WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff37c6e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99ed52b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the citipy module to determine city based on latitude and longitude.\n",
    "from citipy import citipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22445eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x249fe697308>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a set of random latitude and longitude combinations.\n",
    "lats = np.random.uniform(low=-90.000, high=90.000, size=1500)\n",
    "lngs = np.random.uniform(low=-180.000, high=180.000, size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "lat_lngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04f0d8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f3372fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "604"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list for holding the cities.\n",
    "cities = []\n",
    "# Identify the nearest city for each latitude and longitude combination.\n",
    "for coordinate in coordinates:\n",
    "    city = citipy.nearest_city(coordinate[0], coordinate[1]).city_name\n",
    "\n",
    "    # If the city is unique, then we will add it to the cities list.\n",
    "    if city not in cities:\n",
    "        cities.append(city)\n",
    "# Print the city count to confirm sufficient count.\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d8ae539",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the time library and the datetime module from the datetime library \n",
    "import time\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddf2d124",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the requests library.\n",
    "import requests\n",
    "\n",
    "# Import the API key.\n",
    "from config import weather_api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17b5eb17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the query url\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?units=Imperial&APPID=\" + weather_api_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9fa8cd73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \n",
      "-----------------------------\n",
      "Processing Record 1 of Set 1 | tuktoyaktuk\n",
      "Processing Record 2 of Set 1 | tasiilaq\n",
      "Processing Record 3 of Set 1 | laguna\n",
      "Processing Record 4 of Set 1 | iowa city\n",
      "Processing Record 5 of Set 1 | mar del plata\n",
      "Processing Record 6 of Set 1 | busselton\n",
      "Processing Record 7 of Set 1 | cabo san lucas\n",
      "Processing Record 8 of Set 1 | sorland\n",
      "Processing Record 9 of Set 1 | guerrero negro\n",
      "Processing Record 10 of Set 1 | stoyba\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 1 | salinopolis\n",
      "Processing Record 12 of Set 1 | namatanai\n",
      "Processing Record 13 of Set 1 | ixtapa\n",
      "Processing Record 14 of Set 1 | hilo\n",
      "Processing Record 15 of Set 1 | broken hill\n",
      "Processing Record 16 of Set 1 | kodiak\n",
      "Processing Record 17 of Set 1 | puerto ayora\n",
      "Processing Record 18 of Set 1 | constitucion\n",
      "Processing Record 19 of Set 1 | halalo\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 1 | georgetown\n",
      "Processing Record 21 of Set 1 | lokosovo\n",
      "Processing Record 22 of Set 1 | albany\n",
      "Processing Record 23 of Set 1 | east london\n",
      "Processing Record 24 of Set 1 | new norfolk\n",
      "Processing Record 25 of Set 1 | alakurtti\n",
      "Processing Record 26 of Set 1 | khatanga\n",
      "Processing Record 27 of Set 1 | shuiji\n",
      "Processing Record 28 of Set 1 | yellowknife\n",
      "Processing Record 29 of Set 1 | ushuaia\n",
      "Processing Record 30 of Set 1 | qaanaaq\n",
      "Processing Record 31 of Set 1 | anadyr\n",
      "Processing Record 32 of Set 1 | zhangye\n",
      "Processing Record 33 of Set 1 | kahului\n",
      "Processing Record 34 of Set 1 | formoso do araguaia\n",
      "City not found. Skipping...\n",
      "Processing Record 35 of Set 1 | mahebourg\n",
      "Processing Record 36 of Set 1 | mogadishu\n",
      "Processing Record 37 of Set 1 | thinadhoo\n",
      "Processing Record 38 of Set 1 | kaitangata\n",
      "Processing Record 39 of Set 1 | butaritari\n",
      "Processing Record 40 of Set 1 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 1 | dunedin\n",
      "Processing Record 42 of Set 1 | talnakh\n",
      "Processing Record 43 of Set 1 | kamaishi\n",
      "Processing Record 44 of Set 1 | kastamonu\n",
      "Processing Record 45 of Set 1 | clyde river\n",
      "Processing Record 46 of Set 1 | sambava\n",
      "Processing Record 47 of Set 1 | taoudenni\n",
      "Processing Record 48 of Set 1 | mataura\n",
      "Processing Record 49 of Set 1 | khonuu\n",
      "City not found. Skipping...\n",
      "Processing Record 50 of Set 1 | sao felix do xingu\n",
      "Processing Record 1 of Set 2 | lagoa\n",
      "Processing Record 2 of Set 2 | lebu\n",
      "Processing Record 3 of Set 2 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 2 | upernavik\n",
      "Processing Record 5 of Set 2 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 2 | rawannawi\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 2 | tiksi\n",
      "Processing Record 8 of Set 2 | geraldton\n",
      "Processing Record 9 of Set 2 | olinda\n",
      "Processing Record 10 of Set 2 | avarua\n",
      "Processing Record 11 of Set 2 | harer\n",
      "Processing Record 12 of Set 2 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 2 | faya\n",
      "Processing Record 14 of Set 2 | bredasdorp\n",
      "Processing Record 15 of Set 2 | port elizabeth\n",
      "Processing Record 16 of Set 2 | luganville\n",
      "Processing Record 17 of Set 2 | terme\n",
      "Processing Record 18 of Set 2 | hermanus\n",
      "Processing Record 19 of Set 2 | port hardy\n",
      "Processing Record 20 of Set 2 | castro\n",
      "Processing Record 21 of Set 2 | port alfred\n",
      "Processing Record 22 of Set 2 | cherskiy\n",
      "Processing Record 23 of Set 2 | bilibino\n",
      "Processing Record 24 of Set 2 | nenjiang\n",
      "Processing Record 25 of Set 2 | la ronge\n",
      "Processing Record 26 of Set 2 | lac-megantic\n",
      "Processing Record 27 of Set 2 | jacareacanga\n",
      "Processing Record 28 of Set 2 | waitara\n",
      "Processing Record 29 of Set 2 | pevek\n",
      "Processing Record 30 of Set 2 | pisco\n",
      "Processing Record 31 of Set 2 | opuwo\n",
      "Processing Record 32 of Set 2 | mizdah\n",
      "Processing Record 33 of Set 2 | abu dhabi\n",
      "Processing Record 34 of Set 2 | puri\n",
      "Processing Record 35 of Set 2 | coquimbo\n",
      "Processing Record 36 of Set 2 | te anau\n",
      "Processing Record 37 of Set 2 | punta arenas\n",
      "Processing Record 38 of Set 2 | zyryanovsk\n",
      "Processing Record 39 of Set 2 | rikitea\n",
      "Processing Record 40 of Set 2 | emerald\n",
      "Processing Record 41 of Set 2 | dikson\n",
      "Processing Record 42 of Set 2 | saldanha\n",
      "Processing Record 43 of Set 2 | margate\n",
      "Processing Record 44 of Set 2 | fort smith\n",
      "Processing Record 45 of Set 2 | kontagora\n",
      "Processing Record 46 of Set 2 | sarahan\n",
      "Processing Record 47 of Set 2 | cooma\n",
      "Processing Record 48 of Set 2 | teeli\n",
      "Processing Record 49 of Set 2 | codrington\n",
      "Processing Record 50 of Set 2 | henties bay\n",
      "Processing Record 1 of Set 3 | ribeira grande\n",
      "Processing Record 2 of Set 3 | yian\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 3 | atikokan\n",
      "Processing Record 4 of Set 3 | boysun\n",
      "Processing Record 5 of Set 3 | pochutla\n",
      "Processing Record 6 of Set 3 | nabinagar\n",
      "Processing Record 7 of Set 3 | dicabisagan\n",
      "Processing Record 8 of Set 3 | ninh binh\n",
      "Processing Record 9 of Set 3 | faanui\n",
      "Processing Record 10 of Set 3 | nikolskoye\n",
      "Processing Record 11 of Set 3 | cidreira\n",
      "Processing Record 12 of Set 3 | vaini\n",
      "Processing Record 13 of Set 3 | otkaznoye\n",
      "Processing Record 14 of Set 3 | barabinsk\n",
      "Processing Record 15 of Set 3 | kachug\n",
      "Processing Record 16 of Set 3 | ishigaki\n",
      "Processing Record 17 of Set 3 | hithadhoo\n",
      "Processing Record 18 of Set 3 | torbay\n",
      "Processing Record 19 of Set 3 | pangai\n",
      "Processing Record 20 of Set 3 | fortuna\n",
      "Processing Record 21 of Set 3 | la seyne-sur-mer\n",
      "Processing Record 22 of Set 3 | sinkat\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 3 | kalmunai\n",
      "Processing Record 24 of Set 3 | hobart\n",
      "Processing Record 25 of Set 3 | port lincoln\n",
      "Processing Record 26 of Set 3 | lumberton\n",
      "Processing Record 27 of Set 3 | vila velha\n",
      "Processing Record 28 of Set 3 | katsuura\n",
