WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x26cf116c888>"
      ]
     },
     "execution_count": 2,
     "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "611"
      ]
     },
     "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,
   "metadata": {
    "collapsed": true
   },
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the datetime module from the datetime library.\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 1 of Set 1 | guerrero negro\n",
      "Processing Record 2 of Set 1 | south yuba city\n",
      "Processing Record 3 of Set 1 | tasiilaq\n",
      "Processing Record 4 of Set 1 | tuktoyaktuk\n",
      "Processing Record 5 of Set 1 | esperance\n",
      "Processing Record 6 of Set 1 | kapaa\n",
      "Processing Record 7 of Set 1 | bethel\n",
      "Processing Record 8 of Set 1 | east london\n",
      "Processing Record 9 of Set 1 | busselton\n",
      "Processing Record 10 of Set 1 | yellowknife\n",
      "Processing Record 11 of Set 1 | punta arenas\n",
      "Processing Record 12 of Set 1 | talnakh\n",
      "Processing Record 13 of Set 1 | neuquen\n",
      "Processing Record 14 of Set 1 | rikitea\n",
      "Processing Record 15 of Set 1 | arraial do cabo\n",
      "Processing Record 16 of Set 1 | surt\n",
      "Processing Record 17 of Set 1 | ponta delgada\n",
      "Processing Record 18 of Set 1 | clyde river\n",
      "Processing Record 19 of Set 1 | jamestown\n",
      "Processing Record 20 of Set 1 | hermanus\n",
      "Processing Record 21 of Set 1 | panzhihua\n",
      "Processing Record 22 of Set 1 | casa nova\n",
      "Processing Record 23 of Set 1 | hasaki\n",
      "Processing Record 24 of Set 1 | provideniya\n",
      "Processing Record 25 of Set 1 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 1 | naryan-mar\n",
      "Processing Record 27 of Set 1 | ofaqim\n",
      "Processing Record 28 of Set 1 | dovolnoye\n",
      "Processing Record 29 of Set 1 | omboue\n",
      "Processing Record 30 of Set 1 | severo-kurilsk\n",
      "Processing Record 31 of Set 1 | darhan\n",
      "Processing Record 32 of Set 1 | bluff\n",
      "Processing Record 33 of Set 1 | san patricio\n",
      "Processing Record 34 of Set 1 | cape town\n",
      "Processing Record 35 of Set 1 | maniitsoq\n",
      "Processing Record 36 of Set 1 | mattru\n",
      "Processing Record 37 of Set 1 | mar del plata\n",
      "Processing Record 38 of Set 1 | port elizabeth\n",
      "Processing Record 39 of Set 1 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 1 | padang\n",
      "Processing Record 41 of Set 1 | tilichiki\n",
      "Processing Record 42 of Set 1 | qaanaaq\n",
      "Processing Record 43 of Set 1 | jurbarkas\n",
      "Processing Record 44 of Set 1 | baykit\n",
      "Processing Record 45 of Set 1 | geraldton\n",
      "Processing Record 46 of Set 1 | tabukiniberu\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 1 | butaritari\n",
      "Processing Record 48 of Set 1 | mataura\n",
      "Processing Record 49 of Set 1 | vila velha\n",
      "Processing Record 50 of Set 1 | airai\n",
      "Processing Record 1 of Set 2 | sao filipe\n",
      "Processing Record 2 of Set 2 | half moon bay\n",
      "Processing Record 3 of Set 2 | grand-santi\n",
      "Processing Record 4 of Set 2 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 2 | nome\n",
      "Processing Record 6 of Set 2 | hobart\n",
      "Processing Record 7 of Set 2 | puerto ayora\n",
      "Processing Record 8 of Set 2 | san quintin\n",
      "Processing Record 9 of Set 2 | khatanga\n",
      "Processing Record 10 of Set 2 | conde\n",
      "Processing Record 11 of Set 2 | shiraz\n",
      "Processing Record 12 of Set 2 | udachnyy\n",
      "Processing Record 13 of Set 2 | bredasdorp\n",
      "Processing Record 14 of Set 2 | port lincoln\n",
      "Processing Record 15 of Set 2 | matara\n",
      "Processing Record 16 of Set 2 | rawson\n",
      "Processing Record 17 of Set 2 | mocambique\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 2 | thompson\n",
      "Processing Record 19 of Set 2 | dunedin\n",
      "Processing Record 20 of Set 2 | bathsheba\n",
      "Processing Record 21 of Set 2 | pitimbu\n",
      "Processing Record 22 of Set 2 | draper\n",
      "Processing Record 23 of Set 2 | benguela\n",
      "Processing Record 24 of Set 2 | bambous virieux\n",
      "Processing Record 25 of Set 2 | chicama\n",
      "Processing Record 26 of Set 2 | narasannapeta\n",
      "Processing Record 27 of Set 2 | buenavista\n",
      "Processing Record 28 of Set 2 | rodino\n",
      "Processing Record 29 of Set 2 | vaini\n",
      "Processing Record 30 of Set 2 | kodiak\n",
      "Processing Record 31 of Set 2 | albany\n",
      "Processing Record 32 of Set 2 | saint-philippe\n",
      "Processing Record 33 of Set 2 | carutapera\n",
      "Processing Record 34 of Set 2 | chokurdakh\n",
      "Processing Record 35 of Set 2 | opuwo\n",
      "Processing Record 36 of Set 2 | tahe\n",
      "Processing Record 37 of Set 2 | luderitz\n",
      "Processing Record 38 of Set 2 | ushuaia\n",
      "Processing Record 39 of Set 2 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 2 | avarua\n",
      "Processing Record 41 of Set 2 | salalah\n",
      "Processing Record 42 of Set 2 | nikolskoye\n",
      "Processing Record 43 of Set 2 | novobessergenevka\n",
      "Processing Record 44 of Set 2 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 2 | lorengau\n",
      "Processing Record 46 of Set 2 | atuona\n",
      "Processing Record 47 of Set 2 | pingliang\n",
      "Processing Record 48 of Set 2 | moshi\n",
      "Processing Record 49 of Set 2 | lebu\n",
      "Processing Record 50 of Set 2 | simpang\n",
      "Processing Record 1 of Set 3 | kavaratti\n",
      "Processing Record 2 of Set 3 | pevek\n",
      "Processing Record 3 of Set 3 | barrow\n",
      "Processing Record 4 of Set 3 | hilo\n",
      "Processing Record 5 of Set 3 | cherskiy\n",
