WeatherPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55ac2fba",
   "metadata": {},
   "source": [
    "# weatherPy #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b123ed7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the dependencies.\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from citipy import citipy\n",
    "import json\n",
    "import requests\n",
    "# Import the time library and the datetime module from the datetime library \n",
    "import time\n",
    "from datetime import datetime\n",
    "# Import linregress\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bcd30ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the requests library.\n",
    "import requests\n",
    "# Import the API key.\n",
    "from config import weather_api_key\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4db013b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Output File (CSV)\n",
    "output_data_file = \"weather_data/weather_df.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "832135a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Range of latitudes and longitudes\n",
    "lat_range = (-90, 90)\n",
    "lng_range = (-180, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f273816",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the latitudes and longitudes to a list.\n",
    "#coordinates = list(lat_lngs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "edf93365",
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = []\n",
    "lat_lngs=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce84e6dc",
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1ff348b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "608"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify nearest city for each lat, lng combination\n",
    "for lat_lng in lat_lngs:\n",
    "    city = citipy.nearest_city(lat_lng[0], lat_lng[1]).city_name\n",
    "# If the city is unique, then add it to a our cities list\n",
    "    if city not in cities:\n",
    "        cities.append(city)\n",
    "\n",
    "# Print the city count to confirm sufficient count\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "531081b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list for holding the cities.\n",
    "units = \"Imperial\"\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?\"\n",
    "# Starting URL for Weather Map API Call.\n",
    "query_url = f\"{url}&units={units}&appid={weather_api_key}&q=\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b88027fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval     \n",
      "***********************\n"
     ]
    }
   ],
   "source": [
    "# Setting list for the loop call:\n",
    "city_data = []\n",
    "city_lat = []\n",
    "city_lng = []\n",
    "city_max_temp = []\n",
    "city_humidity = []\n",
    "city_clouds = []\n",
    "city_wind = []\n",
    "city_country = []\n",
    "city_date = []\n",
    "\n",
    "# define count of pass throughs\n",
    "record_count = 1\n",
    "set_count = 1\n",
    "# Loop through all the cities in our list.\n",
    "#for i in range(len(cities)):\n",
    "\n",
    "print(\"Beginning Data Retrieval     \")\n",
    "print(\"***********************\")\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5234b03f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 1 of Set 1 | avarua\n",
      "Processing Record 2 of Set 1 | punta arenas\n",
      "Processing Record 3 of Set 1 | harper\n",
      "Processing Record 4 of Set 1 | zhangjiakou\n",
      "Processing Record 5 of Set 1 | arraial do cabo\n",
      "Processing Record 6 of Set 1 | carnarvon\n",
      "Processing Record 7 of Set 1 | rikitea\n",
      "Processing Record 8 of Set 1 | vaini\n",
      "Processing Record 9 of Set 1 | busselton\n",
      "Processing Record 10 of Set 1 | puerto ayora\n",
      "Processing Record 11 of Set 1 | bandarbeyla\n",
      "Processing Record 12 of Set 1 | umzimvubu\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 1 | narsaq\n",
      "Processing Record 14 of Set 1 | barrow\n",
      "Processing Record 15 of Set 1 | sao joao da barra\n",
      "Processing Record 16 of Set 1 | kavaratti\n",
      "Processing Record 17 of Set 1 | zhigansk\n",
      "Processing Record 18 of Set 1 | thompson\n",
      "Processing Record 19 of Set 1 | barentsburg\n",
      "City not found. Skipping...\n",
      "Processing Record 20 of Set 1 | snezhnogorsk\n",
      "Processing Record 21 of Set 1 | port alfred\n",
      "Processing Record 22 of Set 1 | tsihombe\n",
      "City not found. Skipping...\n",
      "Processing Record 23 of Set 1 | lishan\n",
      "Processing Record 24 of Set 1 | dibaya\n",
      "City not found. Skipping...\n",
      "Processing Record 25 of Set 1 | hithadhoo\n",
      "Processing Record 26 of Set 1 | saint anthony\n",
      "Processing Record 27 of Set 1 | ushuaia\n",
      "Processing Record 28 of Set 1 | coahuayana\n",
      "Processing Record 29 of Set 1 | oktyabrskiy\n",
      "Processing Record 30 of Set 1 | cidreira\n",
      "Processing Record 31 of Set 1 | boden\n",
      "Processing Record 32 of Set 1 | longyearbyen\n",
      "Processing Record 33 of Set 1 | kalemie\n",
      "Processing Record 34 of Set 1 | dikson\n",
      "Processing Record 35 of Set 1 | souillac\n",
      "Processing Record 36 of Set 1 | khatanga\n",
      "Processing Record 37 of Set 1 | lavrentiya\n",
      "Processing Record 38 of Set 1 | provideniya\n",
      "Processing Record 39 of Set 1 | kermanshah\n",
      "Processing Record 40 of Set 1 | hilo\n",
      "Processing Record 41 of Set 1 | cape town\n",
      "Processing Record 42 of Set 1 | barranca\n",
      "Processing Record 43 of Set 1 | komsomolskiy\n",
      "Processing Record 44 of Set 1 | kavieng\n",
      "Processing Record 45 of Set 1 | mataura\n",
      "Processing Record 46 of Set 1 | hobart\n",
      "Processing Record 47 of Set 1 | umm kaddadah\n",
      "Processing Record 48 of Set 1 | ericeira\n",
      "Processing Record 49 of Set 1 | mahebourg\n",
      "Processing Record 50 of Set 1 | port hardy\n",
      "Processing Record 0 of Set 2 | butaritari\n",
      "Processing Record 1 of Set 2 | mount isa\n",
      "Processing Record 2 of Set 2 | petrolina\n",
      "Processing Record 3 of Set 2 | rio gallegos\n",
      "Processing Record 4 of Set 2 | malkara\n",
      "Processing Record 5 of Set 2 | turinsk\n",
      "Processing Record 6 of Set 2 | poum\n",
      "Processing Record 7 of Set 2 | ust-maya\n",
      "Processing Record 8 of Set 2 | mar del plata\n",