      "Processing Record 29 of Set 3 | san jeronimo\n",
      "Processing Record 30 of Set 3 | clive\n",
      "Processing Record 31 of Set 3 | jamestown\n",
      "Processing Record 32 of Set 3 | avera\n",
      "Processing Record 33 of Set 3 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 34 of Set 3 | iwanai\n",
      "Processing Record 35 of Set 3 | sinnamary\n",
      "Processing Record 36 of Set 3 | barrow\n",
      "Processing Record 37 of Set 3 | taunggyi\n",
      "Processing Record 38 of Set 3 | huron\n",
      "Processing Record 39 of Set 3 | strelka\n",
      "Processing Record 40 of Set 3 | ilulissat\n",
      "Processing Record 41 of Set 3 | barstow\n",
      "Processing Record 42 of Set 3 | chuy\n",
      "Processing Record 43 of Set 3 | sitka\n",
      "Processing Record 44 of Set 3 | isangel\n",
      "Processing Record 45 of Set 3 | katherine\n",
      "Processing Record 46 of Set 3 | coutances\n",
      "Processing Record 47 of Set 3 | beloha\n",
      "Processing Record 48 of Set 3 | kapaa\n",
      "Processing Record 49 of Set 3 | itarema\n",
      "Processing Record 50 of Set 3 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 4 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 2 of Set 4 | tiznit\n",
      "Processing Record 3 of Set 4 | bluff\n",
      "Processing Record 4 of Set 4 | ozark\n",
      "Processing Record 5 of Set 4 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 4 | camacha\n",
      "Processing Record 7 of Set 4 | mikkeli\n",
      "Processing Record 8 of Set 4 | ayan\n",
      "Processing Record 9 of Set 4 | lolua\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 4 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 4 | moncton\n",
      "Processing Record 12 of Set 4 | maceio\n",
      "Processing Record 13 of Set 4 | alice springs\n",
      "Processing Record 14 of Set 4 | grand baie\n",
      "Processing Record 15 of Set 4 | provideniya\n",
      "Processing Record 16 of Set 4 | rundu\n",
      "Processing Record 17 of Set 4 | severo-kurilsk\n",
      "Processing Record 18 of Set 4 | huayucachi\n",
      "Processing Record 19 of Set 4 | camacari\n",
      "Processing Record 20 of Set 4 | rena\n",
      "Processing Record 21 of Set 4 | olafsvik\n",
      "Processing Record 22 of Set 4 | mersing\n",
      "Processing Record 23 of Set 4 | yamada\n",
      "Processing Record 24 of Set 4 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 25 of Set 4 | green river\n",
      "Processing Record 26 of Set 4 | warqla\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 4 | janakkala\n",
      "Processing Record 28 of Set 4 | sinfra\n",
      "Processing Record 29 of Set 4 | sulangan\n",
      "Processing Record 30 of Set 4 | leh\n",
      "Processing Record 31 of Set 4 | saint-leu\n",
      "Processing Record 32 of Set 4 | san pedro de macoris\n",
      "Processing Record 33 of Set 4 | hasaki\n",
      "Processing Record 34 of Set 4 | sorong\n",
      "Processing Record 35 of Set 4 | bengkulu\n",
      "Processing Record 36 of Set 4 | karlovo\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 37 of Set 4 | saint-philippe\n",
      "Processing Record 38 of Set 4 | atuona\n",
      "Processing Record 39 of Set 4 | carnarvon\n",
      "Processing Record 40 of Set 4 | arraial do cabo\n",
      "Processing Record 41 of Set 4 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 4 | along\n",
      "Processing Record 43 of Set 4 | kosh-agach\n",
      "Processing Record 44 of Set 4 | voh\n",
      "Processing Record 45 of Set 4 | lompoc\n",
      "Processing Record 46 of Set 4 | cape town\n",
      "Processing Record 47 of Set 4 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 4 | dakar\n",
      "Processing Record 49 of Set 4 | thompson\n",
      "Processing Record 50 of Set 4 | norman wells\n",
      "Processing Record 1 of Set 5 | bani\n",
      "Processing Record 2 of Set 5 | sanford\n",
      "Processing Record 3 of Set 5 | balkhash\n",
      "Processing Record 4 of Set 5 | bilma\n",
      "Processing Record 5 of Set 5 | kargasok\n",
      "Processing Record 6 of Set 5 | otse\n",
      "Processing Record 7 of Set 5 | trapani\n",
      "Processing Record 8 of Set 5 | jatai\n",
      "Processing Record 9 of Set 5 | nanortalik\n",
      "Processing Record 10 of Set 5 | dongkan\n",
      "Processing Record 11 of Set 5 | merke\n",
      "Processing Record 12 of Set 5 | ancud\n",
      "Processing Record 13 of Set 5 | karamay\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 5 | san carlos de bariloche\n",
      "Processing Record 15 of Set 5 | mumford\n",
      "Processing Record 16 of Set 5 | aklavik\n",
      "Processing Record 17 of Set 5 | french harbor\n",
      "Processing Record 18 of Set 5 | vao\n",
      "Processing Record 19 of Set 5 | elat\n",
      "Processing Record 20 of Set 5 | sao joao de pirabas\n",
      "Processing Record 21 of Set 5 | boende\n",
      "Processing Record 22 of Set 5 | klaksvik\n",
      "Processing Record 23 of Set 5 | chapais\n",
      "Processing Record 24 of Set 5 | petropavlovsk-kamchatskiy\n",
      "Processing Record 25 of Set 5 | monrovia\n",
      "Processing Record 26 of Set 5 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 5 | meyungs\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 5 | lavrentiya\n",
      "Processing Record 29 of Set 5 | spornoye\n",
      "Processing Record 30 of Set 5 | esperance\n",
      "Processing Record 31 of Set 5 | ocos\n",
      "Processing Record 32 of Set 5 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 5 | padang\n",
      "Processing Record 34 of Set 5 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 35 of Set 5 | plouzane\n",
      "Processing Record 36 of Set 5 | bolotnoye\n",
      "Processing Record 37 of Set 5 | springdale\n",
      "Processing Record 38 of Set 5 | mao\n",
      "Processing Record 39 of Set 5 | tanout\n",
      "Processing Record 40 of Set 5 | huarmey\n",
      "Processing Record 41 of Set 5 | high level\n",
      "Processing Record 42 of Set 5 | saint george\n",
      "Processing Record 43 of Set 5 | esso\n",
      "Processing Record 44 of Set 5 | yining\n",
      "Processing Record 45 of Set 5 | batagay\n",
      "Processing Record 46 of Set 5 | alofi\n",
      "Processing Record 47 of Set 5 | ravar\n",
      "Processing Record 48 of Set 5 | narsaq\n",
      "Processing Record 49 of Set 5 | kaputa\n",
      "Processing Record 50 of Set 5 | praia\n",
      "Processing Record 1 of Set 6 | martil\n",
      "Processing Record 2 of Set 6 | zhigalovo\n",
      "Processing Record 3 of Set 6 | san patricio\n",
      "Processing Record 4 of Set 6 | vardo\n",
      "Processing Record 5 of Set 6 | kanker\n",
      "Processing Record 6 of Set 6 | haines junction\n",
      "Processing Record 7 of Set 6 | anan\n",
      "Processing Record 8 of Set 6 | egvekinot\n",
      "Processing Record 9 of Set 6 | mayo\n",
      "Processing Record 10 of Set 6 | moron\n",
      "Processing Record 11 of Set 6 | pozhva\n",
      "Processing Record 12 of Set 6 | ostrovnoy\n",
      "Processing Record 13 of Set 6 | nioro\n",
      "Processing Record 14 of Set 6 | orcopampa\n",
      "Processing Record 15 of Set 6 | bella union\n",
      "Processing Record 16 of Set 6 | bethel\n",
      "Processing Record 17 of Set 6 | casper\n",
      "Processing Record 18 of Set 6 | calvinia\n",
      "Processing Record 19 of Set 6 | licheng\n",
      "Processing Record 20 of Set 6 | pamanukan\n",
      "Processing Record 21 of Set 6 | chokurdakh\n",
      "Processing Record 22 of Set 6 | port-cartier\n",
      "Processing Record 23 of Set 6 | la palma\n",
      "Processing Record 24 of Set 6 | bairiki\n",
      "Processing Record 25 of Set 6 | blackwater\n",
      "Processing Record 26 of Set 6 | asau\n",
      "Processing Record 27 of Set 6 | san quintin\n",
      "Processing Record 28 of Set 6 | oranjemund\n",
      "Processing Record 29 of Set 6 | damaturu\n",
      "Processing Record 30 of Set 6 | kalomo\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 6 | amahai\n",
      "Processing Record 32 of Set 6 | sioux lookout\n",
      "Processing Record 33 of Set 6 | mount isa\n",
      "Processing Record 34 of Set 6 | yongan\n",
      "Processing Record 35 of Set 6 | los llanos de aridane\n",
      "Processing Record 36 of Set 6 | moranbah\n",
      "Processing Record 37 of Set 6 | laredo\n",
      "Processing Record 38 of Set 6 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 6 | anakapalle\n",
      "Processing Record 40 of Set 6 | lasa\n",
      "Processing Record 41 of Set 6 | moree\n",
      "Processing Record 42 of Set 6 | humaita\n",
      "Processing Record 43 of Set 6 | gonzalez\n",
      "Processing Record 44 of Set 6 | nokaneng\n",
      "Processing Record 45 of Set 6 | turukhansk\n",
      "Processing Record 46 of Set 6 | grand centre\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 6 | tera\n",
      "Processing Record 48 of Set 6 | tsaratanana\n",
      "Processing Record 49 of Set 6 | balakhninskiy\n",
      "Processing Record 50 of Set 6 | eyl\n",
