WeatherPy_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ef2ebd8",
   "metadata": {},
   "source": [
    "# WeatherPy\n",
    "----\n",
    "\n",
    "#### Note\n",
    "* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f943f876",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import time\n",
    "from scipy.stats import linregress\n",
    "import os\n",
    "import csv\n",
    "from datetime import date\n",
    "\n",
    "# Import API key\n",
    "from api_keys import weather_api_key\n",
    "\n",
    "# Incorporated citipy to determine city based on latitude and longitude\n",
    "from citipy import citipy\n",
    "\n",
    "# Range of latitudes and longitudes\n",
    "lat_range = (-90, 90)\n",
    "lng_range = (-180, 180)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f466f922",
   "metadata": {},
   "source": [
    "## Generate Cities List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b146bcb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "581"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List for holding lat_lngs and cities\n",
    "lat_lngs = []\n",
    "cities = []\n",
    "\n",
    "# Create a set of random lat and lng combinations\n",
    "lats = np.random.uniform(lat_range[0], lat_range[1], size=1500)\n",
    "lngs = np.random.uniform(lng_range[0], lng_range[1], size=1500)\n",
    "lat_lngs = zip(lats, lngs)\n",
    "\n",
    "# Identify nearest city for each lat, lng combination\n",
    "for lat_lng in lat_lngs:\n",
    "    city = citipy.nearest_city(lat_lng[0], lat_lng[1]).city_name\n",
    "    \n",
    "    # If the city is unique, then add it to a our cities list\n",
    "    if city not in cities:\n",
    "        cities.append(city)\n",
    "\n",
    "# Print the city count to confirm sufficient count\n",
    "len(cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2689f592",
   "metadata": {},
   "source": [
    "### Perform API Calls\n",
    "* Perform a weather check on each city using a series of successive API calls.\n",
    "* Include a print log of each city as it'sbeing processed (with the city number and city name).\n",
    "\n",
    "> **HINT:** The OpenWeatherMap API only allows 60 calls per minute under their free plan. Try using `time.sleep(60)` after each set of 50 cities to avoid API errors due to large calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f6c73eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning Data Retrieval\n",
      "---------------------------\n",
      "Processing Record 1 of Set 1 | punta arenas\n",
      "Processing Record 2 of Set 1 | lorengau\n",
      "Processing Record 3 of Set 1 | new norfolk\n",
      "Processing Record 4 of Set 1 | qaanaaq\n",
      "Processing Record 5 of Set 1 | kapaa\n",
      "Processing Record 6 of Set 1 | el cope\n",
      "City not found. Skipping...\n",
      "Processing Record 7 of Set 1 | ampanihy\n",
      "Processing Record 8 of Set 1 | atuona\n",
      "Processing Record 9 of Set 1 | beringovskiy\n",
      "City not found. Skipping...\n",
      "Processing Record 10 of Set 1 | mangrol\n",
      "Processing Record 11 of Set 1 | rikitea\n",
      "Processing Record 12 of Set 1 | ancud\n",
      "Processing Record 13 of Set 1 | coquimbo\n",
      "Processing Record 14 of Set 1 | upington\n",
      "Processing Record 15 of Set 1 | cidreira\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 1 | uray\n",
      "Processing Record 17 of Set 1 | fort nelson\n",
      "Processing Record 18 of Set 1 | westport\n",
      "Processing Record 19 of Set 1 | sibolga\n",
      "Processing Record 20 of Set 1 | hamilton\n",
      "Processing Record 21 of Set 1 | cutro\n",
      "Processing Record 22 of Set 1 | hilo\n",
      "Processing Record 23 of Set 1 | hermanus\n",
      "Processing Record 24 of Set 1 | lebu\n",
      "Processing Record 25 of Set 1 | yangjiang\n",
      "Processing Record 26 of Set 1 | dikson\n",
      "Processing Record 27 of Set 1 | hamina\n",
      "Processing Record 28 of Set 1 | faanui\n",
      "Processing Record 29 of Set 1 | ushuaia\n",
      "Processing Record 30 of Set 1 | norman wells\n",
      "Processing Record 31 of Set 1 | bredasdorp\n",
      "Processing Record 32 of Set 1 | olafsvik\n",
      "Processing Record 33 of Set 1 | yar-sale\n",
      "City not found. Skipping...\n",
      "Processing Record 34 of Set 1 | busselton\n",
      "Processing Record 35 of Set 1 | avarua\n",
      "Processing Record 36 of Set 1 | kruisfontein\n",
      "City not found. Skipping...\n",
      "Processing Record 37 of Set 1 | vostok\n",
      "Processing Record 38 of Set 1 | hobart\n",
      "Processing Record 39 of Set 1 | mar del plata\n",
      "Processing Record 40 of Set 1 | tiznit\n",
      "Processing Record 41 of Set 1 | camapua\n",
      "Processing Record 42 of Set 1 | okhotsk\n",
      "Processing Record 43 of Set 1 | castro\n",
      "Processing Record 44 of Set 1 | beloha\n",
      "City not found. Skipping...\n",
      "Processing Record 45 of Set 1 | pisco\n",
      "Processing Record 46 of Set 1 | poum\n",
      "Processing Record 47 of Set 1 | carnarvon\n",
      "Processing Record 48 of Set 1 | guerrero negro\n",
      "Processing Record 49 of Set 1 | thompson\n",
      "Processing Record 50 of Set 1 | dalby\n",
      "Processing Record 0 of Set 2 | vila velha\n",
      "Processing Record 1 of Set 2 | yendi\n",
      "Processing Record 2 of Set 2 | vaini\n",
      "Processing Record 3 of Set 2 | tuktoyaktuk\n",
      "Processing Record 4 of Set 2 | najran\n",
      "Processing Record 5 of Set 2 | price\n",
      "Processing Record 6 of Set 2 | tostedt\n",
      "Processing Record 7 of Set 2 | victoria\n",
      "Processing Record 8 of Set 2 | yumen\n",
      "Processing Record 9 of Set 2 | cape town\n",
      "Processing Record 10 of Set 2 | nanortalik\n",
      "Processing Record 11 of Set 2 | vilhena\n",
      "Processing Record 12 of Set 2 | mount isa\n",
      "Processing Record 13 of Set 2 | pangai\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 2 | bluff\n",
      "Processing Record 15 of Set 2 | saint-philippe\n",
      "Processing Record 16 of Set 2 | luderitz\n",
      "Processing Record 17 of Set 2 | nhulunbuy\n",
      "Processing Record 18 of Set 2 | yellowknife\n",
      "Processing Record 19 of Set 2 | kaikalur\n",
      "Processing Record 20 of Set 2 | hami\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 2 | fortuna\n",
      "Processing Record 22 of Set 2 | say\n",
      "Processing Record 23 of Set 2 | tiksi\n",
      "Processing Record 24 of Set 2 | clearwater\n",
      "Processing Record 25 of Set 2 | porto murtinho\n",
      "Processing Record 26 of Set 2 | roald\n",
      "Processing Record 27 of Set 2 | flinders\n",
      "Processing Record 28 of Set 2 | isiro\n",
      "Processing Record 29 of Set 2 | michigan city\n",
      "Processing Record 30 of Set 2 | kodiak\n",
      "Processing Record 31 of Set 2 | plettenberg bay\n",
      "Processing Record 32 of Set 2 | mataura\n",
      "Processing Record 33 of Set 2 | ternate\n",
      "Processing Record 34 of Set 2 | albany\n",
      "Processing Record 35 of Set 2 | mayo\n",
      "Processing Record 36 of Set 2 | abu samrah\n",
      "Processing Record 37 of Set 2 | sao filipe\n",
      "Processing Record 38 of Set 2 | henties bay\n",
      "Processing Record 39 of Set 2 | mahebourg\n",
      "Processing Record 40 of Set 2 | general roca\n",
      "Processing Record 41 of Set 2 | constitucion\n",
      "Processing Record 42 of Set 2 | puerto ayora\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 2 | san diego de los banos\n",
      "Processing Record 44 of Set 2 | san quintin\n",
      "Processing Record 45 of Set 2 | nikolskoye\n",
      "Processing Record 46 of Set 2 | upernavik\n",
      "Processing Record 47 of Set 2 | dongsheng\n",
      "Processing Record 48 of Set 2 | omutninsk\n",
      "Processing Record 49 of Set 2 | saint john\n",
      "Processing Record 50 of Set 2 | hobyo\n",
      "Processing Record 0 of Set 3 | port alfred\n",
      "Processing Record 1 of Set 3 | arraial do cabo\n",
      "Processing Record 2 of Set 3 | port elizabeth\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 3 | khash\n",
      "Processing Record 4 of Set 3 | comodoro rivadavia\n",
      "Processing Record 5 of Set 3 | adrar\n",
      "Processing Record 6 of Set 3 | vung tau\n",
      "Processing Record 7 of Set 3 | jalu\n",
      "Processing Record 8 of Set 3 | anadyr\n",
      "Processing Record 9 of Set 3 | mao\n",
      "Processing Record 10 of Set 3 | poronaysk\n",
      "Processing Record 11 of Set 3 | sorland\n",
      "Processing Record 12 of Set 3 | maldonado\n",
      "Processing Record 13 of Set 3 | do gonbadan\n",
      "Processing Record 14 of Set 3 | faya\n",
      "Processing Record 15 of Set 3 | clyde river\n",
      "Processing Record 16 of Set 3 | jamestown\n",
      "Processing Record 17 of Set 3 | sao felix do xingu\n",