      "Processing Record 9 of Set 2 | sao filipe\n",
      "Processing Record 10 of Set 2 | airai\n",
      "Processing Record 11 of Set 2 | saint-philippe\n",
      "Processing Record 12 of Set 2 | nuuk\n",
      "Processing Record 13 of Set 2 | victoria\n",
      "Processing Record 14 of Set 2 | buariki\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 2 | praia da vitoria\n",
      "Processing Record 16 of Set 2 | hejobaba\n",
      "Processing Record 17 of Set 2 | mys shmidta\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 2 | aban\n",
      "Processing Record 19 of Set 2 | new norfolk\n",
      "Processing Record 20 of Set 2 | kamiiso\n",
      "Processing Record 21 of Set 2 | artigas\n",
      "Processing Record 22 of Set 2 | le pradet\n",
      "Processing Record 23 of Set 2 | illoqqortoormiut\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 2 | kapaa\n",
      "Processing Record 25 of Set 2 | prince george\n",
      "Processing Record 26 of Set 2 | jamestown\n",
      "Processing Record 27 of Set 2 | ostrovnoy\n",
      "Processing Record 28 of Set 2 | east london\n",
      "Processing Record 29 of Set 2 | nara\n",
      "Processing Record 30 of Set 2 | magadan\n",
      "Processing Record 31 of Set 2 | luderitz\n",
      "Processing Record 32 of Set 2 | sangar\n",
      "Processing Record 33 of Set 2 | bamiantong\n",
      "Processing Record 34 of Set 2 | key west\n",
      "Processing Record 35 of Set 2 | garowe\n",
      "Processing Record 36 of Set 2 | itarema\n",
      "Processing Record 37 of Set 2 | miro khan\n",
      "Processing Record 38 of Set 2 | severo-kurilsk\n",
      "Processing Record 39 of Set 2 | seoul\n",
      "Processing Record 40 of Set 2 | florianopolis\n",
      "Processing Record 41 of Set 2 | padang\n",
      "Processing Record 42 of Set 2 | kodiak\n",
      "Processing Record 43 of Set 2 | tilichiki\n",
      "Processing Record 44 of Set 2 | haines junction\n",
      "Processing Record 45 of Set 2 | pisco\n",
      "Processing Record 46 of Set 2 | touros\n",
      "Processing Record 47 of Set 2 | lebu\n",
      "Processing Record 48 of Set 2 | belushya guba\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 2 | los llanos de aridane\n",
      "Processing Record 0 of Set 3 | yar-sale\n",
      "Processing Record 1 of Set 3 | albany\n",
      "Processing Record 2 of Set 3 | kenai\n",
      "Processing Record 3 of Set 3 | katsuura\n",
      "Processing Record 4 of Set 3 | grand gaube\n",
      "Processing Record 5 of Set 3 | bose\n",
      "Processing Record 6 of Set 3 | qaanaaq\n",
      "Processing Record 7 of Set 3 | dingle\n",
      "Processing Record 8 of Set 3 | warqla\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 3 | schattdorf\n",
      "Processing Record 10 of Set 3 | tema\n",
      "Processing Record 11 of Set 3 | hami\n",
      "Processing Record 12 of Set 3 | bluff\n",
      "Processing Record 13 of Set 3 | anadyr\n",
      "Processing Record 14 of Set 3 | ribeira grande\n",
      "Processing Record 15 of Set 3 | yellowknife\n",
      "Processing Record 16 of Set 3 | burica\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 3 | marcona\n",
      "City not found. Skipping...\n",
      "Processing Record 18 of Set 3 | baykit\n",
      "Processing Record 19 of Set 3 | sitka\n",
      "Processing Record 20 of Set 3 | itoman\n",
      "Processing Record 21 of Set 3 | sanmenxia\n",
      "Processing Record 22 of Set 3 | chokurdakh\n",
      "Processing Record 23 of Set 3 | zhezkazgan\n",
      "Processing Record 24 of Set 3 | lompoc\n",
      "Processing Record 25 of Set 3 | mehamn\n",
      "Processing Record 26 of Set 3 | tiksi\n",
      "Processing Record 27 of Set 3 | bredasdorp\n",
      "Processing Record 28 of Set 3 | kailua\n",
      "Processing Record 29 of Set 3 | sept-iles\n",
      "Processing Record 30 of Set 3 | caconda\n",
      "Processing Record 31 of Set 3 | bathsheba\n",
      "Processing Record 32 of Set 3 | atuona\n",
      "Processing Record 33 of Set 3 | sioux lookout\n",
      "Processing Record 34 of Set 3 | nikolskoye\n",
      "Processing Record 35 of Set 3 | klyuchi\n",
      "Processing Record 36 of Set 3 | leningradskiy\n",
      "Processing Record 37 of Set 3 | torbay\n",
      "Processing Record 38 of Set 3 | georgetown\n",
      "Processing Record 39 of Set 3 | makakilo city\n",
      "Processing Record 40 of Set 3 | lolua\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 3 | acarau\n",
      "Processing Record 42 of Set 3 | evensk\n",
      "Processing Record 43 of Set 3 | kaitangata\n",
      "Processing Record 44 of Set 3 | yanan\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 3 | faya\n",
      "Processing Record 46 of Set 3 | shagonar\n",
      "Processing Record 47 of Set 3 | cam pha\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 3 | taolanaro\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 3 | tuktoyaktuk\n",
      "Processing Record 0 of Set 4 | enfield\n",
      "Processing Record 1 of Set 4 | tasiilaq\n",
      "Processing Record 2 of Set 4 | hasaki\n",
      "Processing Record 3 of Set 4 | svetlogorsk\n",
      "Processing Record 4 of Set 4 | neryungri\n",
      "Processing Record 5 of Set 4 | jeremie\n",
      "Processing Record 6 of Set 4 | zabol\n",
      "Processing Record 7 of Set 4 | huarmey\n",
      "Processing Record 8 of Set 4 | upernavik\n",
      "Processing Record 9 of Set 4 | san quintin\n",
      "Processing Record 10 of Set 4 | riyadh\n",
      "Processing Record 11 of Set 4 | ballina\n",
      "Processing Record 12 of Set 4 | samusu\n",
      "City not found. Skipping...\n",
      "Processing Record 13 of Set 4 | fonte boa\n",
      "Processing Record 14 of Set 4 | marsh harbour\n",
      "Processing Record 15 of Set 4 | porto novo\n",
      "Processing Record 16 of Set 4 | dongzhen\n",
      "Processing Record 17 of Set 4 | yerbogachen\n",
      "Processing Record 18 of Set 4 | uchaly\n",
      "Processing Record 19 of Set 4 | chuy\n",
      "Processing Record 20 of Set 4 | mullaitivu\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 4 | sioni\n",
      "Processing Record 22 of Set 4 | muyezerskiy\n",
      "Processing Record 23 of Set 4 | nyrob\n",
      "Processing Record 24 of Set 4 | antalaha\n",
      "Processing Record 25 of Set 4 | sentyabrskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 4 | broome\n",
      "Processing Record 27 of Set 4 | saint george\n",
      "Processing Record 28 of Set 4 | mogadishu\n",
      "Processing Record 29 of Set 4 | naze\n",
      "Processing Record 30 of Set 4 | adrar\n",