      "Processing Record 1 of Set 7 | ukhta\n",
      "Processing Record 2 of Set 7 | ponta grossa\n",
      "Processing Record 3 of Set 7 | kirakira\n",
      "Processing Record 4 of Set 7 | datong\n",
      "Processing Record 5 of Set 7 | zapolyarnyy\n",
      "Processing Record 6 of Set 7 | matay\n",
      "Processing Record 7 of Set 7 | pilar\n",
      "Processing Record 8 of Set 7 | saint-paul\n",
      "Processing Record 9 of Set 7 | altamira\n",
      "Processing Record 10 of Set 7 | kulachi\n",
      "Processing Record 11 of Set 7 | karatau\n",
      "Processing Record 12 of Set 7 | vostok\n",
      "Processing Record 13 of Set 7 | mount pleasant\n",
      "Processing Record 14 of Set 7 | victoria\n",
      "Processing Record 15 of Set 7 | ketchikan\n",
      "Processing Record 16 of Set 7 | lorengau\n",
      "Processing Record 17 of Set 7 | beihai\n",
      "Processing Record 18 of Set 7 | mountain home\n",
      "Processing Record 19 of Set 7 | sibolga\n",
      "Processing Record 20 of Set 7 | raudeberg\n",
      "Processing Record 21 of Set 7 | walvis bay\n",
      "Processing Record 22 of Set 7 | lianzhou\n",
      "Processing Record 23 of Set 7 | sandwick\n",
      "Processing Record 24 of Set 7 | hofn\n",
      "Processing Record 25 of Set 7 | burns lake\n",
      "Processing Record 26 of Set 7 | belle fourche\n",
      "Processing Record 27 of Set 7 | el carmen\n",
      "Processing Record 28 of Set 7 | santiago del estero\n",
      "Processing Record 29 of Set 7 | souillac\n",
      "Processing Record 30 of Set 7 | angra\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 7 | caravelas\n",
      "Processing Record 32 of Set 7 | samaipata\n",
      "Processing Record 33 of Set 7 | hay river\n",
      "Processing Record 34 of Set 7 | manavalakurichi\n",
      "Processing Record 35 of Set 7 | brokopondo\n",
      "Processing Record 36 of Set 7 | berezovyy\n",
      "Processing Record 37 of Set 7 | karratha\n",
      "Processing Record 38 of Set 7 | kruisfontein\n",
      "Processing Record 39 of Set 7 | barra do garcas\n",
      "Processing Record 40 of Set 7 | port macquarie\n",
      "Processing Record 41 of Set 7 | kingaroy\n",
      "Processing Record 42 of Set 7 | kostomuksha\n",
      "Processing Record 43 of Set 7 | passo de camaragibe\n",
      "Processing Record 44 of Set 7 | omsukchan\n",
      "Processing Record 45 of Set 7 | buchanan\n",
      "Processing Record 46 of Set 7 | inirida\n",
      "Processing Record 47 of Set 7 | barcelos\n",
      "Processing Record 48 of Set 7 | marica\n",
      "Processing Record 49 of Set 7 | santa rosalia\n",
      "Processing Record 50 of Set 7 | coihaique\n",
      "Processing Record 1 of Set 8 | belyy yar\n",
      "Processing Record 2 of Set 8 | bjala\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 8 | bam\n",
      "Processing Record 4 of Set 8 | yumen\n",
      "Processing Record 5 of Set 8 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 8 | kasungu\n",
      "Processing Record 7 of Set 8 | pangnirtung\n",
      "Processing Record 8 of Set 8 | wanlaweyn\n",
      "Processing Record 9 of Set 8 | severodvinsk\n",
      "Processing Record 10 of Set 8 | santa cruz\n",
      "Processing Record 11 of Set 8 | evensk\n",
      "Processing Record 12 of Set 8 | ponta do sol\n",
      "Processing Record 13 of Set 8 | shizunai\n",
      "Processing Record 14 of Set 8 | vangaindrano\n",
      "Processing Record 15 of Set 8 | hamilton\n",
      "Processing Record 16 of Set 8 | gimli\n",
      "Processing Record 17 of Set 8 | palembang\n",
      "Processing Record 18 of Set 8 | millinocket\n",
      "Processing Record 19 of Set 8 | nome\n",
      "Processing Record 20 of Set 8 | poum\n",
      "Processing Record 21 of Set 8 | port hedland\n",
      "Processing Record 22 of Set 8 | sragen\n",
      "Processing Record 23 of Set 8 | vestmanna\n",
      "Processing Record 24 of Set 8 | praia da vitoria\n",
      "Processing Record 25 of Set 8 | honiara\n",
      "Processing Record 26 of Set 8 | luderitz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 27 of Set 8 | mehamn\n",
      "Processing Record 28 of Set 8 | vanavara\n",
      "Processing Record 29 of Set 8 | vokhtoga\n",
      "Processing Record 30 of Set 8 | sinop\n",
      "Processing Record 31 of Set 8 | shimoda\n",
      "Processing Record 32 of Set 8 | darlowo\n",
      "Processing Record 33 of Set 8 | sakakah\n",
      "Processing Record 34 of Set 8 | kavieng\n",
      "Processing Record 35 of Set 8 | saskylakh\n",
      "Processing Record 36 of Set 8 | cabra\n",
      "Processing Record 37 of Set 8 | yulara\n",
      "Processing Record 38 of Set 8 | yueyang\n",
      "Processing Record 39 of Set 8 | dok kham tai\n",
      "Processing Record 40 of Set 8 | bathsheba\n",
      "Processing Record 41 of Set 8 | rudnogorsk\n",
      "Processing Record 42 of Set 8 | volgodonsk\n",
      "Processing Record 43 of Set 8 | comodoro rivadavia\n",
      "Processing Record 44 of Set 8 | kieta\n",
      "Processing Record 45 of Set 8 | porbandar\n",
      "Processing Record 46 of Set 8 | janovice nad uhlavou\n",
      "Processing Record 47 of Set 8 | bara\n",
      "Processing Record 48 of Set 8 | kholtoson\n",
      "Processing Record 49 of Set 8 | acapulco\n",
      "Processing Record 50 of Set 8 | laval\n",
      "Processing Record 1 of Set 9 | taltal\n",
      "Processing Record 2 of Set 9 | richards bay\n",
      "Processing Record 3 of Set 9 | luena\n",
      "Processing Record 4 of Set 9 | labuhan\n",
      "Processing Record 5 of Set 9 | noumea\n",
      "Processing Record 6 of Set 9 | lesnyye polyany\n",
      "Processing Record 7 of Set 9 | falmouth\n",
      "Processing Record 8 of Set 9 | hervey bay\n",
      "Processing Record 9 of Set 9 | tsiroanomandidy\n",
      "Processing Record 10 of Set 9 | honningsvag\n",
      "Processing Record 11 of Set 9 | ginir\n",
      "Processing Record 12 of Set 9 | cozumel\n",
      "Processing Record 13 of Set 9 | camana\n",
      "Processing Record 14 of Set 9 | machesney park\n",
      "Processing Record 15 of Set 9 | hurghada\n",
      "Processing Record 16 of Set 9 | soyo\n",
      "Processing Record 17 of Set 9 | itoman\n",
      "Processing Record 18 of Set 9 | yenagoa\n",
      "Processing Record 19 of Set 9 | rafraf\n",
      "Processing Record 20 of Set 9 | quatre cocos\n",
      "Processing Record 21 of Set 9 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 9 | gangotri\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 9 | bimbo\n",
      "Processing Record 24 of Set 9 | abalak\n",
      "Processing Record 25 of Set 9 | lugovoy\n",
      "Processing Record 26 of Set 9 | onalaska\n",
      "Processing Record 27 of Set 9 | sohag\n",
      "Processing Record 28 of Set 9 | kawanoe\n",
      "Processing Record 29 of Set 9 | necochea\n",
      "Processing Record 30 of Set 9 | bambous virieux\n",
      "Processing Record 31 of Set 9 | airai\n",
      "Processing Record 32 of Set 9 | aykhal\n",
      "Processing Record 33 of Set 9 | westport\n",
      "Processing Record 34 of Set 9 | iqaluit\n",
      "Processing Record 35 of Set 9 | borovoy\n",
      "Processing Record 36 of Set 9 | yuzhno-kurilsk\n",
      "Processing Record 37 of Set 9 | matara\n",
      "Processing Record 38 of Set 9 | wanganui\n",
      "Processing Record 39 of Set 9 | novoorsk\n",
      "Processing Record 40 of Set 9 | paamiut\n",
      "Processing Record 41 of Set 9 | vung tau\n",
      "Processing Record 42 of Set 9 | gornopravdinsk\n",
      "Processing Record 43 of Set 9 | amapa\n",
      "Processing Record 44 of Set 9 | beringovskiy\n",
      "Processing Record 45 of Set 9 | chillicothe\n",
      "Processing Record 46 of Set 9 | skelleftea\n",
      "Processing Record 47 of Set 9 | binucayan\n",
      "Processing Record 48 of Set 9 | boa vista\n",
      "Processing Record 49 of Set 9 | jiuquan\n",
      "Processing Record 50 of Set 9 | rocha\n",
      "Processing Record 1 of Set 10 | port arthur\n",
      "Processing Record 2 of Set 10 | nakamura\n",
      "Processing Record 3 of Set 10 | tabiauea\n",
      "City not found. Skipping...\n",
      "Processing Record 4 of Set 10 | mananjary\n",
      "Processing Record 5 of Set 10 | sao jose da coroa grande\n",
      "Processing Record 6 of Set 10 | natal\n",
      "Processing Record 7 of Set 10 | kisanga\n",
      "Processing Record 8 of Set 10 | najran\n",
      "Processing Record 9 of Set 10 | nadym\n",
      "Processing Record 10 of Set 10 | maykain\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 10 | boguchany\n",
      "Processing Record 12 of Set 10 | meulaboh\n",
      "Processing Record 13 of Set 10 | chippewa falls\n",
      "Processing Record 14 of Set 10 | hare bay\n",
      "Processing Record 15 of Set 10 | palm springs\n",
      "Processing Record 16 of Set 10 | talara\n",
      "Processing Record 17 of Set 10 | pundaguitan\n",
      "Processing Record 18 of Set 10 | foam lake\n",
      "Processing Record 19 of Set 10 | dingle\n",
      "Processing Record 20 of Set 10 | boundiali\n",