      "Processing Record 6 of Set 3 | new norfolk\n",
      "Processing Record 7 of Set 3 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 3 | sao jose da coroa grande\n",
      "Processing Record 9 of Set 3 | pindobacu\n",
      "Processing Record 10 of Set 3 | mandera\n",
      "Processing Record 11 of Set 3 | tiksi\n",
      "Processing Record 12 of Set 3 | carnarvon\n",
      "Processing Record 13 of Set 3 | oyem\n",
      "Processing Record 14 of Set 3 | asau\n",
      "Processing Record 15 of Set 3 | fairbanks\n",
      "Processing Record 16 of Set 3 | hami\n",
      "Processing Record 17 of Set 3 | barreirinha\n",
      "Processing Record 18 of Set 3 | urdzhar\n",
      "City not found. Skipping...\n",
      "Processing Record 19 of Set 3 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 3 | tubruq\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 3 | meulaboh\n",
      "Processing Record 22 of Set 3 | yar-sale\n",
      "Processing Record 23 of Set 3 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 3 | nurota\n",
      "Processing Record 25 of Set 3 | saint-prosper\n",
      "Processing Record 26 of Set 3 | dikson\n",
      "Processing Record 27 of Set 3 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 3 | bodden town\n",
      "Processing Record 29 of Set 3 | georgetown\n",
      "Processing Record 30 of Set 3 | nemuro\n",
      "Processing Record 31 of Set 3 | marmora\n",
      "Processing Record 32 of Set 3 | lagoa\n",
      "Processing Record 33 of Set 3 | sao paulo de olivenca\n",
      "Processing Record 34 of Set 3 | ahipara\n",
      "Processing Record 35 of Set 3 | ponta do sol\n",
      "Processing Record 36 of Set 3 | kenscoff\n",
      "Processing Record 37 of Set 3 | san nicolas\n",
      "Processing Record 38 of Set 3 | banepa\n",
      "Processing Record 39 of Set 3 | port pirie\n",
      "Processing Record 40 of Set 3 | codrington\n",
      "Processing Record 41 of Set 3 | tiarei\n",
      "Processing Record 42 of Set 3 | xining\n",
      "Processing Record 43 of Set 3 | havelock\n",
      "Processing Record 44 of Set 3 | shimanovsk\n",
      "Processing Record 45 of Set 3 | twin falls\n",
      "Processing Record 46 of Set 3 | tsienyane\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 3 | olafsvik\n",
      "Processing Record 48 of Set 3 | mahebourg\n",
      "Processing Record 49 of Set 3 | grindavik\n",
      "Processing Record 50 of Set 3 | bull savanna\n",
      "Processing Record 1 of Set 4 | kawalu\n",
      "Processing Record 2 of Set 4 | xinpu\n",
      "Processing Record 3 of Set 4 | mackay\n",
      "Processing Record 4 of Set 4 | dorado\n",
      "Processing Record 5 of Set 4 | adrar\n",
      "Processing Record 6 of Set 4 | longyearbyen\n",
      "Processing Record 7 of Set 4 | faya\n",
      "Processing Record 8 of Set 4 | monatele\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 4 | maceio\n",
      "Processing Record 10 of Set 4 | karaton\n",
      "Processing Record 11 of Set 4 | mwinilunga\n",
      "Processing Record 12 of Set 4 | kalemie\n",
      "Processing Record 13 of Set 4 | utiroa\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 4 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 4 | karratha\n",
      "Processing Record 16 of Set 4 | peruibe\n",
      "Processing Record 17 of Set 4 | doha\n",
      "Processing Record 18 of Set 4 | komsomolskiy\n",
      "Processing Record 19 of Set 4 | nabire\n",
      "Processing Record 20 of Set 4 | oboyan\n",
      "Processing Record 21 of Set 4 | narrabri\n",
      "Processing Record 22 of Set 4 | coruripe\n",
      "Processing Record 23 of Set 4 | castro\n",
      "Processing Record 24 of Set 4 | eldorado\n",
      "Processing Record 25 of Set 4 | goure\n",
      "Processing Record 26 of Set 4 | brae\n",
      "Processing Record 27 of Set 4 | pangai\n",
      "Processing Record 28 of Set 4 | biak\n",
      "Processing Record 29 of Set 4 | abha\n",
      "Processing Record 30 of Set 4 | dakar\n",
      "Processing Record 31 of Set 4 | qaqortoq\n",
      "Processing Record 32 of Set 4 | saint george\n",
      "Processing Record 33 of Set 4 | puerto escondido\n",
      "Processing Record 34 of Set 4 | umm lajj\n",
      "Processing Record 35 of Set 4 | doctor pedro p. pena\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 4 | westport\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 37 of Set 4 | jalu\n",
      "Processing Record 38 of Set 4 | alice springs\n",
      "Processing Record 39 of Set 4 | troitskoye\n",
      "Processing Record 40 of Set 4 | chagda\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 4 | nouadhibou\n",
      "Processing Record 42 of Set 4 | great bend\n",
      "Processing Record 43 of Set 4 | payson\n",
      "Processing Record 44 of Set 4 | hay river\n",
      "Processing Record 45 of Set 4 | tura\n",
      "Processing Record 46 of Set 4 | artyk\n",
      "City not found. Skipping...\n",
      "Processing Record 47 of Set 4 | amapa\n",
      "Processing Record 48 of Set 4 | miri\n",
      "Processing Record 49 of Set 4 | nishihara\n",
      "Processing Record 50 of Set 4 | ostrovnoy\n",
      "Processing Record 1 of Set 5 | sataua\n",
      "City not found. Skipping...\n",
      "Processing Record 2 of Set 5 | grao mogol\n",
      "Processing Record 3 of Set 5 | kutum\n",
      "Processing Record 4 of Set 5 | nogent-le-rotrou\n",
      "Processing Record 5 of Set 5 | iksha\n",
      "Processing Record 6 of Set 5 | lodja\n",
      "Processing Record 7 of Set 5 | san matias\n",
      "Processing Record 8 of Set 5 | cap malheureux\n",
      "Processing Record 9 of Set 5 | pontian kecil\n",
      "Processing Record 10 of Set 5 | kasongo-lunda\n",
      "Processing Record 11 of Set 5 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 5 | redlands\n",
      "Processing Record 13 of Set 5 | calvia\n",
      "Processing Record 14 of Set 5 | vila franca do campo\n",