      "Processing Record 18 of Set 3 | bharatpur\n",
      "Processing Record 19 of Set 3 | hovd\n",
      "Processing Record 20 of Set 3 | viedma\n",
      "Processing Record 21 of Set 3 | souillac\n",
      "Processing Record 22 of Set 3 | hofn\n",
      "Processing Record 23 of Set 3 | lasa\n",
      "Processing Record 24 of Set 3 | colquechaca\n",
      "Processing Record 25 of Set 3 | bozeman\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 3 | saint-ambroise\n",
      "Processing Record 27 of Set 3 | colon\n",
      "Processing Record 28 of Set 3 | crateus\n",
      "Processing Record 29 of Set 3 | shingu\n",
      "Processing Record 30 of Set 3 | maragogi\n",
      "Processing Record 31 of Set 3 | saint-pierre\n",
      "Processing Record 32 of Set 3 | kaitangata\n",
      "City not found. Skipping...\n",
      "Processing Record 33 of Set 3 | verkhnetulomskiy\n",
      "Processing Record 34 of Set 3 | sulangan\n",
      "Processing Record 35 of Set 3 | cap malheureux\n",
      "Processing Record 36 of Set 3 | dingle\n",
      "Processing Record 37 of Set 3 | new milford\n",
      "Processing Record 38 of Set 3 | dhidhdhoo\n",
      "Processing Record 39 of Set 3 | sao joao da barra\n",
      "Processing Record 40 of Set 3 | severo-kurilsk\n",
      "Processing Record 41 of Set 3 | seminole\n",
      "Processing Record 42 of Set 3 | bairiki\n",
      "Processing Record 43 of Set 3 | changli\n",
      "Processing Record 44 of Set 3 | fare\n",
      "Processing Record 45 of Set 3 | jaleswar\n",
      "Processing Record 46 of Set 3 | nam som\n",
      "Processing Record 47 of Set 3 | omama\n",
      "Processing Record 48 of Set 3 | bethel\n",
      "Processing Record 49 of Set 3 | rocha\n",
      "Processing Record 50 of Set 3 | sitka\n",
      "Processing Record 0 of Set 4 | gympie\n",
      "Processing Record 1 of Set 4 | huron\n",
      "Processing Record 2 of Set 4 | salalah\n",
      "Processing Record 3 of Set 4 | longyearbyen\n",
      "Processing Record 4 of Set 4 | santa cruz\n",
      "Processing Record 5 of Set 4 | khatanga\n",
      "Processing Record 6 of Set 4 | umtata\n",
      "Processing Record 7 of Set 4 | east london\n",
      "Processing Record 8 of Set 4 | leningradskiy\n",
      "Processing Record 9 of Set 4 | bilibino\n",
      "Processing Record 10 of Set 4 | vaitape\n",
      "Processing Record 11 of Set 4 | torbay\n",
      "City not found. Skipping...\n",
      "Processing Record 12 of Set 4 | amapa\n",
      "Processing Record 13 of Set 4 | sayyan\n",
      "Processing Record 14 of Set 4 | benicia\n",
      "Processing Record 15 of Set 4 | lavrentiya\n",
      "Processing Record 16 of Set 4 | vidim\n",
      "Processing Record 17 of Set 4 | arlit\n",
      "Processing Record 18 of Set 4 | vestmannaeyjar\n",
      "Processing Record 19 of Set 4 | saint anthony\n",
      "Processing Record 20 of Set 4 | loa janan\n",
      "Processing Record 21 of Set 4 | fez\n",
      "Processing Record 22 of Set 4 | atherton\n",
      "Processing Record 23 of Set 4 | toora-khem\n",
      "Processing Record 24 of Set 4 | padang\n",
      "Processing Record 25 of Set 4 | belaya gora\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 4 | airai\n",
      "Processing Record 27 of Set 4 | saldanha\n",
      "Processing Record 28 of Set 4 | meulaboh\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 4 | mattru\n",
      "Processing Record 30 of Set 4 | pedasi\n",
      "Processing Record 31 of Set 4 | gribanovskiy\n",
      "Processing Record 32 of Set 4 | smidovich\n",
      "Processing Record 33 of Set 4 | parabel\n",
      "Processing Record 34 of Set 4 | ranong\n",
      "Processing Record 35 of Set 4 | codrington\n",
      "Processing Record 36 of Set 4 | le port\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 37 of Set 4 | garowe\n",
      "Processing Record 38 of Set 4 | cairns\n",
      "Processing Record 39 of Set 4 | nome\n",
      "Processing Record 40 of Set 4 | santa fe\n",
      "Processing Record 41 of Set 4 | salta\n",
      "Processing Record 42 of Set 4 | ostrovnoy\n",
      "Processing Record 43 of Set 4 | saskylakh\n",
      "Processing Record 44 of Set 4 | buta\n",
      "Processing Record 45 of Set 4 | serebryansk\n",
      "Processing Record 46 of Set 4 | paamiut\n",
      "Processing Record 47 of Set 4 | husavik\n",
      "Processing Record 48 of Set 4 | provideniya\n",
      "Processing Record 49 of Set 4 | namibe\n",
      "Processing Record 50 of Set 4 | uyskoye\n",
      "Processing Record 0 of Set 5 | bom jardim\n",
      "Processing Record 1 of Set 5 | sinaia\n",
      "Processing Record 2 of Set 5 | biskamzha\n",
      "Processing Record 3 of Set 5 | borogontsy\n",
      "Processing Record 4 of Set 5 | aquiraz\n",
      "Processing Record 5 of Set 5 | naliya\n",
      "Processing Record 6 of Set 5 | damietta\n",
      "Processing Record 7 of Set 5 | progreso\n",
      "Processing Record 8 of Set 5 | bengkulu\n",
      "Processing Record 9 of Set 5 | warrnambool\n",
      "Processing Record 10 of Set 5 | georgetown\n",
      "Processing Record 11 of Set 5 | savannah bight\n",
      "Processing Record 12 of Set 5 | jinxiang\n",
      "Processing Record 13 of Set 5 | narayanganj\n",
      "City not found. Skipping...\n",
      "Processing Record 14 of Set 5 | cabo san lucas\n",
      "Processing Record 15 of Set 5 | nykoping\n",
      "City not found. Skipping...\n",
      "City not found. Skipping...\n",
      "Processing Record 16 of Set 5 | eyl\n",
      "Processing Record 17 of Set 5 | noumea\n",
      "Processing Record 18 of Set 5 | chuy\n",
      "Processing Record 19 of Set 5 | luanda\n",
      "Processing Record 20 of Set 5 | leo\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 5 | doka\n",
      "Processing Record 22 of Set 5 | arequipa\n",
      "Processing Record 23 of Set 5 | ambunti\n",
      "Processing Record 24 of Set 5 | barrow\n",
      "Processing Record 25 of Set 5 | saucillo\n",
      "Processing Record 26 of Set 5 | majene\n",
      "City not found. Skipping...\n",
      "Processing Record 27 of Set 5 | brae\n",
      "Processing Record 28 of Set 5 | kalundborg\n",
      "Processing Record 29 of Set 5 | ribeira grande\n",
      "Processing Record 30 of Set 5 | semnan\n",
      "Processing Record 31 of Set 5 | kushmurun\n",
      "Processing Record 32 of Set 5 | terrace\n",
      "Processing Record 33 of Set 5 | alekseyevsk\n",
      "Processing Record 34 of Set 5 | matara\n",
      "Processing Record 35 of Set 5 | wilmington\n",
      "Processing Record 36 of Set 5 | pangody\n",
      "Processing Record 37 of Set 5 | kahului\n",
      "Processing Record 38 of Set 5 | honiara\n",
      "Processing Record 39 of Set 5 | kinarut\n",
      "Processing Record 40 of Set 5 | camacha\n",
      "Processing Record 41 of Set 5 | sioux lookout\n",
      "Processing Record 42 of Set 5 | thinadhoo\n",
      "City not found. Skipping...\n",
      "Processing Record 43 of Set 5 | hithadhoo\n",
      "Processing Record 44 of Set 5 | mahajanga\n",
      "Processing Record 45 of Set 5 | samarai\n",
      "Processing Record 46 of Set 5 | youkounkoun\n",
      "Processing Record 47 of Set 5 | klaksvik\n",
      "Processing Record 48 of Set 5 | turbat\n",
      "Processing Record 49 of Set 5 | basay\n",
      "Processing Record 50 of Set 5 | tupiza\n",
      "Processing Record 0 of Set 6 | gizo\n",
      "City not found. Skipping...\n",
      "Processing Record 1 of Set 6 | anicuns\n",
      "Processing Record 2 of Set 6 | hasaki\n",
      "Processing Record 3 of Set 6 | sovetskiy\n",
      "Processing Record 4 of Set 6 | homer\n",
      "Processing Record 5 of Set 6 | sisimiut\n",
      "Processing Record 6 of Set 6 | svetlaya\n",
      "Processing Record 7 of Set 6 | maturin\n",
      "Processing Record 8 of Set 6 | xichang\n",
      "Processing Record 9 of Set 6 | arcachon\n",
      "Processing Record 10 of Set 6 | ostersund\n",
      "Processing Record 11 of Set 6 | geraldton\n",
      "Processing Record 12 of Set 6 | san policarpo\n",
      "Processing Record 13 of Set 6 | basar\n",
      "Processing Record 14 of Set 6 | port lincoln\n",
      "Processing Record 15 of Set 6 | umarkot\n",
      "Processing Record 16 of Set 6 | sydney\n",
      "Processing Record 17 of Set 6 | salinopolis\n",
      "Processing Record 18 of Set 6 | iqaluit\n",
      "Processing Record 19 of Set 6 | havre\n",
      "Processing Record 20 of Set 6 | aykhal\n",
      "City not found. Skipping...\n",
      "City not found. Skipping...\n",
      "Processing Record 21 of Set 6 | pilot butte\n",
      "Processing Record 22 of Set 6 | bandarbeyla\n",
      "Processing Record 23 of Set 6 | ostuacan\n",
      "Processing Record 24 of Set 6 | tasiilaq\n",
      "Processing Record 25 of Set 6 | nefteyugansk\n",
      "Processing Record 26 of Set 6 | bukan\n",
      "Processing Record 27 of Set 6 | zamora\n",
      "Processing Record 28 of Set 6 | montepuez\n",
      "Processing Record 29 of Set 6 | chumphon\n",