      "Processing Record 31 of Set 4 | superior\n",
      "Processing Record 32 of Set 4 | anar darreh\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 4 | ust-nera\n",
      "Processing Record 34 of Set 4 | hermanus\n",
      "Processing Record 35 of Set 4 | tumannyy\n",
      "City not found. Skipping...\n",
      "Processing Record 36 of Set 4 | oarta de jos\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 37 of Set 4 | xuddur\n",
      "Processing Record 38 of Set 4 | saleaula\n",
      "City not found. Skipping...\n",
      "Processing Record 39 of Set 4 | nuevo ideal\n",
      "Processing Record 40 of Set 4 | russellville\n",
      "Processing Record 41 of Set 4 | chahal\n",
      "Processing Record 42 of Set 4 | mecca\n",
      "Processing Record 43 of Set 4 | lindi\n",
      "Processing Record 44 of Set 4 | mutsamudu\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 4 | namatanai\n",
      "Processing Record 46 of Set 4 | meadow lake\n",
      "Processing Record 47 of Set 4 | akyab\n",
      "Processing Record 48 of Set 4 | satitoa\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 4 | ketchikan\n",
      "Processing Record 0 of Set 5 | pedreiras\n",
      "Processing Record 1 of Set 5 | tolaga bay\n",
      "Processing Record 2 of Set 5 | samfya\n",
      "Processing Record 3 of Set 5 | barrhead\n",
      "Processing Record 4 of Set 5 | cabras\n",
      "Processing Record 5 of Set 5 | ancud\n",
      "Processing Record 6 of Set 5 | kamenka\n",
      "Processing Record 7 of Set 5 | tabuk\n",
      "Processing Record 8 of Set 5 | hamilton\n",
      "Processing Record 9 of Set 5 | beringovskiy\n",
      "Processing Record 10 of Set 5 | nogliki\n",
      "Processing Record 11 of Set 5 | lake city\n",
      "Processing Record 12 of Set 5 | merrill\n",
      "Processing Record 13 of Set 5 | panzhihua\n",
      "Processing Record 14 of Set 5 | vila velha\n",
      "Processing Record 15 of Set 5 | vaitupu\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 5 | nosy varika\n",
      "Processing Record 17 of Set 5 | te anau\n",
      "Processing Record 18 of Set 5 | maku\n",
      "Processing Record 19 of Set 5 | bengkulu\n",
      "Processing Record 20 of Set 5 | sarangani\n",
      "Processing Record 21 of Set 5 | mapiri\n",
      "Processing Record 22 of Set 5 | tarko-sale\n",
      "Processing Record 23 of Set 5 | san andres\n",
      "Processing Record 24 of Set 5 | saldanha\n",
      "Processing Record 25 of Set 5 | barstow\n",
      "Processing Record 26 of Set 5 | vao\n",
      "Processing Record 27 of Set 5 | henties bay\n",
      "Processing Record 28 of Set 5 | shkotovo-22\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 5 | matagami\n",
      "Processing Record 30 of Set 5 | houma\n",
      "Processing Record 31 of Set 5 | foam lake\n",
      "Processing Record 32 of Set 5 | nizhneyansk\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 5 | ust-kachka\n",
      "Processing Record 34 of Set 5 | vestmannaeyjar\n",
      "Processing Record 35 of Set 5 | norman wells\n",
      "Processing Record 36 of Set 5 | punta gorda\n",
      "Processing Record 37 of Set 5 | zhicheng\n",
      "Processing Record 38 of Set 5 | mahanoro\n",
      "Processing Record 39 of Set 5 | castro\n",
      "Processing Record 40 of Set 5 | necochea\n",
      "Processing Record 41 of Set 5 | kokkola\n",
      "Processing Record 42 of Set 5 | sindand\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 5 | juneau\n",
      "Processing Record 44 of Set 5 | creel\n",
      "Processing Record 45 of Set 5 | kahului\n",
      "Processing Record 46 of Set 5 | mitsamiouli\n",
      "Processing Record 47 of Set 5 | faanui\n",
      "Processing Record 48 of Set 5 | brownwood\n",
      "Processing Record 49 of Set 5 | puerto madryn\n",
      "Processing Record 0 of Set 6 | san lorenzo\n",
      "Processing Record 1 of Set 6 | maniitsoq\n",
      "Processing Record 2 of Set 6 | jiaozuo\n",
      "Processing Record 3 of Set 6 | tigil\n",
      "Processing Record 4 of Set 6 | pinhao\n",
      "Processing Record 5 of Set 6 | pevek\n",
      "Processing Record 6 of Set 6 | onega\n",
      "Processing Record 7 of Set 6 | vanavara\n",
      "Processing Record 8 of Set 6 | codrington\n",
      "Processing Record 9 of Set 6 | del rio\n",
      "Processing Record 10 of Set 6 | margate\n",
      "Processing Record 11 of Set 6 | kuche\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 6 | aklavik\n",
      "Processing Record 13 of Set 6 | khorinsk\n",
      "Processing Record 14 of Set 6 | amderma\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 6 | okhotsk\n",
      "Processing Record 16 of Set 6 | changchun\n",
      "Processing Record 17 of Set 6 | cockburn town\n",
      "Processing Record 18 of Set 6 | san antonio\n",
      "Processing Record 19 of Set 6 | luwuk\n",
      "Processing Record 20 of Set 6 | karlskrona\n",
      "Processing Record 21 of Set 6 | endicott\n",
      "Processing Record 22 of Set 6 | port moresby\n",
      "Processing Record 23 of Set 6 | grindavik\n",
      "Processing Record 24 of Set 6 | monte patria\n",
      "Processing Record 25 of Set 6 | tuatapere\n",
      "Processing Record 26 of Set 6 | geraldton\n",
      "Processing Record 27 of Set 6 | bethel\n",
      "Processing Record 28 of Set 6 | pinotepa nacional\n",
      "Processing Record 29 of Set 6 | vero beach\n",
      "Processing Record 30 of Set 6 | talnakh\n",
      "Processing Record 31 of Set 6 | denpasar\n",
      "Processing Record 32 of Set 6 | sabang\n",
      "Processing Record 33 of Set 6 | tautira\n",
      "Processing Record 34 of Set 6 | north platte\n",
      "Processing Record 35 of Set 6 | san buenaventura\n",
      "Processing Record 36 of Set 6 | marovoay\n",
      "Processing Record 37 of Set 6 | gornyak\n",
      "Processing Record 38 of Set 6 | clyde river\n",
      "Processing Record 39 of Set 6 | russell\n",
      "Processing Record 40 of Set 6 | malgrat de mar\n",
      "Processing Record 41 of Set 6 | kloulklubed\n",
      "Processing Record 42 of Set 6 | phonhong\n",
      "Processing Record 43 of Set 6 | otavi\n",
      "Processing Record 44 of Set 6 | lorengau\n",
      "Processing Record 45 of Set 6 | cabo san lucas\n",
      "Processing Record 46 of Set 6 | iqaluit\n",
      "Processing Record 47 of Set 6 | ponta do sol\n",
      "Processing Record 48 of Set 6 | port elizabeth\n",
      "Processing Record 49 of Set 6 | misratah\n",
      "Processing Record 0 of Set 7 | sola\n",
      "Processing Record 1 of Set 7 | tello\n",