      "Processing Record 15 of Set 5 | ketchikan\n",
      "Processing Record 16 of Set 5 | la asuncion\n",
      "Processing Record 17 of Set 5 | torbay\n",
      "Processing Record 18 of Set 5 | toliary\n",
      "City not found. Skipping...\n",
      "Processing Record 19 of Set 5 | san carlos\n",
      "Processing Record 20 of Set 5 | dalby\n",
      "Processing Record 21 of Set 5 | santa ines\n",
      "Processing Record 22 of Set 5 | iquique\n",
      "Processing Record 23 of Set 5 | sur\n",
      "Processing Record 24 of Set 5 | tabiauea\n",
      "City not found. Skipping...\n",
      "Processing Record 25 of Set 5 | guipavas\n",
      "Processing Record 26 of Set 5 | sorland\n",
      "Processing Record 27 of Set 5 | zeya\n",
      "Processing Record 28 of Set 5 | kaeo\n",
      "Processing Record 29 of Set 5 | port said\n",
      "Processing Record 30 of Set 5 | parvatipuram\n",
      "Processing Record 31 of Set 5 | kaputa\n",
      "Processing Record 32 of Set 5 | camana\n",
      "Processing Record 33 of Set 5 | rio grande\n",
      "Processing Record 34 of Set 5 | kahului\n",
      "Processing Record 35 of Set 5 | alenquer\n",
      "Processing Record 36 of Set 5 | buin\n",
      "Processing Record 37 of Set 5 | kuytun\n",
      "Processing Record 38 of Set 5 | yithion\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 5 | norman wells\n",
      "Processing Record 40 of Set 5 | ribeira grande\n",
      "Processing Record 41 of Set 5 | bilibino\n",
      "Processing Record 42 of Set 5 | juneau\n",
      "Processing Record 43 of Set 5 | maragogi\n",
      "Processing Record 44 of Set 5 | tibati\n",
      "Processing Record 45 of Set 5 | akure\n",
      "Processing Record 46 of Set 5 | yei\n",
      "Processing Record 47 of Set 5 | manoel urbano\n",
      "Processing Record 48 of Set 5 | saskylakh\n",
      "Processing Record 49 of Set 5 | henties bay\n",
      "Processing Record 50 of Set 5 | cestas\n",
      "Processing Record 1 of Set 6 | lusambo\n",
      "Processing Record 2 of Set 6 | paamiut\n",
      "Processing Record 3 of Set 6 | rastolita\n",
      "Processing Record 4 of Set 6 | nanortalik\n",
      "Processing Record 5 of Set 6 | pilao arcado\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 6 | jiangyou\n",
      "Processing Record 7 of Set 6 | constitucion\n",
      "Processing Record 8 of Set 6 | san cristobal\n",
      "Processing Record 9 of Set 6 | paita\n",
      "Processing Record 10 of Set 6 | ziro\n",
      "Processing Record 11 of Set 6 | wilmington\n",
      "Processing Record 12 of Set 6 | buqayq\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 6 | poum\n",
      "Processing Record 14 of Set 6 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 6 | oranjestad\n",
      "Processing Record 16 of Set 6 | hithadhoo\n",
      "Processing Record 17 of Set 6 | tabou\n",
      "Processing Record 18 of Set 6 | mount gambier\n",
      "Processing Record 19 of Set 6 | huilong\n",
      "Processing Record 20 of Set 6 | los llanos de aridane\n",
      "Processing Record 21 of Set 6 | loandjili\n",
      "Processing Record 22 of Set 6 | sokolka\n",
      "Processing Record 23 of Set 6 | saint anthony\n",
      "Processing Record 24 of Set 6 | laguna\n",
      "Processing Record 25 of Set 6 | kiruna\n",
      "Processing Record 26 of Set 6 | aklavik\n",
      "Processing Record 27 of Set 6 | terney\n",
      "Processing Record 28 of Set 6 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 6 | port alfred\n",
      "Processing Record 30 of Set 6 | belomorsk\n",
      "Processing Record 31 of Set 6 | agadez\n",
      "Processing Record 32 of Set 6 | agirish\n",
      "Processing Record 33 of Set 6 | jiwani\n",
      "Processing Record 34 of Set 6 | victoria\n",
      "Processing Record 35 of Set 6 | antofagasta\n",
      "Processing Record 36 of Set 6 | brindisi\n",
      "Processing Record 37 of Set 6 | catamarca\n",
      "Processing Record 38 of Set 6 | oranjemund\n",
      "Processing Record 39 of Set 6 | cururupu\n",
      "Processing Record 40 of Set 6 | zhangzhou\n",
      "Processing Record 41 of Set 6 | vardo\n",
      "Processing Record 42 of Set 6 | kavieng\n",
      "Processing Record 43 of Set 6 | faanui\n",
      "Processing Record 44 of Set 6 | judenburg\n",
      "Processing Record 45 of Set 6 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 6 | zhigansk\n",
      "Processing Record 47 of Set 6 | igarka\n",
      "Processing Record 48 of Set 6 | yaring\n",
      "Processing Record 49 of Set 6 | kanina\n",
      "Processing Record 50 of Set 6 | margate\n",
      "Processing Record 1 of Set 7 | doorn\n",
      "Processing Record 2 of Set 7 | araguari\n",
      "Processing Record 3 of Set 7 | aswan\n",
      "Processing Record 4 of Set 7 | roma\n",
      "Processing Record 5 of Set 7 | upernavik\n",
      "Processing Record 6 of Set 7 | springside\n",
      "Processing Record 7 of Set 7 | mumford\n",
      "Processing Record 8 of Set 7 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 7 | smolenka\n",
      "Processing Record 10 of Set 7 | tomatlan\n",
      "Processing Record 11 of Set 7 | beringovskiy\n",
      "Processing Record 12 of Set 7 | curup\n",
      "Processing Record 13 of Set 7 | kardailovo\n",
      "Processing Record 14 of Set 7 | sakakah\n",
      "Processing Record 15 of Set 7 | san cosme y damian\n",
      "Processing Record 16 of Set 7 | quelimane\n",
      "Processing Record 17 of Set 7 | payo\n",
      "Processing Record 18 of Set 7 | darovskoy\n",
      "Processing Record 19 of Set 7 | vyshneve\n",
      "Processing Record 20 of Set 7 | souillac\n",
      "Processing Record 21 of Set 7 | bababe\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 7 | port-gentil\n",
      "Processing Record 23 of Set 7 | kuche\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 7 | kaitangata\n",
      "Processing Record 25 of Set 7 | sobolevo\n",
      "Processing Record 26 of Set 7 | skjervoy\n",