      "Processing Record 30 of Set 6 | magan\n",
      "Processing Record 31 of Set 6 | banda aceh\n",
      "Processing Record 32 of Set 6 | chokurdakh\n",
      "Processing Record 33 of Set 6 | siniscola\n",
      "City not found. Skipping...\n",
      "City not found. Skipping...\n",
      "Processing Record 34 of Set 6 | cherskiy\n",
      "Processing Record 35 of Set 6 | ayorou\n",
      "Processing Record 36 of Set 6 | potosi\n",
      "Processing Record 37 of Set 6 | havoysund\n",
      "Processing Record 38 of Set 6 | porto novo\n",
      "Processing Record 39 of Set 6 | antsohihy\n",
      "City not found. Skipping...\n",
      "Processing Record 40 of Set 6 | ilulissat\n",
      "Processing Record 41 of Set 6 | celestun\n",
      "Processing Record 42 of Set 6 | hearst\n",
      "Processing Record 43 of Set 6 | divnogorsk\n",
      "Processing Record 44 of Set 6 | brandon\n",
      "Processing Record 45 of Set 6 | gornozavodsk\n",
      "Processing Record 46 of Set 6 | lebedinyy\n",
      "Processing Record 47 of Set 6 | lufilufi\n",
      "Processing Record 48 of Set 6 | richards bay\n",
      "Processing Record 49 of Set 6 | kushima\n",
      "Processing Record 50 of Set 6 | tautira\n",
      "Processing Record 0 of Set 7 | khvoynaya\n",
      "Processing Record 1 of Set 7 | laguna\n",
      "Processing Record 2 of Set 7 | bambous virieux\n",
      "Processing Record 3 of Set 7 | broome\n",
      "Processing Record 4 of Set 7 | narsaq\n",
      "Processing Record 5 of Set 7 | chama\n",
      "Processing Record 6 of Set 7 | caninde de sao francisco\n",
      "Processing Record 7 of Set 7 | talnakh\n",
      "Processing Record 8 of Set 7 | tres arroyos\n",
      "Processing Record 9 of Set 7 | pakokku\n",
      "Processing Record 10 of Set 7 | kumluca\n",
      "Processing Record 11 of Set 7 | berlevag\n",
      "Processing Record 12 of Set 7 | alice springs\n",
      "Processing Record 13 of Set 7 | mancora\n",
      "Processing Record 14 of Set 7 | chapleau\n",
      "Processing Record 15 of Set 7 | mandalgovi\n",
      "Processing Record 16 of Set 7 | fairlie\n",
      "Processing Record 17 of Set 7 | gorontalo\n",
      "Processing Record 18 of Set 7 | yelovo\n",
      "Processing Record 19 of Set 7 | cukai\n",
      "Processing Record 20 of Set 7 | kavieng\n",
      "Processing Record 21 of Set 7 | suraabad\n",
      "Processing Record 22 of Set 7 | butaritari\n",
      "Processing Record 23 of Set 7 | ribeira brava\n",
      "City not found. Skipping...\n",
      "Processing Record 24 of Set 7 | tiruchchendur\n",
      "Processing Record 25 of Set 7 | vilyuysk\n",
      "Processing Record 26 of Set 7 | neiafu\n",
      "Processing Record 27 of Set 7 | mertoyudan\n",
      "Processing Record 28 of Set 7 | caravelas\n",
      "Processing Record 29 of Set 7 | liwale\n",
      "Processing Record 30 of Set 7 | iranshahr\n",
      "City not found. Skipping...\n",
      "Processing Record 31 of Set 7 | jertih\n",
      "Processing Record 32 of Set 7 | alyangula\n",
      "Processing Record 33 of Set 7 | yulara\n",
      "Processing Record 34 of Set 7 | huarmey\n",
      "Processing Record 35 of Set 7 | vardo\n",
      "Processing Record 36 of Set 7 | los llanos de aridane\n",
      "City not found. Skipping...\n",
      "Processing Record 37 of Set 7 | mocuba\n",
      "Processing Record 38 of Set 7 | hualmay\n",
      "Processing Record 39 of Set 7 | araouane\n",
      "Processing Record 40 of Set 7 | san javier\n",
      "City not found. Skipping...\n",
      "Processing Record 41 of Set 7 | leuwiliang\n",
      "Processing Record 42 of Set 7 | san patricio\n",
      "Processing Record 43 of Set 7 | boguchany\n",
      "Processing Record 44 of Set 7 | natchez\n",
      "Processing Record 45 of Set 7 | sambava\n",
      "Processing Record 46 of Set 7 | kalemie\n",
      "Processing Record 47 of Set 7 | russell\n",
      "City not found. Skipping...\n",
      "Processing Record 48 of Set 7 | benguela\n",
      "Processing Record 49 of Set 7 | soyo\n",
      "Processing Record 50 of Set 7 | manicore\n",
      "Processing Record 0 of Set 8 | nishihara\n",
      "Processing Record 1 of Set 8 | lompoc\n",
      "Processing Record 2 of Set 8 | vila\n",
      "Processing Record 3 of Set 8 | usinsk\n",
      "Processing Record 4 of Set 8 | saint-georges\n",
      "Processing Record 5 of Set 8 | charyshskoye\n",
      "Processing Record 6 of Set 8 | peterhead\n",
      "Processing Record 7 of Set 8 | ixtapa\n",
      "Processing Record 8 of Set 8 | udachnyy\n",
      "Processing Record 9 of Set 8 | avera\n",
      "Processing Record 10 of Set 8 | broken hill\n",
      "City not found. Skipping...\n",
      "Processing Record 11 of Set 8 | panama city\n",
      "Processing Record 12 of Set 8 | zima\n",
      "Processing Record 13 of Set 8 | tobol\n",
      "Processing Record 14 of Set 8 | touros\n",
      "Processing Record 15 of Set 8 | kilosa\n",
      "Processing Record 16 of Set 8 | solnechnyy\n",
      "Processing Record 17 of Set 8 | kawalu\n",
      "Processing Record 18 of Set 8 | nanakuli\n",
      "Processing Record 19 of Set 8 | biak\n",
      "Processing Record 20 of Set 8 | minab\n",
      "Processing Record 21 of Set 8 | lethem\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Record 22 of Set 8 | vrede\n",
      "Processing Record 23 of Set 8 | tyukhtet\n",
      "Processing Record 24 of Set 8 | the valley\n",
      "Processing Record 25 of Set 8 | portland\n",
      "Processing Record 26 of Set 8 | makakilo city\n",
      "Processing Record 27 of Set 8 | laguna de perlas\n",
      "Processing Record 28 of Set 8 | karakulino\n",
      "Processing Record 29 of Set 8 | namatanai\n",
      "City not found. Skipping...\n",
      "Processing Record 30 of Set 8 | alofi\n",
      "Processing Record 31 of Set 8 | porto belo\n",
      "Processing Record 32 of Set 8 | nuuk\n",
      "Processing Record 33 of Set 8 | kant\n",
      "Processing Record 34 of Set 8 | baiyin\n",
      "Processing Record 35 of Set 8 | flin flon\n",
      "Processing Record 36 of Set 8 | inirida\n",
      "Processing Record 37 of Set 8 | putina\n",
      "Processing Record 38 of Set 8 | sabya\n",
      "Processing Record 39 of Set 8 | genhe\n",
      "Processing Record 40 of Set 8 | bombay\n",
      "Processing Record 41 of Set 8 | santa\n",
      "Processing Record 42 of Set 8 | port macquarie\n",
      "Processing Record 43 of Set 8 | saint-augustin\n",
      "Processing Record 44 of Set 8 | petropavlovsk-kamchatskiy\n",
      "Processing Record 45 of Set 8 | whitianga\n",
      "Processing Record 46 of Set 8 | coxim\n",
      "Processing Record 47 of Set 8 | lagoa\n",
      "Processing Record 48 of Set 8 | ust-tsilma\n",
      "Processing Record 49 of Set 8 | sinnamary\n",
      "Processing Record 50 of Set 8 | burnie\n",
      "Processing Record 0 of Set 9 | mgandu\n",
      "Processing Record 1 of Set 9 | san cristobal\n",
      "Processing Record 2 of Set 9 | kutum\n",
      "Processing Record 3 of Set 9 | san juan\n",
      "Processing Record 4 of Set 9 | isangel\n",
      "Processing Record 5 of Set 9 | pevek\n",
      "Processing Record 6 of Set 9 | vila franca do campo\n",
      "Processing Record 7 of Set 9 | dakar\n",
      "Processing Record 8 of Set 9 | tyshkivka\n",
      "City not found. Skipping...\n",
      "Processing Record 9 of Set 9 | misratah\n",
      "Processing Record 10 of Set 9 | ucluelet\n",
      "Processing Record 11 of Set 9 | igrim\n",
      "Processing Record 12 of Set 9 | tuatapere\n",
      "Processing Record 13 of Set 9 | inhambane\n",
      "Processing Record 14 of Set 9 | iquitos\n",
      "Processing Record 15 of Set 9 | opuwo\n",
      "Processing Record 16 of Set 9 | bongaree\n",
      "Processing Record 17 of Set 9 | nova olimpia\n",
      "Processing Record 18 of Set 9 | marand\n",
      "City not found. Skipping...\n",
      "Processing Record 19 of Set 9 | kumeny\n",
      "Processing Record 20 of Set 9 | katangli\n",
      "Processing Record 21 of Set 9 | yemelyanovo\n",
      "Processing Record 22 of Set 9 | kieta\n",
      "Processing Record 23 of Set 9 | jodhpur\n",
      "Processing Record 24 of Set 9 | rorvik\n",
      "Processing Record 25 of Set 9 | saint george\n",
      "City not found. Skipping...\n",
      "Processing Record 26 of Set 9 | brza palanka\n",
      "Processing Record 27 of Set 9 | qurayyat\n",
      "Processing Record 28 of Set 9 | verkhnyaya inta\n",
      "Processing Record 29 of Set 9 | yerbogachen\n",
      "Processing Record 30 of Set 9 | tura\n",
      "Processing Record 31 of Set 9 | sao pedro do sul\n",
      "Processing Record 32 of Set 9 | mapimi\n",
      "Processing Record 33 of Set 9 | haines junction\n",
      "Processing Record 34 of Set 9 | necochea\n",
      "Processing Record 35 of Set 9 | tazovskiy\n",
      "Processing Record 36 of Set 9 | tilichiki\n",
      "Processing Record 37 of Set 9 | kirkcudbright\n",