      "Processing Record 2 of Set 7 | abu samrah\n",
      "Processing Record 3 of Set 7 | altay\n",
      "Processing Record 4 of Set 7 | laguna\n",
      "Processing Record 5 of Set 7 | kuytun\n",
      "Processing Record 6 of Set 7 | narrabri\n",
      "Processing Record 7 of Set 7 | raudeberg\n",
      "Processing Record 8 of Set 7 | mogochin\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 7 | tateyama\n",
      "Processing Record 10 of Set 7 | taoudenni\n",
      "Processing Record 11 of Set 7 | hammerfest\n",
      "Processing Record 12 of Set 7 | hirara\n",
      "Processing Record 13 of Set 7 | abnub\n",
      "Processing Record 14 of Set 7 | dharampuri\n",
      "Processing Record 15 of Set 7 | san francisco\n",
      "Processing Record 16 of Set 7 | muroto\n",
      "Processing Record 17 of Set 7 | la ligua\n",
      "Processing Record 18 of Set 7 | buluang\n",
      "Processing Record 19 of Set 7 | ashqelon\n",
      "Processing Record 20 of Set 7 | cayenne\n",
      "Processing Record 21 of Set 7 | biak\n",
      "Processing Record 22 of Set 7 | hambantota\n",
      "Processing Record 23 of Set 7 | izumo\n",
      "Processing Record 24 of Set 7 | lagoa\n",
      "Processing Record 25 of Set 7 | hay river\n",
      "Processing Record 26 of Set 7 | jodar\n",
      "Processing Record 27 of Set 7 | alice springs\n",
      "Processing Record 28 of Set 7 | nyuzen\n",
      "Processing Record 29 of Set 7 | lazaro cardenas\n",
      "Processing Record 30 of Set 7 | flin flon\n",
      "Processing Record 31 of Set 7 | santo antonio do ica\n",
      "Processing Record 32 of Set 7 | toftir\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 7 | bermejo\n",
      "Processing Record 34 of Set 7 | comodoro rivadavia\n",
      "Processing Record 35 of Set 7 | fort nelson\n",
      "Processing Record 36 of Set 7 | zistersdorf\n",
      "Processing Record 37 of Set 7 | sfantu gheorghe\n",
      "Processing Record 38 of Set 7 | cherskiy\n",
      "Processing Record 39 of Set 7 | srednekolymsk\n",
      "Processing Record 40 of Set 7 | husavik\n",
      "Processing Record 41 of Set 7 | atambua\n",
      "Processing Record 42 of Set 7 | canitas\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 7 | araouane\n",
      "Processing Record 44 of Set 7 | las vegas\n",
      "Processing Record 45 of Set 7 | guajara\n",
      "Processing Record 46 of Set 7 | pangkalanbuun\n",
      "Processing Record 47 of Set 7 | la orilla\n",
      "Processing Record 48 of Set 7 | yirol\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 7 | grenaa\n",
      "Processing Record 0 of Set 8 | ixtapa\n",
      "Processing Record 1 of Set 8 | ussel\n",
      "Processing Record 2 of Set 8 | vila franca do campo\n",
      "Processing Record 3 of Set 8 | diamantina\n",
      "Processing Record 4 of Set 8 | nanchong\n",
      "Processing Record 5 of Set 8 | attawapiskat\n",
      "City not found. Skipping...\n",
      "Processing Record 6 of Set 8 | beloha\n",
      "Processing Record 7 of Set 8 | rio grande\n",
      "Processing Record 8 of Set 8 | luganville\n",
      "Processing Record 9 of Set 8 | kudahuvadhoo\n",
      "Processing Record 10 of Set 8 | berezanka\n",
      "Processing Record 11 of Set 8 | caravelas\n",
      "Processing Record 12 of Set 8 | pericos\n",
      "Processing Record 13 of Set 8 | samarinda\n",
      "Processing Record 14 of Set 8 | callaguip\n",
      "Processing Record 15 of Set 8 | sao goncalo dos campos\n",
      "Processing Record 16 of Set 8 | toliary\n",
      "City not found. Skipping...\n",
      "Processing Record 17 of Set 8 | zelenoborskiy\n",
      "Processing Record 18 of Set 8 | hannibal\n",
      "Processing Record 19 of Set 8 | esperance\n",
      "Processing Record 20 of Set 8 | san patricio\n",
      "Processing Record 21 of Set 8 | pandan\n",
      "Processing Record 22 of Set 8 | ahipara\n",
      "Processing Record 23 of Set 8 | moranbah\n",
      "Processing Record 24 of Set 8 | kushima\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 25 of Set 8 | san pedro\n",
      "Processing Record 26 of Set 8 | mudgee\n",
      "Processing Record 27 of Set 8 | bharatpur\n",
      "Processing Record 28 of Set 8 | hobyo\n",
      "Processing Record 29 of Set 8 | ferrol\n",
      "Processing Record 30 of Set 8 | hailar\n",
      "Processing Record 31 of Set 8 | kielce\n",
      "Processing Record 32 of Set 8 | tawkar\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 8 | vermilion\n",
      "Processing Record 34 of Set 8 | beruwala\n",
      "Processing Record 35 of Set 8 | kaniama\n",
      "Processing Record 36 of Set 8 | mookane\n",
      "Processing Record 37 of Set 8 | muros\n",
      "Processing Record 38 of Set 8 | villa bruzual\n",
      "Processing Record 39 of Set 8 | saskylakh\n",
      "Processing Record 40 of Set 8 | kalmunai\n",
      "Processing Record 41 of Set 8 | tucuman\n",
      "Processing Record 42 of Set 8 | ituni\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 8 | bowen\n",
      "Processing Record 44 of Set 8 | palana\n",
      "Processing Record 45 of Set 8 | wooster\n",
      "Processing Record 46 of Set 8 | guozhen\n",
      "Processing Record 47 of Set 8 | ostrovskoye\n",
      "Processing Record 48 of Set 8 | ust-kishert\n",
      "Processing Record 49 of Set 8 | usinsk\n",
      "Processing Record 0 of Set 9 | camacha\n",
      "Processing Record 1 of Set 9 | palabuhanratu\n",
      "City not found. Skipping...\n",
      "Processing Record 2 of Set 9 | ondorhaan\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 9 | fortuna\n",
      "Processing Record 4 of Set 9 | wewak\n",
      "Processing Record 5 of Set 9 | resavica\n",
      "Processing Record 6 of Set 9 | palmer\n",
      "Processing Record 7 of Set 9 | tanout\n",
      "Processing Record 8 of Set 9 | pacific grove\n",
      "Processing Record 9 of Set 9 | nanning\n",
      "Processing Record 10 of Set 9 | sao miguel do araguaia\n",
      "Processing Record 11 of Set 9 | kualakapuas\n",
      "Processing Record 12 of Set 9 | pangnirtung\n",
      "Processing Record 13 of Set 9 | paka\n",
      "Processing Record 14 of Set 9 | kananga\n",
      "Processing Record 15 of Set 9 | pakxan\n",
      "Processing Record 16 of Set 9 | peterhead\n",
      "Processing Record 17 of Set 9 | plettenberg bay\n",
      "Processing Record 18 of Set 9 | grand river south east\n",
      "City not found. Skipping...\n",
      "Processing Record 19 of Set 9 | korla\n",
      "Processing Record 20 of Set 9 | matay\n",