      "Processing Record 27 of Set 7 | halalo\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 7 | hobyo\n",
      "Processing Record 29 of Set 7 | kamenskoye\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 7 | egvekinot\n",
      "Processing Record 31 of Set 7 | moindou\n",
      "Processing Record 32 of Set 7 | jacareacanga\n",
      "Processing Record 33 of Set 7 | oistins\n",
      "Processing Record 34 of Set 7 | sioux lookout\n",
      "Processing Record 35 of Set 7 | lishui\n",
      "Processing Record 36 of Set 7 | nortelandia\n",
      "Processing Record 37 of Set 7 | talawdi\n",
      "City not found. Skipping...\n",
      "Processing Record 38 of Set 7 | linxia\n",
      "Processing Record 39 of Set 7 | walvis bay\n",
      "Processing Record 40 of Set 7 | lucea\n",
      "Processing Record 41 of Set 7 | one hundred mile house\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 7 | lithakia\n",
      "Processing Record 43 of Set 7 | orange\n",
      "Processing Record 44 of Set 7 | port hardy\n",
      "Processing Record 45 of Set 7 | azimur\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 7 | sept-iles\n",
      "Processing Record 47 of Set 7 | acapulco\n",
      "Processing Record 48 of Set 7 | rio rancho\n",
      "Processing Record 49 of Set 7 | tambura\n",
      "Processing Record 50 of Set 7 | honningsvag\n",
      "Processing Record 1 of Set 8 | staryy nadym\n",
      "Processing Record 2 of Set 8 | goya\n",
      "Processing Record 3 of Set 8 | ancud\n",
      "Processing Record 4 of Set 8 | derzhavinsk\n",
      "Processing Record 5 of Set 8 | arlit\n",
      "Processing Record 6 of Set 8 | el faiyum\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 8 | monrovia\n",
      "Processing Record 8 of Set 8 | ati\n",
      "Processing Record 9 of Set 8 | alibag\n",
      "Processing Record 10 of Set 8 | hualmay\n",
      "Processing Record 11 of Set 8 | saint marys\n",
      "Processing Record 12 of Set 8 | ambunti\n",
      "Processing Record 13 of Set 8 | doctor mora\n",
      "Processing Record 14 of Set 8 | pecos\n",
      "Processing Record 15 of Set 8 | kapuskasing\n",
      "Processing Record 16 of Set 8 | shache\n",
      "Processing Record 17 of Set 8 | leningradskiy\n",
      "Processing Record 18 of Set 8 | lompoc\n",
      "Processing Record 19 of Set 8 | saint-pierre\n",
      "Processing Record 20 of Set 8 | broome\n",
      "Processing Record 21 of Set 8 | saint-francois\n",
      "Processing Record 22 of Set 8 | rio gallegos\n",
      "Processing Record 23 of Set 8 | coolum beach\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 24 of Set 8 | alta floresta\n",
      "Processing Record 25 of Set 8 | ilulissat\n",
      "Processing Record 26 of Set 8 | dingle\n",
      "Processing Record 27 of Set 8 | jiddah\n",
      "City not found. Skipping...\n",
      "Processing Record 28 of Set 8 | calama\n",
      "Processing Record 29 of Set 8 | porto santo\n",
      "Processing Record 30 of Set 8 | krasnoselkup\n",
      "Processing Record 31 of Set 8 | borogontsy\n",
      "Processing Record 32 of Set 8 | kandrian\n",
      "Processing Record 33 of Set 8 | rochester\n",
      "Processing Record 34 of Set 8 | coihaique\n",
      "Processing Record 35 of Set 8 | angoche\n",
      "Processing Record 36 of Set 8 | aragarcas\n",
      "Processing Record 37 of Set 8 | salou\n",
      "Processing Record 38 of Set 8 | ndele\n",
      "Processing Record 39 of Set 8 | okha\n",
      "Processing Record 40 of Set 8 | uruguaiana\n",
      "Processing Record 41 of Set 8 | sambava\n",
      "Processing Record 42 of Set 8 | achisay\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 8 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 44 of Set 8 | necochea\n",
      "Processing Record 45 of Set 8 | cayenne\n",
      "Processing Record 46 of Set 8 | tanete\n",
      "Processing Record 47 of Set 8 | kununurra\n",
      "Processing Record 48 of Set 8 | jizan\n",
      "Processing Record 49 of Set 8 | cidreira\n",
      "Processing Record 50 of Set 8 | sakaiminato\n",
      "Processing Record 1 of Set 9 | rayachoti\n",
      "Processing Record 2 of Set 9 | guadalupe victoria\n",
      "Processing Record 3 of Set 9 | haimen\n",
      "Processing Record 4 of Set 9 | muriwai beach\n",
      "Processing Record 5 of Set 9 | xingtai\n",
      "Processing Record 6 of Set 9 | port blair\n",
      "Processing Record 7 of Set 9 | erzin\n",
      "Processing Record 8 of Set 9 | cordoba\n",
      "Processing Record 9 of Set 9 | chiredzi\n",
      "Processing Record 10 of Set 9 | meiganga\n",
      "Processing Record 11 of Set 9 | te anau\n",
      "Processing Record 12 of Set 9 | liverpool\n",
      "Processing Record 13 of Set 9 | morondava\n",
      "Processing Record 14 of Set 9 | sokoni\n",
      "Processing Record 15 of Set 9 | salinopolis\n",
      "Processing Record 16 of Set 9 | toamasina\n",
      "Processing Record 17 of Set 9 | caravelas\n",
      "Processing Record 18 of Set 9 | lauda-konigshofen\n",
      "Processing Record 19 of Set 9 | inta\n",
      "Processing Record 20 of Set 9 | general roca\n",
      "Processing Record 21 of Set 9 | shimoda\n",
      "Processing Record 22 of Set 9 | eureka\n",
      "Processing Record 23 of Set 9 | abu dhabi\n",
      "Processing Record 24 of Set 9 | sitka\n",
      "Processing Record 25 of Set 9 | bhadrachalam\n",
      "Processing Record 26 of Set 9 | nicoya\n",
      "Processing Record 27 of Set 9 | fort wellington\n",
      "Processing Record 28 of Set 9 | rexburg\n",
      "Processing Record 29 of Set 9 | narsaq\n",
      "Processing Record 30 of Set 9 | canon city\n",
      "Processing Record 31 of Set 9 | karamea\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 9 | inuvik\n",
      "Processing Record 33 of Set 9 | tretten\n",
      "Processing Record 34 of Set 9 | abalak\n",
      "Processing Record 35 of Set 9 | luanda\n",
      "Processing Record 36 of Set 9 | banjar\n",
