMxLeo7UCY9pIPc4dC6JyZTti6Zo7mf4YilvXV3d/PPf+0hq7Ta+1lldi8WSz5mc2ZN3Lu6ujAYDFG1q2QQ6f5G07YnTJjwNynlOUFfaPl4JvIFnAM4gC+53z8CLAp3zFDy40+Un3isvsOprm+9PtyBpFpuPQTeEz0yR+sfH4lo25vW9Vc99JDa6xEn9NzfePjxp+IxdwA4IF2mJHCtI8xPgRwJYyDTtETtSs3E7eqZEBtnIMRyT7TMMzlFFbz66qtMnDgx4e3N1ySYU1BGd8dhRk58elDcj3QgWWFBwm3guizcK9YLSimbgf1CiM+5P7oAl9lnUBAPN71Ub77RQzJ80NPJ/S1d0PJ37zjUxE8WPpC09jZlymRWLVtKT4eN/LLh7D9wQNd1I7WZwbyvQS9JC5euNQ1wzRB42v16BWgHtrpfbcCLoY7T8wLOBN4DPgR+DZSE+32mmHrCTdPSWe5waMkdq/klWgZi1sjE+o7WHFgy8lQpjCZZ/PXr4mL20UvgfXlkw/MRrxupzSSrTfmSrm0kkvktoaYeKeX1AEKI3wH/T0p5yP1+GPD4AB82H+Cy9Q8qhsIINZnml3Ryf0snPOaZV199lZ8sfIDCc68AkuePHtjOhdEU9rqR2oye75MdkC+VJCMIoZ7UizUepe/mMK7cu4oAUpXVKpk0NTVhtJT6PdwM+aUJe7hlSmycZGO1Wpk4cSInvBvtktfeAtu5dPSGvW6kAVG47yOZTgereSjR5l49i7tvCSG2AfW4dtlOBt5MiDQZzlAYoVosFrpaDpDv44Nutx1IqOtk4CJopowAEy1nqtpb4HWdP5ke9rpa+xaOtR7ytplQ+xosFkvwTGDadMrKSqmtrWX79jfSOhtaWqNl/wl84cq3+5D7NUnPMfF8ZYqN34OW62QmyK1FoNwNDQ3SUj5CZuUWSFPlKTIrt0BayobLhoaGpMij1xac6vqOxWYdSuZIrrjJDMGsdd3XX3894m899VEw7DNSGE3SUj7Cr1607NoNDQ2ybPTn5eh5v/O+jMXDZFHVaJlnKZQ5Zouu9Z9Q9ZPqNhIrSXPnlFK+CLyYsKfPICMTXSf1UlNTQ3/vMconLfDmN7W/skylPvQhnnLqiaWTqvbmua6eFIZTpkzmzDO/SO248VivvI+8UWfQ61MvWnZtm80WNBNw9hyl4Jq19OxppPPtZyO6PSYzR3KmzERBh43f7b75TyFEpxDiqBCiSwhxNBnCKdIPzzTf/soy+t5ag/2VZWmR+jCdiJec6RTGNx7Y7XYs5dXe3LiB9RJo1/aN2NnxzO00P3MHZRffjMFcRN6YWpz2Nv/1jfZm2tvbvfUTqf7iGY8/naKt6kHPiH8Z8F/SFY5ZkWTScRShUh+GJ15yNjY2YiosT+scv9EQS7142lpjYyPfu/xKsstGAuC0t5FtNNK5ZSF5JVXYjxxESsnVdbd5R/Zjx54S8gG8ffsbNB9u5sGH19Dd+il3zpvL5Zdfht1uj7pNh5rhnXnmF9m/fz8AtbW16XXPtOw/vi/g7Ui/SfQr02z8WsQidyp8mwNJt/rWG2Ig1XLHEnrDV+ZNm+plnqVQiuycuIVnSBR6Q000NDTI1avXxBySRKtOW1pa5LZt22SepTConnbu3Km5D8Tz+SMbnpfll86VIidfGkuGaa496EFrLcJSPlya8tznzc6Rprz8uPXfZNn43xNCPIdro9UJnweGsvknkEyxZyebVM02omUgcnruffGVD9LXup/mTfMx5BVicnazfs3qlJU51tlnoJ191fKlnFVbG/V5fEf/cHIUXVJSgrlseNDI3m63a3o92e12Vzz+LANtrz1B1dU/885ADtffSfmkBd6+BkQsc+BMpmffhxzrOELVNSu9523eNJ8b66alTf/Vo/gLgW7gIp/PJGqxN6EkK2ZHJpIpi+exyul7700VY8gd/UW6nl/A8796nosuuijyCRLAQBK2BA5gZs2ZF3Ooby0XzgsvvCCkCWncuHGceeYXaWhoYOzYsZhMJiwWCz3tzfSf6MZYVOHXx4xFFWRl55JXXMmatWtZsmxFxDIHurfaWw9iLvd/EGUXV2GiL236b0TFL907eBXJJVPs2em4BpHpBN57p72Nvp6j1NbWpkSegcw+4zmACSVH0+5PQu5n8DywsnLyOdZuo6CiGoe9jSlXXYGzy0Zf2wF6ffqYo7OF/r7jdLcdYsnS5RRd8YCuMvvO8CwWC+d86ct+5+3raMaQbUib/htS8Qsh5koplwkhHsM1wvdDSnlrQiUb4iRqc048FXUyXeWGAh4vk5qamrTaCDgQ5R1uABNtWwwnRyh30KnTZ2D5zlyOvLTEz/Ty1IbZrFq5EkOWoPW5BeQUVWC3HSC/qBz7K8u4c95cHl7/TFRl9p3hrV+zmuun3oTTVIDzmGsh+sm1a9JmcBRuxO/x4nkvGYIogom3PTueilqtQcSX+vrNXi8Tz71p2v1JWsymBjL7DDWAiWXXbSQ5Ak1rngdFVnZusEmnZARZhRVYr15Oxwt3sWXDE4wcOdLr1QOwZPmKmGfcodYj0oVwiv8UIcQ44FkppSNZAin8iZc9O5zLWSwubGoNIn547s2SR9dimbzCz4Qxbty4VIs34Nln4AAGoGbsqX5t8ca6ad5QDOFG1NHI4XlQGPqO4+hs8TO9OLuOIAxGTBVjMJcOo6SkhNNOO83v+IHOuqxWa8rWZCIRTvFX48qO9XkhxIfAX4C3gb9KKduSIVy8SQd7dKpk0FLUhvxSaseNx1Je7bdQpke+TFmDyAQ890YYXTl3o3mIDrQ96T1+oLNP3wHMjh07/NpiX+t+jp84wRXXz8DZ1cr6NatDjv6jkcP3QWHOL6B542ws5SOwHzlI0VevhixD2HYb6VrJ6MsJu4aWj6fvCzABXwHuwBWP/1NgZ6Tj4vmKhx9/qn3i33zzzahkiHf8Fa3Y9sJokhVTFnvf55gtMs9S6CdfuPpOVJpILdmjrYtE+/HH8/547s0jG56Pyl/frz0VFMlFDzwQlTzx6hPR1rVvW6ye+awUJrNfu8wxW+K6V8Fzr3bu3Om3l+DhR38ec7mToU9CXSMefvx6FH8R8C1gEbAdl83/6UjHxfM1UMUf7zylsfD666/rliFRjcpXUefmF0pL+YigAFhll9zhJ1+kAFyJDhCWjjl39cgUbb1s2lQvVz300IBy34rsHJlnKdRVR/HsEwPZnFhgHSGNxcOC2uG2bduiPmckfO9JS0uLfOWVV6Iqr+8DJNH6JF5JnUIp/nCpF9cKId4GngO+jMvUc6WU8hyZYS6e6RDjpbe3V5cMiYzP4hvb/v0d79Dfe8wv1onzWBt5Y2r95Ovt7Q17zljjhoeLo+75bteuXWkXq0bP/YklbsuUKZP5wumn8/stG3nvnb8wduwpYcup1aazS6sxn1+nq46i7RPxjnvvaYsP3j0P57G2oHYYbwLvyfbtb2A2m0O228Dy+h5/1rhzyTLlJ1SfJFpnhQvSNgrIAZqBg7iSpHfE5apJJh0SpJhMJl0yDPSGR+qgHkV92mmneQNg2etn07llIdlGI057m598JpMptgKHIZxi9P2udtz4oKQvqQ7KFun+DOTBbTQa2b37X5xz7lciPjS02rSjs4W8MbW66iiaPpGoAGRWq5XJkyeTbTTSvGk+hzbcRvOm+WQbjTHvWdBq/6HuicOh7bMSWN41a9b6HV9y1YMc67DRs+9DIDH6JNE6K1zqxW8JIQTwH7hs/LOB04UQbbgWeO+NiwRJIB0SpBiNRl0yWCwW7EcOYNj3oTd0rd4bHq27ZuDildfFzkc+o1FX5G5d2Gw2GhsbmTptut/GmBtumsaZZ36R8vJyP88jw74Psb1wn1/Sl1QvIGsuarc3e2UaiLeTw+HQ7SLrbdPTptNrMOPsOUrZxTfjtLfpqqPAPtHddog7580N+l2i3XatVisbnlzPjXXTMNGHIdsQs797qPYf6p5ozWa1ynvb7NnkB4SEsFir6fzNYpxlIxKiTxKts8L2areN6CMhRAfQ6X5dAowHMkbxQ3rEeIkkg6fh5hRVuBReUTn9fd26bnisHdTX20JLPj2x1vXgKVu2uYheo9mvEzlNFmrHjWfhgvl+HTRv1BnkF5XT/vxd5JcNS/lGJjjZIQM352zf/gZTpkwekLdTOHNguN2ia9asZfHSZcgPXqYzijryHr92LUuWLufh9c+wZPkKvwFDMtx249E3w7X/UPckcDZrs9l49dVXyS329/k3F1fS3dpMjq876LE2Gnc0xOQKrZdE6qxwO3dvxTXS/yrQh9uVE3gK+HvcJEgi6RDjJZQMgQ03v2UP7c/fxfs73gnyL9YiHh3U13UMXG53oabD0eBbNoOllKNr6/x9qnu6KJl0J0uWLkVK6ddB+/u6eX/HOwntYNFy4YUXkJWVheVrP/DGhfd9yMY6UvOYA6N5aFitVhYuvItp0+piVhBLlq3QnIGddtppIWc4nrj38bofA+mboRS2p/2PGzdO854YjUZvm3///UZmzZ1HTmE5Hc37/JT8ia5WHl61nFlz5vkd7+mXHvNSItpnonRWuBF/DbAF+In0T7aeMcTTBzbRPrtaiju/bBh2u13X8QP1q/edJtttB5BSUlAxkrtun4bNdmRAI4/AspVdfDPNz9yB0VKGs6eL0otmYCofTba5iBnX/4BHf3G3ZgeLROCDK1E0NTVhLhuO5T/OB3AlBvGxq48dewrvvfOXqB9Wes2BWvgqiGjaqla788zAnl63jilTJvsHINOIez9sWFXI8ye633hnyRoK27f9+46ee3t72b17N4cPH+Y7353kPdYT0kG+s4XmjbMpHlbDic4W7wzovP/8TxoaGhg/fry3TWZs2BItV590e8XizhlPl8h4nCuS3PFwr4vVr17TNTAnX1bPfFY+suF5acrLl+aCopjLr3X+3HyLzDHny4opi/1iopsLiuTqNWuidhENvEdbt74YtYx6rxnoh152yR0yz1J4MtZ8jPXkaSMDcZHVaqvhzqd1b7JyC2TFlMV+7S9c3PtQLr+J9nUPlL3469dJYTTJkpGnhrzeLbfcKoXRJLNLR8gVK1fK4q9fJ6uuXSWzK8b4uZSWjDhFbtiwwVv+UPWaCjfxpOXcTQRCCAOuPQEHpZSXxPPc8VyQSlZMmngs5sRqE9R0DSyuwtF5GJE1mj6Hg7LJP4u5/Fple2rdOgBurJvG8RMnqPrhCu9IzRO2F9A1hQ68R4Z9H9K0t4ldu3ZFnC3YbDavjTuvdJiuUZunPNfdOJU+hwODpRThcDLzttspv3pZVGEIQp0/Xnbu66feRFZWFuay4Zpl85Tlhpum4TRZvDOwvFFn4PQxFVqtoePe610k1dtu9M4SAttt0blXIHb/iYcW3cnEiRODjt21axePr17tHdlnl3Vz9N37yPvsl3AePeJnfjzR1eo9x65du7j+ppsouvRO8kad4S3LSy88l7FhSyLm3E0gt3EyEFxciacPbDL3APj62Tft/iSmKWMsfvVarmN9Hc0YiyrpP9GNIQ4ulVplmzJlMr/e+gJFlSODzr9mzVrdLoS+9+jYrj9y5KUlkGXgrHHnhj2uvn4zoz8zlnt+uoiiKx6Iyv3ywgsvwGg0Yr3iXsr/aw4lk+7C4XRy4tN/ACfDEHz/xpuTloNV22xTgPn8urBlmzJlMu/veIes3qOUT1pA/mnnaZoKQ7kYarn8xtpvonEd1ZLHV2EH0tDQgLHQ6pVJGE0YCsqRvT1YzvoOzRtn0/ns7XRuvdsvrPNZ486lP6eIIy8t4diuP3rLAqTcTTxWUjLiF0JUA98BHgRmxfv88Ywjk+yYNJ4G6+kgoRaC42k3DRyR248cJDsLel6+n/4x0zGc6PIbDcUaVldrJFtbW0tvwGirp72ZxUuXUXzlg7pGi557ZNj3IW2vPUHllMVkl4+k5KoHNY/zdSs1n1+Hc8dLUY/ampqaEDlmjry0BGNRBY7OFrLyimn7n/XkjPwPWrc97jeLSUbkUq22qrUpT6tsp512Gk+vW8fU6TNwhphxhpqVarn8xtJvws0SwD8TlqftrVq2lFlz5+maJY8fPx7HUZu3rUlHL472gxz97c/o6epweY61N/PIypVMmTLZK0/JVQ96y3C4/k6y8ovpaW+mtrY25Cw9HeKChUO4zEBJvqgQW4AlQAFwh5apRwhRB9QBVFZWnr15s+vJb7fbsVgsEa/R1tbO3n17yTJk0+/sY/So0ZSWlsQkbzzOFS+541muQBwOB729vd4RXG9vL06nk74+R9A1gYTVb1VVFS22VrJKq72/6W87wGdPGYPZbA55jqa9TZBlILt8NJV5cLgn+DjPtUSWgf5+SXZZNb22vWSXVSOMJqSjF2f7Qb5w+ulh9zAcP36cj3fuJLtspPe4vtb9IAxkCeiXYCofCVkGXfKD/jYSDr+6dPQhkRhL9ZfNtw3o/U0ouaNtq93d3fzzX3tc973fiXQ66D/aQmVFOc3Nzd7zlJeVcaS11ft+ZHU1ZrNZl8w2m40jra2ILCMjhlXS3d1Ne0cnxtIRQXXU29t7Uh43fba9SKcDkSWoGV1DaWlJUH0kso9CdO1kwoQJf5NSnhP0hZbhP5EvXPsAfuH+/3zgd5GOiTVWT7wDaQ3kXHoTUodbLErFYpLWgmNLS4srmNsld8jqmc/GRY7A88dSzp07d8rcfNfi46PP/Dps/VXPfFaKnHw57PrHTi4uF7sWl/UsQjY0NMiCYf4LgtllI6UxJ0fmmC2uJNs5+bL80rm65Y9XfCHfukxGIL1wcgf2Gz0LzcVfv05m5RbI7IoxUhhN0pR7MohbxZTFUhhNAY4ChXLnzp0hZQhcmF2+YoXcsGGD/P3vfx+UKL165rOyqHKU3LZtWwinB7McNvWJkPc0GX00Uxd3vwpcKoSYCOQChUKIZ6SUP4z3heLpA5uMPQCRfPEH6qs/kOmnb/kfePBBjp84gXPHS7RvX+NaDBzgolZg/cay0H3aaafx1Lo1TJ0+g/7aB/xstRDarbSwYiS52QbunH0z06bV6SpDTU0NzmPtQen1TDk5lH5/ifez5mfuIMeUzVPr1iVtyu9b3gsvvCCqhC6JMCN6zhPJ9dFqtbJq2VJm3DLTP1H5M3dgsJQCkJWdi6HA6tcHHAHup4HlCTQf3Xv/3TTt/oSPP/7YzyTV17qf1m2PYzAXMumKq1i/ZrW3HWYXlHO0ZT9l374NU9lIAM02n4wNb/Eg6Yu7UsoFUspqKWUNMBn4n0Qo/UwkUnyOUN9bLJaIAbRCLZoFxjaJFOvHZrOxZOlyqn64gmHXPULllMW0bnuc7rZDcV33iHWh23PcZ08ZE3RcTU0NPW2HvPWXXTaS3Jwcnn/qcfb+ezcLF96lO2iXx97dufVujm6aRfvzdzF/7h1Bm4gMljLPTDeh+MrnudffuuKHjBozlhdfeklzwX/Xrl388pe/ZNcul49FomLyeOSLFMPIZrORm5tDUWW1fx3ml9Kzx5XJqr/vOM4um39Qt64jFFx0q+bCdWNjI9nmIu+DI3CR2XMfO164i9bfP0LV1T9jRN06iq54gKnTplNWVsp77/yF5596nBxTNrK/H2d3Z8g1i0h9OG3QmgYk60WCTT3pRNT7D0JMzQO/v+WWW3WFCA6cfuZZCuX8+Qv84u8Hnmv16jVBoWsDp8aeMLrz5y9IaHjmaNGq702b6qUpL1+K7BxpLB4mc8wWXaaPcP7ovqaLnTt3BpkhtHzio5FZD77y5VkKZY7ZEpR3YfnyFX73x9efXRhN8sYbp8ZsotAjt1a7KRv1OdnQ0OBXhtJRp0phNMnir18XnCfCHU48p6BUZuUWSFPlKTIrt0Aaiipl1bWr/M7nPWdBkZ/ZrWLKYpmTb5E7d+70k3v+/AXSWBIcHrqoarS3b3hNeNk50pSXH7LtJNq8lqmmHt+HzlvAW6mUId2I5Ivv+73FYuGcc78S0Vc6cPrpcTVcsWYDzj4n+V+8lKKykTy+ejbWK+9z+XC/s4UZt8zkoVWruHLKD7zTci1vjawTnTz6+C9YV/9S2u5e9Iw4yyb/DIPFNYLsfmut12Mk0nGh6tjXlNHU1ER+sZXmTfMxmIvoD+ETr1dePSaXQPn4+C06337Wb8RsLKpkzvz5lI74DMc7WvjpPQv9/Nl7W/bw5IafUDx8TMJMFOG8fALLkNOyh+aNsxG7/8SJrlaeXr8uuM1PWkBWdi79fcc58tIS+vuO+42sved0h6LobdnDoV/NQghBQUU155z7FTY+/ZT3t4/8/HGcfc6AUCJHKbhmLb1H9vL46vv86qtzy8KQbScd4oJFIqWKf7ATawiBSOsJnu8DU9iF6qy+nc5gKQ1yNTxcfyfDb1qNsaCcrOxcnN2dHH13K1XXrCSrtNo7LdeKRdPddgghsvxivaRj0vXAh5/lP86HD3+ry21Tr822pqaG/t5jlE28jdZXHsZ6+cKoI6xCdGEAAuXLG1NL27bH/BSYo/MwFVfdT96oM8hp2cNdC+f7+bObKsZgtJTR03qIvAS5LYfboKjVjouH1QRtxPL89ZxH5BZxrPUg+YVl2F9ZFnY9x2ApRWRl+bX7vfv2evuouWw4+V+8lMP1d2IoKMPRfoiyibdhMBe5krUH1FdOhIdiOsQFC4dS/AkisPN6RheRiGZxTa+vtG+ny84rxGAuDBgRVtCzpxFH1xH6+47j6DyMobDc9Zv+E0hnHzkFZd6G7juiaW9v5+q629J+MSvSiDNUnUfjj261Wlm1fCm3z5qDuaAI2wv3YbFW4zzWpnsXdrQ7XoPkO7IXg4BDv5pFdmk1jqMtZJmLyRt1BuC+P6WVHG054D+6PdbOj388g/VPL3S12YAF9Xgs+oYaCWvV8YnOlpAbsaZMmczRo0e5ffYcioeP4XjbYR5etTx4PcfnnD17GoM2ImYZmmhsdK0ddLd+SnHZSIbftJpj/3ib9jfWke1exO3vO+7n/9/bsoeOQ67AbuPGjdNd/rTy7dey/6TbK9Ns/Fo29VUPPRRdDlWdtsFo7ImeeCvmgiLNlH0eG3/JiFO8dtYVK1d53epWr1mjq6yxuK9FcvOLR85drbrSU+d669jzu5LqsTIn3xJkV9cjcyRbeLjrWsqHS2E0ycLhn5Ems0Uac3JlUVVN0LqDubBY3njjVJeNv8R1jNGU610jWLTIP3dvpDqKR5+Mth3raXO+59Ra+1i5apV3jSvHbJGmvHz/tbOCIlk0Yqw0FxTJG6e668s6RmblFsjir18XVTuPZ9yipOTcTYdXpil+rc778KM/D9t5B6JAY8nv6u1kAUm6PedavmKFFEaTrgTgA13MCtcp4p1zN9b9ApHqOB4PwDfffDPm8/z5z38OUmzmgiK5bds2uXrNGs37s3PnTvnoo4/KvPyC8HtHAgYKgfIkYv9BOKJ5OIbc01BQJFeuWqVZX57f5uUXSEtFtczLL5CLHnhAllSPlVXXrpLVM5/V9UD2lSGevv0Zv7g7WNGauvZXOcLaS7VsyTlFFbz66quMHz8+bIjfaO2J4RaffM9VMrwGYTR55QllwhnIYlakbfpa35155hdjjs/vWz69aySBx2kRL//tWIL11ddv5vqbbqI/t8j/+iVVlJSUcNFFF3HZpElB9+e0007DbrdjLh8RUu41a9YGJc5JlClPT7gSiN785jlPoIly1z/+iami5mS53PUFcP3Um/z2YyxavABDVha5hmwM5qKo1kDS0bdfKf4EoNV5R1/yVNibrNWYOw41ccu8eznWeoj8Yiv9vcdYv/qJuHgMBCqywIXo9vZ2etqakY5ewBi0ZyDcAyMawnUKIOg7Q34ptePGYymvpqftEAvmzWFanb5NV4EE2YH3fYi99aDfdni9dtlYYtNoYbPZdMXz98hlsVhcD8dL7+TIS0s0YyrByfvjmzQEPPf5UMi1j8VLlwV5u/immrTZbHR3d8clKYveRW1P/7qxbhqm/GJ6j3Xw5No1QORorr718PHOnZr19eabb+LMKfBrd/2mAmbNuI5HH787qk2FkPx4X7rQmgak2yvTTD0efKeZeuT22ohHBvsye/zBBxobP9x1y0Z/Xpry8mWO2SLLRn9eZuea5YoVK7124Isu/pauPQPRmJ3CTYM1t8wbTbJiymK/9QmTOV+uXu2//hDtvglf+3g09n/NeozR5LV164u6rucrV06+RRYO/4wcPe93svzSua5QByXDZW5+Yeg9IL42bd97HiC3x6TiOa