      "Processing Record 38 of Set 9 | albania\n",
      "Processing Record 39 of Set 9 | wahpeton\n",
      "Processing Record 40 of Set 9 | qandala\n",
      "Processing Record 41 of Set 9 | dharmadam\n",
      "Processing Record 42 of Set 9 | chupa\n",
      "Processing Record 43 of Set 9 | rypefjord\n",
      "Processing Record 44 of Set 9 | wattegama\n",
      "Processing Record 45 of Set 9 | teahupoo\n",
      "Processing Record 46 of Set 9 | ossora\n",
      "Processing Record 47 of Set 9 | seoul\n",
      "Processing Record 48 of Set 9 | rio gallegos\n",
      "Processing Record 49 of Set 9 | kameda\n",
      "Processing Record 50 of Set 9 | jacqueville\n",
      "Processing Record 0 of Set 10 | ludvika\n",
      "Processing Record 1 of Set 10 | kopong\n",
      "Processing Record 2 of Set 10 | lavumisa\n",
      "Processing Record 3 of Set 10 | simao\n",
      "Processing Record 4 of Set 10 | kupang\n",
      "Processing Record 5 of Set 10 | lamu\n",
      "Processing Record 6 of Set 10 | akbarpur\n",
      "Processing Record 7 of Set 10 | fukue\n",
      "Processing Record 8 of Set 10 | baykit\n",
      "Processing Record 9 of Set 10 | ambon\n",
      "Processing Record 10 of Set 10 | hongjiang\n",
      "Processing Record 11 of Set 10 | hambantota\n",
      "Processing Record 12 of Set 10 | ciudad bolivar\n",
      "Processing Record 13 of Set 10 | kichera\n",
      "Processing Record 14 of Set 10 | oranjemund\n",
      "Processing Record 15 of Set 10 | la libertad\n",
      "Processing Record 16 of Set 10 | lima\n",
      "Processing Record 17 of Set 10 | bakchar\n",
      "Processing Record 18 of Set 10 | channel-port aux basques\n",
      "Processing Record 19 of Set 10 | walvis bay\n",
      "Processing Record 20 of Set 10 | ahipara\n",
      "Processing Record 21 of Set 10 | accra\n",
      "Processing Record 22 of Set 10 | nunkini\n",
      "Processing Record 23 of Set 10 | morgantown\n",
      "Processing Record 24 of Set 10 | juneau\n",
      "Processing Record 25 of Set 10 | scituate\n",
      "Processing Record 26 of Set 10 | sovetskaya gavan\n",
      "Processing Record 27 of Set 10 | ekhabi\n",
      "Processing Record 28 of Set 10 | mount gambier\n",
      "Processing Record 29 of Set 10 | umm lajj\n",
      "Processing Record 30 of Set 10 | awbari\n",
      "Processing Record 31 of Set 10 | bathsheba\n",
      "City not found. Skipping...\n",
      "Processing Record 32 of Set 10 | arman\n",
      "Processing Record 33 of Set 10 | katsuura\n",
      "Processing Record 34 of Set 10 | sharan\n",
      "Processing Record 35 of Set 10 | mariakani\n",
      "Processing Record 36 of Set 10 | den helder\n",
      "Processing Record 37 of Set 10 | nahrin\n",
      "Processing Record 38 of Set 10 | ponta delgada\n",
      "Processing Record 39 of Set 10 | raudeberg\n",
      "Processing Record 40 of Set 10 | ayan\n",
      "Processing Record 41 of Set 10 | madang\n",
      "Processing Record 42 of Set 10 | esperance\n",
      "Processing Record 43 of Set 10 | takoradi\n",
      "Processing Record 44 of Set 10 | cockburn town\n",
      "Processing Record 45 of Set 10 | paramonga\n",
      "Processing Record 46 of Set 10 | state college\n",
      "Processing Record 47 of Set 10 | balakhninskiy\n",
      "Processing Record 48 of Set 10 | missungwi\n",
      "Processing Record 49 of Set 10 | nuevo progreso\n",
      "Processing Record 50 of Set 10 | benghazi\n",
      "Processing Record 0 of Set 11 | dzaoudzi\n",
      "Processing Record 1 of Set 11 | jiaozuo\n",
      "Processing Record 2 of Set 11 | kayseri\n",
      "City not found. Skipping...\n",
      "Processing Record 3 of Set 11 | grumazesti\n",
      "Processing Record 4 of Set 11 | dunedin\n",
      "Processing Record 5 of Set 11 | port moresby\n",
      "Processing Record 6 of Set 11 | hay river\n",
      "Processing Record 7 of Set 11 | garanhuns\n",
      "Processing Record 8 of Set 11 | zonguldak\n",
      "Processing Record 9 of Set 11 | beira\n",
      "Processing Record 10 of Set 11 | peleduy\n",
      "Processing Record 11 of Set 11 | bubaque\n",
      "Processing Record 12 of Set 11 | neosho\n",
      "Processing Record 13 of Set 11 | sandpoint\n",
      "Processing Record 14 of Set 11 | ingham\n",
      "Processing Record 15 of Set 11 | blyth\n",
      "Processing Record 16 of Set 11 | lithakia\n",
      "Processing Record 17 of Set 11 | amla\n",
      "Processing Record 18 of Set 11 | nakambala\n",
      "Processing Record 19 of Set 11 | sumbe\n",
      "Processing Record 20 of Set 11 | balabac\n",
      "Processing Record 21 of Set 11 | port-gentil\n",
      "Processing Record 22 of Set 11 | evensk\n",
      "Processing Record 23 of Set 11 | carutapera\n",
      "Processing Record 24 of Set 11 | port hedland\n",
      "Processing Record 25 of Set 11 | bereda\n",
      "Processing Record 26 of Set 11 | taft\n",
      "Processing Record 27 of Set 11 | cartagena\n",
      "Processing Record 28 of Set 11 | tabou\n",
      "City not found. Skipping...\n",
      "Processing Record 29 of Set 11 | chongwe\n",
      "Processing Record 30 of Set 11 | illapel\n",
      "Processing Record 31 of Set 11 | christchurch\n",
      "---------------------------\n",
      "Data Retrieval Complete\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "# Set url and assign units measure in F \n",
    "url = 'http://api.openweathermap.org/data/2.5/weather?'\n",
    "units = 'imperial'\n",
    "\n",
    "# Build partial query URL\n",
    "query_url = f'{url}appid={weather_api_key}&units={units}&q='\n",
    "\n",
    "number = 1\n",
    "set_num = 1\n",
    "\n",
    "# List that's used to store the data for each city\n",
    "weather_data = []\n",
    "\n",
    "print('Beginning Data Retrieval')\n",
    "print('---------------------------')\n",
    "for city in cities:\n",
    "    response = requests.get(query_url + city).json()\n",
    "    \n",
    "    try:\n",
    "        weather_check = response['weather']\n",
    "        weather_data.append(response)     #collecting the data for 'weather_data' list\n",
    "        \n",
    "        print(f'Processing Record {number} of Set {set_num} | {city}')\n",
    "        number +=1 \n",
    "        if number == 51:\n",
    "            number=0\n",
    "            set_num +=1 \n",
    "    except:\n",
    "        print('City not found. Skipping...')\n",
    "        pass\n",
    "print('---------------------------')\n",
    "print('Data Retrieval Complete')\n",
    "print('---------------------------')\n",
    "\n",
    "# I wrote the code to match the output that was given (the answer in the cell without the code).\n",
    "# If it was up to me how to group the Sets, I would start counting Processing Record from 1 \n",
    "# instead 0. That way we would get 50 Records per Set. \n",
    "\n",
    "# Otherwise, when we start counting Processing Records from 0 (in each Set), in results \n",
    "# it gives us 51 records per Set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8053c802",
   "metadata": {},
   "source": [
    "### Convert Raw Data to DataFrame\n",
    "* Export the city data into a .csv.\n",
    "* Display the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d6ef44b",
   "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>punta arenas</td>\n",
       "      <td>-53.1500</td>\n",
       "      <td>-70.9167</td>\n",
       "      <td>53.11</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>20.83</td>\n",
       "      <td>CL</td>\n",
       "      <td>1647907311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lorengau</td>\n",
       "      <td>-2.0226</td>\n",
       "      <td>147.2712</td>\n",
       "      <td>84.36</td>\n",
       "      <td>69</td>\n",
       "      <td>74</td>\n",
       "      <td>4.00</td>\n",
       "      <td>PG</td>\n",
       "      <td>1647907311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>new norfolk</td>\n",
       "      <td>-42.7826</td>\n",
       "      <td>147.0587</td>\n",
       "      <td>59.04</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>0.81</td>\n",
       "      <td>AU</td>\n",
       "      <td>1647907312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qaanaaq</td>\n",
       "      <td>77.4840</td>\n",
       "      <td>-69.3632</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>5.75</td>\n",
       "      <td>GL</td>\n",
       "      <td>1647907312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kapaa</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>80.28</td>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "      <td>1.99</td>\n",
       "      <td>US</td>\n",
       "      <td>1647907313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0  punta arenas -53.1500  -70.9167     53.11        66         100   \n",
       "1      lorengau  -2.0226  147.2712     84.36        69          74   \n",
       "2   new norfolk -42.7826  147.0587     59.04        77         100   \n",