      "Processing Record 37 of Set 9 | bonfim\n",
      "Processing Record 38 of Set 9 | muros\n",
      "Processing Record 39 of Set 9 | guamal\n",
      "Processing Record 40 of Set 9 | oswego\n",
      "Processing Record 41 of Set 9 | lata\n",
      "Processing Record 42 of Set 9 | lumphat\n",
      "Processing Record 43 of Set 9 | diu\n",
      "Processing Record 44 of Set 9 | plettenberg bay\n",
      "Processing Record 45 of Set 9 | mezen\n",
      "Processing Record 46 of Set 9 | evanston\n",
      "Processing Record 47 of Set 9 | tuatapere\n",
      "Processing Record 48 of Set 9 | sao felix do xingu\n",
      "Processing Record 49 of Set 9 | gat\n",
      "Processing Record 50 of Set 9 | synya\n",
      "Processing Record 1 of Set 10 | husavik\n",
      "Processing Record 2 of Set 10 | monterey\n",
      "Processing Record 3 of Set 10 | cabo san lucas\n",
      "Processing Record 4 of Set 10 | chuy\n",
      "Processing Record 5 of Set 10 | bom jesus\n",
      "Processing Record 6 of Set 10 | dauphin\n",
      "Processing Record 7 of Set 10 | vao\n",
      "Processing Record 8 of Set 10 | aljezur\n",
      "Processing Record 9 of Set 10 | havre-saint-pierre\n",
      "Processing Record 10 of Set 10 | west bend\n",
      "Processing Record 11 of Set 10 | metehara\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 10 | yerbogachen\n",
      "Processing Record 13 of Set 10 | nantucket\n",
      "Processing Record 14 of Set 10 | iqaluit\n",
      "Processing Record 15 of Set 10 | sao joao da barra\n",
      "Processing Record 16 of Set 10 | boca do acre\n",
      "Processing Record 17 of Set 10 | hambantota\n",
      "Processing Record 18 of Set 10 | pacific grove\n",
      "Processing Record 19 of Set 10 | alakurtti\n",
      "Processing Record 20 of Set 10 | illapel\n",
      "Processing Record 21 of Set 10 | road town\n",
      "Processing Record 22 of Set 10 | port-de-paix\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 10 | hastings\n",
      "Processing Record 24 of Set 10 | comodoro rivadavia\n",
      "Processing Record 25 of Set 10 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 10 | zamora\n",
      "Processing Record 27 of Set 10 | marzuq\n",
      "Processing Record 28 of Set 10 | mortka\n",
      "Processing Record 29 of Set 10 | kendari\n",
      "Processing Record 30 of Set 10 | placido de castro\n",
      "Processing Record 31 of Set 10 | vestmanna\n",
      "Processing Record 32 of Set 10 | beeville\n",
      "Processing Record 33 of Set 10 | ksenyevka\n",
      "City not found. Skipping...\n",
      "Processing Record 34 of Set 10 | takoradi\n",
      "Processing Record 35 of Set 10 | svetlyy\n",
      "Processing Record 36 of Set 10 | sinnar\n",
      "Processing Record 37 of Set 10 | fortuna\n",
      "Processing Record 38 of Set 10 | san-pedro\n",
      "Processing Record 39 of Set 10 | xinan\n",
      "Processing Record 40 of Set 10 | tacuarembo\n",
      "Processing Record 41 of Set 10 | skibbereen\n",
      "Processing Record 42 of Set 10 | la ronge\n",
      "Processing Record 43 of Set 10 | sayhat\n",
      "Processing Record 44 of Set 10 | wulanhaote\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 10 | prnjavor\n",
      "Processing Record 46 of Set 10 | aksu\n",
      "Processing Record 47 of Set 10 | rocha\n",
      "Processing Record 48 of Set 10 | vostok\n",
      "Processing Record 49 of Set 10 | parauna\n",
      "Processing Record 50 of Set 10 | port arthur\n",
      "Processing Record 1 of Set 11 | kumertau\n",
      "Processing Record 2 of Set 11 | kattivakkam\n",
      "Processing Record 3 of Set 11 | bad freienwalde\n",
      "Processing Record 4 of Set 11 | trairi\n",
      "Processing Record 5 of Set 11 | xichang\n",
      "Processing Record 6 of Set 11 | russell\n",
      "Processing Record 7 of Set 11 | uba\n",
      "Processing Record 8 of Set 11 | hvolsvollur\n",
      "Processing Record 9 of Set 11 | yilan\n",
      "Processing Record 10 of Set 11 | piacabucu\n",
      "Processing Record 11 of Set 11 | jieshi\n",
      "Processing Record 12 of Set 11 | la macarena\n",
      "Processing Record 13 of Set 11 | guiyang\n",
      "Processing Record 14 of Set 11 | port macquarie\n",
      "Processing Record 15 of Set 11 | fort nelson\n",
      "Processing Record 16 of Set 11 | chipaque\n",
      "Processing Record 17 of Set 11 | lakatoro\n",
      "Processing Record 18 of Set 11 | shingu\n",
      "Processing Record 19 of Set 11 | sembe\n",
      "Processing Record 20 of Set 11 | grass valley\n",
      "Processing Record 21 of Set 11 | mountain home\n",
      "Processing Record 22 of Set 11 | cascais\n",
      "Processing Record 23 of Set 11 | oussouye\n",
      "Processing Record 24 of Set 11 | anadyr\n",
      "Processing Record 25 of Set 11 | broken hill\n",
      "Processing Record 26 of Set 11 | mulege\n",
      "Processing Record 27 of Set 11 | okhotsk\n",
      "Processing Record 28 of Set 11 | formoso do araguaia\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 11 | quimper\n",
      "Processing Record 30 of Set 11 | quatre cocos\n",
      "Processing Record 31 of Set 11 | bowen\n",
      "Processing Record 32 of Set 11 | namibe\n",
      "Processing Record 33 of Set 11 | sivas\n",
      "Processing Record 34 of Set 11 | phek\n",
      "Processing Record 35 of Set 11 | manacapuru\n",
      "Processing Record 36 of Set 11 | skole\n",
      "Processing Record 37 of Set 11 | siparia\n",
      "Processing Record 38 of Set 11 | mitchell\n",
      "Processing Record 39 of Set 11 | wukari\n",
      "Processing Record 40 of Set 11 | neiafu\n",
      "Processing Record 41 of Set 11 | gravelbourg\n",
      "Processing Record 42 of Set 11 | bandar-e lengeh\n",
      "Processing Record 43 of Set 11 | mehamn\n",
      "Processing Record 44 of Set 11 | vilyuysk\n",
      "Processing Record 45 of Set 11 | ardistan\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 11 | yulara\n",
      "Processing Record 47 of Set 11 | itanhem\n",
      "Processing Record 48 of Set 11 | suphan buri\n",
      "Processing Record 49 of Set 11 | mga\n",
      "Processing Record 50 of Set 11 | mponela\n",
      "Processing Record 1 of Set 12 | ciudad bolivar\n",
      "Processing Record 2 of Set 12 | ambon\n",
      "Processing Record 3 of Set 12 | togur\n",
      "Processing Record 4 of Set 12 | ambodifototra\n",
      "City not found. Skipping...\n",
      "Processing Record 5 of Set 12 | makakilo city\n",
      "Processing Record 6 of Set 12 | cockburn town\n",
      "Processing Record 7 of Set 12 | samarai\n",
      "Processing Record 8 of Set 12 | bhanpuri\n",
      "Processing Record 9 of Set 12 | itarema\n",
      "Processing Record 10 of Set 12 | funadhoo\n",
      "Processing Record 11 of Set 12 | puerto madryn\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 12 of Set 12 | kibala\n",
      "Processing Record 13 of Set 12 | rosetta\n",
      "Processing Record 14 of Set 12 | kasaragod\n",
      "Processing Record 15 of Set 12 | koslan\n",
      "Processing Record 16 of Set 12 | beja\n",
      "Processing Record 17 of Set 12 | ous\n",
      "Processing Record 18 of Set 12 | coquimbo\n",
      "Processing Record 19 of Set 12 | burnie\n",
      "Processing Record 20 of Set 12 | banda aceh\n",
      "Processing Record 21 of Set 12 | diffa\n",
      "Processing Record 22 of Set 12 | mbanza-ngungu\n",
      "Processing Record 23 of Set 12 | barentu\n",
      "Processing Record 24 of Set 12 | tarko-sale\n",
      "Processing Record 25 of Set 12 | bilma\n",
      "Processing Record 26 of Set 12 | sigayevo\n",
      "Processing Record 27 of Set 12 | luce\n",
      "Processing Record 28 of Set 12 | mount darwin\n",
      "Processing Record 29 of Set 12 | louisbourg\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 12 | rorvik\n",
      "Processing Record 31 of Set 12 | slonim\n",
      "Processing Record 32 of Set 12 | san juan\n",
      "Processing Record 33 of Set 12 | sadao\n",
      "Processing Record 34 of Set 12 | ohara\n",
      "Processing Record 35 of Set 12 | alofi\n",
      "Processing Record 36 of Set 12 | panlaitan\n",
      "Processing Record 37 of Set 12 | kasangulu\n",
      "Processing Record 38 of Set 12 | gulfport\n",
      "Processing Record 39 of Set 12 | yanam\n",
      "Processing Record 40 of Set 12 | pahrump\n",
      "Processing Record 41 of Set 12 | tarudant\n",
      "City not found. Skipping...\n",
      "Processing Record 42 of Set 12 | bage\n",
      "Processing Record 43 of Set 12 | cartagena\n",
      "Processing Record 44 of Set 12 | jiuquan\n",
      "Processing Record 45 of Set 12 | haibowan\n",
      "City not found. Skipping...\n",
      "Processing Record 46 of Set 12 | palmer\n",
      "Processing Record 47 of Set 12 | burkhala\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 12 | abasolo\n",
      "Processing Record 49 of Set 12 | wilkie\n",
      "Processing Record 50 of Set 12 | heishan\n",
      "Processing Record 1 of Set 13 | roald\n",
      "Processing Record 2 of Set 13 | gourdon\n",
      "Processing Record 3 of Set 13 | benghazi\n",
      "Processing Record 4 of Set 13 | jardim\n",
      "Processing Record 5 of Set 13 | lalibela\n",
      "Processing Record 6 of Set 13 | balakhninskiy\n",
      "Processing Record 7 of Set 13 | kracheh\n",
      "City not found. Skipping...\n",
      "Processing Record 8 of Set 13 | tessalit\n",
      "Processing Record 9 of Set 13 | santiago\n",
      "Processing Record 10 of Set 13 | hofn\n",
      "Processing Record 11 of Set 13 | avera\n",
      "-----------------------------\n",
      "Data Retrieval Complete      \n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "# 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",
    "    # 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(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Guerrero Negro</td>\n",
       "      <td>27.9769</td>\n",
       "      <td>-114.0611</td>\n",
       "      <td>58.23</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>14.36</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Yuba City</td>\n",
       "      <td>39.1166</td>\n",
       "      <td>-121.6391</td>\n",
       "      <td>52.00</td>\n",
       "      <td>58</td>\n",
       "      <td>90</td>\n",
       "      <td>5.75</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tasiilaq</td>\n",
       "      <td>65.6145</td>\n",
       "      <td>-37.6368</td>\n",
       "      <td>30.20</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>5.75</td>\n",
       "      <td>GL</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>77</td>\n",
       "      <td>90</td>\n",
       "      <td>8.05</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Esperance</td>\n",
       "      <td>-33.8667</td>\n",
       "      <td>121.9000</td>\n",
       "      <td>80.60</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>8.05</td>\n",
       "      <td>AU</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kapaa</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>78.80</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>11.50</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bethel</td>\n",
       "      <td>41.3712</td>\n",
       "      <td>-73.4140</td>\n",
       "      <td>32.00</td>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>4.61</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East London</td>\n",
       "      <td>-33.0153</td>\n",
       "      <td>27.9116</td>\n",
       "      <td>71.73</td>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
       "      <td>16.22</td>\n",
       "      <td>ZA</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Busselton</td>\n",
       "      <td>-33.6500</td>\n",
       "      <td>115.3333</td>\n",
       "      <td>86.00</td>\n",
       "      <td>54</td>\n",
       "      <td>100</td>\n",
       "      <td>1.01</td>\n",