+p8hQpsnPkogce8Dvfw4/+fMBtMVpziMdHv7jaZYPXs58lEG99B6z5mMz5UhhzgtbBPGagWParxNO3X9n4Mwi9cre0tMgNGzbIkuqxfjZMU+UpsuKq+6XIzombrdBzPa34NZ4EIytWrpIVV93vjZHiq3QDrx2rPT5cp/D9Lje/UFrKRwRtsjG4H0y+i8/RtBPfGD++9t5wtu9w9RmLYmhpaZGrHnoo4vUCFWRg7BrfDUrhjvNNtuPZ0OdRbFrH+CacCXwoa8VFiqbcDQ0Nctu2bVHZ7cPVQTSxkbTWfCqmLJYiO0+KnHzXwy4nX5ry8r2/iXWjYrxih8VD8acyHr9CA6vVysSJEznhDlUMuH2xW+jraI5LbHxffE0tjs7DZBdX0de6n0/XTadrx0uA5MShf5I36gyvn7/n2gZzCZs3b8Zms+nakq+Fr2lDKzyDb+iG93e8Q3/vMf/t+j1HGfbD5VRds5LbZ82JeD0t7HY7+eX+CUaEuYTeLFPUdW21Rp8PAVz3IcuQHfF6QX77Pgnp7fWzsb+yjKfXrQtKQKNlUvMk2zFVjMFcOoySkhLNMMydW++m5+X76fnjep5cuyZi3Ci9+IaI+N7lV2K3HdAV6iDw2lp+9npkcTgcfmY8z3nzRp1B2bdnAuDs7sRkEGx4cj3bt78xoJAWsbaNRKBs/GmI7xqBIb8Uu+0A+UXlHP/LMxj6+0PacWPB1/5oLKqkt/2Qa4PX1a6MW9ll3XTtuJec6tO8fv4Ane9swX7kILPuXcKc+XeycMH8qBewAm26q5Yt9asD3//9Nu9Mm06vwYzzWBtl33ZtsjGYi8grrYppwUzLBtvd+ikyyxjSth1vampq+PPbb0e8t5qOAzoS0msd50m2E64d6fG9h5FRt0WtRf3WzfPp3KK9jyBcWbT87CPJUl+/mebDzTz48BrvesKFF17gPW/+aeeRlV9M528W0/heA+Xl5dSMPTXhmfiShtY0IN1eQ8nU44tnarhz586Ehtn1PacxJ1caiiq8bmuPPvNrmV0yXObkW7y+zYbCiqCpdV5+QUS3v8Cy+U7XPflSS0eFzpfqe+z8+QskhuyQ14u2vr2xa9xmo5yCUrdt2yKzrTV+tu1EoWVz1iKwDaxerS9Hse9xgX7rsbSjgdj4Q7lkbtu2LagsWiaSkDGrotgHoOWqHHheTwjn5557Lur9FYlC2fgziIEofi07b7wDonn995e7/PezK1wbVR5a87RfvPNt27bJ/LIqmV35Gb9OUDRirFz0wAO6O59vx6+e+azMytVnT/ct++o1a2RufqFmcm09ye21bLtV167y2owLvzxZipx8aSgZLjFka25giyeBNmc98keb4D2w3ANtRy0tLUGB/PQepyeng94AeVrvQ+Fpe48+8+uTydVHnupNru7Z6Phfl37XbzBgiPP6WqwoP/5BTDjXtkTEAfGc7/wLL/ILRuW0t/HwyuVem3FtbS39J7px2P3TMfYdtTGtro5pdXVRuz9KZ9/JEBGcdNsMNNto1cm+Pbv9rudxc3Q4HCGvHXieBXPv8Np2PVjKR3C0YQvDrn3IW8ZZc+Zx2aRJcat7LVfRcG62geYvcN2vaMwPgecfaFmsVismkynI9z6SG+yLL76E0+mk8+1nadv2GNlGIxueXB9Udr0B8rTKFgpP2/N1Ve441MTMuQuZccutXP39q3jm2U2c6Otj2LWrvPe/eeNs2p9fQH7ZiKjcOdMRpfjTkF27dnH9TTdRdOmd5I06IyH2RK2OqbVgZ8hu4qyzar3HWa1Wnly7hutunErzM3eQlVdE1okuHnvkYT/ZIm3C8YsfZCnF0XbQ70Fitx0I8qfXUgJNuz/xxkvxVeh33T4Nm+2IrsQcS5YuRErpb//uaqW4apRfkK/svEIaGxu56KKLBlj7Gusby5cysrrazx8+kl97PDYGBbaDcApb6zstWzmgKbfn+D/84Y/MXXBnxGiXidr45Gl7zYcPcnTTE3QcakIiOCFy6Ovt48mnN5BdOgLR0Uxf635XO6kYg8FSyt1zZvLNb34zPeLtDAStaUC6vYaSqWfTpnqZm18os0tHyKzcAll+6dy42xNDTZ+jiTHU0tIi5y9YIHPzC/zs8tG6dHrcV/NLq/xirFvKhvuVN9I2/UDZH9nwvOZUPNR5Fi3yN1N5whz4pWYs0Z+aMVKZtfYnPPToY9460+PXPtBQAIH3KpwvvNZ91bSVFxTJPEthkEwek1RJ9ViJwSizK/zTVpaMPDWofbe0tASFocgxW+JmXnn99dflo48+KglYr/J1c/X735gjn3vuubhceyAoU0+SSXR0Pc9otOSqB/1CJmflF8dtp1+k6XM0O44fffwJSq5a7Aov0bKHG+umIYSIKkSzx31V3no75T4x1u2vLPMrb6Tdj4GjQ2E0RQxR7XueadPqmDbN30xVWFDIjXXT6Dl+nLJv3UremFqc9rYBz760RrLGkhFkFVR43WBfeuG5iKPdWMI7eAhsB4Z9H/rFnNcTOsMjo29Yj+xCKzj7/EOPFJRx++w5lFz1INLZh/HVh3G63ZW996BN22NKSknzpvkuN+OOZrJF/LKZGY1GPve5z2EsKNN0c80ZdiqGvEKan52L81gHhizBhAkT4nb9VKIUv06iiZEeiVAPEE1TS14Bnb9ZzNNxytkaafoc6L738ccf6z6PKb8Yka3t+x5Odj0KLNJvAhW6dPRGDFGtdR7fa06ZMpkP//4hS1esoiuO+YW1Hj7OriMIg9FbZ4Cubf6xxkmK1hdeq814ZPS1lfcdtSGl9FPq3R2HyS9z7ZNwdnfSb2+n8EuXu2Pfl+NoP8gTjz8WJHtTUxMFFSPJu/Rueg//C4DW//45a9asZeHCu6Kudy1qa2vJOhGwXuXj5uq0t2KyFJNtzOLJdesz27zjg1L8Oog2Rno4wj1AtBSCodfO+zsagjbkxIqeuCF6Fsm0ztN7rAMhREz7DPQosEjB5XwVuvMn00OOfvUqS5vNxqM//4Vf8o7mTfPJzTZE5a+utYB7w7XX8vjq2RgLynF0HSHvs18GwP7xW3S3HaK2tlb3aD7S/dKSIVpf+J72Zgz7PgSng76OZo77yNh8+CD2+tUuGdes5i9/+atf2W740bXUv7CFHPe5C790OZ1vb6KwoprjnS088fPHmFZXFyR3jTtHsvPD1zn67lYMheX0d3dw/wMPMm1abPGZAnnxxZfodzpcCd3zS+F4J9lZgmO//ik9bc0s+9livn7eeZlv0w9Ey/6Tbq9U2/hjiZEeiN6Qu9HGJU903JBw9R02vn2C8o1GwlMnr7/+uu7fhqq/UPmFFy3S588faS3F130UQ7ZcsXKlNJb45wIeqMtlYI4A35hGgffqxqlTQ7rH3nLLrRJDthRGV77i7Fyz9/vXX389rGus75pJtHsP5i9YELRnxBM3Z6C88MILXtdlkZMv8z73NZnrDncxkDpPhLu1L8rGnyTiFV1Pj5eC3tHoQExP8coJGuo8qcw36hn9vvXWW2F/51d/IaJ8at13k7ObadOCR6eBhJsl+oYGAFdYAJGVRXbZSEbUraNXY0YZyUtK6/qNjY3cWDeNnLMvo/PdrRgKrcy4ZSYImFZX53f/3n+/kVlz55FXUkF326c8snKltz3ZbDae3LABYTR5d3T3tuxh6rTpXHjhBRiNRq9n1WuvvUa2uQhT+WgM5iJX+YorOau2lqbdn2h6EIUr14Tzz2fF6g1Brr5a5Y2mzdlsNvbt30+pz9pN8zN3IKVk64svsvCu2ExJ8TQJJxStp0G6vVI94pdy4NH1BpJkI5B4J3aIJHcmEDjKCie3pleNOwtZyIiWUd73cLPEwOuXXXKHNJYM89tQ5PntQLKyFVePlRhNUuSY/cqam18YlXdQQ0ODLKoaLU2Vp/iVJ7+yRjY0NHjr2hMx01gyTIqcfFl+6dyYNmYF3qtInj2x1NGiBx6QK1au9HqRlV86V5oqT5Fll9whzQVFMQdhS0a/VCP+JBKPUfJAvDB8SZR/c6aiNcoaNqwq5O+16i+7tBrzuElBI+1Y73ukWeKCuXewZKkrLk132yEM/f2auQ+iXVsK8tb5+C06//ysf1sJiGnkWx/O7k6cx9ox5OR79yzU1NRwoquVPkfAom3rp1gsFo4dO3byum6Prt6WPTQ/cwc5pmzumj8/rIzhymW1Wnl6/TqmTptOdqGVvqM2nl5/0tFB77kC800sWbqcJY+tY9h1j3jXbsCVrJ4P/evHN/dBuHhImdQvk674hRAjgV8BVUA/sFZK+Uiy5YiFeOyYjccDJF6mp0xEa8ORVsd/eesLIc+hVX+OzhZ3pw/uqLHc91AP+e3b3/A+pKSU3H7TNUybVseLL73EsTbXhqITnS2sX/0Edrs9akUSqHzyxtTS9t+P+SnsE50tmq6yzne20PnO8xjyinDa2/iv705iw1NPAq5IlrLP6V0EdfZ0kl9Ujt1u17yuJ+Kno8vGw+ufYcnyFV6zR7QKMlSfsdlsvPrqq+QE7PoOPFfQTu05d5BXOszPDdWQV0j+Fy7EaW/z60ueY7Ny8jnWbqOgohqHvU3ThJNJ/TIVI34HMFtK+b4QogD4mxDidSnlzhTIkhIG+gCJ18wh09Aa2Y8de4qmEunt7Q15Hm/9eaJ89hyl7OKbgzp9OPTYlAMVFhAU4XHJ8ruxWq3MmjuPBx9Y5Gdft9lsUSuSQOXjtLeRbTTQ/vyd5JUO8z5UAh9sq5YtZcYtM/120zZvms+NN9WBEJRedg+2Fx+k+LwfkV1SBQajd6/Fxx9/rKn0uls/xXrlfUG7z2NRkIF9xtMWcosr6Gjeh3xnC0XnXhF0Lq2BweKld+EMmGE57a1k/fsvdP7vb7314znW8p25HHlpiV/daM0qMqlfJl3xSykPAYfc/3cJIXYBI4C0V/yJ3sAVDfFaoI0niawfzZH9tOk8vX6tZurArKyssGn4PPW3Zs1aFi9dhvzgZTrDdFTfsvmO2iMt4PkqLK0cv76bmwylIym9agmz5s7jsssm6VYkgfUeeMyGp56M2FbOOquWwsqRQRuZsvpPYDQYXDHqL76ZtteewJBXgKHXzlPr1mhuJsspKONYazPmwlLvArbvSHzcuHGaqROjWbj2bQs57jg6YvefONHV6qe4X331VXKLK/zKlVtkxX7kU/paD3Bow+OujWFGI5uf+gUjR47Ebrd76zSvpAqcDrLMhRgspUFlCZQ5HfulFsJl/0/RxYWoAf4InC6lPBrwXR1QB1BZWXn25s2upAd2u90vhkuyaGtrZ+++vWQZsul39jF61GhKS0t0H58quQeKr9wOh4Pe3l5MJhNGo/+YYaD1E4nu7m7++a89ZJVWez/rs+0lK0vQ73QFZDMYTfQ7+ygvKyMnJ4dDh226ZAlXrqCyOfqQSIyl1QijCenoxdl+kC+cfrrmsb4cP36cnbt2YSgeRpYpD+noxdF2AIPRRFZpNZV5cLgH+tsO8NlTxmA2myPKF6reI5VJqw7+/vePMJSO8Jarr/UAQri+95S3v7cHZ8chPnfqqQghMJlMHD9+3NtGjhw5wr79B8gyGnH29WKwlGLIL/HW02mf/zz9/f10d3ezf/8BhNGIdDgYPVp/e9FqC862A4wcXkVRURFGo9FbL8LgkiO7bKS3XJ46HzZiBM1HexEGI/0dh6goL6P5cLO3LkdWV7Nv/36kBJFlQPY7ycorIMtkpr+rRdc9TwTR6JIJEyb8TUp5TtAXWiu+yXgBFuBvwGWRfptqr554rNYPVO5E+waHup7HHz5SeNxEejN4wuQGxvvXSh24c+fOkLHWY6nDUB5A1TOfjWpPh6f+Cod/RgqjSVrKhrvivS9fIXPyLbJiyuKoUxjGu943baqXOWaLNBYPkyI7R5JllJbyEdJktkhjTq4sGXGKZkyfrVtflC0tLfK5556TuQHpKoXRFHRcSfXYmFIl6i13qFwPnv0JnrhBkWIM5VkKpcntUeSN11Q8zJt/WkuuZPTRjPXqEUJkA1uBZ6WUL6ZChmhI9Wq9Ht/geJpZAqNc/uvf/2bWnHkhPScSWT++svT19XFk01xyS6qwt37ql33LkzrQsyDqu3CXV1zJmjVrWbJ8hWYdhqs7rbIZ8kvp2dOI5T/O12WfDjRN5Lfsof35u1h03z3cu+gBcooqsL1wH44vLadz670R7cIeedvb2+Na7x4zxcsvv8yMW2ZSfMlc8k/9itdDp7v9MA/cfx/33v+AX1to2tvElB9eg9OYi8i2+MlTPKyGhxbdyfjx4znn3K9QdPkiV7ye/34sZrkjmcAC71nRuVcgdv+JhxbdycSJE11xmAoL/XYcL5g3h4fXPaMZgsRgKeVw/Z1+exhe3zibXbt2eXfUZ4z/vptUePUI4Elgl5RyVbKvHwupXK3X464W7zhCfu6AJd3cfvNU8koqQnbURNWPryx9rftxbHscg7mI3qM2jO4NT4Cf++P+/fvpaTvk7xrZ3szipcsovvLBoDqMZK/XKpuht4vut9bCh7/VtYCn/WC0cvd993sD8uW37EH2O3jvnb+EDc8RuPHM4XD413t7M3v27KG9vd1rr45mMLB9+xvMvH02Ir+Mtt8/Bk4H+aedR3ZpNZZxk1h4733euDuesiD+RcEFM8gbU8vBtXVBHkQTJ04MdhkNDNIWor2EeiiHs6Vr3bMTXa1epe85fvv27fx+y8aTLp7LVmiGIOnZ04ixyL/9GwvKaWhwhVKJ1EfTaW3QQypG/F8FrgH+LoT4wP3ZnVLKV1Mgiy5SuVofaTQdr9j9oUaRwmgir7SK7rZPyQ3RURNVP56ya4242p5bQMcLd2EuHUZPx2FuuPZazjn3K+SVVOFwOHC0HcBevybkaC6vuJLGxsagDntj3TTKykqpra31LswGlu3p9euiWsDT9HjxCVzmkclgbPK6R4a6R4Hy+taD/chBHI4+Jv/wWgyWUpxdreQXW+nvPaZrMKAVHbZ503ycx+30dTSTN6aW/sZKulubvXF3elv2ICsc5I2pxWAuouzim2l+5g6MRVU4Og75BV/zXYQv/NLlNG+cjcFShsnZzarly4J28UYa0AR6+vgqWD3t0XfHMRB0zJNr1wBw40119J3o9XtQObqOMH78eCB8H43GESCZpMKr58+ASPZ1B0qqVutr3IGqtEbT9fWbueGmafTnFHHkpSWUXjSD/NPOi3q679vBuls/pb+/3y/K5YnOFh5ZudK1pT9ER0pE/XgUJhojLkv5CDate5SSkhIsFovXjOBRiAIHm9Y9Sm2tK4lM4Giup+Mw4B91sq91P8dPnOD7N95M79Ej3k4aqmx6y6j18PDUp68C7a9yhJwlhfJZ99QDwHcvu5x+YaTqBycfkIfr76R80gJuuGkaZ575xbCziUAF1te6H5wOuhpeAiT2D1+nt6uVRT+9h7vvvYu80ipOdBxGnH4vTnsbBnORaxaWZaTkGzfS/YenGOMzOFgwbw73/PQOskurcXS2UHJBHc4PfsOMG37sDhdxUjleeOEFUW1e83Xv7G5t5uFVy3nvnb/Q0NDA+PHjdQU5DBeCZOpNN/HbjScDz908fbr3nKFmvJ4NeJbvzCUrOxdD3/G0SdCudu5GQTw2cEXL9u1v4HCcjB5o6O3i6fXrAOISu19rFNm6eT6dW1y7Sp0/mc6qZUs566xa3nvnL+zfvx/Aq1B9iXf9eBTmjXXT6DtxIsg04DFl7N+/P2jElWVsoqSkxCuP1giwtrbW22ENllJatz3uF4kzXJq/aNFUKgJunzXHpUA7W0LmPojks15bW0tTUxM5BWX0S6O/SaKogqzsXJwmC7XjxvP0unUhR5y+CsxTH9Yr7/XmSLC9cB83/Oha7r3/Ab+YPlarlda7r6XfZMHZ00XZxTdjyC/hWNshvnf5lTy5dg1TpkxmWl0di3+2DPO4Sd74OB1vd/Doz38RlMNBTz4CD1runTNunonRaKSwclRUI22t+/ziiy+xbfsb5JdWcLzjCPcsvJP77r3X75jAaKs3T5+O3W4nKyefIy8twVhUgaOzBXN+QVrs5FWKP43xNOiyyT/DYHEtKHa/tdYv0JefqSCG2P1a5ymwVntH03v37uVHN95EXkkVdtsBpJQUVIyMetoaq53T62+/dq03xEGgaSdwlqI1eg41mvOmf8wrxGAuTOgCvq9Sqa/fzKw5/kHRSktLgkILNDY2MnXadK9i9Pisy3+8xfGuI17FC9Brb6evz+lvkuhsob/vOM6eLkom3Rl2xOk7M8nOK0QYTX5KK6+wlGc2b6b0qiUuf/iWPcyaO4+Xt77AgaZ/s+qhh3jokUfp+ONG16a4b99GdtlIv2s+uXYNU6fPgA9dD+A7583l4fXBZjjQl48AXG040FffWDKCvo5D5F16N6YBJM9Zs2YtM26ZibF0BN1Hj1D45cnc/8BihlUN8wbrs9lsPPWrX2G98j7vQ/KpXy5l8uSrONZu898Ut3F2Wrh1K8WfIvQowkClbPmP8+HD33qP6279FD5+yzt6iiV2f+Aor2dPozcePMCf337bbzZw6Nl5GL50NRaDUXdnCmer1VMPVquVhXfdxeWXXUZDQwNjx47loomXBM1SOl64C1NRBX1HbYy+5GnNuC+hNtw0NjYy6YqrYsolEC2BI1SPAv3lk+uoGXuq30M2p6CEExj9Ng+Zi8o4fvQI+eXDmTV3Hgg4q7aWh1Ys59afzDoZVsHe6h1xll40g7xRZ+CM8DDz1Mebb77J5B9cE6y0SquQzj6c3Z1eJd3b2+vaffyTnzB82DDuemAZRdes9UbnDJfoB2DJ8mAznFY+glXLlgatAwDuvuC/7uDsOuKXSSuWh7jNZuP22XP86uBw/Z0Yiqq4bfZs7yY7Tz/1bFYDaDOY2fjMMxRUVPub5qzVYddxkoVS/DEw0FV6vV444bxltm9/g/7+fjrffpa2bY+RbTSy4cn1USds8YzyrrtxKn0Oh2vDTX8/27e/wdixp5BlyMZUUQO4bL5COml/62mcR4/omraG83gIXPhatWwpY8a4ruVZXNWqs2NHPsWYVxDgeldEX/dRcPZ59olEVQcXXXQR69esjjmNYTTtQWuWlVNQxr79B/zqqfnZefR1tmPIK+Tg2jrKLr6ZrPxiujtbvcrIY9YorhrFiaNHePThVV67en5+Phdc/C2KJrkW/vU+zKxWK2PGjAlSWrmlVXS1fsrx/34M59EjFH7pcno7DmMymfzMUfa2Fgwfvq4ZQsFzft96CrUQqx02OrjPWK1WHl613GXeKRmBs8slW+c7L3gzacUaRt1cdvI+GSyliNwCHEdtFFQMp7Gx0bvG1N36qX9GtZ6j/GrjsxCQmMh5rC0p3oCRUIo/Cmw2m3eLv7lseEyr9NFGJtTqFOCy75d+f4m3QXVuWejNjxotnpjqZZN9Yq1Pn8F77/yFfmefy4VRwwYeadoaasu8lkeN850tzLhlJoaCMnd8GdeDzBOzJrDOmjfOxrzvQ/JGnUHPvg851nHEb2S2d99ebDZbTGYlLSUeSrnX1292hR6wlNBrb/fas8MRysvHYDz5kDVVjMFgLqLoaz/w7hdofuYOTEZjkEI2lowg5xs/JteQzaw582ja/YlXxqfXrWPq9Bk4fUbNjY2NQPDDNVBGh73NT2kdb2um4qr7vQ+R5o2zWbZkMV1dXVw/9SZvewwVQiGWevf8f/6FF4XtM9Pq6kDCbbNnYymrpHvHFrKzoOfl+2P2MqupqeF4Rws5LXvoa91P67bHMeQVIgQcbTnApCuuIq90GHbbARy9vTRvvMPlTdXTSdnFtyA/eJnbb7qGJcvvTrvYPUrx66S+frMrqJfRjLPPSf4XL6UowH6ph6amJnIKy4Omy6FGzlqdQivmS15Jla6p7K5du4I8HZqamsgrHRaknO12O6NHjabz3hs0beDhpq2eEWB2fgmdLfsxupW0lkeNs7uTo+9uCQ4QVjct5HqGxVpN528W4ywbgb31YJAyzDI0xWSf1zIHaXmMTKurw2azuWZK/eCURvr6nFx349SQ7SGcu+EjK1fS3dMdNDrMG1PrLVOBtZp1Dy3m+pum+f+u6wjGokoM5qKwiX3ef7+RW38yC6cxF2dPF6bsbO/DVasefGU81nqI/GKrX+wdc1EZd91zL0sWL6a3t4++1v0YLKVIZx+FFdU89ODdfr7zoYg0Y4rk0uxh2rQ6Lrtskp8ZaSAzc1/nguMnTgQPei6Z53Whbt40H+sV93Ji7//S2fASWfnFHG09xOWXX8a0aXXKjz8T0Yo1frj+TobftDpq2+H77zfS0bwPY8B0Odz0L1AZRbNhylfR/+IXq3l89WqMhVYcR21M+f5VXPTNCxk7dmzI8x07doym3Z9o2sB9p62Bi5JTp8/AVPs9jr67leyS4dheuA9zURmyryfIo0Y6+zAUlPl17OziKkz0ec8ZlJz8WBuNOxq8cUvOOfcrfrKFc42MhlAeI0gYM6aGPofDXyE8c4c3jr0vWua9wIxUL774Ep333kBecSVdtgNIp9PrJtnbsgeHvZUJEyacDIZWVEHHoSaKvnq19zehEswDfO3r59PXL8g2F9Pfe5zePof34Rpp0BFYxz37PvSanLKtI6m6ZgXNz84DIVyLwW0HOX7ixIDWfzxE094D+8pAFe2FF17A0sUPcPfSR/xnWQXlZGXnet9n5Vqwbfkp2cVViH4nLZvvxlJWxTnnfoVVy5dyloYXXErRiuOQbq9Ux+rRyqbkzdajM8ZIqAxcwmjyy4OqF63MUIGxQm655VZXTtHSEe58qaagaxuLq7yxR/xyoq5ZE5S7NlQ2qsA4PosWPSBLqsfKrNyCoMxJO3fuDDpfyYhTgmXLyZd5lkJv/tPAnK2RMmVt3fpi1HWqRUNDgywddaocPe93snrms7Lq2lXSWDZK5uRb5HPPPSeNJcOCcvIG5oPVG1PH00YWLXpA5lkKZX6ZKy5MwbAxmvGRGhoavHFnImUI27ZtmxTZOUExhwqsI3Tnjvat45x8i8yvqpGj5/3OmznMWFwlSy76ccj4OYFxbHbu3OmNUxQpZk/g/fW0z4HExdHSJb5y+uYr1uo7HrkrpiwO/t5klsOmPnEyXlH12LjloM7YWD2ZhtaIo6/1AF1vPMGGJ9cPyHWyeFgNZ50V/Wgg0AS0ffsbXo+QnvZmfnrPQh5fvdprPrFrZGIyloyg7NszEYZsXt84mz+99T+YTCbXQtqced5YPTbbkZAbmULFO+/vd2Io9Lftm8uG+5mG/MwQjY3cevssnIZcnMddZogbr7vO67LpWfw966xazSlzoGwff/xx1HWqRU1NDXbbQfrf2cLRd7diKCzH2dlMTkEJxcXFGE50+Y2CRU8HI0eO9DuHXlOFhyXLV3jDS/Ts+5DO3yymMcBbyzOyHTdunJ95I1xbNFhK/d1/80vptXdQU1MTdX4Bi8XCOV/6Mr0te4CR7pj27XT84ZcYci1+Gwm1dq8CUW0+DFronaO90DsQtDYyetYt5DtbaN44m+JhNZzobOHm6dN56lfLcBZX0tm8l6y8Aj/PK0N+Mc2/mk3Zt27xW4NRG7gyCKvVyqrlS328Bmzkn/FNnJ/80W9BNVLn0XqABGZD8iXS+TydX0v5LrxngV9H18rE5GsbNhaUs3v3biZOnOi3kGYo6Wbq9LqQG5m0lFpuSRXTfng5S5evDJv5ybcMu3f/C6PRSF5hMSdkL4t+em9QMLBZc/0XLkPVR7REqud+p5POtzcFuTaOHDnSmxZQZudx/GgbBRXVnHPuV/yUUTSmisA1oLxRZ7jWMcK4AIYrt6dsI0eO9HtIeZKP/Pznj/kp5u7WT7lz3lymTauL2OYWzJvDogfn03fGYpo33UnZRJffvu9GQq30kTfeVIcESq5a7Gc6jbT5UO9CbywE9iE+fovOt08OlDyB3n465xY+97nPUVtbyz33LGTN2rUs/tky+owmr+dVdtlI+nu6sF6+ENuLDwKEXINJFUrx6+Ss2lqKq0aR840fe2+i/cg/vTdRj60ylJdOuN2aekY1miPK0io6DjV5O7rT3obsd7rioxSU4+w6Qt5nv3zSfuyOPRJ4LmE0hW2sWkqt41ATNaNreOLxx/x2poZKIhK4Sam3ZQ9333sneSWVIUfJ8Qp8pVXPvjOHpqYm8ksr6XagOXuZMmUyZ575Rc4ad27IDE3hvLMCk8XEsgakt2w33XADT/1yIcaCcno7W3ji8ce4bNIkb1awvtb9HN/2OD9d9ThLli1n/ZrVmm3O97wIEAJG1J302/fdSKiVPrJXmMjKzvGffejcfBjKFfbVV1/VtZAc6bwGSyknDn2CqeoUnAFeTcfampl/193klQ7zzkCXLDs5O3MNCO4Ag5Gyi28mb9QZGPIKyB1zTtg1mFSgFL9OampqOHH0CLmG7KCbGI2Lpp6YNtGczyNboPLtPXqE8887jz/4KPosISj32V1oe+E+Dq6+Eeexdm/skcB0f9LRG3EU5j8bOkLRV6/2js4vmxTaDOH1/DEX0Ws0+3VmR7aFLtsBzcBwobxsokWrnq+fehNZWVmuwHStzSz66T302ttxBOyIld3t3jqx2+3klw8P+ZDSuu+BpjnXQ7GcWXPn+T1ADv1qFrfe/GMaGxvDul/qKdtTv7qb9979q1/ETo+HmFYgvFBJy/2it+77kH6nw28R2ncjoVb6SGdvD9LhH4JD7+bDwLbe+c4WOg/vZ9Y9i/nxrbfHbPapqamhq2U/bWvryC6uoq+jmSzwhi7pbjuEEMIvtMRts2cHB9qzlFIwfhL5p51Hb8sesnrtOP/5R+yt/1TunJlIuNG6pntlmFFyJJNEtDZhLdlWLV/KrDnz/LaR27b8FFP5aO/IrKhqFLfecDWTJ0+mvLzcO/r0PZfzJ9MjNlbN2dCev9LkTrMX6eFmsJRyNCCcr7PnKEaRRdvm+ZhLq7z+4EBIL5tAV75IaNWz01SAxe07n9Oyh7nzZ3PDj65l46b6oHhJnnLpNeWEM81NnT6D+o2/DA6SBjz2xGp+sfF5DCdc19Wj2EK1Ibvd7heR0iO7ViA8rTYXeN68UWeQZfgX7c/fRX6ZK1LqU+vWeBW4b9vMLijnaMt+yr99G4DLvJNXgNGdxlHP5kPf8+UUlNF5eL/fZjbPw8ojazQzQiEEVVef3BvT9twC74Oyvb2dq+tu85/1FQdHKjX0dnH8L8+Q9X9vuNJeRhnJNVkoxR8FoUbr0dhw9RDL+QJl83RQ323kgQlEjre3MHnyZD744H9Duhp2dnZy4YUXRpQ31GwoFIEKpOzim2neeAfGkmE4u1qxnP4N7B++RmFFNT0dNh5etZwpUyazY8cOvw1hBkspWZZybrn1Nn5yxxzvxrqNTz+lr54DIp8G+s4bS0bwzObNfPC3HX4B6gJdBqMJSx3KXHH8+HGvPAZLKUf+++cIo8l/FD5tui57djRtaMHcO1i8ZCl9vf6hh7V+r3Veqvp5f8c7IWP/+4aB+P7VPyS7zJXbNyu/mJbn7+GtP7zFV7/61bDl0Trfq6++yqx7Fgc9rNasXcuSZdpJd0LR1NSExeq/F8Rjzhs3bpzmzOVEVysPr1ruWmiOELI7XRS+B6X4o0RrtB5tx9dzjVjOFyhbqAQivX97EbvtAPnFVs750pdxOBzeXbue0WfT7k8YN24cb731VkLkDVQg2WUjkbIfS+1Eckd9geaNd/j5x8+aM89lj/aJy+K7m9LZL8k99Tws35yOSefO3cDIp1m9XRizsvzMFs6uI1jKXCPliy66yLu24Cm3h2jCUocyVxw6bMPR10fr5vnkFJRhyMnDYC72U0bZhVbv9SMt/Ee6J/62esEV3/ser2w5GQhP6x5qnXf0JU9FHK1bra4wEJaSCg7X3+kN/GYprsBkMoU9NtT5Jk6cyI9vvd1vxN3ddoglS5cHRfuM9LAMtVb1/vuN3lmrVn1OmTJZ05yZboo+CC0fz3R7pdqPXy/hcm7GIvdAc3hq+d1r+U6HyyEbjdweeT2+95HkDpTPk5O1qHJUkH+8r0yr16xx70vw90vPyrV4y/Hwoz/X9E/3ldHjW18981lZdskdMs9SKJevWOHa+2AdI7NyC2Tx16/z+paHyzscLatXr5E5+RZZVFXj9QH35NzFaJKmXLPMzsuXIiff3/+/oOik774OOUK1oVB7C/TeO9/z6m0jnmtWTFksq65dJSumLB5wbubANrRo0QNBe25C5UT27JvwlGP1mjUh771WuVOF8uNPI6LxMtH721jdEz2ECvdgKa/223ofbQ7ZcPJGk3FIS7577lkYMVLmtLo6mvY0sWLNBv+FtQIrjs7DOO1tmjt3wwV680Q+/fp55/HEzx/zxnw50fjrk2sLPp5HA3EjrK/fzKy588gvG86xI59iLvLsWna4zl02koJxk+h64wmyhfRbW3jkoZVh8x9r3ZNowiAErgHE0q57e3t5//33qaysZMKECX5eWKuWLQ2b0CfSuQPl8HhVeXanl5eXa0b71GrPbW3tfgvs37n4YrLyiyn79sygtSrfkbzn/3RMqagbradBur3SfcSvZyTokdt3N2BOviWmXbsDQWukl2O2yDxLoebuz2hH/Hp2qOrBd1evVj21tLRIc0FR0G7KkhGnaO7cDbVrOtSu0cCR3aJFD4SdhQykjjxyeEb8WbkFrlnIqM/Jbdu2eV8emfSOaKOVI/BehWrXgZ9v3fqi9zNzxWgpjCZJVrY0Fg+T2blm70zO8/tod92G619a34XaYR5Y/lUPPeRXfgzZmjOssDuJ4zD7ixY14k8DonG99PzWVPs9Ot/diqHQyoxbZoIgJnfEaOX0jE4GmkM2FNF6I4VjypTJHD16lNtnzyG/zBV3vrCw0C8UryeMck5BGd0dh1n2s8V8/bzzqNHYuaslm2+gt8DRZ+DIbvHSZTgDXDp72pujnhk1aSQNsVir6Xz5QfrGuzdCXXwzTnubNy59YN3Fw5Eg0hpAqHZ95plfDPp87969LLz3Pr99GM2b5lN1zQqc9jYeXz0b65X3eQOaBUYQDUe4/gVofte0+5OgOEha98E35LipYgzGQiv5p1/gXYPoazvA/ffeHbIfx3sTWTJRin+ARKPsPLsyO9/dSuWUk7sWb581h8smTUpYo9ETIAwGviAVjSdJpGmyzWZj1tx53tSSORqdK/DhcO/9D7B+9RN+popwsvkGegv3wGtqasJcNpz8L14aUSlE4v33G2n/tMk/acixNhrf28EHH/wvudkG5Acv0xnF4mqsjgThFqRDteuGhga/qKrS2QdZBkxFVv9FaJ8kKIaAgGbRDAbC9S8g5Heh3Ig91NTU8Oe33w7ayZ5TfRoFZ36Lnj2NtP7+ES6/7LKoZFKKf4gQjbKrqamhu70ZQ6F/J8kr1RdSORZCjU48XjvxRK9S0rMrWU/nCvdw0CubHt9xzz0uKhvJ8JtWe1NgRjtL88hb9NWrXZmcCspxtB/kiccf47TTTuPw4cPs/ffuiDOveCa2D7UGEKpdjx8/np72Zpw+sYv6v3gL3Uc+Jc/nt30dzd4kKM6uI/T3HQeIeoYSqX/FOvuxWq3ekOOesNOmPItfqklLsVUzVEa83bdTgVL8AySaEZjVauWRlSuZccvMiDFs4kWyRyeRlJLeabKezhVpNBitbKHQusdPrl0Tdf155LWcewWWM76Jo/MwJ9543Buy1+Fw6JZtoAv/kQj3oFy1bCkzbpnp3TiVXdaNyMqi44W7EOZietqakf3SlYzl+FG/gGbRzlAi9a+BzH5KS0u8M19P2OmiSQu8Gx7tryzT7Jfxdt9OBUrxx4FoFMq0aXUgiBjDJl6kYnQSTinpfRDp6VzhyhYqOmesCjMeo+xAeZ32Nk50tVLjDkPRfLiZBx9eE9eIkwMhVJnPOquWkuE13nsojCYs5SPYtO5RSkpKNL167rlnYcx1F67uB3pffNtDNMo8nrOulKC14pvoF/At4B/AbmB+pN+nu1ePHgLlTqY/sB4vh1DEu76j9fzZuXOn3LBhg18cf19ClS1d20moPArmwmL5yIbn/fYUpNJXPByB9/CRDc/L3PzCkPcoGfJEu/dAysjx+NOVeHj1pELpG4B/AZ8BTMD/Av8v3DGDUfEnm1gbdCLk1vsg0usyp1W2VNd3OALl9bhoPrz+GZmVWyBNladIkZ0jFz3wQIolDY3n3hQM+4xcsXKltJSPSLpbo68ckdqI1u/SuY2EI1PdOccDu6WU/wYQQmwGvgvsTIEsQ4ZE24SjId4RStOpbHoIlLempobu1k9xdLb4eXstWbqQaXXacfFTjWfjVO248RhLhlN245qQET0Thd42Eup3L299IeEypivC9VBI4gWFuAL4lpRyqvv9NcCXpJS3BPyuDqgDqKysPHvz5s0A3hyrmYaSOzq6u7v557/2kFVa7f2sv+0Anz1lDGazOeLxmVbfhw41k2XIoqUvx/tZNOVNBZ57NGxENYd7XJ8lU2a9bSTU78aMHklhYWHC5Yw30bTtCRMm/E1KeU7QF1rTgES+gCuB9T7vrwEeC3eMMvWkjlTJPdBdwJlW3y0tLXLVqofisus5WfiuTaRCZr1tJNTvfPNJZxKZauo5APgmJa0GPk2BHIo0ZjC4zEWD1Wpl9OjRdN53Q8aU13OPmg8fxF6/Ouky620joX5nNA5dp8ZUlHwH8FkhxBjgIDAZuDoFcijSnIx3mYsSX7/yTCnvlCmT2b59O7/fsjElMuttI1q/0xNyfLCSdMUvpXQIIW4BtuHy8HlKSqntdK0Y8mTawu1AycTyGo3GuO8Cjwa9dZaJdZsoUjLXkVK+CryaimsrFArFUCcr1QIoFAqFIrkoxa9QKBRDDKX4FQqFYoihFL9CoVAMMZK+czcWhBA2YK/7bTlwJIXixIqSO7lkotyZKDMouZNNNHKPllIGuTJlhOL3RQjxntTagpzmKLmTSybKnYkyg5I72cRDbmXqUSgUiiGGUvwKhUIxxMhExb821QLEiJI7uWSi3JkoMyi5k82A5c44G79CoVAoBkYmjvgVCoVCMQCU4lcoFIohRkYpfiHETCHEP4QQHwshlvl8vkAIsdv93cWplDEUQog7hBBSCFHu81layi2EWC6E+D8hxIdCiJeEEMU+36WlzB6EEN9yy7ZbCDE/1fKEQggxUgjxphBil7s93+b+vFQI8boQ4p/uvyWpljUQIYRBCNEohPid+30myFwshNjibte7hBBfzhC5f+JuHx8JIeqFELlxkVsrO0s6voAJwHYgx/2+wv33/+FK2J4DjMGVyN2QankDZB+JKwz1XqA83eUGLgKM7v+XAkvTXWa3fAa3TJ8BTG5Z/1+q5Qoh6zDgLPf/BcAn7vpdBsx3fz7fU/fp9AJmAZuA37nfZ4LMvwSmuv83AcXpLjcwAtgD5LnfPw9cFw+5M2nEPwP4mZTyBICUssX9+XeBzVLKE1LKPcBuXAnd04mHgLmA70p62sotpXxNSulwv30HV5Y0SGOZ3YwHdksp/y2l7AU245I57ZBSHpJSvu/+vwvYhaujfxeXksL993spETAEQohq4DvAep+P013mQuA84EkAKWWvlLKDNJfbjRHIE0IYATOubIUDljuTFP+pwH8KId4VQvxBCOHJ/DAC2O/zuwPuz9ICIcSlwEEp5f8GfJXWcvtwA/B79//pLnO6y6eJEKIGqAXeBSqllIfA9XAAKlIomhYP4xrE9Pt8lu4yfwawAU+7TVTrhRD5pLncUsqDwApgH3AI6JRSvkYc5E6rpJNCiO1AlcZXd+GStQQ4FxgHPC+E+AwgNH6fVB/VCHLfict0EnSYxmdJkzuczFLKl92/uQtwAM96DtP4fTr5A6e7fEEIISzAVuB2KeVRIbSKkB4IIS4BWqSUfxNCnJ9icaLBCJwFzJRSviuEeASXiSStcdvuv4vLrNoBvCCE+GE8zp1Wil9KeWGo74QQM4AXpcuw1SCE6McVrCjlydtDyS2E+AKum/a/7g5dDbwvhBhPiuUOV9cAQogfAZcAF7jrHNKgriOQ7vL5IYTIxqX0n5VSvuj++LAQYpiU8pAQYhjQEvoMSeerwKVCiIlALlAohHiG9JYZXO3igJTyXff7LbgUf7rLfSGwR0ppAxBCvAh8hTjInUmmnl8D3wAQQpyKa4HmCPAbYLIQIsedwP2zQEOqhPRFSvl3KWWFlLJGSlmDqwGeJaVsJo3lFkJ8C5gHXCql7Pb5Km1ldrMD+KwQYowQwgRMxiVz2iFcI4EngV1SylU+X/0G+JH7/x8BLydbtlBIKRdIKavdbXky8D9Syh+SxjIDuPvbfiHE59wfXQDsJM3lxmXiOVcIYXa3lwtwrQUNXO5Ur1xHscJtAp4BPgLeB77h891duLw5/gF8O9WyhilDE26vnnSWG9ei7X7gA/drdbrL7CPfRFweMv/CZbZKuUwh5PwaLjPUhz71PBEoA94A/un+W5pqWUPIfz4nvXrSXmbgTOA9d33/GpfZOBPk/inwf269txGXR92A5VYhGxQKhWKIkUmmHoVCoVDEAaX4FQqFYoihFL9CoVAMMZTiVygUiiGGUvwKhUIxxFCKXzHkEELYo/jt+UKIr/i8ny6EuNb9/3VCiOExXL/JN0qrQpFs0mrnrkKRhpwP2IG/AEgpV/t8dx0u/+q03R2sUGihFL9CAQgh/gtYiGujYCvwAyAPmA443TFSZuLaPWnHtRnvHOBZIUQP8GVcuyrPkVIeEUKcA6yQUp4vhCgD6gErrp3Owue6PwRudV/3XeDHUkpn4kusGMooU49C4eLPwLlSylpc4ZznSimbgNXAQ1LKM6WUf/L8WEq5BddO0B+4v+sJc+57gT+7z/0bYBSAEOI04PvAV6WUZwJOXA8chSKhqBG/QuGiGnjOHfTKhCsBRrw4D7gMQEr5ihCi3f35BcDZwA53EL880i9QmGIQohS/QuHiMWCVlPI37pDD98VwDgcnZ9G5Ad9pxUYRwC+llAtiuJZCETPK1KNQuCgCDrr//5HP5124UiNqEfhdE64RPMDlPp//EbcJRwjxbVwBwsAVYOsKIUSF+7tSIcToGOVXKHSjFL9iKGIWQhzwec3CNcJ/QQjxJ1zhvj38FpgkhPhACPGfAefZAKx2f5eHK5LiI+5z+C7Q/hQ4TwjxPq6kPPsApJQ7cS0ovyaE+BB4HVcuXoUioajonAqFQjHEUCN+hUKhGGIoxa9QKBRDDKX4FQqFYoihFL9CoVAMMZTiVygUiiGGUvwKhUIxxFCKX6FQKIYY/x+0/+kjO6WD3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This scatter shows that wind speed doesn't not correlate with Latitude, it is very constant with a few outliers\n"
     ]
    }
   ],
   "source": [
    "city_df.plot.scatter(x='Lat', y='Wind Speed', grid=True,edgecolors='black')\n",
    "plt.title(\"City Latitude vs. Wind Speed\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.show()\n",
    "print(\"This scatter shows that wind speed doesn't not correlate with Latitude, it is very constant with a few outliers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>komsomolskiy</td>\n",
       "      <td>40.4272</td>\n",
       "      <td>71.7189</td>\n",
       "      <td>16.38</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>UZ</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>butaritari</td>\n",
       "      <td>3.0707</td>\n",
       "      <td>172.7902</td>\n",
       "      <td>28.18</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.61</td>\n",
       "      <td>KI</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>goderich</td>\n",
       "      <td>43.7501</td>\n",
       "      <td>-81.7165</td>\n",
       "      <td>6.21</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.20</td>\n",