      "Processing Record 21 of Set 9 | koumac\n",
      "Processing Record 22 of Set 9 | trenggalek\n",
      "Processing Record 23 of Set 9 | talcahuano\n",
      "Processing Record 24 of Set 9 | the pas\n",
      "Processing Record 25 of Set 9 | tanete\n",
      "Processing Record 26 of Set 9 | colwyn bay\n",
      "Processing Record 27 of Set 9 | ilulissat\n",
      "Processing Record 28 of Set 9 | vestbygda\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 9 | guerrero negro\n",
      "Processing Record 30 of Set 9 | constitucion\n",
      "Processing Record 31 of Set 9 | kokemaki\n",
      "Processing Record 32 of Set 9 | freeport\n",
      "Processing Record 33 of Set 9 | vychegodskiy\n",
      "Processing Record 34 of Set 9 | romny\n",
      "Processing Record 35 of Set 9 | dale\n",
      "Processing Record 36 of Set 9 | bagdarin\n",
      "Processing Record 37 of Set 9 | lichinga\n",
      "Processing Record 38 of Set 9 | chicama\n",
      "Processing Record 39 of Set 9 | buala\n",
      "Processing Record 40 of Set 9 | dunedin\n",
      "Processing Record 41 of Set 9 | inhambane\n",
      "Processing Record 42 of Set 9 | phan rang\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 9 | kinanah\n",
      "City not found. Skipping...\n",
      "Processing Record 44 of Set 9 | imbituba\n",
      "Processing Record 45 of Set 9 | yaan\n",
      "Processing Record 46 of Set 9 | spencer\n",
      "Processing Record 47 of Set 9 | phumi samraong\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 9 | bitkine\n",
      "Processing Record 49 of Set 9 | bogandinskiy\n",
      "Processing Record 0 of Set 10 | nanortalik\n",
      "Processing Record 1 of Set 10 | jaguarao\n",
      "Processing Record 2 of Set 10 | lao cai\n",
      "Processing Record 3 of Set 10 | kibala\n",
      "Processing Record 4 of Set 10 | gudari\n",
      "Processing Record 5 of Set 10 | coihaique\n",
      "Processing Record 6 of Set 10 | shaki\n",
      "Processing Record 7 of Set 10 | ila\n",
      "Processing Record 8 of Set 10 | haapiti\n",
      "Processing Record 9 of Set 10 | sechelt\n",
      "Processing Record 10 of Set 10 | isangel\n",
      "Processing Record 11 of Set 10 | utiroa\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 10 | juba\n",
      "Processing Record 13 of Set 10 | andenes\n",
      "Processing Record 14 of Set 10 | mrirt\n",
      "City not found. Skipping...\n",
      "Processing Record 15 of Set 10 | whitecourt\n",
      "Processing Record 16 of Set 10 | great falls\n",
      "Processing Record 17 of Set 10 | nianzishan\n",
      "Processing Record 18 of Set 10 | keti bandar\n",
      "Processing Record 19 of Set 10 | karratha\n",
      "Processing Record 20 of Set 10 | nogent-le-rotrou\n",
      "Processing Record 21 of Set 10 | carballo\n",
      "Processing Record 22 of Set 10 | yulara\n",
      "Processing Record 23 of Set 10 | bentiu\n",
      "Processing Record 24 of Set 10 | stellenbosch\n",
      "Processing Record 25 of Set 10 | nabire\n",
      "Processing Record 26 of Set 10 | salem\n",
      "Processing Record 27 of Set 10 | imperia\n",
      "Processing Record 28 of Set 10 | kazachinskoye\n",
      "Processing Record 29 of Set 10 | kommunar\n",
      "Processing Record 30 of Set 10 | ponta delgada\n",
      "Processing Record 31 of Set 10 | solginskiy\n",
      "Processing Record 32 of Set 10 | shache\n",
      "Processing Record 33 of Set 10 | erzincan\n",
      "Processing Record 34 of Set 10 | meulaboh\n",
      "Processing Record 35 of Set 10 | ourinhos\n",
      "Processing Record 36 of Set 10 | rila\n",
      "Processing Record 37 of Set 10 | pahrump\n",
      "Processing Record 38 of Set 10 | severo-yeniseyskiy\n",
      "Processing Record 39 of Set 10 | cap malheureux\n",
      "Processing Record 40 of Set 10 | susanville\n",
      "Processing Record 41 of Set 10 | half moon bay\n",
      "Processing Record 42 of Set 10 | taltal\n",
      "Processing Record 43 of Set 10 | bambous virieux\n",
      "Processing Record 44 of Set 10 | palu\n",
      "Processing Record 45 of Set 10 | atoyac\n",
      "Processing Record 46 of Set 10 | deer lake\n",
      "Processing Record 47 of Set 10 | namibe\n",
      "Processing Record 48 of Set 10 | wahran\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 10 | saint-joseph\n",
      "Processing Record 0 of Set 11 | gizo\n",
      "Processing Record 1 of Set 11 | deputatskiy\n",
      "Processing Record 2 of Set 11 | linjiang\n",
      "Processing Record 3 of Set 11 | rorvik\n",
      "Processing Record 4 of Set 11 | priargunsk\n",
      "Processing Record 5 of Set 11 | vila\n",
      "Processing Record 6 of Set 11 | olinda\n",
      "Processing Record 7 of Set 11 | ilesha\n",
      "Processing Record 8 of Set 11 | jiwani\n",
      "Processing Record 9 of Set 11 | mlonggo\n",
      "Processing Record 10 of Set 11 | takoradi\n",
      "Processing Record 11 of Set 11 | burhaniye\n",
      "Processing Record 12 of Set 11 | kimbe\n",
      "Processing Record 13 of Set 11 | balimo\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 11 | sironko\n",
      "Processing Record 15 of Set 11 | bollnas\n",
      "Processing Record 16 of Set 11 | belaya gora\n",
      "Processing Record 17 of Set 11 | brumunddal\n",
      "Processing Record 18 of Set 11 | farah\n",
      "Processing Record 19 of Set 11 | krasnoselkup\n",
      "Processing Record 20 of Set 11 | wadi musa\n",
      "Processing Record 21 of Set 11 | doctor pedro p. pena\n",
      "City not found. Skipping...\n",
      "Processing Record 22 of Set 11 | vostok\n",
      "Processing Record 23 of Set 11 | port said\n",
      "Processing Record 24 of Set 11 | rundu\n",
      "Processing Record 25 of Set 11 | tazovskiy\n",
      "Processing Record 26 of Set 11 | balabac\n",
      "Processing Record 27 of Set 11 | mahibadhoo\n",
      "Processing Record 28 of Set 11 | grand centre\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 11 | languyan\n",
      "Processing Record 30 of Set 11 | tall kayf\n",
      "Processing Record 31 of Set 11 | christchurch\n",
      "Processing Record 32 of Set 11 | tura\n",
      "Processing Record 33 of Set 11 | bara\n",
      "Processing Record 34 of Set 11 | port blair\n",
      "Processing Record 35 of Set 11 | chekhov\n",
      "Processing Record 36 of Set 11 | nizhniy chir\n",
      "Processing Record 37 of Set 11 | dong hoi\n",
      "Processing Record 38 of Set 11 | asau\n",
      "Processing Record 39 of Set 11 | najran\n",
      "Processing Record 40 of Set 11 | silifke\n",