       "      <td>AU</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yellowknife</td>\n",
       "      <td>62.4560</td>\n",
       "      <td>-114.3525</td>\n",
       "      <td>-7.01</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>9.22</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0   Guerrero Negro  27.9769 -114.0611     58.23        71          50   \n",
       "1  South Yuba City  39.1166 -121.6391     52.00        58          90   \n",
       "2         Tasiilaq  65.6145  -37.6368     30.20        80          75   \n",
       "3      Tuktoyaktuk  69.4541 -133.0374     -7.60        77          90   \n",
       "4        Esperance -33.8667  121.9000     80.60        39          42   \n",
       "5            Kapaa  22.0752 -159.3190     78.80        73           1   \n",
       "6           Bethel  41.3712  -73.4140     32.00        64          20   \n",
       "7      East London -33.0153   27.9116     71.73        87          92   \n",
       "8        Busselton -33.6500  115.3333     86.00        54         100   \n",
       "9      Yellowknife  62.4560 -114.3525     -7.01        84           1   \n",
       "\n",
       "   Wind Speed Country                 Date  \n",
       "0       14.36      MX  2021-02-15 02:28:30  \n",
       "1        5.75      US  2021-02-15 02:28:30  \n",
       "2        5.75      GL  2021-02-15 02:28:30  \n",
       "3        8.05      CA  2021-02-15 02:28:30  \n",
       "4        8.05      AU  2021-02-15 02:28:31  \n",
       "5       11.50      US  2021-02-15 02:28:31  \n",
       "6        4.61      US  2021-02-15 02:28:31  \n",
       "7       16.22      ZA  2021-02-15 02:28:31  \n",
       "8        1.01      AU  2021-02-15 02:28:31  \n",
       "9        9.22      CA  2021-02-15 02:28:31  "
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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>Guerrero Negro</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>27.9769</td>\n",
       "      <td>-114.0611</td>\n",
       "      <td>58.23</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>14.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Yuba City</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>39.1166</td>\n",
       "      <td>-121.6391</td>\n",
       "      <td>52.00</td>\n",
       "      <td>58</td>\n",
       "      <td>90</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tasiilaq</td>\n",
       "      <td>GL</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>65.6145</td>\n",
       "      <td>-37.6368</td>\n",
       "      <td>30.20</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>77</td>\n",
       "      <td>90</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Esperance</td>\n",
       "      <td>AU</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "      <td>-33.8667</td>\n",
       "      <td>121.9000</td>\n",
       "      <td>80.60</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>8.05</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>550</th>\n",
       "      <td>Balakhninskiy</td>\n",
       "      <td>RU</td>\n",
       "      <td>2021-02-15 02:29:51</td>\n",
       "      <td>58.0106</td>\n",
       "      <td>114.2769</td>\n",
       "      <td>-17.97</td>\n",
       "      <td>81</td>\n",
       "      <td>58</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Tessalit</td>\n",
       "      <td>ML</td>\n",
       "      <td>2021-02-15 02:29:51</td>\n",
       "      <td>20.1986</td>\n",
       "      <td>1.0114</td>\n",
       "      <td>69.73</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>10.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>CL</td>\n",
       "      <td>2021-02-15 02:25:18</td>\n",
       "      <td>-33.4569</td>\n",
       "      <td>-70.6483</td>\n",
       "      <td>69.01</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>Hofn</td>\n",
       "      <td>IS</td>\n",
       "      <td>2021-02-15 02:29:52</td>\n",
       "      <td>64.2539</td>\n",
       "      <td>-15.2082</td>\n",
       "      <td>42.19</td>\n",
       "      <td>92</td>\n",
       "      <td>100</td>\n",
       "      <td>12.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>Avera</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:29:52</td>\n",
       "      <td>33.1940</td>\n",
       "      <td>-82.5271</td>\n",
       "      <td>42.80</td>\n",
       "      <td>95</td>\n",
       "      <td>90</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>555 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                City Country                 Date      Lat       Lng  \\\n",
       "0     Guerrero Negro      MX  2021-02-15 02:28:30  27.9769 -114.0611   \n",
       "1    South Yuba City      US  2021-02-15 02:28:30  39.1166 -121.6391   \n",
       "2           Tasiilaq      GL  2021-02-15 02:28:30  65.6145  -37.6368   \n",
       "3        Tuktoyaktuk      CA  2021-02-15 02:28:30  69.4541 -133.0374   \n",
       "4          Esperance      AU  2021-02-15 02:28:31 -33.8667  121.9000   \n",
       "..               ...     ...                  ...      ...       ...   \n",
       "550    Balakhninskiy      RU  2021-02-15 02:29:51  58.0106  114.2769   \n",
       "551         Tessalit      ML  2021-02-15 02:29:51  20.1986    1.0114   \n",
       "552         Santiago      CL  2021-02-15 02:25:18 -33.4569  -70.6483   \n",
       "553             Hofn      IS  2021-02-15 02:29:52  64.2539  -15.2082   \n",
       "554            Avera      US  2021-02-15 02:29:52  33.1940  -82.5271   \n",
       "\n",
       "     Max Temp  Humidity  Cloudiness  Wind Speed  \n",
       "0       58.23        71          50       14.36  \n",
       "1       52.00        58          90        5.75  \n",
       "2       30.20        80          75        5.75  \n",
       "3       -7.60        77          90        8.05  \n",
       "4       80.60        39          42        8.05  \n",
       "..        ...       ...         ...         ...  \n",