       "3       qaanaaq  77.4840  -69.3632     -5.64        88         100   \n",
       "4         kapaa  22.0752 -159.3190     80.28        65          72   \n",
       "\n",
       "   Wind Speed Country        Date  \n",
       "0       20.83      CL  1647907311  \n",
       "1        4.00      PG  1647907311  \n",
       "2        0.81      AU  1647907312  \n",
       "3        5.75      GL  1647907312  \n",
       "4        1.99      US  1647907313  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_path = os.path.join('..','output_data','cities_data.csv')\n",
    "with open (output_path, 'w', encoding = 'utf-8') as csvfile:\n",
    "    csvwriter = csv.writer(csvfile, delimiter = \",\")\n",
    "    csvwriter.writerow (['City','Lat','Lng','Max Temp','Humidity','Cloudiness','Wind Speed','Country','Date'])    \n",
    "    \n",
    "    for index in range(len(weather_data)):    \n",
    "        City = cities[index]\n",
    "        Lat = weather_data[index]['coord']['lat']\n",
    "        Lng = weather_data[index]['coord']['lon']\n",
    "        Max_Temp = weather_data[index]['main']['temp']\n",
    "        Humidity = weather_data[index]['main']['humidity']                                \n",
    "        Cloudiness = weather_data[index]['clouds']['all']\n",
    "        Wind_Speed = weather_data[index]['wind']['speed']\n",
    "        Country = weather_data[index]['sys']['country']\n",
    "        Date = weather_data[index]['dt']\n",
    "        \n",
    "        csvwriter.writerow ([City, Lat, Lng, Max_Temp, Humidity, Cloudiness, Wind_Speed, Country, Date])\n",
    "    \n",
    "# Creating Data Frame\n",
    "Cities_df = pd.read_csv(output_path)\n",
    "Cities_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2bd802f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>5.410000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.666730</td>\n",
       "      <td>19.514504</td>\n",
       "      <td>55.420832</td>\n",
       "      <td>74.255083</td>\n",
       "      <td>57.556377</td>\n",
       "      <td>8.527412</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.929723</td>\n",
       "      <td>90.877321</td>\n",
       "      <td>25.634711</td>\n",
       "      <td>19.892340</td>\n",
       "      <td>39.218135</td>\n",
       "      <td>6.552844</td>\n",
       "      <td>1.036890e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-54.800000</td>\n",
       "      <td>-175.200000</td>\n",
       "      <td>-18.800000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-10.166700</td>\n",
       "      <td>-60.700000</td>\n",
       "      <td>36.720000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.760000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.333300</td>\n",
       "      <td>25.570100</td>\n",
       "      <td>64.710000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>6.910000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.932900</td>\n",
       "      <td>98.634800</td>\n",
       "      <td>76.320000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.218600</td>\n",
       "      <td>179.316700</td>\n",
       "      <td>89.110000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>46.550000</td>\n",
       "      <td>1.647908e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Lat         Lng    Max Temp    Humidity  Cloudiness  Wind Speed  \\\n",
       "count  541.000000  541.000000  541.000000  541.000000  541.000000  541.000000   \n",
       "mean    18.666730   19.514504   55.420832   74.255083   57.556377    8.527412   \n",
       "std     33.929723   90.877321   25.634711   19.892340   39.218135    6.552844   \n",
       "min    -54.800000 -175.200000  -18.800000    5.000000    0.000000    0.000000   \n",
       "25%    -10.166700  -60.700000   36.720000   65.000000   17.000000    3.760000   \n",
       "50%     21.333300   25.570100   64.710000   79.000000   67.000000    6.910000   \n",
       "75%     48.932900   98.634800   76.320000   89.000000   99.000000   11.500000   \n",
       "max     78.218600  179.316700   89.110000  100.000000  100.000000   46.550000   \n",
       "\n",
       "               Date  \n",
       "count  5.410000e+02  \n",
       "mean   1.647907e+09  \n",
       "std    1.036890e+02  \n",
       "min    1.647907e+09  \n",
       "25%    1.647907e+09  \n",
       "50%    1.647907e+09  \n",
       "75%    1.647907e+09  \n",
       "max    1.647908e+09  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stats_df = Cities_df.describe()\n",
    "Stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c15f33",
   "metadata": {},
   "source": [
    "## Inspect the data and remove the cities where the humidity > 100%.\n",
    "----\n",
    "Skip this step if there are no cities that have humidity > 100%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23f075aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Max Temp</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Cloudiness</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>541.000000</td>\n",
       "      <td>5.410000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.666730</td>\n",
       "      <td>19.514504</td>\n",
       "      <td>55.420832</td>\n",
       "      <td>74.255083</td>\n",
       "      <td>57.556377</td>\n",
       "      <td>8.527412</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.929723</td>\n",
       "      <td>90.877321</td>\n",
       "      <td>25.634711</td>\n",
       "      <td>19.892340</td>\n",
       "      <td>39.218135</td>\n",
       "      <td>6.552844</td>\n",
       "      <td>1.036890e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-54.800000</td>\n",
       "      <td>-175.200000</td>\n",
       "      <td>-18.800000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-10.166700</td>\n",
       "      <td>-60.700000</td>\n",
       "      <td>36.720000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>3.760000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.333300</td>\n",
       "      <td>25.570100</td>\n",
       "      <td>64.710000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>6.910000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.932900</td>\n",
       "      <td>98.634800</td>\n",
       "      <td>76.320000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>1.647907e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.218600</td>\n",
       "      <td>179.316700</td>\n",
       "      <td>89.110000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>46.550000</td>\n",
       "      <td>1.647908e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Lat         Lng    Max Temp    Humidity  Cloudiness  Wind Speed  \\\n",
       "count  541.000000  541.000000  541.000000  541.000000  541.000000  541.000000   \n",
       "mean    18.666730   19.514504   55.420832   74.255083   57.556377    8.527412   \n",
       "std     33.929723   90.877321   25.634711   19.892340   39.218135    6.552844   \n",
       "min    -54.800000 -175.200000  -18.800000    5.000000    0.000000    0.000000   \n",
       "25%    -10.166700  -60.700000   36.720000   65.000000   17.000000    3.760000   \n",
       "50%     21.333300   25.570100   64.710000   79.000000   67.000000    6.910000   \n",
       "75%     48.932900   98.634800   76.320000   89.000000   99.000000   11.500000   \n",
       "max     78.218600  179.316700   89.110000  100.000000  100.000000   46.550000   \n",
       "\n",
       "               Date  \n",
       "count  5.410000e+02  \n",
       "mean   1.647907e+09  \n",
       "std    1.036890e+02  \n",
       "min    1.647907e+09  \n",
       "25%    1.647907e+09  \n",
       "50%    1.647907e+09  \n",
       "75%    1.647907e+09  \n",
       "max    1.647908e+09  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I don't have cities with over 100% of Humidity at the moment of working with the current Data Set.\n",
    "#In case I re-run my code, and different random cities appear which do have over 100% of Humidity...\n",
    "#then the code below will filter out and keep the cities with no more than 100% Humidity.\n",
    "\n",
    "Filtered_df = Cities_df.loc[Cities_df['Humidity'] <=100, :]\n",
    "Updated_Stats_df = Filtered_df.describe()\n",
    "Updated_Stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8df83d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Get the indices of cities that have humidity over 100%.\n",
    "\n",
    "# Again, I did it just because if I re-run my code and cities with over 100% Humidity will appear.\n",
    "#I didn't need to do this step for my curent Data Set.\n",
    "\n",
    "Index_list = Cities_df[Cities_df['Humidity'] > 100].index.tolist()\n",
    "Index_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba1db7c6",
   "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>punta arenas</td>\n",
       "      <td>-53.1500</td>\n",
       "      <td>-70.9167</td>\n",
       "      <td>53.11</td>\n",
       "      <td>66</td>\n",