      "Processing Record 21 of Set 10 | nemuro\n",
      "Processing Record 22 of Set 10 | zermatt\n",
      "Processing Record 23 of Set 10 | emba\n",
      "Processing Record 24 of Set 10 | iskateley\n",
      "Processing Record 25 of Set 10 | kaeo\n",
      "Processing Record 26 of Set 10 | barbar\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 10 | katobu\n",
      "Processing Record 28 of Set 10 | gibara\n",
      "Processing Record 29 of Set 10 | iskandar\n",
      "Processing Record 30 of Set 10 | balasinor\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 10 | grand-santi\n",
      "Processing Record 32 of Set 10 | malindi\n",
      "Processing Record 33 of Set 10 | bet shemesh\n",
      "Processing Record 34 of Set 10 | leningradskiy\n",
      "Processing Record 35 of Set 10 | bud\n",
      "Processing Record 36 of Set 10 | erenhot\n",
      "Processing Record 37 of Set 10 | tessalit\n",
      "Processing Record 38 of Set 10 | jalu\n",
      "Processing Record 39 of Set 10 | aksu\n",
      "Processing Record 40 of Set 10 | solovetskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 10 | colares\n",
      "Processing Record 42 of Set 10 | bayanday\n",
      "Processing Record 43 of Set 10 | gat\n",
      "Processing Record 44 of Set 10 | greeley\n",
      "Processing Record 45 of Set 10 | lovozero\n",
      "Processing Record 46 of Set 10 | ust-kamchatsk\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 10 | maan\n",
      "Processing Record 48 of Set 10 | tigzirt\n",
      "Processing Record 49 of Set 10 | dalvik\n",
      "Processing Record 50 of Set 10 | barrhead\n",
      "Processing Record 1 of Set 11 | rodino\n",
      "Processing Record 2 of Set 11 | coahuayana\n",
      "Processing Record 3 of Set 11 | longyearbyen\n",
      "Processing Record 4 of Set 11 | benton harbor\n",
      "Processing Record 5 of Set 11 | rungata\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 11 | champerico\n",
      "Processing Record 7 of Set 11 | taki\n",
      "Processing Record 8 of Set 11 | lingao\n",
      "Processing Record 9 of Set 11 | bengkalis\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 11 | bonaventure\n",
      "Processing Record 11 of Set 11 | cockburn town\n",
      "Processing Record 12 of Set 11 | winona\n",
      "Processing Record 13 of Set 11 | buckeburg\n",
      "Processing Record 14 of Set 11 | hambantota\n",
      "Processing Record 15 of Set 11 | shingu\n",
      "Processing Record 16 of Set 11 | cumpas\n",
      "Processing Record 17 of Set 11 | mosquera\n",
      "Processing Record 18 of Set 11 | broome\n",
      "Processing Record 19 of Set 11 | samarai\n",
      "Processing Record 20 of Set 11 | trybukhivtsi\n",
      "Processing Record 21 of Set 11 | mergui\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 11 | stornoway\n",
      "Processing Record 23 of Set 11 | wulanhaote\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 11 | isla mujeres\n",
      "Processing Record 25 of Set 11 | gamba\n",
      "Processing Record 26 of Set 11 | moose factory\n",
      "Processing Record 27 of Set 11 | ahipara\n",
      "Processing Record 28 of Set 11 | mandalgovi\n",
      "Processing Record 29 of Set 11 | bur gabo\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 11 | umm kaddadah\n",
      "Processing Record 31 of Set 11 | crab hill\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 11 | nyurba\n",
      "Processing Record 33 of Set 11 | zhigansk\n",
      "Processing Record 34 of Set 11 | kungurtug\n",
      "Processing Record 35 of Set 11 | phrai bung\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 11 | mount gambier\n",
      "Processing Record 37 of Set 11 | cayenne\n",
      "Processing Record 38 of Set 11 | seddon\n",
      "Processing Record 39 of Set 11 | fairmont\n",
      "Processing Record 40 of Set 11 | bowen\n",
      "Processing Record 41 of Set 11 | lubao\n",
      "Processing Record 42 of Set 11 | sao raimundo nonato\n",
      "Processing Record 43 of Set 11 | santa rosa\n",
      "Processing Record 44 of Set 11 | makokou\n",
      "Processing Record 45 of Set 11 | sedalia\n",
      "Processing Record 46 of Set 11 | valenca do piaui\n",
      "Processing Record 47 of Set 11 | camocim\n",
      "Processing Record 48 of Set 11 | chimore\n",
      "Processing Record 49 of Set 11 | shitanjing\n",
      "Processing Record 50 of Set 11 | gudari\n",
      "Processing Record 1 of Set 12 | saurimo\n",
      "Processing Record 2 of Set 12 | srednekolymsk\n",
      "Processing Record 3 of Set 12 | namibe\n",
      "Processing Record 4 of Set 12 | petrolia\n",
      "Processing Record 5 of Set 12 | ca mau\n",
      "Processing Record 6 of Set 12 | ishinomaki\n",
      "Processing Record 7 of Set 12 | mayor pablo lagerenza\n",
      "Processing Record 8 of Set 12 | port victoria\n",
      "Processing Record 9 of Set 12 | puerto penasco\n",
      "Processing Record 10 of Set 12 | la rochelle\n",
      "Processing Record 11 of Set 12 | havre-saint-pierre\n",
      "Processing Record 12 of Set 12 | pestravka\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 13 of Set 12 | burica\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 12 | vilyuysk\n",
      "Processing Record 15 of Set 12 | touros\n",
      "Processing Record 16 of Set 12 | beni\n",
      "Processing Record 17 of Set 12 | artyk\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 12 | el vigia\n",
      "Processing Record 19 of Set 12 | jiddah\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 12 | marawi\n",
      "Processing Record 21 of Set 12 | altay\n",
      "Processing Record 22 of Set 12 | jumla\n",
      "Processing Record 23 of Set 12 | udimskiy\n",
      "Processing Record 24 of Set 12 | parintins\n",
      "Processing Record 25 of Set 12 | hualmay\n",
      "Processing Record 26 of Set 12 | jutai\n",
      "Processing Record 27 of Set 12 | luanda\n",
      "Processing Record 28 of Set 12 | hami\n",
      "Processing Record 29 of Set 12 | eldikan\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 12 | palmer\n",
      "Processing Record 31 of Set 12 | mamontovo\n",
      "Processing Record 32 of Set 12 | oranjestad\n",
      "Processing Record 33 of Set 12 | boffa\n",
      "Processing Record 34 of Set 12 | riyadh\n",
      "Processing Record 35 of Set 12 | shambu\n",
      "Processing Record 36 of Set 12 | qui nhon\n",
      "Processing Record 37 of Set 12 | les cayes\n",
      "Processing Record 38 of Set 12 | kristiinankaupunki\n",
      "Processing Record 39 of Set 12 | khambhat\n",
      "Processing Record 40 of Set 12 | saryshagan\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 12 | farkhor\n",
      "Processing Record 42 of Set 12 | flinders\n",
      "Processing Record 43 of Set 12 | kurumkan\n",
      "Processing Record 44 of Set 12 | inhambane\n",
      "Processing Record 45 of Set 12 | trelew\n",
      "Processing Record 46 of Set 12 | isla vista\n",
      "Processing Record 47 of Set 12 | inverness\n",
      "Processing Record 48 of Set 12 | pozoblanco\n",
      "Processing Record 49 of Set 12 | avesta\n",
      "Processing Record 50 of Set 12 | bacuit\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 13 | minna\n",
      "Processing Record 2 of Set 13 | tautira\n",
      "Processing Record 3 of Set 13 | goderich\n",
      "Processing Record 4 of Set 13 | ossora\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# Create an empty list to hold the weather data.\n",
    "city_data = []\n",
    "# Print the beginning of the logging.\n",
    "print(\"Beginning Data Retrieval     \")\n",
    "print(\"-----------------------------\")\n",
    "\n",
    "# Create counters.\n",
    "record_count = 1\n",
    "set_count = 1\n",
    "\n",
    "# Loop through all the cities in the list.\n",
    "for i, city in enumerate(cities):\n",
    "\n",
    "    # Group cities in sets of 50 for logging purposes.\n",
    "    if (i % 50 == 0 and i >= 50):\n",
    "        set_count += 1\n",
    "        record_count = 1\n",
    "        time.sleep(60)\n",
    "\n",
    "    # Create endpoint URL with each city.\n",
    "    city_url = url + \"&q=\" + city.replace(\" \",\"+\")\n",
    "\n",
    "    # Log the URL, record, and set numbers and the city.\n",
    "    print(f\"Processing Record {record_count} of Set {set_count} | {city}\")\n",
    "    # Add 1 to the record count.\n",
    "    record_count += 1\n",
    "    \n",
    "# Run an API request for each of the cities.\n",
    "    try:\n",
    "        # Parse the JSON and retrieve data.\n",
    "        city_weather = requests.get(city_url).json()\n",
    "        # Parse out the needed data.\n",
    "        city_lat = city_weather[\"coord\"][\"lat\"]\n",
    "        city_lng = city_weather[\"coord\"][\"lon\"]\n",
    "        city_max_temp = city_weather[\"main\"][\"temp_max\"]\n",
    "        city_humidity = city_weather[\"main\"][\"humidity\"]\n",
    "        city_clouds = city_weather[\"clouds\"][\"all\"]\n",
    "        city_wind = city_weather[\"wind\"][\"speed\"]\n",
    "        city_country = city_weather[\"sys\"][\"country\"]\n",