      "Processing Record 41 of Set 11 | poyarkovo\n",
      "Processing Record 42 of Set 11 | tidore\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 11 | kapit\n",
      "Processing Record 44 of Set 11 | jesus maria\n",
      "Processing Record 45 of Set 11 | zeya\n",
      "Processing Record 46 of Set 11 | taiobeiras\n",
      "Processing Record 47 of Set 11 | champerico\n",
      "Processing Record 48 of Set 11 | chegdomyn\n",
      "Processing Record 49 of Set 11 | port-gentil\n",
      "Processing Record 0 of Set 12 | sampit\n",
      "Processing Record 1 of Set 12 | ohrid\n",
      "Processing Record 2 of Set 12 | labuhan\n",
      "Processing Record 3 of Set 12 | chekmagush\n",
      "Processing Record 4 of Set 12 | sobolevo\n",
      "Processing Record 5 of Set 12 | broken hill\n",
      "Processing Record 6 of Set 12 | ayan\n",
      "Processing Record 7 of Set 12 | roald\n",
      "Processing Record 8 of Set 12 | djenne\n",
      "Processing Record 9 of Set 12 | shaikhpura\n",
      "Processing Record 10 of Set 12 | griffith\n",
      "Processing Record 11 of Set 12 | gondanglegi\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 12 of Set 12 | vargashi\n",
      "Processing Record 13 of Set 12 | aranos\n",
      "Processing Record 14 of Set 12 | vardo\n",
      "Processing Record 15 of Set 12 | idrinskoye\n",
      "Processing Record 16 of Set 12 | carutapera\n",
      "Processing Record 17 of Set 12 | kampot\n",
      "Processing Record 18 of Set 12 | troitskoye\n",
      "Processing Record 19 of Set 12 | ko samui\n",
      "Processing Record 20 of Set 12 | pula\n",
      "Processing Record 21 of Set 12 | mandalgovi\n",
      "Processing Record 22 of Set 12 | carberry\n",
      "Processing Record 23 of Set 12 | finschhafen\n",
      "Processing Record 24 of Set 12 | bilma\n",
      "Processing Record 25 of Set 12 | molde\n",
      "Processing Record 26 of Set 12 | mumford\n",
      "Processing Record 27 of Set 12 | dniprorudne\n",
      "Processing Record 28 of Set 12 | albufeira\n",
      "Processing Record 29 of Set 12 | athabasca\n",
      "Processing Record 30 of Set 12 | coquimbo\n",
      "Processing Record 31 of Set 12 | kalutara\n",
      "Processing Record 32 of Set 12 | choya\n",
      "Processing Record 33 of Set 12 | tucuma\n",
      "Processing Record 34 of Set 12 | cuichapa\n",
      "Processing Record 35 of Set 12 | mbanza-ngungu\n",
      "Processing Record 36 of Set 12 | hilton head island\n",
      "Processing Record 37 of Set 12 | petropavlovsk-kamchatskiy\n",
      "Processing Record 38 of Set 12 | ipora\n",
      "Processing Record 39 of Set 12 | yumen\n",
      "Processing Record 40 of Set 12 | great bend\n",
      "Processing Record 41 of Set 12 | lincolnton\n",
      "Processing Record 42 of Set 12 | saint-pierre\n",
      "Processing Record 43 of Set 12 | makokou\n",
      "Processing Record 44 of Set 12 | kruisfontein\n",
      "Processing Record 45 of Set 12 | riberalta\n",
      "Processing Record 46 of Set 12 | tual\n",
      "Processing Record 47 of Set 12 | bolungarvik\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 12 | urdzhar\n",
      "City not found. Skipping...\n",
      "Processing Record 49 of Set 12 | dingzhou\n",
      "Processing Record 0 of Set 13 | dubovskoye\n",
      "Processing Record 1 of Set 13 | jacareacanga\n",
      "Processing Record 2 of Set 13 | sretensk\n",
      "Processing Record 3 of Set 13 | kiboga\n",
      "Processing Record 4 of Set 13 | calabozo\n",
      "Processing Record 5 of Set 13 | quatre cocos\n",
      "Processing Record 6 of Set 13 | wageningen\n",
      "Processing Record 7 of Set 13 | batagay\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",
    "# Create endpoint URL with each city.\n",
    "    city_url = query_url + city\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 = 0\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",
    "# Run an API request for each of the cities.\n",
    "    try:\n",
    "# Parse the JSON and retrieve data.\n",
    "        response = requests.get(city_url).json()\n",
    "# Parse out the needed data.\n",
    "        city_data.append(response[\"name\"])\n",
    "        city_lat.append(response[\"coord\"][\"lat\"])\n",
    "        city_lng.append(response[\"coord\"][\"lon\"])\n",
    "        city_max_temp.append(response[\"main\"][\"temp_max\"])\n",
    "        city_humidity.append(response[\"main\"][\"humidity\"])\n",
    "        city_clouds.append(response[\"clouds\"][\"all\"])\n",
    "        city_wind.append(response[\"wind\"][\"speed\"])\n",
    "        city_country.append(response[\"sys\"][\"country\"])\n",
    "        city_date.append(response[\"dt\"])\n",
    "        \n",
    "        #to alleviate getting locked\n",
    "        time.sleep(2) \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",
    "    \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": 12,
   "id": "1e674fd9",
   "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>Humidity</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avarua</td>\n",
       "      <td>CK</td>\n",
       "      <td>1638654016</td>\n",
       "      <td>83</td>\n",
       "      <td>-21.2078</td>\n",
       "      <td>-159.7750</td>\n",
       "      <td>78.85</td>\n",
       "      <td>40</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Punta Arenas</td>\n",
       "      <td>CL</td>\n",
       "      <td>1638653661</td>\n",
       "      <td>50</td>\n",
       "      <td>-53.1500</td>\n",
       "      <td>-70.9167</td>\n",
       "      <td>55.51</td>\n",
       "      <td>40</td>\n",
       "      <td>25.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harper</td>\n",
       "      <td>LR</td>\n",
       "      <td>1638654020</td>\n",
       "      <td>81</td>\n",
       "      <td>4.3750</td>\n",
       "      <td>-7.7169</td>\n",
       "      <td>79.90</td>\n",
       "      <td>6</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Zhangjiakou</td>\n",
       "      <td>CN</td>\n",
       "      <td>1638654023</td>\n",
       "      <td>45</td>\n",
       "      <td>40.8100</td>\n",
       "      <td>114.8794</td>\n",
       "      <td>28.35</td>\n",
       "      <td>0</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arraial do Cabo</td>\n",
       "      <td>BR</td>\n",
       "      <td>1638654025</td>\n",
       "      <td>69</td>\n",
       "      <td>-22.9661</td>\n",
       "      <td>-42.0278</td>\n",
       "      <td>76.96</td>\n",
       "      <td>40</td>\n",
       "      <td>10.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Carnarvon</td>\n",