       "      <td>100</td>\n",
       "      <td>20.83</td>\n",
       "      <td>CL</td>\n",
       "      <td>1647907311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lorengau</td>\n",
       "      <td>-2.0226</td>\n",
       "      <td>147.2712</td>\n",
       "      <td>84.36</td>\n",
       "      <td>69</td>\n",
       "      <td>74</td>\n",
       "      <td>4.00</td>\n",
       "      <td>PG</td>\n",
       "      <td>1647907311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>new norfolk</td>\n",
       "      <td>-42.7826</td>\n",
       "      <td>147.0587</td>\n",
       "      <td>59.04</td>\n",
       "      <td>77</td>\n",
       "      <td>100</td>\n",
       "      <td>0.81</td>\n",
       "      <td>AU</td>\n",
       "      <td>1647907312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qaanaaq</td>\n",
       "      <td>77.4840</td>\n",
       "      <td>-69.3632</td>\n",
       "      <td>-5.64</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>5.75</td>\n",
       "      <td>GL</td>\n",
       "      <td>1647907312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kapaa</td>\n",
       "      <td>22.0752</td>\n",
       "      <td>-159.3190</td>\n",
       "      <td>80.28</td>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "      <td>1.99</td>\n",
       "      <td>US</td>\n",
       "      <td>1647907313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City      Lat       Lng  Max Temp  Humidity  Cloudiness  \\\n",
       "0  punta arenas -53.1500  -70.9167     53.11        66         100   \n",
       "1      lorengau  -2.0226  147.2712     84.36        69          74   \n",
       "2   new norfolk -42.7826  147.0587     59.04        77         100   \n",
       "3       qaanaaq  77.4840  -69.3632     -5.64        88         100   \n",
       "4         kapaa  22.0752 -159.3190     80.28        65          72   \n",
       "\n",
       "   Wind Speed Country        Date  \n",
       "0       20.83      CL  1647907311  \n",
       "1        4.00      PG  1647907311  \n",
       "2        0.81      AU  1647907312  \n",
       "3        5.75      GL  1647907312  \n",
       "4        1.99      US  1647907313  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new DataFrame equal to the city data to drop all humidity outliers by index.\n",
    "# Passing \"inplace=False\" will make a copy of the city_data DataFrame, which we call \"clean_city_data\".\n",
    "\n",
    "# I did it above with the .loc function... So I call it. (but as I stated above, this whole step wasn't needed for my curent Data Set)\n",
    "Filtered_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77137c2a",
   "metadata": {},
   "source": [
    "## Plotting the Data\n",
    "* Use proper labeling of the plots using plot titles (including date of analysis) and axes labels.\n",
    "* Save the plotted figures as .pngs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8294a4",
   "metadata": {},
   "source": [
    "## Latitude vs. Temperature Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f081ce1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a general function to create Scatter Plot Graphs\n",
    "def weather_scatter_plot(x_values, y_values,title, x_label, y_label):\n",
    "    \n",
    "    #Create Scatter Plot\n",
    "    plt.scatter(x_values,y_values, edgecolors='black')\n",
    "    plt.grid(color='#9FA095', alpha=1)\n",
    "    \n",
    "    #Title and Labels\n",
    "    plt.title(title)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1048daee",
   "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": [
    "#Get today's date and format it mm/dd/yy\n",
    "Today = date.today()\n",
    "Todays_date = Today.strftime('%m/%d/%y')\n",
    "\n",
    "#Creating City Latitude vs Max Temperature scatter plot\n",
    "weather_scatter_plot(Filtered_df['Lat'], Filtered_df['Max Temp'],\n",
    "                     f'City Latitude vs. Max Temperature ({Todays_date})',\n",
    "                     'Latitude', 'Max Temperature (f)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/Lat_vs_MaxTemp.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cacbf593",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "This graph shows us that the furthest we are from the equator the maximum temperature drops. The reason behind it - earth shape is round and not every place on earth receives the same amount of sunlight heat (warmth). Also, we see our Max Temperature isn't exactly on 0 degree Latitude but rather between 20 and 30 degree Latitudes. This makes sense as we know that the maximum temperature on Earth are around the Tropic of Cancer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1cdf57c",
   "metadata": {},
   "source": [
    "## Latitude vs. Humidity Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93a2f0e4",
   "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": [
    "#Creating City Latitude vs Humidity scatter plot\n",
    "weather_scatter_plot(Filtered_df['Lat'], Filtered_df['Humidity'],\n",
    "                     f'City Latitude vs. Humidity ({Todays_date})',\n",
    "                     'Latitude', 'Humidity (%)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/Lat_vs_Humidity.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af9da2a",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "It doesn't seem like there is any correlation between Latitude and Humidity. The Humidity in cities is spread out across all Latitudes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f800c73c",
   "metadata": {},
   "source": [
    "## Latitude vs. Cloudiness Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7b23a80",
   "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": [
    "#Creating City Latitude vs Cloudiness scatter plot\n",
    "weather_scatter_plot(Filtered_df['Lat'], Filtered_df['Cloudiness'],\n",
    "                     f'City Latitude vs. Cloudiness ({Todays_date})',\n",
    "                     'Latitude', 'Cloudiness (%)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/Lat_vs_Cloudiness.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1481bdd",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "There is no correlations between Latitude and Cloudiness. However, we do see there are bands of cities at various levels of Cloudiness (for example, Cloudiness: 0, 20, 40, 75,90, 100)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593ca121",
   "metadata": {},
   "source": [
    "## Latitude vs. Wind Speed Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa78e147",
   "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": [
    "#Creating City Latitude vs Wind Speed scatter plot\n",
    "weather_scatter_plot(Filtered_df['Lat'], Filtered_df['Wind Speed'],\n",
    "                     f'City Latitude vs. Wind Speed ({Todays_date})',\n",
    "                     'Latitude', 'Wind Speed (mph)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/Lat_vs_WindSpeed.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5404fde3",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "There is no strong corelation between Latitude and Wind Speed. The plot shows that most of the cities fall into 0 to 15 mph Wind Speed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbee0e2",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1614e50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a general function to create Linear Regression Plots\n",
    "def plot_linear_regression(x_values, y_values, x_line_eq, y_line_eq, x_label, y_label):\n",
    "    \n",
    "    #Perform  Linear Regression\n",
    "    (slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values) \n",
    "    \n",
    "    #Get regression values\n",
    "    regress_values = x_values * slope + intercept\n",
    "    \n",
    "    #Create line equation string\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    \n",
    "    #Create Scatter Plot\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r-\")\n",
    "    \n",
    "    #Displaying equation on the plot\n",
    "    plt.annotate(line_eq,(x_line_eq, y_line_eq),fontsize=15,color=\"red\")\n",
    "    \n",
    "    #Labels\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    \n",
    "    #Printing r-values\n",
    "    print(f'The r-value is: {rvalue**2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c3dbf6",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Max Temp vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cf5460bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.7923485717506374\n"
     ]
    },
    {
     "data": {