    "        # Convert the date to ISO standard.\n",
    "        city_date = datetime.utcfromtimestamp(city_weather[\"dt\"]).strftime('%Y-%m-%d %H:%M:%S')\n",
    "        # Append the city information into city_data list.\n",
    "        city_data.append({\"City\": city.title(),\n",
    "                          \"Lat\": city_lat,\n",
    "                          \"Lng\": city_lng,\n",
    "                          \"Max Temp\": city_max_temp,\n",
    "                          \"Humidity\": city_humidity,\n",
    "                          \"Cloudiness\": city_clouds,\n",
    "                          \"Wind Speed\": city_wind,\n",
    "                          \"Country\": city_country,\n",
    "                          \"Date\": city_date})\n",
    "\n",
    "# If an error is experienced, skip the city.\n",
    "    except:\n",
    "        print(\"City not found. Skipping...\")\n",
    "        pass\n",
    "\n",
    "# Indicate that Data Loading is complete.\n",
    "print(\"-----------------------------\")\n",
    "print(\"Data Retrieval Complete      \")\n",
    "print(\"-----------------------------\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b1e2dfc",
   "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>Tuktoyaktuk</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>-20.20</td>\n",
       "      <td>76</td>\n",
       "      <td>90</td>\n",
       "      <td>4.61</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-12-30 16:26:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tasiilaq</td>\n",
       "      <td>65.6145</td>\n",
       "      <td>-37.6368</td>\n",
       "      <td>10.56</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>GL</td>\n",
       "      <td>2021-12-30 16:26:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Laguna</td>\n",
       "      <td>38.4210</td>\n",
       "      <td>-121.4238</td>\n",
       "      <td>45.10</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>3.00</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-12-30 16:26:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iowa City</td>\n",
       "      <td>41.6611</td>\n",
       "      <td>-91.5302</td>\n",
       "      <td>28.69</td>\n",
       "      <td>88</td>\n",
       "      <td>90</td>\n",
       "      <td>8.05</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-12-30 16:25:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mar Del Plata</td>\n",
       "      <td>-38.0023</td>\n",
       "      <td>-57.5575</td>\n",
       "      <td>86.02</td>\n",
       "      <td>49</td>\n",
       "      <td>75</td>\n",
       "      <td>1.99</td>\n",
       "      <td>AR</td>\n",
       "      <td>2021-12-30 16:25:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Busselton</td>\n",
       "      <td>-33.6500</td>\n",
       "      <td>115.3333</td>\n",
       "      <td>64.09</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>15.17</td>\n",
       "      <td>AU</td>\n",
       "      <td>2021-12-30 16:26:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cabo San Lucas</td>\n",
       "      <td>22.8909</td>\n",
       "      <td>-109.9124</td>\n",
       "      <td>75.51</td>\n",
       "      <td>80</td>\n",
       "      <td>88</td>\n",
       "      <td>3.76</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-12-30 16:23:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sorland</td>\n",
       "      <td>67.6670</td>\n",
       "      <td>12.6934</td>\n",
       "      <td>32.07</td>\n",
       "      <td>69</td>\n",
       "      <td>86</td>\n",
       "      <td>12.19</td>\n",
       "      <td>NO</td>\n",
       "      <td>2021-12-30 16:26:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Guerrero Negro</td>\n",
       "      <td>27.9769</td>\n",
       "      <td>-114.0611</td>\n",
       "      <td>57.16</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>9.62</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-12-30 16:26:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Salinopolis</td>\n",
       "      <td>-0.6136</td>\n",
       "      <td>-47.3561</td>\n",
       "      <td>81.86</td>\n",
       "      <td>81</td>\n",
       "      <td>83</td>\n",
       "      <td>9.44</td>\n",
       "      <td>BR</td>\n",
       "      <td>2021-12-30 16:26:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0     Tuktoyaktuk  69.4541 -133.0374    -20.20        76          90   \n",
       "1        Tasiilaq  65.6145  -37.6368     10.56        55           0   \n",
       "2          Laguna  38.4210 -121.4238     45.10        90          90   \n",
       "3       Iowa City  41.6611  -91.5302     28.69        88          90   \n",
       "4   Mar Del Plata -38.0023  -57.5575     86.02        49          75   \n",
       "5       Busselton -33.6500  115.3333     64.09        88           0   \n",
       "6  Cabo San Lucas  22.8909 -109.9124     75.51        80          88   \n",
       "7         Sorland  67.6670   12.6934     32.07        69          86   \n",
       "8  Guerrero Negro  27.9769 -114.0611     57.16        86         100   \n",
       "9     Salinopolis  -0.6136  -47.3561     81.86        81          83   \n",
       "\n",
       "   Wind Speed Country                 Date  \n",
       "0        4.61      CA  2021-12-30 16:26:11  \n",
       "1        4.90      GL  2021-12-30 16:26:12  \n",
       "2        3.00      US  2021-12-30 16:26:12  \n",
       "3        8.05      US  2021-12-30 16:25:02  \n",
       "4        1.99      AR  2021-12-30 16:25:36  \n",
       "5       15.17      AU  2021-12-30 16:26:13  \n",
       "6        3.76      MX  2021-12-30 16:23:14  \n",
       "7       12.19      NO  2021-12-30 16:26:13  \n",
       "8        9.62      MX  2021-12-30 16:26:14  \n",
       "9        9.44      BR  2021-12-30 16:26:14  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the array of dictionaries to a Pandas DataFrame.\n",
    "city_data_df = pd.DataFrame(city_data)\n",
    "city_data_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c4152dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_column_order = [\"City\", \"Country\", \"Date\", \"Lat\", \"Lng\", \"Max Temp\", \"Humidity\", \"Cloudiness\", \"Wind Speed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5257272a",
   "metadata": {},
   "outputs": [],
   "source": [
    "city_data_df = city_data_df[new_column_order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c290540e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output file (CSV).\n",
    "output_data_file = \"weather_data/cities.csv\"\n",
    "# Export the City_Data into a CSV.\n",
    "city_data_df.to_csv(output_data_file, index_label=\"City_ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7630963a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract relevant fields from the DataFrame for plotting.\n",
    "lats = city_data_df[\"Lat\"]\n",
    "max_temps = city_data_df[\"Max Temp\"]\n",
    "humidity = city_data_df[\"Humidity\"]\n",
    "cloudiness = city_data_df[\"Cloudiness\"]\n",
    "wind_speed = city_data_df[\"Wind Speed\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32f81527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1640890385.9644732"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the time module.\n",
    "import time\n",
    "# Get today's date in seconds.\n",
    "today = time.time()\n",
    "today"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "81dbb4b2",
   "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"
    }
   ],
   "source": [
    "# Build the scatter plot for latitude vs. max temperature.\n",
    "plt.scatter(lats,\n",
    "            max_temps,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Max Temperature \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Max Temperature (F)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig1.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "300cbf49",
   "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"
    }
   ],
   "source": [
    "# Build the scatter plot for latitude vs. humidity.\n",
    "plt.scatter(lats,\n",
    "            humidity,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Humidity \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Humidity (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig2.png\")\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5639b0a8",
   "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"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. cloudiness.\n",
    "plt.scatter(lats,\n",
    "            cloudiness,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Cloudiness (%) \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Cloudiness (%)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig3.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "235c2c52",
   "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"
    }
   ],
   "source": [
    "# Build the scatter plots for latitude vs. wind speed.\n",
    "plt.scatter(lats,\n",
    "            wind_speed,\n",