       "      <td>AU</td>\n",
       "      <td>1638654028</td>\n",
       "      <td>94</td>\n",
       "      <td>-24.8667</td>\n",
       "      <td>113.6333</td>\n",
       "      <td>69.87</td>\n",
       "      <td>0</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rikitea</td>\n",
       "      <td>PF</td>\n",
       "      <td>1638653748</td>\n",
       "      <td>71</td>\n",
       "      <td>-23.1203</td>\n",
       "      <td>-134.9692</td>\n",
       "      <td>76.06</td>\n",
       "      <td>40</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Vaini</td>\n",
       "      <td>TO</td>\n",
       "      <td>1638653867</td>\n",
       "      <td>78</td>\n",
       "      <td>-21.2000</td>\n",
       "      <td>-175.2000</td>\n",
       "      <td>82.56</td>\n",
       "      <td>40</td>\n",
       "      <td>13.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Busselton</td>\n",
       "      <td>AU</td>\n",
       "      <td>1638653838</td>\n",
       "      <td>68</td>\n",
       "      <td>-33.6500</td>\n",
       "      <td>115.3333</td>\n",
       "      <td>56.08</td>\n",
       "      <td>1</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Puerto Ayora</td>\n",
       "      <td>EC</td>\n",
       "      <td>1638653688</td>\n",
       "      <td>83</td>\n",
       "      <td>-0.7393</td>\n",
       "      <td>-90.3518</td>\n",
       "      <td>75.16</td>\n",
       "      <td>78</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City Country        Date  Humidity      Lat       Lng  Max Temp  \\\n",
       "0           Avarua      CK  1638654016        83 -21.2078 -159.7750     78.85   \n",
       "1     Punta Arenas      CL  1638653661        50 -53.1500  -70.9167     55.51   \n",
       "2           Harper      LR  1638654020        81   4.3750   -7.7169     79.90   \n",
       "3      Zhangjiakou      CN  1638654023        45  40.8100  114.8794     28.35   \n",
       "4  Arraial do Cabo      BR  1638654025        69 -22.9661  -42.0278     76.96   \n",
       "5        Carnarvon      AU  1638654028        94 -24.8667  113.6333     69.87   \n",
       "6          Rikitea      PF  1638653748        71 -23.1203 -134.9692     76.06   \n",
       "7            Vaini      TO  1638653867        78 -21.2000 -175.2000     82.56   \n",
       "8        Busselton      AU  1638653838        68 -33.6500  115.3333     56.08   \n",
       "9     Puerto Ayora      EC  1638653688        83  -0.7393  -90.3518     75.16   \n",
       "\n",
       "   Cloudiness  Wind Speed  \n",
       "0          40        5.75  \n",
       "1          40       25.32  \n",
       "2           6        8.05  \n",
       "3           0        0.27  \n",
       "4          40       10.36  \n",
       "5           0        5.75  \n",
       "6          40       10.00  \n",
       "7          40       13.80  \n",
       "8           1       10.65  \n",
       "9          78        7.70  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new dataframe for cities data:\n",
    "city_data_df ={\n",
    "    \"City\": city_data, \n",
    "    \"Country\": city_country,\n",
    "    \"Date\": city_date,\n",
    "    \"Humidity\": city_humidity,\n",
    "    \"Lat\": city_lat, \n",
    "    \"Lng\": city_lng, \n",
    "    \"Max Temp\": city_max_temp,\n",
    "    \"Cloudiness\": city_clouds,\n",
    "    \"Wind Speed\": city_wind,\n",
    "    }\n",
    "\n",
    "weather_df = pd.DataFrame(city_data_df)\n",
    "weather_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "87720fab",
   "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": 38,
   "id": "38317541",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporting the city data to .csv file:\n",
    "\n",
    "weather_df.to_csv(\"weather_database/WeatherPy_database.csv\", index=False, header=True)\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": 36,
   "id": "e4ffbeef",
   "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",
    "# Plot 1: Latitude vs. Temperature Plot\n",
    "x_values = lats\n",
    "y_values = max_temps\n",
    "\n",
    "plt.scatter(x_values,y_values, marker = \"o\", facecolor = \"steelblue\", edgecolor=\"black\")\n",
    "\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Max Temprature (F)')\n",
    "plt.title(\"City Latitude vs. Max Temperature (%s)\" % time.strftime(\"%x\"))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2cee076f",
   "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/cityLatitudeVsHumidity.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee8dc442",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "282b8843",
   "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/CityLatitudeVSCloudiness.png\")\n",
    "# Show plot.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "054713c7",
   "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/CityLatitudeVSWindSpeed.png\")\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fa1636",
   "metadata": {},
   "source": [
    "# Linear regression\n",
    "'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "faf5e235",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = -0.45x + 92.94\n",
      "The p-value is: 0.011\n"
     ]
    }
   ],
   "source": [
    "# Import linear regression from the SciPy stats module.\n",
    "from scipy.stats import linregress\n",
    "# Create an equal number of latitudes and temperatures.\n",
    "lats = [42.5, 43.9, 8.1, 36.8, 79.9, 69.1, 25.7, 15.3, 12.7, 64.5]\n",
    "temps = [80.5, 75.3, 90.9, 90.0, 40.4, 62.3, 85.4, 79.6, 72.5, 72.0]\n",
    "# Perform linear regression.\n",
    "(slope, intercept, r_value, p_value, std_err) = linregress(lats, temps)\n",
    "# Get the equation of the line.\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "print(line_eq)\n",
    "print(f\"The p-value is: {p_value:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43027eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the regression line \"y values\" from the slope and intercept.\n",
    "regress_values = [(lat * slope + intercept) for lat in lats]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cc4d5e2d",
   "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 Matplotlib.\n",
    "import matplotlib.pyplot as plt\n",
    "# Create a scatter plot of the x and y values.\n",
    "plt.scatter(lats,temps)\n",
    "# Plot the regression line with the x-values and the y coordinates based on the intercept and slope.\n",
    "plt.plot(lats,regress_values,\"r\")\n",
    "# Annotate the text for the line equation and add its coordinates.\n",