      "image/png": 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VBEqkEqo18a26to7Rz5jSEpmUfvayEo9MyCnv8+c/GyVx/PGxk0tKYM89Te6EB9zuUbmiyrE5RiSh0A92ikaS0wQhfVq0Wckplt9qesi2SWLawnW2pTIiYasR80w69/GyErcqxMh9IqaXkbNWMm3huvjP+dprUF9vMqxra+Gbb6CwEE46CV5+OeX97D5LZEXv5GpQyv534/VzRZDkNEFInxatHFIpgHQnaTfcJqZMJy0nf0UEu52Pm1M++tkLC6GmBr77Dnbf3Yy98oqZxa+6Cu6/35ecqXY41TV1cb+bVNVXnXZ0kpwmCOnTos1KEN/TwamcdDZxm5gynbTcekMo4Nye9k5jz6aXNm2MqWnLltjYAw8YJXHXXZ7lTKUEI99D5HczY2h3x/pSbma3dEyDUl1VEAwtXjk0Nk5VUCOlMjIhYuO3q+SqgSXvfZE0npbpZd99jZJYvTo2dsMNRknMmZNSTjclaDd52/kupg/tzsYUCt2v/0Yc2IIQo8UnwbmRqzDIyhVVTJq3JuqUznZSl1PGsQI2TBkYN5aVpLvFi+MrwAIsWwYnnGB7uF2CIDR+clvi7/e773fZVofNdgKiIAQFSYJLA0+2+DTJhS/Dih9bu5tT3jN9+5qdxMyZcNllZqx3b/P80UfQpUvc4bly9qfCqgwS+2C7+TVy6cCWPAwhqMjOwYHGKmORC/yW7sj6BHXTTXDLLfFjX30F7dqlf00PuH0Op92KF+x+59n4zhqrjpYgOCE7hzRoymGQflfmWd/J3HyzeVxwATzzjBlr394879gBu+3m+5KpJuNUO710srEhgwgvD2RaokUQcokoBweaehhkrk1Xnnj6aWNuOvJIWLvWjLVubVqXbtoUbTaUCi+TcaqJ1qtST1U118u9vNKUFyBC80eUgwNZscULRgG8+y7U1UFRkRmrqjLNhs46C/7xj5SX8DIZp5poU+WAQKw3R2MVR2zqCxCheSOhrA5kUsZCsMkXWP252UVs3x476J//NMrjN79xvZaXydhpQi0NN1Cyy3kIFSjalYR8/3791N1yy5uQJkFCkJGdgwuBMM00QVKagbQ2ZqXOnc0Jd95pHvfdB1dfnXQ9Lyvs0QO6MvrZt5NKk3y7Y1e0ZhVkJ0LKy66yckUVE+euiQuNTfwe8hW1JQhekGglIev4ivRauRJ69IgfW7AAfvzj6EuvUT3dJy1qtDyFTCKjmkLEm9AyCGy0klKqFHgYOAqTxHs5sA6YBZQDG4ELtNbb8iOhkA6+bPLdu5udxD/+AQPDCXqR5xUroHt3zyvs7TaKIfG+2QrbddtVTpy7xjUyqqq6li5jF8hOQQg0+fY5/AF4Xmt9GHAMsBYYC7yotT4EeDH8WmhCpNUL48c/Nkri3ntjYz16GJ/Ep59m5b6NUR6jckWV7e4lESnPIQSdvCkHpdSewCnAIwBa651a62rgbGBm+LCZQEU+5Gsu+C0kl43Ccxk5Wn/5S6MkRo2Kje2/PxXHdmL7Z1+6Tqqp7tsY/R38Xkv6SwhBJZ87hwOBL4C/KKVWKKUeVkq1AfbRWm8BCD+796cUHPG7Us7WyjorkV533mmUxFlnRYdWz7iAjVMH0ap+l+2kmuq+TuauqurarK3e08lRkLwGIYjkzSGtlOoFLANO1Fq/ppT6A/A1cJ3WutRy3DatdVLdBaXUlcCVAJ07d+758ccfN47gTQi/JUCCWjKky5j5LL3vZ3T8Zmt0bH37TvT/+X1smDoo5fnWvtpOpFu2IrGIorVvt5VIcl0Qv1+h5eLmkM7nzuFT4FOt9Wvh188CxwKfKaX2Awg/f253stb6Qa11L611rw4dOjSKwE0Nv8laQc3Y7diuhB9d8yiH3vD36NjBX33KhtsHw9Chrucm9tV2Ih3zjl3LVzvFEEmuGz2gK6GC+KzwbJRqF4RckDfloLX+H7BJKRX5z+gHvAvMBS4Nj10KPJcH8ZoFfh3DaTmSG4FID4ydrUKUj5nPMdc/GXvz6aeN0/qmm2zP9VNTqaq61pe/xanlqxWlEposJVQMqWvQvPnxV57kE4TGJN/RStcBTyil3gG6A78HpgD9lVIfAP3Dr4U08OsYDmrGbkWPMtoUxaKutxfvQfmY+Zx81cOxg265xczEM2fGnetn16PAl78l1W4EjNtk9vKqqGnLTpk8sewTiVgSAkdelYPWemXYNHS01rpCa71Na/2l1rqf1vqQ8LMsq9LEr2M4yCVD7HIYNpXuS/mY+Qz92fTY4GWXGSWxeDHgvOtJLPln5ytIZWqy67hnR21dPTc8/bajMtH4j3IShFwj5TOaOX5LgAS1ZIhb4bzXfnAIJ972IksPrYZzzzWD4c50Nz+7mF+tLEzKrj63ZxlL3vsimgzndG23nUe9j2COVMfmw68jjYYEN0Q5CE0Cu3pGVjZX18I55xg7zl13mZ7WQL/z+rIWGPTbZ1hTX+w4CTpFarn5W8o8VHr1ih+/Ti4aDWWz06HQPMi3z0EQPBExeTmZcuIm11GjjJK46qro0Pzfn8+GqYNovbOWkbNWZqVCqt056aCAPod5i7jLVi5KYyQECk0b2TkITYbIitZzn4377zePk0+GV14B4MUJg/i+MMTho55lxKyVTJq3Jq6HQ+KKHGK7ikKlqNeasoTVut05kTGloCGF9UkDs17fBBBn6pJGQ0I+kaqsQuCwmk1KS0KmDURtne3ka/e+tTpq5LhCNG9NH8qeO2ui91m536FUXHKXYwKcW3VVr0lzTpVivWB3jy5jF9jmUihgw5SBnq8d1IRHoXEJahKcICSRaDbZVlNHdW1dnAkFYOnYvkwf2p0ddQ1J71euqEq6zi4UR498mq6jZkfv1X3L+2ycOoiJz91la05xy5HwaoJxqhTrBbt7ZCsXJahhy0JwEOUgBIpUSWvWCdPNxOJ0ne9Du1E+Zj49f/V4dGzoqhdYOq4fTJ0ad2wqE4sXE0ymCYSJ98jWpB7ksGUhGIjPQQgUXibcyDGZ2M2/bFNK+Zj5HPjlpyx+ONx9buxY85g1Cy64IGXfaS8Tf6ooq1Qk3iOb3eOCGrYsBAPZOQiBwsuEW6AU4ytXUeASueR1xf7RXp04fPw/efmhZ2ODQ4eCUty2z9eO0UheV+t2K/SLe3f2JFuo0L7uUkWPMpaO7cuGKQNZOravTPBCThCHtBAoUrXYTEXEiQswYtZKx+MiGdFlCU7u45b+gxnz74w79oIb/srrrdo7RiulgxdHdWlxiJUTzoi+lqQ1Idu4OaRFOQiBIzFaqbqmzjZCJ5FCpbjzgmOiE+YR//dPauoaHI+PRObYKaRRy2Zx/b8fiz9h61bYa6+cJKHZYY1A8tpHWxD8IMpBaNI4hW8mkhjOmeq8yPFOYZ2FSnHn3GlUvPtS3PgxY59ju46ZmzLpBeHWZ8IaVuomY4PWspMQ0kJCWYUmjVf/QeJxqc6LvO/kwK7XmhGDf0P5jfNYu8+B0fG3p5zN8j9ehNJmV5JuZnHEdzBjaPeUEUhuMko/aiEXiHIQAo+XMhV2DmK386zHp1Q+SnHWZX/klMmLaAjXc92r9ms23D6Ex5/6HZBZZrGXsFIvCtKLkspGj3ChZeDJrKSUOhY4CePDW6q1fivXgvlBzErNn0Q7f5/DOqQsNWE9z638xfjKVTyx7JOUpiuFmaS3ff4V704/P+69Z374E85/dU6WPm0yXh31bpnS4rcQEsnI56CUugk4H4j85VcAz2itJ2dTyEwQ5SCki5/oqIhSiRz/g2++5PV7L40/6O674Ve/irt+tiKMrNcqCCs6Oxmdyl9IyQwhkUyVw1qgh9Z6R/h1MfCW1vrwrEuaJqIchHRxmjATm/9YV9iJE/4tXerpO+yM+AvMnUtlp2NztlJPZxeQrbpMkftLWG3Tx005eMmQ3gi0BnaEX+8GfJgd0QQhvzj5CiI5EHYVWiNj04d2j02IQzUsWgQDBpjXQ4ZQATx86QxW73tw9Lp2FVQrV1Qxad4attWYvIfS4hAThxzpOtmmkyntlPHtt8SH9IJoGXjZOVQCxwEvYP5n+gOvAJ8DaK2vz62IqZGdg5AuXk0tvlbqDz0EV14Zf5+r/0xV2x8AyfkLo599O6m3dAHQNpzjka2VebZ8DkE3T8muxjuZhrL+HfgtsAR4Cfgd8E9gefghCDmhMSJrvBay89Uc5xe/AK157NRh0aGl91/OxqmD2OP77+JW6tMWrktSDAANmIq0XsNUvXxX2Sq2F+ReENlqhiR4MCtprWc2hiCCYKWxTBdezTPpTIh7TL+Dw+dcyh+fmkj/9a8BsGrGUPPmDTshFPI8obo19PHzXWWj2F62zFO5wEmJj5i1kmkL18kuwgcplYNSahBwC3BA+HgFaK31njmWTWjBZKvjmRe8TJjpTIiRa05s83uu3FbDKw9dSdm2LebNoiI46CA6XvEAVdt3OF7DipMiaczvqnJFFTU7dyWNF4cK6XNYhzifTGNMxIkmJLcquuIb8YcXh/QM4BxglW4OtTaEJkEQTBeJNZ5CBYo6S89PL5VZ4xTP1M2wcyfstpt5/eGHLP3t6Sw8pDdXnTM+pTxOiqixviunsN+SUAHn9Cxj9vKqRnVS2+2YEqPMEsmV0myOePE5bAJWi2IQGpNsdTxLF7uOdCgzEUbYrZX9v4+r/b+oCLSGbduiQwM+WMbGqYMY/W9nC66bImqs78qpgVJtXQML3tni3SeTQ3k0YF/IPUYQfCNNAS/K4UbgH0qpcUqpUZFHrgUTWjb5bmNpN/HU1WtqLVVeq2vrkpydnh2ipaWgNSdd/Uh06Nplz7Bx6iAueHsREJvkUjmOG+u7cgv7jYThJuJm5smlPGUuitGL0pQyI96Uw61ADSbXYQ/LQxByRr7bWLpNPFYSV8e+opoAfUA55WPmM/iS6dGx25//IxunDuLxA75mo4eGPl6+q2xMdunsRFT43pngJLuTPJGQWi8FDZ3uJxFP3vIc3nSKgw0KkucgZBunWH47rHkLfrOQE+3m/T9YxkNzEirTvPMOdOvmQ3r3e0ByfoOX3IDKFVWMnLXSU/l0K5nkP7jJDmTlcyUS9DyObJJpnsO/lFJnpD4sPZRShUqpFUqp+eHX7ZVSLyilPgg/t8vVvQXBCS+VYCO0LQ5Ff/Zr/09c9b97XB8q3/oU/vCH2EFHHw1KwebNnuW3kmo343WlXNGjjOG9OyfZ9P3Y+P3uYFJFYqXaMaXTUjUIwRBBwItyuBZ4Xim1Qyn1tVLqG6XU11mU4dfAWsvrscCLWutDgBfDr1su994LAwfCXnuZCeKll7yd98ILcOGFUF5uzps40fnYOXPguOOguNjc58wz4bvvsiB8FtAabr0VOneG1q3h2GNh4cL4Y3buhNGj4eSTzWdw6C1tS3U1XH45tG8Pu+8OZ50F69dHJ552JSGe+ttYNk4dZPs4tmpt3O282P8TJ0ggOoGNHtCVaQvX0WXzQZx424t8NPSy2IXKysxn+/Zb14+UeH2nHVBksvNjCptc0Y3pQ7vHTcipdhIRxZiOuSbVRJ2Lftr5DoYICl6S4HLmX1BKdQIGYvwaESf32cBp4Z9nYrKyx+RKhsDz17+aCWHAAHjySe/nPf+8MUf06wdPPeV83MMPmyqiN94I06aZKJrFi2FXcix7XpgyBW6+2Ty6d4fHH4fBg2HpUqPQAGpqzOc4/nj40Y+M/F4ZOhRWrzYr9bZtYfJk852tMmGYO+oaGH/GNezxfU3caSNfeYIjP/uQt/c7lAaLMzZVUl1ieXBryCeQFJo58JCh3PbWZCp+c0nsc+2xh/mbqKuDwnhF5Ce8M1WzI6fxxLwQNwVkVYx+8jEi5iAnxZPORO3VxGStvGv3OVoKXpLgFDAc6KK1vkUptT+wn9b69SzcfwYmGsqqgPbRWm8B0FpvUUr9wEGuK4ErATp37pwFUQLKq69CQYGZwPwoh2nT4M47zc/PPWd/zNatMHKkKTP9i1/Exn/yk/TldaO83OxgLrvM2/E7d8Jtt8GYMeYBRkm++y5MmgTz55ux0lL46iszYd5zj3fl8N//mmJ5L74IfcO25BNOgC5d4MEHmVRzNLV19azfO/7vK1Rfx9H/+4D5h51MfUFhUmSMdfKMTEgjZ62kbXGI6trkqB7rKt1x8nzxRWhogH32Mb83raFVK97f72AG/HQ6HduVRHcdduGdiYQKVVyzo0wynu0mU4B2JSEmDI4VEPSqhFKVUU9novabRQ7+iho2R7wkwd2LKfXSF5Mp/S3wJ0wxvrQJZ15/rrVerpQ6ze/5WusHgQfBOKQzkQWABQvMivTDD83kEGHDBjjoIKishCFDMr6NbwrSbNbn5bynnzbPl17qfMyyZXDSSfDgg8b8ArB9Oxx1FJx6qlnJ54oPP4RvvoHTT48f798fpk83yqOoyIz5MSVFWLkSWrUynyPCPvvA0Ufzxaw5bOtnX5X+1I/eonTHt8w94lQUOE5UiROSnWKI4GbPjrxX+fYWpt0wi61bt7PuznMAOHTLejbcPpjZR/Zh3Hc3eupLAcRpjExXyk6TaWRs5KyVdCwtdlSObYtDcZnV332/y/FzWBs1+XE2+80iz0aZkaaOl5nnBK31tYRLdmuttwFFWbj3icAQpdRG4Cmgr1LqceAzpdR+AOHnz7Nwr9SceSZ07AgzExKRHn0UOnSAH//Y+dz6emOGcXs0NDifny9eew26doVHHoFOnSAUMivnV1+NHdO7t7HnjxwJn3xixq6/3nyeu+/OrXw7wmUlihL+3HbbzSiGjz7K/PqtWiWZZthtNwree8/xtMFr/8OW3ffijU5HMrx3Z18TkhMdS4tdbd1We/33rYooHzOfHtc9ET3m3DVLWDv5LK5/dZan+9U16OhuJRthw4m2fyDJv/Ddzl2ECuKVeKhA8d3OXXHHOSlRBVG/gl//hTiZ/eNFOdQppQoJrzWUUh0wO4mM0FqP01p30lqXA8OAxVrri4G5QGQpeyngYBPJMoWFxtwxc6bZshshzeuf/tRMIk7062cmVrdHZNUdJP73P1i3ztjZp06FefOgTRujKD/7LHbcpElwwAHmMzz3nPGDPPQQtEsRSJaoIMEoFeuYWyj1gQeaHcEbb8SPvx62aH71lf/PbOXgg42CWBWz+VNbC6tXs0fNN7antK7bQb8PX+el7n2YPqwHkyu6OUbgeJ14IrsPN2e2naLZVtKW8jHzOe0XD0THRr38GBunDmLIuy+lvK9Vvmw7dp2SCHdv3SpqhitQRknZVaW1o0Cp6Hc8ad4aX/kk4mT2j6NyUEo9Gv7xj5iy3T9QSt2K6eXw+xzKNAXor5T6ANM7YkoO7xXP5ZfDxx/HIoKWLDGvf/Yz9/MeeMBMYG4Pt2ghreMnzHqPpoFMaWgwkS+PPALDhxulUFlpFOU998SOKyoyCuE//zEO3J//3H0nFSFRQX78MVxxRfxY4k7NStu2JuLq1lvN7+Krr8xu5V//Mu8nrvj9MmCAMSFedZVRklu2wNVXw/btaAez3JBPlrP7zlounD6Gih5ljK9cxchZK21XsF4mHgXR3YfbCt5N0WxsX0b5mPmcN3xqdOyP8+5g49RBHL9pteN5uZwYneStrqlj9ICuhAoVDT6NwfVaR79jt4xsO0Wd74z7poibz+FoAK31E0qp5UA/zN9yhdZ6rct5vtFav4SJSkJr/WX4Xo3PgQfCaafBX/4CffqY5+OPhyOPdD/v4IPdV8Dg7gP497/N/SKceqr3kNVMaN/ePJ92Wmxszz2hZ0/j9LVy9NFwxBHw9ttwzTXerp+44h8yxDTBGTQoNmb179gxY4ZRSBGH8f77w/jxRtnus483OZwoKjKRXBdeCIcdZsZOOgkuuYT651+gOFSYZIcf8eVb5vfdqxeVK6riIo8iRFawdrb8UIFi99atHJv4ONm6U1UcBXj7gKOofOtTyhbO5bhx5nf09N9MJHjfn9/PR3t1ivssuZwYS0tCthN4aUnIsYeFlXYlIUqKWrn2y3Yi8j3ZOZ1z6WRubk2G3MxKJUqpHkqpY4ES4L/Aq0BxeKx58vOfw+zZUFVl4v9T7Rogc7NSz57xu4wHHnA+Npscfrgx2yT+42mdrMz+8Ad47z0ziV53nTcfSq9e8Y+iIhOxZB3bay/3a3ToYKKPNm0yEVsffWRMX/vua66VKccfD+vXm8+2fj28/DJ8/jklp5yYtIqf1v8AOr76klEm4Bpqubm6NmknUFocclUMbnhJymtTZNZ6l3zXhfIx87n9lEui7y1++Go2Th3EXjXbG6UUidNcrnVqc1txqJAJg4+MmrkaMqj5mevifxGaY8kNt51DGXAn9gmQGhO91Pw45xy49loYNsxMgMOGpT7ngQdMVI0be+/t/N4ee5iJsrEZNMj4E5YsiZmJtm+H5cvhN7+JHbduHfzud8Y3ceaZRplNnw433NB4snbqZB47dsCf/5xdH45SxjEP8MEHxmw1b17yKv7RR+H776PKwW0lHzHZRK6RafMi68rX6b7ba+vibP33/vAC7v3hBUxbMIPzVxtT3PK7h5uDf11je41ssd3Bqby9ts51F1SoVJLicjq+tDhEm91apdxRba6uzXnzqMbsqdFYuCmH9Vrr5qkA3Gjd2tjf//QnMwmUlqY+p2sO7ZZvvgkbN5qVMxgT1NatsRU4GH/A5Zeb0M8DDjBjH38cM+vs3GnMRM8+a1bdZ51lxnv1grPPNn6AKVOMArv9drPTufZac0x9vQl17dEDRo0yO4pJk4xpZ+DAmDkmVzz2mEn2OvBAEy01fbrxNYwbF3/cP/9psrpXrjSvn33WPB93XOw7ueIK8/2tXx8775ZbzGfYe2/jmL7lFrMg6N8/WZannoJjjjE7LsxE5mTu8NNm1OvkEVE0TklnHUuLbVfloweO4MaBI9jw4kTz9wRQUmJ8Ol99lX64tAtuuROjB3S17ZsdKlBMO/+YpO/DKdR24hCTQ+FUz8p6z0y+/1TmosoVVSmz0Jsi2f+raA5UVJjnIEQY3XMPnH++mZjB2NrPPz/eYdzQYCZx60S1ZIk57vzzza7mmWfMz7/8Zfz1H3/cfN5Ro+C884xiWLw4Fol0++1m0nz00dgkMnq0yVa+9NLcO88bGkwk1Zlnmvsef7zxx+y+e/xxv/yl+XyPhEtgRz77kiWxYyIhx1a+/BJGjDDX/+MfzY7p4YeT5di61STLWXaSbnZwaxKclxIWfnBzrrpG5bzxhvn8kdDg7duNorXmeWQJNxkrepQx7bxjaFcSq0lVWhyKUwzWCLBpC9dxbs+yJEc9mOxsN8UQuWe6oaypzEWR951