    "            edgecolor=\"black\", linewidths=1, marker=\"o\",\n",
    "            alpha=0.8, label=\"Cities\")\n",
    "\n",
    "# Incorporate the other graph properties.\n",
    "plt.title(f\"City Latitude vs. Wind Speed \"+ time.strftime(\"%x\"))\n",
    "plt.ylabel(\"Wind Speed (mph)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.grid(True)\n",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig4.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a5de7200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import linregress\n",
    "from scipy.stats import linregress\n",
    "\n",
    "# Create a function to create perform linear regression on the weather data\n",
    "# and plot a regression line and the equation with the data.\n",
    "def plot_linear_regression(x_values, y_values, title, y_label, text_coordinates):\n",
    "\n",
    "    # Run regression on hemisphere weather data.\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "\n",
    "    # Calculate the regression line \"y values\" from the slope and intercept.\n",
    "    regress_values = x_values * slope + intercept\n",
    "    # Get the equation of the line.\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    # Create a scatter plot and plot the regression line.\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r\")\n",
    "    # Annotate the text for the line equation.\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "162dc4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                  Broken Hill\n",
       "Country                        AU\n",
       "Date          2021-12-30 16:24:18\n",
       "Lat                        -31.95\n",
       "Lng                      141.4333\n",
       "Max Temp                    77.54\n",
       "Humidity                       25\n",
       "Cloudiness                      0\n",
       "Wind Speed                   7.45\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index13 = city_data_df.loc[13]\n",
    "index13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0631fbc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "546     True\n",
       "547     True\n",
       "548    False\n",
       "549     True\n",
       "550     True\n",
       "Name: Lat, Length: 551, dtype: bool"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df[\"Lat\"] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2ad0b6c4",
   "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>Country</th>\n",
       "      <th>Date</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-12-30 16:26:11</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>-20.20</td>\n",
       "      <td>76</td>\n",
       "      <td>90</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tasiilaq</td>\n",
       "      <td>GL</td>\n",
       "      <td>2021-12-30 16:26:12</td>\n",
       "      <td>65.6145</td>\n",
       "      <td>-37.6368</td>\n",
       "      <td>10.56</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>4.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Laguna</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-12-30 16:26:12</td>\n",
       "      <td>38.4210</td>\n",
       "      <td>-121.4238</td>\n",
       "      <td>45.10</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iowa City</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-12-30 16:25:02</td>\n",
       "      <td>41.6611</td>\n",
       "      <td>-91.5302</td>\n",
       "      <td>28.69</td>\n",
       "      <td>88</td>\n",
       "      <td>90</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cabo San Lucas</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-12-30 16:23:14</td>\n",
       "      <td>22.8909</td>\n",
       "      <td>-109.9124</td>\n",
       "      <td>75.51</td>\n",
       "      <td>80</td>\n",
       "      <td>88</td>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "0     Tuktoyaktuk      CA  2021-12-30 16:26:11  69.4541 -133.0374    -20.20   \n",
       "1        Tasiilaq      GL  2021-12-30 16:26:12  65.6145  -37.6368     10.56   \n",
       "2          Laguna      US  2021-12-30 16:26:12  38.4210 -121.4238     45.10   \n",
       "3       Iowa City      US  2021-12-30 16:25:02  41.6611  -91.5302     28.69   \n",
       "6  Cabo San Lucas      MX  2021-12-30 16:23:14  22.8909 -109.9124     75.51   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        76          90        4.61  \n",
       "1        55           0        4.90  \n",
       "2        90          90        3.00  \n",
       "3        88          90        8.05  \n",
       "6        80          88        3.76  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.loc[(city_data_df[\"Lat\"] >= 0)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "41f91c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Northern and Southern Hemisphere DataFrames.\n",
    "northern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] >= 0)]\n",
    "southern_hemi_df = city_data_df.loc[(city_data_df[\"Lat\"] < 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "24d457ac",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for Maximum Temperature', 'Max Temp',(10,-50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c04f91e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=-1.4880870353818334, intercept=96.90088259461045, rvalue=-0.8506553000856338, pvalue=2.564098175407404e-107, stderr=0.047365455766869735, intercept_stderr=2.076900829209705)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use linear regression on the data. Note the r values, lower than .05 = low correlation; greater than .05 = hiogh correlation\n",
    "linregress(northern_hemi_df[\"Lat\"],northern_hemi_df[\"Max Temp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c441f432",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Max Temp\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for Maximum Temperature', 'Max Temp',(-50,90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8b562164",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere, humidity\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7cf01780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.4715982118708706, intercept=57.03419329558739, rvalue=0.44565289822566145, pvalue=6.837745207650902e-20, stderr=0.048789643598994355, intercept_stderr=2.1393492283985274)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use linear regression on the data. Note the r values, lower than .05 = low correlation; greater than .05 = hiogh correlation\n",
    "linregress(northern_hemi_df[\"Lat\"],northern_hemi_df[\"Humidity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e9203463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nxJutGxsCLQ+KucoNo7zUgmL+QES2AvC+P/SWLwK2TSu3DfBehWUzjNAZO7QfDfUdwyUa6usYO7RfKPs3V7lhlJdaUMxTgdHe79HAvWnLTxSR9USkL7AT8HwE8hlGqDQPaGLCiN1oamxAgKbGBiaM2C00V3MlXOWGUcskqo9ZRG4HBgM9RWQRcBkwEZgiIqcB7wDHA6jqXBGZArwKrAbOVNU1WXdsGFVG84CmsvX5bt3YQFsWJRyWq9wwap1EKWZVHeWz6lCf8lcBV5VPIsNIHmOH9uPCe17u4M4O01VuGLVOohSzYRjlJ2WJhxmVbVHehtGOKWbDMAITpqs8FeWdssBTUd6p4xhGrVELwV+GYcQYi/I2jI6YYjYMI1IsytswOmKK2TCMSCl3QhTDqDasj9kwqoBqCY4KKmdLaxtffLm60/IkRXlX+txlOx6EG6xnlBdRzZqF0vBh4MCBOnPmzKjFMGqIzOAocIorzKQhYRBUzmzlAXp0r+eyY3aNVd2KpdLnLtvx6rsICKxa0/6sr/T1IyKzVHVgRQ6WAMyVbRgxp1qCo4LKma08QPduXataKafPhX3elDkVPXfZ2nTVWu2glIPKYHN7Vx5zZRtGzKmW4KigclZLvYKQabGu8fFIlquOQfZbSFkbyhYNppiNwFRLf2dSqJYUmEHlrJZ6BcHPC5BJGHXMdh/6tWmxMuTygtg9Xz7MlW0Ewqb8qzzlni0qLILKWS31CkIhVmgYdfS7D4f079WpTeu7CPV1HWe5LVSGJHo1qgFTzEYgqqW/M0mUe7aosAgqZ7XUKwh+VmidSKh19LsPH5+3pFObXnv87lz7rd2LamcbyhYN5so2AmFv0NFQztmiwiQzj3bqhS2Xcq6GehWK3wQfYb9w5LoP/dq0mOPbhCXRYIrZCEQS+wWN8Kj1YKFyTPCRjUrdh5Wqj9ERG8cckFofx1wtY2rLjQXAZWfQxOlZFUZTYwMzxh0SgUTRkH59bNJQjwgsW74q67VSzLVU7H0Y1XVr45iDYRazEQh7gzarMBfW1dH5+li2YtW6dZnXSrHXUjH3oV231YMpZiMwSesXDIoNIfHHujryD5lKv1byBVPmUrxB78NCr1vzBkWPRWUbRkDMKvQniUOgglLIdZAq41c2Zc2GOSyxkOvWhkPGA1PMhhEQG0LiTxKHQAWlkOsgVSbX8KqwhyUWct3acMh4YIrZMHzwyxGczSoUYEj/XhFIWV6C5kmOyg0ap3zO2a6PdNI9CH4ehnKk8izEm2HeoHhgitkwspDLpdc8oInj9moiPZeSAnfPakuUyy+oWzMqN2jc3K+ZXoPGhnp6dK/P6kHw8zA0lcErU4g3w7xB8cCGSwWk1odLJQ0/Cy/fsJ9aGBYUtI5RtUkSz0VUwxLLdVwbLhUMi8o2apZcw0fyufRqweVXLbNFJfFcRDUs0YZDxgNTzEbNkivQJd+wn1oYFlQts0X5HbeLyLquh2rEbzhUufvxa304ZBywPmajZsllaeULlKmFYUHVMluUX7DVGtXEDfWJW3+6UR7MYjaqijCthVwWXj6XXi24/ILWsdDyYVt8qW3PmzKnUzRzuRO/VDoKPWhyG0sWUp1Y8FdALPgrOsIOTLG835WnnG3ed9w0sj3NBFgw8eiS9p2NKK6fIHWM0/VtwV/BMFe2UTWEnfzAkmFUnnImsKj0UJ8oknEEqaMlC6lezJVt5CUu7rBc6QsHTZxelHwW6FIZUtdQtq4DCCeCutJzB0cRDR6kjkmMVq8VTDEbOYnTjDR+fcLiyQU2Y04cyeZSzSQMq7bS/f5RRKEHqWMtjBxIKqaYjZzEaSalbNaCQKc+tyTO9BS11yLd4q0TYY0qTQXKkW+2pTCt2kp6QIJa6MWcw0taXub2595ljSp1Iozad1uubC6su6VYD0LU15phitnIQ5zcYdmshXK6RuNC1F6LzOOnIp8LlSPXuShUuceRINZrMefwkpaX+duz76z7v0Z13f8rm3cLVb5S5DTCxxSzkZO4ucMyLSK/dIxJctdF7bXIZfFmypHN2vK7hqolZWYuC7JQC72Yc3j7c+/6Li9EMQeRrxQ5jfCxqGwjJ3FPpBF3+cIgaq9FvuOk1vslvxjSv1fVnqOwEnoUcw79ZpjyWx4GUV9rhsMsZiMncU+kUQn5WlrbGD91LstWrAKgR/d6Ljtm14q1QTm8FkH6EXN1GaTL4WdtPT5vCRNG7NahDdevrw6bIN+Qo1LbMNc5TPXlZ1teLpKa3rTaMMVs5CXuQ4rKKV9Laxtj75zDqrXtD8ily1cx9q45645dbsIeBhS0H3Hs0H6d2iBFfRdZJ0c+a+vL1WvXLVu6fFVV9F3mGqIXtA2DnsNR+27boY85fXm5yCYntKc3hXifr6RQHa+thhER1z48P6tCWrVGK5aoIexEKEETTzQPaGLD9bO/w2+4ftd1cuRKflGtyS786lQnErgNg57DK5t346T9eq+zkOtEOGm/3gX3LxdDSs5sVnk1nK+kUBMWs4icC5yOG1nzMnAK0B2YDPQBFgInqOrSiEQ0YkquvrVK9ruF6RXIZQX6uSuXLV+VdZv05bmswnMnzw4kSy4qOZzHr05+wXC56lPIOcxWt2IUcSFt5FemeUBTqOfLCE7iLWYRaQJ+BAxU1a8CdcCJwDjgMVXdCXjM+28YHcjVB1itkd+55PYLbCokFWQuqzCsdJmVnl3Jr05NZUj/GVbdCtlPvjKVTm9qdCTxitmjK9AgIl1xlvJ7wHDgVm/9rUBzNKIZcWbs0H7Ud+ns1quvk6qIKs6G3zSJ4O+uLDT6vXlAEzPGHcKCiUczY9wh66y0sKLnS3GJt7S2MWjidPqOm8agidMLVnjZ6lSO0QBhufsL2U++MkP69yLzqq+WSPokkHhXtqq2icgvgHeAFcAjqvqIiGyhqou9MotFZHO/fYjIGGAMQO/evSshthETUoolyqjssEnJfU4Ad2Wp0e9hRc8XO5wn7MQZ5RgNENZQpUL2k6tMS2sbd89q65BRT4Dj9op3EGiSSLxiFpEeOOu4L7AMuFNETgqyD1W9AbgB3LSPYctoxJuw+nfjlOqweUCT76QSfu7KQtshV/rOUhOKFDt0bPzUuaEnzgj7uvB7sAR1H+dqo0KOlc2aVuDxeUsCyWEUTy24sg8DFqjqElVdBdwDHAB8ICJbAXjfH0Yoo5FwKt03WghD+vcKtLwQ0usJndN3llrfYlzILa1t67wdmUQdzJTZXpkU4z72a6Mh/XsVdCxLMhI9taCY3wH2E5HuIiLAocBrwFRgtFdmNHBvRPIZNUAchwv5WUClWEaFpO8shWKGHeU6ZtTBTLnaq9hhcX5t9Pi8JQUdywK/oifxrmxVfU5E7gJeBFYDrTi39IbAFBE5Dae8j49OSqNcxMV9HEcrJAyZMts3V4YwyD0kq1CCupBz1SfqYCY/2QRKcvtnayO/IVCZx6r0vNZGZxKvmAFU9TLgsozFX+KsZyOhxGmmnLhNBpI6dikyZWvfbNNwZlLpc+BXzx7d6yMPZqrkdVHosZp324Jezz3Fxzf9H0PmPM5GK71t/rEfPPNM6HIZnakFV7ZRAsUOMYkDcXIfx3GyjUJl8rsG/IKE8mVyrvQ58KvnZcfsWjEZ/Mh3DsK8/zKPJbqWA96fx1/n/A222AJE3Ke+nkH/O4pjX3igXSkDDBpU9LGNYNSExWwUR5wszmKIk/s4jpOBFCJTrmvArx0V12eZy61d6axpEK+2T5FLttDuP1WYPZvmyZP5xv/dxgbvZZ9OsgP77w8jR8K3vgVN0bdTrSFaxinEksjAgQN15syZUYtREfzmOobqmODeT/5qmQc4alpa2zhvypysMxylMl/la99qv4aipKjrd948mDzZfV57Lf9B9tjDKeATToDtty9N4ByIyCxVHVi2AyQMc2UbvuSyauIw3CcfcXQfVwspa81v7t/3lq0oqH1zZRmrhmsoSnJ6fBYuhGuugb32andBi8DOO8P48Z2Vcr9+cOml8MorzoJOfVpbYdy4siplIzjmyjZ82aSh3nf8J5SeoKHcxNmFWQyVijDPZSmnSAUMrde1yzpX6wbd6qiv68K5k2dz7cPzO8jnl8yk2GsoLtH25WTrxgZWLmrjqPkzOHreU+yz6NX2lVf7bNS7t7N+R45cp7Q7tNV9HzB2ZWPi2ippmGI2stLS2sYXK1fnLRf3pANxn0u6UCrV35/PUoaOySrSg7++WLkGyC5f84Am+o6bljViO+g1VO2xD1n55BP4+9+dC/rRRwGYkat8z55O+Y4c6YKyumR3fiayrWoAc2UbWbn24fmsWpM//sCSDlSGSkWY50p4AW5O4HzJKvzkCytxRZyi7QPz+edw220wfHhHF/Rmm8Hpp69TyilWN3TnvoFH8N0TruDrVz5My4uLnAt6yRL43e/goIN8lTJUeVvVMGYxG1kpxIqx/tp2SnWt5tu+UhHmufbXUF+3LjuUX7KKTNLd12ElrihHYpSwXOGp/X700aeM+OBlzvxwFts8MjXvdmukC9P6H8i/9jqUA878NsfutwPgHtDHeJ9iKGXSj6R3FcQZU8xGVvySEdSJsFbVbtY0SnUXFrJ9pRJR5Drv6ekhC8nyBW5McyrTV1h9/uVIjFK0e3fVKpg+HSZPZvXtd9D83xX5548dPty5oIcNo+WNzzq9rNw77XXWrrd+KPdWMW1l7u/oMVe2kRW/iNvrTti901y7tU6p7sJCtq9UhHmu855+vnNFW6ejdMxV7TdfcxgyFtoWRZ2vtWvhySfhzDNd/27KBd2tGxxxBNx8M13/21EBPtlnAGOP/BFH/PTvHSOhW1pg1CjYaKOyu5qLaSs/mS6/b24oMhn5MYvZyErSIprLSamu1UK2r9T5KPQ42cr5WdBhu9tLbYuc7a0KM2e2jwVetCj/Dg88EEaOZO95jSzZsEen1bKySFlCoJi28jv20uWrSs5zbhSGKWbDl6RENJebUl2rBecwrtC80IUeJ7OcX0KMuAUIptp7pyVvM2zeUwyb9xQ7fOKNpfYbhgRu+NHIkXD88dCnT4dVLa1tfNI2xyn2LMfLJ0uQbYKSqZxT1rjfOc71khXn4ZFJwhSzYZRIqUFNlZzNp5z9h5WqR+A6vPkmTJniLOA5c3IPQwLYZZf2bFj9+xcsT7YhZvnqX4k2C9peY4f24xyf4L64D49MCqaYDaNEmgc0MfPtT7j9uXdZo0qdCMftVbh1W8lug1z9h6Uerxz1yGbd+9Xh5ilP0/zPxU4BP/ts3n239diKe/sN4vm9D6P55KNp3nObomT64svVWYeOZQbMZaPc594vWUyuxC7NA5oYP3Vu1uRCcfN+JBXLlR2QWsqVbRRGpkUCHYcWxYk+46b5rvv1yD1iJa9fuzZ8+glH/PtfHD3vKQa9/VL+HW2xRXsyjv32yznutxiZ/BBgwcSjiz5WqeSTNZd8YV/Tlis7GGYxG0aJ5IqsjZOiA2fF+WX1ipu8f777Wc59ZBJjXvh7QeX/s94GbPjd/3EK+OCDoWv4j7d8CVjSidq6zCdrLvks+DNaTDEbRkAyXZmVikYOg1ypNsshb0trGxfd8xLLV60F3Aijb+/bmyubd2sv9Nln8Otfw2WXddjWz7Zf2aUrD+3ydVr6HcjTfQawsmt9xTwUhbZRHJLv5EsWk08+C/6MDlPMhhGAbIE0AllzQEdtMWUj1zzJYcvb0trGj6fMZq3XOOut+pKTZj/IOb+cBCsLfwn4zQGjuHHv4Xy2/oZA+3SR8x+ez6plKyo6faTfi1iP7vV079Y1VtZloclijPhhitkwApDNPajQSTmXajGVKyXi2KH9GHvXnE550Ou7SHgW3urV8Ne/csC5F/DWZx8Vvt0558AFF8CWWwL+/ZyptqiEYsk8D0P69+LuWW2dZLrsmF1jp+j8Ir5NKccfU8yGEQA/a1NxllwYirScQ5pS219+31yWLndRt40N9Yw/tgjFsnYt3HMP/PSnMG9ep9Wb+2x2x9cO53cHjOTp608tSNao+jmznYe7Z7Vx3F5NPD5vSays42xE3X5G8cQuKltEzgImqerSqGXJhkVl1wZ+FusOFz7g208bVlSzX6KOpsYGZow7pOT9B0YVHnkELr0Unn++4M0e2+1gJu59Aq/32q7TujoR3pxwVJhShk5U5yGJE0hYVHYw4pgre0vgBRGZIiJHiIhELZBRW6QspbZlK1DaLdaW1racwVOpMqVSqZmksvLss/CNb3SckrBLF5cP2k8pH344PPNMx3zQqnx+6yTe3LyzUgYYte+2ZaxEOERxHnJde0btEDvFrKqXADsBNwInA6+LyM9FZIdIBTNqhlzDn5pyBEiFNflAWPMW5+SVV+C44zoqYBHYf3/4xz+yb7P//m6+4LVrOyrhhx9244MzaB7QxC9P2IPu9e2PGRE4ab+MqOwy0NLaxqCJ0+k7bhqDJk4vSrFV5DxkYPMnGxDTPmZVVRF5H3gfWA30AO4SkUdV9fxopTOSTi5L6Vcj98iZtCEMayrUNI0LFsAVV8AttxS+za67um2am0tKxgHRDLkJq4++kqlSU0TqLTFiQ+wsZhH5kYjMAq4BZgC7qer/AnsBx0UqnFET5LKUmgc0MWHEbtT59LCEYU2ljtHU2IDg+jTzRtK+/76Las60gLff3l8pb7MN3Hyzm1M43QJ+5RUYMaJkpRwVYVmdRZ2HEonCSjfiRxwt5p7ACFV9O32hqq4VkWERyWSEQLUEteSzlFIyl9Oa8rU0ly6FX/4Srryy8J1tsomzgMeMgfXXD0W+OBOm1Vlpiz8KK92IH3FUzH0zlbKI/J+qfkdVX4tKKKM0yjkEKGwKGWZS9qEoy5fD73/vhiJ9+WVh24jA5ZfD2WfDxhuHI0cVUompFMuFDXEyIJ7DpV5U1T3T/tcBL6vqLhGKtQ4bLlUcsRsCFBdWroSbbnLpKD/8sPDtxo51n169yiZa2B6OSnlMLml5mUnPvtMp4Ysl1ogOGy4VjNhYzCJyIXAR0CAin6UWAyuBGyITzAiFmg9qWbPGzQl86aXwxhuFb3fGGXDRRdC7d/lky0LYHo5KeUxaWtu4e1ZbB6UsEGgaTsOImtgoZlWdAEwQkQmqemHU8hjhEqV7saJ926rwwAPOBd3aWvh2//M/Tmn3i0dfYtgzZlVqBi6/lKmPz1tS0PbluFaqJbbCiA+xCbsUkf7ezztFZM/MT6TCGSUzdmg/GurrOiyrRFBLWRM2PPUUDBnSORnHsGH+SnnYMJg5s1MyDiZNio1ShvA9HJXymJRynHJcK5YwxCiG2Chm4Dzv+7osn19EJZQRDlEMPYGQhs7Mng3Dh3ceivT1r8MTT2Tf5qCDYPr0zgr4vvtgr72Krk+lCHvYTqWGAZVynHIk97CEIUYxxMmV/T3ve0jUshj+lOKWq/TQk5bWtmBzJb/+uotqnjSp8IPsvjv87GfOEk5Q9tgwh+20tLax9IvOkeX59lfMtVaK3OWw6v22bVu2gkETpxdcN3OH1xaxUcwiMiLXelW9p1KyGNmppiFPKVmzscXnH3H+i/dAgGHx7zZuyS8O/Db37fx11napS3yUb1jDdlpa2xh75xxWre08+iNXQFax11opcpcjDsJvn0L7TGX56lZN950RDrEZLiUiN3s/NwcOAKZ7/4cAT6hqTsVdKWp5uFQ1DXkaNHE6/1n8IWc8fzc/ePauwjfcbDNnAZ96Kqy33rp9+VneTWa95CRf2/ldN37bNTbUM/uyw0OVEZzyS58KM0UpL2AtrW2MnzqXZSs67jNz7u4Ufu2RhOvPhksFIzYWs6qeAiAi9wO7qOpi7/9WwO+jlM1wxHbI03//Cw8+CJMnuw8ul2tOunVz2bDOPBM23DBn0Vz1M+slN7narph1y1asoqW1LfQhVtnynxc9T3WOffboXt9J+aco5v6y6y+ZxCn4K0WflFL2+AD4Sqk7FZFGEblLROaJyGsisr+IbCoij4rI6953j1KPk2Qiz+O7ahU89BCccgpssEF7EFZDg8vt7CnlTH63/wns8aPb6HPB/Qya8JgLwvryS7jggrxKGfLXL6xgnjBmRIobudqu2HVhB05lC9AC2GC9rkUrO799du/W1XeGsmLvLwsmSx5xVMxPiMjDInKyiIwGpgGPh7Df3wAPqWp/YHfgNWAc8Jiq7gQ85v03fKjYkKe1a+HJJ50127NnuwLu1g2OPNJNyrB8ecdtDj8cbrwRPv4YVGl5cRE7X/IgfS64n198/bssa9i4aFmz1TuTUr0GSR1WM3ZoP+q7dA6Kq6+TnOci17o4DbEqZp9B76NKXH9GvIiNKzuFqp7lBYId5C26QVX/Xso+RWRj4Ou4+Z1R1ZXAShEZDgz2it0KPAFcUMqxkkzoeXxV3ZjelAt60aL82xx0EIwc6eYS3nLLisiavi+/vr5SvQZhJ+CISxRv6pjpfa09utdz2TG5XcTNA5qy9vlCeYZYZe3P7l4fKHK6kH2mZiiDwq/NSlx/RryITfBXORGRPXBpPV/FWcuzgLOBNlVtTCu3VFU7ubNFZAwwBqB37957vf3225lFjHy88opTvlOmwL//nb/8wIFwwglw/PHQp0/ZxSuUbH2HYURo9x03LWtAkAALJh4dCxkrTaXqke049XUCSodo8iDHLpfs1XpuLfgrGLGxmEXkaVU9UEQ+h06pblVVS5kupyuwJ/BDVX1ORH5DALe1qt6Al6974MCByX+TKYU333TKd/JkmDMnf/lddnEW8AknQP/++ctHTLlm/wlzqE6l0l+Wm0rNtJTtOF98ubpTNHWQNiyX7Db7VG1QKxbzlsCzqtrH+38QTjHvCAxW1cVe9PcTqpqzE7KWh0t1oK0N7rzTKeBnn81fvm9fp4BHjnRJORKUjCMMwrSEwrS+oyRKd3xS2jAumMUcjNhYzOl40dHbkiafqr5Y7P5U9X0ReVdE+qnqfOBQnFv7VWA0MNH7vrckwZPIkiVwzz1OAT9eQAzells663fkSNhvP5c72shLmJZQNc9HnCLqpBpJaEOjeomdYhaRn+GCtN4C1nqLFSg1g8UPgUki0s3b9ym4qPQpInIa8A5wfInHqF4+/RSmTnUKeNq0/OU33rjdAj74YOgau0up6ggrZWmY6TSjImp3fBLaMJ24BAMahRHHp+kJwA5e5HRoqOpsIJsr5dAwjxN7li930xJOngx3FZARq76+XQF/4xvrsmEZ8SUJ/ZBRJ7NJQhumiNr7YAQnjor5FaAR+DBiOaqblSvh0UfbhyKtLOA957jjnAI+6iiXwKNGSYJ1UekJQ8ImDq7kam/DFFF7H4zgxFExTwBaReQVYN2UNKp6bHQixZg1a1wyjpQCXrYs/zZHHukU8PDh0NhYbgmrCrMu4kHSXMlRErX3wQhOHBXzrcDVwMu09zEbqvDcc+0KePHi/NsMHuwU8IgRsPnmZRexELJZoxAfl6FZF/Ggml3JcfO4xMH7YAQjjor5I1X9bdRCRIYqvPRSuwJ+66382+y7r1PA3/oWbLtt+WUskmzW6Ng754DAqjW6blmUFqpZF/GhGl3JcfS4mPeh+oijYp4lIhOAqXR0ZRc9XCq2/Pvf7dmwXnklf/mvfc0NRTrhBNhpp/LLFzLZrNFs8/RGaaGadWGUQhw9LtXsfahV4qiYB3jf+6UtC2O4VLTMnAk/+AG88EL+sjvu2B4J/dWvJiYZRxCrMyoL1awLoxSi8LgU4jqvRu9DLRM7xayqQ6KWoSwcfHDnGZGamtoV8N57J0YB++FnjfqVjQKzLoxSqLTHJY6uc6N0YqeYReTSbMtV9YpKyxIqb70Fr73mZkeqyz2FW1LJZo3Wd5EOfcwQvYVq1oVRLJX2uMTRdW6UTuwUM/BF2u/1gWG4uZOrmy22cJ8axs8azbbMHipGNVJpj4sFKyaT2E9iISLrAVNVdWjUsoBNYlGrxG0IjGEADJo4PavrvKmxgRnj4hOWY5NYBKMaZhjoDmwftRBG7ZLqx2tbtgKlvR+vpbUtatGMGmfs0H401HfsGou6K8gondi5skXkZdrnY64DegHV3b9sVDXWj2fEFQtWTCaxU8y4PuUUq4EPVHV1VMJUCnOVxhfrxzPijAUrJo/YKGYR2dT7+XnGqo1FBFX9pNIyVQob8hBvLOmIYRiVJE59zLOAmd73e2m/U8sTSy5XqRE91o9nGEYliY3FrKp9U79FpFVVB+QqnyTMVRpvrB/PMIxKEhvFnEG8x3CFjLlKw6WltY3L75vL0uWrAGhsqGf8sbuWpEitH89IAhbLUh3EyZVds5irNDxaWtsYe9ecdUoZYNmKVYy9c44NbzJqGhv2Vz3ExmIWkR+n/d084z+q+ssKi1QxzFUaHtc+PL9Des8Uq9aqDW8yKkJcrVIb9lc9xEYxAxul/f5zxv/EY67ScMjVL2999ka5ifMIC4tlqR5io5hV9fKoZTCqn1wzWFmfvVFu4myVWixL9WB9zBWgpbWNQROn03fcNAZNnG59OmVk7NB+1Nd1nj6zvotYn71RduJslVosS/UQG4s5qcTZtZVEUm0adlS2YRRCnK1Si2WpHmI/u1TcCDq7VLXM/mIEJ65BPkZ0ZL6Ig7NKJ4zYraavDZtdKhixdWWLyH4iMl1EZohIc9TyFEucXVtG8djQEyMbzQOamDBiN5oaGxDcC3itK2UjOLFxZYvIlqr6ftqiHwPHAgL8C2iJQq5SibNryyieOAf5GNGSPsIi5VU5d/Js86oYBRMni/mPIvJTEVnf+78M+B9gJPBZZFKViAVcJBPzhBj5MK+KUSyxsZhVtVlEjgHuF5FbgXNwirk70ByhaCVhARfJxDwh5SMpffelelWS0g5GcGKjmAFU9T4ReQD4AXAPcJWqPhWxWCVjyUOSx9ih/bIG+ZgnpDSSNIqhFK9KktrBCE5sXNkicqyIPA1MB14BTgS+KSK3i8gO0UpnGB2xIJ/ykKQpUP28J4V4VZLUDkZw4mQxXwnsDzQAD6jqPsCPRWQn4CqcojaM2GCekPBJUt99KV6VJLWDEZw4KeZPccq3AfgwtVBVX8eUsmHUBEnquy8lviRJ7WAEJ06K+ZvAKGAVLujLMIwaI2l998V6VZLWDkYwYqOYVfUj4P9FLYdhGNFhoxgc1g61jaXkDEjQlJyGUY1kDtUZ0r8Xj89bEkhJlGO4jw0hqk4sJWcwYmMxG4YRD7IN1fnbs++sW1/I0J1yDPexIURGrRCb4VLlRkTqRKRVRO73/m8qIo+KyOved4+oZTSMOJBtqE4m+YbulGO4jw0hMmqFWrKYzwZeAzb2/o8DHlPViSIyzvt/QVTCGaVjbs5wKHRITq5yuYb7FHuebAiRUSvUhMUsItsARwN/SVs8HLjV+30rVZz207C8xGFS6JCcXOX81m3SUF/0eSolYYdhVBM1oZiBXwPnA2vTlm2hqosBvO/NI5DLCAlzc4ZHtolXMsk3dMdv8hYRij5PNiGMUSskXjGLyDDgQ1WdVcI+xojITBGZuWTJkhClM8LC3JzhkS3d6En79Q6UftQvZemy5auyli/kPFkaVKNWSPxwKRGZAHwHWA2sj+tjvgfYGxisqotFZCvgCVXN++ptw6XiyaCJ07NmSmpqbGDGuEMikMjIhp2n2sSGSwUj8Razql6oqtuoah9cas/pqnoSMBUY7RUbDdwbkYhGCJibszqw82QY+amlqOxMJgJTROQ04B3g+IjlMUrAMiUFJ4oodjtPhpGfxLuyw8Zc2UYSyEzWAc5ytT5boxyYKzsYiXdlG4bRGYtiN4z4UsuubMOoWeIexW7JYoxaxixmw6hB4pysw5LFGLWOKWbDqEHiHB1tbnaj1jFXtmHUIHGOjo67m90wyo0pZsOoUZoHNMVCEWeydWND1iQkcXCzG0YlMFe2YRixIs5udsOoBGYxG7HDInJrmzi72Q2jEphiNmJFZuKLVEQuYA/mGiKubnbDqATmyjZihUXkGoZR65jFbMSKOEbkmmvdMIxKYhazESvilvjCkl0YhlFpTDEbsSJuEbnmWjcMo9KYK9uIFXGLyI2ja90wjGRjitmIHXGKyLVkF4ZhVBpzZRtGDuLmWjcMI/mYxWwYOYiba90wjORjitkw8hAn17phGMnHXNmGYRiGESPMYjYMoyJYohbDKAxTzIZhlB3LgW4YhWOubMMwyo4lajGMwjHFbBhG2bFELYZROKaYDcMoO3HLgW4YccYUs2EYZccStRhG4Vjwl2EYZccStRhG4ZhiNowyYkOE2rFELYZRGKaYDaNM2BAhwzCKwfqYDaNM2BAhwzCKwRSzYZQJGyJkGEYxmGI2jDJhQ4QMwygGU8yGUSZsiJBhGMVgitkwykTzgCYmjNiNpsYGBGhsqGf9+i6cO3k2gyZOp6W1LWoRDcOIIaaYDaOMNA9oYsa4Q/jVyD34cvVali5fhdIeoW3K2TCMTEwxG0YFsAhtwzAKxRSzYVQAi9A2DKNQTDEbRgWwCG3DMAqlJhSziGwrIo+LyGsiMldEzvaWbyoij4rI6953j6hljTMtrW0MmjidvuOmWfBSQCxC2zCMQqkJxQysBs5T1Z2B/YAzRWQXYBzwmKruBDzm/TeykEov2bZshQUvFUFmhHZTYwMTRuxmqTkNw+hETeTKVtXFwGLv9+ci8hrQBAwHBnvFbgWeAC6IQMTYkyt4qRaUSxiTUdgkDoVjk38YtUxNKOZ0RKQPMAB4DtjCU9qo6mIR2dxnmzHAGIDevXtXSNJ4UcvBSzYZRWWx9jZqnVpxZQMgIhsCdwPnqOpnhW6nqjeo6kBVHdirV6/yCRhjajl4yYY6VRZrb6PWqRnFLCL1OKU8SVXv8RZ/ICJbeeu3Aj6MSr64U8vBS7XsLYgCa2+j1qkJxSwiAtwIvKaqv0xbNRUY7f0eDdxbadmqhVoOXqplb0EUWHsbtU6t9DEPAr4DvCwis71lFwETgSkichrwDnB8NOJVB7UavDR2aL8OfZ5Q/d6COAdXJbG9DSMINaGYVfVpQHxWH1pJWYzqI6Ww4qrIghL34KqktbdhBEVUNWoZqoqBAwfqzJkzoxbDSBiVtGAHTZxOW5b+2qbGBmaMO6QsxzRqGxGZpaoDo5ajWqgJi9kw4kylLVgLrjKMeFMTwV+GEWcqPTzIgqsMI96YYjaMiKm0BVvLQ98MoxowxWwYEVNpC7aWh74ZRjVgfcxG2Ynz0Jw4EMXwoFod+mYY1YBZzEZZsVmp8mMWLPDqq3DoodC9O2y9NVx6KaxZk3+7FGvXwl57gQjcf3/78jVr4Oqr4aCDYLPN3Ofww+GFF8KvQ7GMH+/kzvaZMKG9nF+Z9dbLvf+77oIDDnB1X3996NcPrrwSVq7sWG7xYjjlFGhqgg03hAEDYNKk0Ktr5McsZqOs1PqsVIVSjAWbGE/E0qVw2GGwyy5w773w5ptw3nlO2V55ZWH7+MtfoC3Ly96KFTBxolM4F17oFNnvfgcHHgj/+pdT5lFz+ulwxBEdl7W0uBeKI49sX/bMM523PeYYGDQo9/4//hiGDIGxY6GxEZ5/3r0MvP++awtwbX3ssa7sNdfAlls6hX7SSe5l6ZvfLKGCRlBsHHNAbBxzMPqOm0a2K0yABROPrrQ4iSFziBU493dVWtoTJjhl8PbbsPHGbtk117Qrj9QyP5Yuha98xSng00+H++6DYcPcujVr4LPPoEeP9vIrV7ryQ4bAzTeHW5dbbnFyL1xY2n6OPhreegtee82/zPPPw777wh13wMiRwfZ/8cXw+9+7thOBefNg551h6lSn7FPsuSfstBNMnlxcPTxsHHMwzJVtlBUbmlMeQh1iNW0adOkCCxZ0XL5ggVs+dWoJkhbAgw/C0KEdFfCJJzpr95//zL/9T3/qrMZDsyTxq6vrqJQBunWDXXeFD9PmrBk2DPr3d8dMcd11zvU7d26w+pTKJ5/Ao4/CqFG5y91xB2ywQUdFWiibbdbRlb1qlfveZJOO5RobwYy3imOK2SgrNjSnPIQ6xOqII1y/7q23dlx+yy3QqxccdZT/tmvWwOrVuT9r1+Y+/rx5Timm07u3c6HOm5d725declbvL36Ru1w6X34Js2Y513mKP/8Zlixx7m5wluoll8DllzslXknuusspyhNP9C+jCnfeCcOHu3YqhDVrYPlyePpp+O1v4X//11nLAF/9qrO+L70UXn/deRluuQVmzIDvf7/kKhnBMMVslBULbCoPoXoi6urg5JOdYk5ZR6ru/3e+A11zhKIceijU1+f+nHpq7uMvXeoss0x69HDrcvHDH8KZZ8KOO+Yul85VV7n9nn56+7KttnL9rb/9LTz2GIwe7YKffvKT3Ptauzb7S0j6siBBbOAs4T33dO52P556ChYtyq28M9lgA/c56CA4+GC49tr2dSLOc7F2rTvuJpvAmDFw001wiKVprTQW/GWUHRuaEz6hD7E69VT4+c/hiSdc3+vjj7s+31NOyb3dn/4En3+eu0zPnvmPn7Lc0lHNvjzFHXfA/PmuT7lQpk1zivm661x0cjqjRsE997j+3S5dYM4c99KSiyuucFZ1JvX17b+3267wPufFi537/uqrc5e7/Xb34jJ0aGH7BRfstny565u+4go46yz4wx/curVr3UvYxx+7/uTNN4cHHoDTTnNu78zgNKOsmGI2jCok9BmYtt8eBg92buFUUNQ+++R34+64Y/4+yC55HHM9esCyZZ2Xf/ppdksanKt37Fi44AKnVJYtc+5XgC++cC8LG23UcZsXXnBBUmecAeeck32/o0Y5V/Kxx7qgp3yMGdMeaAZuqNYNN3Tsl883nCmdKVNce+YK5lq9Gu6+G447zvWXF8qee7rvAw90L0ujR7vo9x12cHJPmwb//nd7vQcPhnffhfPPN8VcaVTVPgE+e+21lxpGIpk0SbV7d9VFi9z39dfn3+bgg1WdKvH/jB6dex8HHaR64okdl73zjtt26tTs2yxdmvuYO+zQsfz8+aq9eqkee6zq6tXZ9/npp6q9e6sOGOD28dBD+eufyc03q263XfDtUuy3n2uPXDz0kJPvsceKP87LL7t9PPqo+3/11e6cZ+K3PCDATI3B87taPmYxG4bhGDHC9deeeKKzQgvpvwzDlX3kka6/M93KnTwZGhpcX2g2NtzQudvTef99Z/H+/Ocd+0UXL3Yu3x12cC5gP/f0Oee4/uDp0107nH46vPJK50jlcrFwITz7bLt72Y/bb3fjjAcPLv5YM2a477593fd22zk39/z5HV38s2ZBnz7FH8cojqjfDKrtYxazkWjOPFMVVEeNqtwxP/lEdcstVQ87zFlwf/qT6gYbqF58ccdyO+ygeuqp/vtZsMDJft997cuWL1fdfXfVTTZRvf9+1Weeaf+8+GJ7ufvuc9s++KD7//HHqlttld/az6QUi3nCBNWuXVU//NC/zH//6+py9tnZ1y9cqFpXp3rrre3Lhg5VvfZa1QceUH34YdVLL3XtO3Jke5nPPnPegv79VW+7zZ2Hc85xbfL73xdXnzQwiznQxyxmwzDaaW52iSfyRVKHSY8eLhL6rLPcmNzGRjj3XJeoI51iIpw/+MAFcUHHvmBoD8r65BPXV/y977X3pW66qRtCNWyY68stZqxwUO64w0W59+rlX+bBB13fu583Q9W1UfoQtb33dkOfFi50Efbbb++SuqQPg9poI3cOLrzQ9Tt/9pnzMPzxj65tjIpimb8CYpm/jERz/vnOjZxKLmIYIWCZv4JhFrNhGK5v8dVX4frr4bLLTCkbRoSYxRwQEVkCvB21HAXQE/goaiHKRJLrBhHU7zno9zXY4DFYdhws+JKsKc7Dws5f9VJs3bZT1Rw+eiMdU8wJRURmJtV1lOS6gdWv2kly/ZJctzhh/irDMAzDiBGmmA3DMAwjRphiTi43RC1AGUly3cDqV+0kuX5JrltssD5mwzAMw4gRZjEbhmEYRowwxWwYhmEYMcIUc4IQkfEi0iYis73PUWnrLhSRN0RkvogEmMQ1fojIT0RERaRn2rKqr5+I/ExEXvLO3SMisnXauqqun4hcKyLzvPr9XUQa09ZVdd0AROR4EZkrImtFZGDGuqqvH4CIHOHV4Q0RGRe1PIkm6mTd9gnvA4wHfpJl+S7AHGA9oC/wJlAXtbxF1nFb4GFckpeeSaofsHHa7x8Bf0xK/YDDga7e76uBq5NSN68eOwP9gCeAgWnLk1K/Ok/27YFuXp12iVqupH7MYq4NhgN3qOqXqroAeAPYJ2KZiuVXwPl0zEyViPqp6mdpfzegvY5VXz9VfURVV3t/nwW28X5Xfd0AVPU1VZ2fZVUi6oeT+Q1VfUtVVwJ34OpmlAFTzMnjLM9deJOI9PCWNQHvppVZ5C2rKkTkWKBNVedkrEpE/QBE5CoReRf4NnCptzgx9fM4FXjQ+520umWSlPolpR5VgU1iUWWIyD+ALbOsuhi4HvgZztL6GXAd7iEoWcrHcpxcnvpdhHOJdtosy7Kqq5+q3quqFwMXi8iFwFnAZVRJ/fLVzStzMbAamJTaLEv52NUNCqtfts2yLItl/fKQlHpUBaaYqwxVPayQciLyZ+B+7+8iXN9sim2A90IWLRT86iciu+H66OaICLg6vCgi+5CA+mXhNmAaTjFXRf3y1U1ERgPDgEPV67ikSuoGgc5dOlVTvzwkpR5VgbmyE4SIbJX295vAK97vqcCJIrKeiPQFdgKer7R8paCqL6vq5qraR1X74B4Ue6rq+ySgfgAislPa32OBed7vqq+fiBwBXAAcq6rL01ZVfd3ykJT6vQDsJCJ9RaQbcCKubkYZMIs5WVwjInvgXEwLgTMAVHWuiEwBXsW5Ec9U1TVRCRk2CarfRBHpB6zFRZ1/HxJTv9/hIpMf9Twez6rq9xNSN0Tkm8D/A3oB00RktqoOTUr9VHW1iJyFGxFRB9ykqnMjFiuxWEpOwzAMw4gR5so2DMMwjBhhitkwDMMwYoQpZsMwDMOIEaaYDcMwDCNGmGI2DMMwjBhhitkwIkJE/hOg7GAROSDt//dF5Lve75PTZ6IKsM+F6TN0GYYRD2wcs2FUB4OB/wD/AlDVP6atOxmXTMYyMRlGAjDFbBgxQkSOAS7BTa33MW4yiwZcspE1InIS8EPgUJyiXggMBCaJyApgf+A13NSDH3lzA/9CVQeLyGbA7bgkGM+Tlv/Y2++PvOM+B/ygGhNhGEYSMFe2YcSLp4H9VHUAbmq981V1IfBH4FequoeqPpUqrKp3ATOBb3vrVuTY92XA096+pwK9AURkZ2AkMEhV9wDW4F4IDMOIALOYDSNebANM9vKedwMWhLjvrwMjAFR1mogs9ZYfCuwFvOCly2wAPgzxuIZhBMAUs2HEi/8H/FJVp4rIYGB8EftYTbs3bP2Mddly8Apwq6peWMSxDMMIGXNlG0a82ARo836PTlv+ObCRzzaZ6xbiLGCA49KWP4nnohaRI4Ee3vLHgG+JyObeuk1FZLsi5TcMo0RMMRtGdHQXkUVpnx/jLOQ7ReQp4KO0svcB3xSR2SJyUMZ+bgH+6K1rAC4HfuPtIz2A63Lg6yLyInA48A6Aqr6KCzh7REReAh4F0qcQNQyjgtjsUoZhGIYRI8xiNgzDMIwYYYrZMAzDMGKEKWbDMAzDiBGmmA3DMAwjRphiNgzDMIwYYYrZMAzDMGKEKWbDMAzDiBH/H6JEFgUY4416AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(-25,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d208211a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.41645289662961776, intercept=77.38487106434721, rvalue=0.28078628352290463, pvalue=0.00019074629724712613, stderr=0.1091774263821252, intercept_stderr=2.6941567889681917)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use linear regression on the data. Note the r values, lower than .05 = low correlation; greater than .05 = hiogh correlation\n",
    "linregress(southern_hemi_df[\"Lat\"],southern_hemi_df[\"Humidity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9f1e5f20",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere, % cloudiness\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Cloudiness', '% Cloudiness',(40,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a602e5f0",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere, cloudiness\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for % Cloudiness', '% Cloudiness',(-50,60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "25c69c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Module states that correlation between the latitude and percent cloudiness is very low because the r-value\n",
    "# is less than –0.09 for the Northern Hemisphere and less than –0.02 for the Southern Hemisphere for the plots\n",
    "# shown here. This means that cloudiness is unpredictable due to changing weather patterns that can increase\n",
    "#  or decrease percent cloudiness. Check the r-values for your plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "76974152",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Northern Hemisphere, wind speed\n",
    "x_values = northern_hemi_df[\"Lat\"]\n",
    "y_values = northern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for Wind Speed', 'Wind Speed',(40,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ac244d9f",
   "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"
    }
   ],
   "source": [
    "# Linear regression on the Southern Hemisphere\n",
    "x_values = southern_hemi_df[\"Lat\"]\n",
    "y_values = southern_hemi_df[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for Wind Speed', 'Wind Speed',(-50,35))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8d9c1dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The correlation between the latitude and wind speed is very low because the r-value is less  than –0.07 for the Northern \n",
    "# Hemisphere and less than –0.3 for the Southern Hemisphere for the plots shown here. This means that wind speed is \n",
    "# unpredictable due to changing weather patterns that can increase or decrease wind speed. Check the r-values\n",
    "# for your plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06e5719",
   "metadata": {},
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