       "      <td>CA</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sisophon</td>\n",
       "      <td>13.5859</td>\n",
       "      <td>102.9737</td>\n",
       "      <td>28.57</td>\n",
       "      <td>63.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>KH</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kapaa</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>23.37</td>\n",
       "      <td>88.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.14</td>\n",
       "      <td>US</td>\n",
       "      <td>1.635224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>abohar</td>\n",
       "      <td>30.1500</td>\n",
       "      <td>74.1833</td>\n",
       "      <td>24.54</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.27</td>\n",
       "      <td>IN</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>notse</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>1.1667</td>\n",
       "      <td>23.44</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>TG</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>solhan</td>\n",
       "      <td>38.9634</td>\n",
       "      <td>41.0293</td>\n",
       "      <td>3.79</td>\n",
       "      <td>47.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.84</td>\n",
       "      <td>TR</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>nishihara</td>\n",
       "      <td>26.1842</td>\n",
       "      <td>127.7558</td>\n",
       "      <td>26.28</td>\n",
       "      <td>52.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.63</td>\n",
       "      <td>JP</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>oranjestad</td>\n",
       "      <td>12.5240</td>\n",
       "      <td>-70.0270</td>\n",
       "      <td>27.89</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>AW</td>\n",
       "      <td>1.635225e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>358 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0    komsomolskiy  40.4272   71.7189     16.38      25.0        70.0   \n",
       "1      butaritari   3.0707  172.7902     28.18      75.0         9.0   \n",
       "2        goderich  43.7501  -81.7165      6.21      81.0       100.0   \n",
       "4        sisophon  13.5859  102.9737     28.57      63.0        83.0   \n",
       "6           kapaa  22.0752 -159.3190     23.37      88.0        40.0   \n",
       "..            ...      ...       ...       ...       ...         ...   \n",
       "578        abohar  30.1500   74.1833     24.54      37.0         0.0   \n",
       "579         notse   6.9500    1.1667     23.44      94.0       100.0   \n",
       "580        solhan  38.9634   41.0293      3.79      47.0        43.0   \n",
       "581     nishihara  26.1842  127.7558     26.28      52.0        20.0   \n",
       "582    oranjestad  12.5240  -70.0270     27.89      80.0         7.0   \n",
       "\n",
       "     Wind Speed Country          Date  \n",
       "0          0.00      UZ  1.635224e+09  \n",
       "1          3.61      KI  1.635224e+09  \n",
       "2          7.20      CA  1.635224e+09  \n",
       "4          3.37      KH  1.635224e+09  \n",
       "6          5.14      US  1.635224e+09  \n",
       "..          ...     ...           ...  \n",
       "578        2.27      IN  1.635225e+09  \n",
       "579        1.60      TG  1.635225e+09  \n",
       "580        1.84      TR  1.635225e+09  \n",
       "581        4.63      JP  1.635225e+09  \n",
       "582        9.96      AW  1.635225e+09  \n",
       "\n",
       "[358 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "north_cities = city_df.loc[city_df.Lat >= 0, :]\n",
    "south_cities = city_df.loc[city_df.Lat < 0, :]\n",
    "north_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Max Temp vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is -0.8909470410826494, showing a high negative correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(north_cities[\"Lat\"], north_cities[\"Max Temp\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "north_cities.plot.scatter(x='Lat',y='Max Temp')\n",
    "plt.plot(north_cities[\"Lat\"], intercept+slope*north_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(6,-10),fontsize=15,color='red')\n",
    "plt.title(\"Northern: Max Temp vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/northtempvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a high negative correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Max Temp vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is 0.5885437021509893, showing a slight positive correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(south_cities[\"Lat\"], south_cities[\"Max Temp\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "south_cities.plot.scatter(x='Lat',y='Max Temp')\n",
    "plt.plot(south_cities[\"Lat\"], intercept+slope*south_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(-30,5),fontsize=15,color='red')\n",