       "550    -17.97        81          58        0.78  \n",
       "551     69.73        19           0       10.11  \n",
       "552     69.01        72           0       10.36  \n",
       "553     42.19        92         100       12.30  \n",
       "554     42.80        95          90        4.61  \n",
       "\n",
       "[555 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_column_order = [\"City\", \"Country\", \"Date\", \"Lat\", \"Lng\", \"Max Temp\", \"Humidity\", \"Cloudiness\", \"Wind Speed\"]\n",
    "\n",
    "city_data_df = city_data_df[new_column_order]\n",
    "\n",
    "city_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": [
    "# Import time module\n",
    "import time\n",
    "\n",
    "# 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": 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"
    }
   ],
   "source": [
    "# Build the scatter plots 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",
    "# Save the figure.\n",
    "plt.savefig(\"weather_data/Fig2.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 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"
    }
   ],
   "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": 23,
   "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.xlabel('Latitude')\n",
    "    plt.ylabel(y_label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                      Rikitea\n",
       "Country                        PF\n",
       "Date          2021-02-15 02:28:32\n",
       "Lat                      -23.1203\n",
       "Lng                      -134.969\n",
       "Max Temp                    80.24\n",
       "Humidity                       76\n",
       "Cloudiness                     55\n",
       "Wind Speed                  16.55\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index13 = city_data_df.loc[13]\n",
    "index13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "550     True\n",
       "551     True\n",
       "552    False\n",
       "553     True\n",
       "554     True\n",
       "Name: Lat, Length: 555, dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df[\"Lat\"] >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Guerrero Negro</td>\n",
       "      <td>MX</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>27.9769</td>\n",
       "      <td>-114.0611</td>\n",
       "      <td>58.23</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>14.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Yuba City</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>39.1166</td>\n",
       "      <td>-121.6391</td>\n",
       "      <td>52.00</td>\n",
       "      <td>58</td>\n",
       "      <td>90</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tasiilaq</td>\n",
       "      <td>GL</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>65.6145</td>\n",
       "      <td>-37.6368</td>\n",
       "      <td>30.20</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuktoyaktuk</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-02-15 02:28:30</td>\n",
       "      <td>69.4541</td>\n",
       "      <td>-133.0374</td>\n",
       "      <td>-7.60</td>\n",
       "      <td>77</td>\n",
       "      <td>90</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kapaa</td>\n",
       "      <td>US</td>\n",
       "      <td>2021-02-15 02:28:31</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>78.80</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>11.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City Country                 Date      Lat       Lng  Max Temp  \\\n",
       "0   Guerrero Negro      MX  2021-02-15 02:28:30  27.9769 -114.0611     58.23   \n",
       "1  South Yuba City      US  2021-02-15 02:28:30  39.1166 -121.6391     52.00   \n",
       "2         Tasiilaq      GL  2021-02-15 02:28:30  65.6145  -37.6368     30.20   \n",
       "3      Tuktoyaktuk      CA  2021-02-15 02:28:30  69.4541 -133.0374     -7.60   \n",
       "5            Kapaa      US  2021-02-15 02:28:31  22.0752 -159.3190     78.80   \n",
       "\n",
       "   Humidity  Cloudiness  Wind Speed  \n",
       "0        71          50       14.36  \n",
       "1        58          90        5.75  \n",
       "2        80          75        5.75  \n",
       "3        77          90        8.05  \n",
       "5        73           1       11.50  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_data_df.loc[(city_data_df[\"Lat\"] >= 0)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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',(0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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',(-50,55))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(40,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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[\"Humidity\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\\n",
    "                        for % Humidity', '% Humidity',(-50,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\\n",
    "                        for % Cloudiness', '% Cloudiness',(40,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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[\"Cloudiness\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Southern Hemisphere \\n'\n",
    "                        'for % Cloudiness', '% Cloudiness',(-50,60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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[\"Wind Speed\"]\n",
    "# Call the function.\n",
    "plot_linear_regression(x_values, y_values,\n",
    "                       'Linear Regression on the Northern Hemisphere \\n'\n",
    "                        'for Wind Speed', 'Wind Speed',(40,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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'\n",
    "                        'for Wind Speed', 'Wind Speed',(-50,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}