    "plt.annotate(line_eq, (10,40), fontsize=15, color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Temp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e0d0821",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating northern and southern hemisphere dataframe for linear regression:\n",
    "\n",
    "north_hemi_df = weather_df.loc[weather_df[\"Lat\"] >= 0]\n",
    "south_hemi_df = weather_df.loc[weather_df[\"Lat\"] < 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0716f72d",
   "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": [
    "# Northern Hemisphere - Max Temp vs. Latitude Linear Regression: \n",
    "x_values = pd.to_numeric(north_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(north_hemi_df['Max Temp']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "#get the equation of the line\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "#create scatter plot and plot of regression line\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r\")\n",
    "plt.annotate(line_eq,(8,10),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Max Temp')\n",
    "plt.title('Linear Regression on Northern Hemisphere For Maximum Temperature')\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/Max_Temp_Northern_Hem.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03600d87",
   "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": [
    "\n",
    "# Southern Hemisphere - Max Temp vs. Latitude Linear Regression: \n",
    "x_values = pd.to_numeric(south_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(south_hemi_df['Max Temp']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "\n",
    "#get the equation of the line\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "#create scatter plot and plot of regression line\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r\")\n",
    "plt.annotate(line_eq,(-90,50),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Max Temp')\n",
    "plt.title('Linear Regression on Southern Hemisphere For Maximum Temperature')\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/Max_Temp_Northern_Hem.png\")\n",
    "plt.show() \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2678c42",
   "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": [
    "# Northern Hemisphere - Humidity (%) vs. Latitude Linear Regression\n",
    "x_values = pd.to_numeric(north_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(north_hemi_df['Humidity']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "#create scatter plot and plot of regression line\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(10,15),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('%Humidity ')\n",
    "plt.title('Linear Regression on the Northen Hemisphere for % Humidity')\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/northLinear_Regression_humidity\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "897c9d0c",
   "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": [
    "# Southern Hemisphere - Humidity (%) vs. Latitude Linear Regression\n",
    "x_values = pd.to_numeric(south_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(south_hemi_df['Humidity']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "#create scatter plot and plot of regression line\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(-50,50),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('%Humidity ')\n",
    "plt.title('Linear Regression on the southern Hemisphere for % Humidity')\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/southLinear_Regression_humidity\")\n",
    "plt.show() \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a3bb1ae",
   "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 Percent Cloudiness for the Northern Hemisphere\n",
    "\n",
    "\n",
    "x_values = pd.to_numeric(north_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(north_hemi_df['Cloudiness']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "#create scatter plot and plot of regression line\n",
    "\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('%Cloudiness ')\n",
    "plt.title('Linear Regression on the Percent Cloudiness for the Northern Hemisphere')\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/cloudinessNorth.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "53a371bd",
   "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 Percent Cloudiness for the south Hemisphere\n",
    "\n",
    "\n",
    "x_values = pd.to_numeric(south_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(south_hemi_df['Cloudiness']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "# Plot 5: Northern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(-50,50),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('%Cloudiness ')\n",
    "plt.title('Linear Regression on the Percent Cloudiness for the Southern Hemisphere')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/cloudinesssouth.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e53472ff",
   "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 Wind Speed for the Northern Hemisphere\n",
    "\n",
    "\n",
    "x_values = pd.to_numeric(north_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(north_hemi_df['Wind Speed']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "#create scatter plot and plot of regression line\n",
    "\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(6,10),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('(MPH)Windspeed ')\n",
    "plt.title('Linear Regression on the Wind Speed for the Northern Hemisphere')\n",
    "\n",
    "\n",
    "plt.savefig(\"weather_data/cloudinessNorth.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b9638bef",
   "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 Wind Speed for the Northern Hemisphere\n",
    "x_values = pd.to_numeric(south_hemi_df['Lat']).astype(float)\n",
    "y_values = pd.to_numeric(south_hemi_df['Wind Speed']).astype(float)\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "#create scatter plot and plot of regression line\n",
    "\n",
    "plt.scatter(x_values,y_values)\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(-40,20),fontsize=15,color=\"red\")\n",
    "plt.xlabel('Latitude')\n",
    "plt.ylabel('Wind Speed (mph')\n",
    "plt.title('Linear Regression on the Wind Speed for the Northern Hemisphere')\n",
    "\n",
    "plt.savefig(\"weather_data/windregressionsouth.png\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e727b206",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}