    "plt.title(\"Southern: Max Temp vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/southtempvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a slight positive correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Humidity (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is 0.24481675104059497, showing a slight positive correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(north_cities[\"Lat\"], north_cities[\"Humidity\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "north_cities.plot.scatter(x='Lat',y='Humidity')\n",
    "plt.plot(north_cities[\"Lat\"], intercept+slope*north_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(47,20),fontsize=15,color='red')\n",
    "plt.title(\"Northern: Humidity % vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/northhumidvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a slight positive correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Humidity (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is 0.17960798809773185, showing a slight positive correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(south_cities[\"Lat\"], south_cities[\"Humidity\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "south_cities.plot.scatter(x='Lat',y='Humidity')\n",
    "plt.plot(south_cities[\"Lat\"], intercept+slope*south_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(-55,25),fontsize=15,color='red')\n",
    "plt.title(\"Southern: Humidity % vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/southhumidvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a slight positive correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is 0.12950005076630258, showing a slight positive correlation, albiet with many outliers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(north_cities[\"Lat\"], north_cities[\"Cloudiness\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "north_cities.plot.scatter(x='Lat',y='Cloudiness')\n",
    "plt.plot(north_cities[\"Lat\"], intercept+slope*north_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(20,28),fontsize=15,color='red')\n",
    "plt.title(\"Northern: Cloudiness % vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/northcloudvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a slight positive correlation, albiet with many outliers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is 0.25246984250658827, showing a small positive correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(south_cities[\"Lat\"], south_cities[\"Cloudiness\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "south_cities.plot.scatter(x='Lat',y='Cloudiness')\n",
    "plt.plot(south_cities[\"Lat\"], intercept+slope*south_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(-50,27),fontsize=15,color='red')\n",
    "plt.title(\"Southern: Cloudiness % vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/southcloudvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a small positive correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is 0.04713645035168065, showing nearly no correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(north_cities[\"Lat\"], north_cities[\"Wind Speed\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "north_cities.plot.scatter(x='Lat',y='Wind Speed')\n",
    "plt.plot(north_cities[\"Lat\"], intercept+slope*north_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(10,11),fontsize=15,color='red')\n",
    "plt.title(\"Northern: Wind Speed (mph) % vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/northwindvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing nearly no correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is -0.16285387219116823, showing a very slight negative correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope,intercept,r,p,se = linregress(south_cities[\"Lat\"], south_cities[\"Wind Speed\"])\n",
    "line_eq= \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "south_cities.plot.scatter(x='Lat',y='Wind Speed')\n",
    "plt.plot(south_cities[\"Lat\"], intercept+slope*south_cities[\"Lat\"],'r',label='fitted line')\n",
    "plt.annotate(line_eq,(-50,8),fontsize=15,color='red')\n",
    "plt.title(\"Southern: Wind Speed (mph) % vs Latitude (10/25/2021)\")\n",
    "plt.savefig(\"Output Images/southwindvslat.jpg\")\n",
    "plt.plot()\n",
    "print(f\"The r-value is {r}, showing a very slight negative correlation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Three main observations from this data\n",
      "1: Temperature increases as Latitude approaches 0\n",
      "2: Wind Speed appears to not have any correllation with Latitude\n",
      "3: While cloudiness appears to have some correlation, there are many outliers so it is not realistic to call a strong relationship between the two.\n"
     ]
    }
   ],
   "source": [
    "print(\"Three main observations from this data\")\n",
    "print(\"1: Temperature increases as Latitude approaches 0\")\n",
    "print(\"2: Wind Speed appears to not have any correllation with Latitude\")\n",
    "print(\"3: While cloudiness appears to have some correlation, there are many outliers so it is not realistic to call a strong relationship between the two.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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