oytFQjlVZlVJnaK0XNbI8aZH1qqw33gizZpkEuBysqoTmQarqnakyfa3H+sVuNQswce6apDyBxEqlgNllJSrYq6+G++7zLYsfGb3skrxWkU313VoztNOttJrqvB43L3KMnLL93gOGW1VWR7NSU1EMWWXdOmN+ue8+mDBBFIPgSqrM4lRJcG7Z1alI9Ic4TZaJJSyitGljdpr/+x/st58Zu/9+87jrLpP06BM7ZeBH8UXOt5uME01Abt9tmY0icoocq9m5iy5jFzgqL7cdR+WKKkfFAAReMaRCZj8rV10FF11knLPXX59vaYSAkyqzOJXJQpO9/spOk2VJUSv3e+y7L5VvfcrFI2Id6Rg1yjjp//53z/fPNFqnckUVo59529W5bP0+nb5baxa1FbvIMZTpYOcmr5uZzi0KKo1iLoHDS7Of1lrrHQlje2utt+ZUMh9Isx8hiKRqGJRpExzrKt3pPk5NhqzXsa6of7RxJX+bNT7+oNdeM74eFzJtkNN90iLX2lPW60UyxjO5n1d53UxcqRofNYXmQJk2+3lDKdXbcrFzMfkOgiC44JabkEkSmt0q3Wmlai05YbeKT9xxvFrenfIx87n1vNGxg044wewkNmzwXSrEq8Pdi2IA81lHzlpJ+V7FGWU8e5XXbXeYytnclCOVwFtV1ouAPyulXgI6AnvRXHMcBCED7Gzut53TLbrKjYS+2tnE/eBUltuub0Mkosoprt9pAnvooFN5eMypTHj9KS5bEq68e+CBVAA3/fopdOvd466ZadlvP2jg1Q+/Ynjvzix574u0MpL9yOuUte5UqtztWk2JlGYlAKVUBfAY8A1witZ6vfsZjYuYlYR84yXCJlu4xfW75V5AsqnDS6/s4lAh/1p2D2X/WhA3fshv/k5dYSiq7JIcvoWKNkWtoolvbpO3W9SP18/ih2z9vipXVDFp3pok2ZtCpBJkaFZSSj0CjMDUWvoZME8pdW1WJRSEJo6fVpuZ4rQiVbjnXkDyTsFLWY7aunou6DeKLjfOY337WH2mD+74Ca/eexmbt9UkmV/alYRAG3ORFwf1hMFHEir058a1axLktT91NsqUR66z4qYzmJHQOrUpKIZUeDErrQZ+rs0WY0PY/3BXbsUShKZFY/YLsFulO7UCTSRRsXgpywHmc3RsV8Lpv7ifVvW7WH9HhbneN1vZcPtgWDOQivnzo9c7ccripNV0quY6VjkiO6B2DgX8Ej+Ln/IYiea/6UO728rkJ0+jOTYHSrlz0FpP1xbbk9Z6u9b6ityKJQhNi8bsF2C36vWiGJwcthU9ylLuICKTY3GokF2FrSgfM5+jRjwdO2DBAuO0Hm0c2ekoy0hPiY1TBnLnBcdQVlpMdU0dbYqS5Ur8LF53bnbO/JGzVlKesNtojoX0/OKlh/QhwG3AEUDryLjW+sAcyiUITYrGbkqfuFJ18h0UKkWD1mm10YxgLXsROXZzdS1t99mLyrc+pWLvBoj0cb/jDrjjDq6pGMGfup6edC03ZWlNgrPuhL7bWZ/Sf+FVGbn12LbuNppjIT2/eDEr/QWYAEwH+mD8Ds0hx0MQska+m9I7KafEkhPWXs1W+dxW9NZrOJpPtIYVK+BYU81/dOUMRjODy86byEsH9YrK47XfduJOqK5e02a3VqyccIbt+V6jj1KZ+SIKQNqKestzKNZav4iJbPpYaz0RCWUVhCSy3WrT773dHKx2ZpIRs1bSfdIi1651ZaXF3j9Hjx5GScyfHx169NmJbJw6iNNqqlydtF76bVeFS1bY4bXTmxczX0R52tHUw1P94CVDeilwMvAssBioAqZorQPTX09CWQXBHbeQ1eJQIef2LGP28qrshuLee2+s9HuETZtMX44EUpXd9iKTmwPZyWRlh1NobuS8TPNUgoRbKKsX5XAcsBYoBW4B2gK3a62XZVnOtBHlIAjupJp8rWUpsm4WGzXK9OGw8vXXpslVGC/5FlZZ/Rb0c5roExWFVfm4KZRU7V6bChkph6aAKAdBcCfV5JuqBlNWOPNMWLgwfqyuDlq18lSCO4JfWd3qKHlRiF4TBZtibkNaJbuVUnPdLqq1HpKpYIIgNA6jB3RlxKyVju83ii39+eeNT6KsDLZsMWOhEBxxBBWrVwPxDv2anbtscxz8yurmXPaSn+DFCd0cI5ncopV+CGwCngReQyKUBCFrpNsIJ10qepTZlnmAzPpK+EYp2LzZ9OJuHY6Mf/ddKCigYtgwKp58MnqoU4kLv7JmWvfJreqtleYWyeQWrbQv8FvgKOAPQH9gq9b631rrfzeGcILQHMlXgtWEwUcmRfQoYHjvzrZZxF5LUaTFbruZXcSXX8bGnnrKKI+JE4HslbjwGsnk53w7mlskk9fCe7sBFwLTgJu11nfnWjA/iM9BaEpk2vsgEzKJ6CkJFVBb15Cbnc5HH8FBB8WPzZwJl1ziSfZUZLpTs57ftjjEdzt3UVcf+4aao8/BVTmElcJAjGIoB+YCf9ZaByqHXJSD0JRwihzK1Cmc6eTp1SEMOZwMly2DH/4wfmzxYipLD220qrdeaGyzYK5ISzkopWZiTEr/BJ7SWq/OnYiZIcpBCApeJo1c7BwyLUHtJ5Q0QkRePxOl52Nnz4bzzosb6nfFfXy49/62MrREsqGg0lUODcB34ZfWgxSgtdZ7+pIih4hyEIKA1wk6F70fMlU4XpPQrChg+tDunj+LW76BY2LZHXdEi/lF6PWrx9japl30/HR3W0159Z+tv6FA5jkopfYH/opxfDcAD2qt/6CUag/MwpixNgIXaK23uV1LlIMQBPxM0NmemNxMVdOHds9KLH8iZWEHrNtntn7OghSNiFyzn08YRMXr8c2GDhv1LHt1aJfWzsHL5Bpk5ZGt3WdQlcN+wH5a67eUUnsAy4EK4DLgK631FKXUWKCd1nqM27VEOQhBIFe+BC84TRalxSG+39WQ1m7Gjcg1Rs5a6aqU/FwzIu/KCWckTcx9DuvA7OVVPPaXUfSqWhs9flfr1rT69lsodI4mspvknfpXWJVakHwciWTrby2jTnC5Qmu9RWv9VvjnbzAlOsqAs4GZ4cNmYhSGIASefBZrcwrXVArH0tPWcNVpC9dxbs+yuLDRi3t3juvsVlocSgopdfvMXorpJVJdW8fwh/7LyFkr40J9Zy+v4tyeZfziyhl0uXEu3xaZ+7basQNatYLevW2v5xQ27LRLiuQqNGZnv3RojL+1QJTPUEqVA//BOMA/0VqXWt7bprVuZ3POlcCVAJ07d+758ccfN46wguBAPlab1lVxaUkIrYnreeC0sgfT4znTcEy3z+x273RoVxJiR11sF9S6bgfv3RXvtOaKK+Dhh6Mv3fpc2Jm4IjuHfO4CvdAYPoe87RwiKKV2B2YDI7TWX3s9T2v9oNa6l9a6V4cOHXInoCB4JFtJW15JXBVvq6nj+10NTB/aPVoy3G0laVUMkN7K2PqZwUy6keu0LQ75/kxubKupi5sMd4RaUz5mPn1Hz4od9MgjJpHu9tsB56zleq0JFcQXfQgVqGhiXNBLdjfG31pedw5KqRAwH1iotb4rPLYOOE1rvSXsl3gpVXlw8TkILREvTkm/voR0V8Z29yksUNQ3NN78ctCXm3jx4V/GjY2/6CYe3//4pGNLbRLZQoWKaecdE63IGmSfQ7ZIq/BerlFKKeARYG1EMYSZC1wKTAk/P5cH8QQh8HjtVtY6VOBZOXhdGSc6eWt27kq6R2MqBoAP99qf8jHzOeGTVcx6chwAk/92M5OBcy6exltlhwMxX0zizqmuXkeL5+W7s18QyJtyAE4EfgqsUkqtDI/9FqMUnlZKXQF8ApyfH/EEIdikKijnd9dgNau4kXhdvyGwEdqVhPh2xy7qsqxEXuvcjS5j5rPhqG3w058CMOdxkytxwQ1/5aKhfRnpUKHWqli9VGxtzuQzWukVrbXSWh+tte4efvxDa/2l1rqf1vqQ8PNX+ZJREIJMqoJyvqOFLCZ4t8J76UQhJRIqVEwYfCTTzj+GQpX9gs8dS4vh4otNcb9wIT+Ap++8hIpjO3FYaKfzeQIQAIe0IAjpkcop6VZC2m5CjphVUlWNzUZp6si9ANfEuHRIqrg6YYJREhdeGB365+Rz2Dh1EEW76pzPa+EEIpQ1U8QhLQjJuDmsN4cn/kQUzuaqiKPbLeGuzW6t4hLXZr2xKcm2b6U4VOi4C1FAa5f3I4QKFW2KWsWF8Dqag7SGY46BVauiQ1tLSqn43TP85qzDW5wZKZAZ0tlElIMgJOMWceOWIeykOCLv25X0dqun5LV8hh0zwqU/nPwahUpx5wXHuE7qtmUwuu1jutBZ6d8fFi1ylSfIJTXSIdB5DoIg5AY3s5Obv8LJ7q6IOZ81MReFW4x9RY8ylo7ty4YpA2lIYyEaOX/G0O628npRDLYmslWfmV3E15bUqhdeMDkSI0b4u1aOmzTlC9k5CEILxWkV7FY9NRE/hd78FvdrVxJixU1n2MrbtjiEUlBd425K8lygrqoKOnWKP+juu+FXv/J/rSZEIPMcBEHIL06hmnYx/qlqEaWickUV332/K2ncSemAaWtqJ69dKO24OaviZE8lX9J4WZnZSbzzjvFJAFx3nXnMmweDBnm/VjNBlIMgCIC7Pd1p1ewl9NMp36JdSYiBR+/H7OVVSbuUSF9rp4qqTkXxEpVDqlyQJI4+2iiJhQvhzDPN2ODBAJz2q/tY0mb/pFOaa/ir+BwEQUhpT0+VU+GGU15ESVErJld049yeZdHQ2kKlGN67M5MruqVdUdVK2nIPGGCUxIMPRof+cs8v2Th1EB2//tzftZooohwEQUhZojqTQm9u5pjKFVXMXl4VjWKq15rZy6uiOwY7mZywW8FnXKDuF78wSuLGG6NDr953ORunDuLQ1vXNrtaSFXFIC4KQ0xLVbo5ccO4k5xZSm0ikuVDOJ+ohQ4wPwsrOnclhsU0ECWUVBMGVXJaodjPtuO0q/Nxbk+yMzglz50JDA3TpEhsrKoJDDjE7jGaEKAdBEDLyKaTCzbTjppTsZHKqwlTmU4m51Y5KiVLw0Ufw/fexsfXroaAAzj3XlxxBRqKVBEHIeYlqp7DZ0QO62mZxW+9t10va7nivjK9cxRPLPomarNxCYV0pKjK7hepqaBduVjlnjlEev/0t3Hqr92sFEPE5CIKQV/yWpMikhEXliirH9qUZJ7Nt3BhvbgLTme7yy9O/Zo6R2kqCIOSNINUjcsvSzlp/6DfegOMTus+98AKcfnrm184ykiEtCEJOSDXx+8lmbgzcspmd/B++ldtxxxlz03PPQUWFGevf3zyvWgVHHZWm9I2LOKQFQUgLL4XoUuVPNDZuRQXt/BYZFds7+2yjJGbMiI1162Z8Elu2pCV/YyLKQRCEtPAy8QetHpFTBFSkXEciWVFuv/61URLXXBMb69jRKIlvv/UjfqMiykEQBN9UrqjyVMYil/kT6WAXVjt9aHcmV3SzPT6ryu1PfzJK4rTTYmN77AGtWkF9Zm1Xc4EoB0EQfBExtThhnfhzmT+RDn79B16Um++ciSVLjDJo3968rq83CqJnz0Al0olyEATBF06F9CB54s+4tlEWScd/kEq5pe2TKCiAL7+EmprY2FtvmfFLL03zE2YXCWUVBMEXTnWYwLT1DGohunSb9aRTytx3zsTWrdChQ/zYrbeaZLocIqGsgiBkDaceCWWlxY2qGPyaiNL1Hzhld2dyzST23tuYlN5/H7qGd16/+515/O1vcOGF/q6XBcSsJAiCL4LgR0jHnJML53jWr3nooUZJvPxybOyii0xkk3WsERDlIAiCL4LgR0gnxNRJqfU5rEPaRfhypihPOskoiSefjI2dcopREu+/n9m1PSJmJUEQfONmamkM0jHneCnk5zeD22/BQt/Z1sOGmcdtt8X8DxGz0/btsOeeKWVMF3FIC4LQ5MiWIzhrDmUP2PXSLg4V+tt1XXYZzJwZe92nDzz/vKkQmwZNstmPUupMpdQ6pdR6pdTYfMsjCEJwcMp0rqqu5cQpixlfucqTqShXGdx2uQ9ZybZ+9FFjbrruOvN6yRIYMyYjWZ0IpHJQShUCfwLOAo4ALlRKHZFfqQRBCApWvwcYxWDtz/D4sk88Oatz4aR2cpZ7ySj3zB//aJLn7rknpiiyTCCVA3A8sF5r/ZHWeifwFHB2nmUSBCFAVPQoY+nYvpSVFqfsNe20Qs+FQ9lph1Co7PvYpa2ICgrg2mvhwAPTOz/V5XNy1cwpAzZZXn8aHouilLpSKfWmUurNL774olGFEwQhOHhdedsdl4vIKyd56rXOewiwH4IarWSnYuMWB1rrB4EHwTikG0MoQRCCh1NSnt1xVhIjh6Z7yO72Em3kliQ4ekDXwDQ+SkVQlcOnwP6W152AzXmSRRCEAGPXh9qOPofFylOk04TI6zmp+mIHVRkkElSz0hvAIUqpLkqpImAYMDfPMglCi8V35dFGJNE05GTbX/JezPycTuSQ13OCkCSYDQK5c9Ba71JK/QpYCBQCf9Zar8mzWILQIglaq087rCvyLmMX2B5j9QWkE8Lq55ymtENwIqg7B7TW/9BaH6q1PkhrfWu+5RGElkrQWn2mwkt4ajohrEFrXJRrAqscBEEIBkFr9ZkKL+Gp6YSwBqHgYGMSSLOSIAjBwSn6JqgrZmu9o6rqWgqVitvpWE0+fiKH0jkn2/iuzZQBUltJEARXslITKA80VbmdyMXnkWY/giCkTRBWzF6xrqwLlKI+YfEb2UHYyd6Yq/J0cPP95EJOUQ6CIKSkKUTfJK6sExVDBDtfSVOIyGps3484pAVBaBbYraztsPOVZCsiK5f5II0dLSXKQRCEZoGXFbRTdFE2VuXptC71Q2NHS4lyEAShWeC0gi5UKmWmcjZW5bnOB2nszGvxOQiC0CxwqmnkZQJ1q4fklcbwCTSm70eUgyAIzYJMoqqyEZHV1PJBUiF5DoIgCFmgKeZVSJ6DIAhCjrDmR7QtDtE6VEB1TV0gcyX8IMpBEATBB+MrV/Hka5uo1xqlTGeyhrABprq2juJQoafGQUFHopUEQRA8Mr5yFY8v+ySaYKd1TDFECHLFWj+IchAEQfDIk69tSn0Qwa1Y6wdRDoIgCB5xKsmRSFONULIiPgdBEFo8XovuFdoU80ukufR4kJ2DIAgtGj9lLy48YX/ba5SECqJZy+f2LGPawnWB7LftB9k5CILQovFTCntyRTeAaLRSoVJceML+0fGmUN3VK6IcBEFo0fgtezG5oltUGSTipGgmzl3T5JSDmJUEQWjRZLMUtpNCqa6ta3LmJVEOgiC0aLJZCttNoTS13AdRDoIgtGiyWQrbTaE0tdwH8TkIgtDisAtdXTq2b8bXrehRxqR5a9hWU5f0XlPLfZCdgyAILYpcd2ybMPjIRu3Ylitk5yAIQosiVeiq14Q4J+x6Q/Q5rAPTFq5j5KyVTaZaqygHQRBaFG6hq9nKU7B2bGuquQ95MSsppaYppd5TSr2jlPq7UqrU8t44pdR6pdQ6pdSAfMgnCELzxS10NRd9oHPdWzpX5Mvn8AJwlNb6aOB9YByAUuoIYBhwJHAmcK9SqtDxKoIgCD5xC13NRR/oxugtnQvyohy01ou01rvCL5cBncI/nw08pbX+Xmu9AVgPHJ8PGQVBaJ64ha5mMyEu1blBj14Kgs/hcmBW+OcyjLKI8Gl4LAml1JXAlQCdO3fOpXyCIDQzrD4BK6MHdLXtA51JpFEurtkY5Ew5KKX+Bexr89bvtNbPhY/5HbALeCJyms3xtvVxtdYPAg8C9OrVy1uRdUEQBBfsIo0yjSzKxTUbg5wpB6316W7vK6UuBQYB/bSOFkj/FLDWxO0EbM6NhIIgCMk47SqCds1ck69opTOBMcAQrXWN5a25wDCl1G5KqS7AIcDr+ZBREAShJZMvn8M9wG7AC0opgGVa66u11muUUk8D72LMTddqretdriMIgiDkgLwoB631wS7v3Qrc2ojiCIIgCAlIbSVBEAQhCVEOgiAIQhIqFijUdFFKfQF87PO0vYGtORAnWwRZPpEtPUS29AmyfE1ZtgO01h3s3mgWyiEdlFJvaq175VsOJ4Isn8iWHiJb+gRZvuYqm5iVBEEQhCREOQiCIAhJtGTl8GC+BUhBkOUT2dJDZEufIMvXLGVrsT4HQRAEwZmWvHMQBEEQHBDlIAiCICTRIpWDUurMcBvS9UqpsXmW5c9Kqc+VUqstY+2VUi8opT4IP7fLk2z7K6WWKKXWKqXWKKV+HRT5lFKtlVKvK6XeDss2KSiyWWQsVEqtUErND6BsG5VSq5RSK5VSbwZJPqVUqVLq2XAr4bVKqR8GQTalVNfw9xV5fK2UGhEE2cLyjQz/L6xWSj0Z/h9JW7YWpxzCbUf/BJwFHAFcGG5Pmi8exbREtTIWeFFrfQjwYvh1PtgF3KC1PhzoDVwb/q6CIN/3QF+t9TFAd+BMpVTvgMgW4dfAWsvrIMkG0Edr3d0SBx8U+f4APK+1Pgw4BvMd5l02rfW68PfVHegJ1AB/D4JsSqky4Hqgl9b6KKAQ03I5fdm01i3qAfwQWGh5PQ4Yl2eZyoHVltfrgP3CP+8HrMv39xaW5Tmgf9DkA0qAt4ATgiIbphfJi0BfYH7Qfq/ARmDvhLG8ywfsCWwgHCwTJNkS5DkDWBoU2TAdMzcB7TEFVeeHZUxbtha3cyD2JUZwbEWaR/bRWm8BCD//IM/yoJQqB3oArxEQ+cJmm5XA58ALWuvAyAbMAG4EGixjQZENTIfFRUqp5eGWuxAM+Q4EvgD+EjbJPayUahMQ2awMA54M/5x32bTWVcAdwCfAFmC71npRJrK1ROXguRWpYFBK7Q7MBkZorb/OtzwRtNb12mzxOwHHK6WOyrNIACilBgGfa62X51sWF07UWh+LMa9eq5Q6Jd8ChWkFHAvcp7XuAXxH/s1vcSilioAhwDP5liVC2JdwNtAF6Ai0UUpdnMk1W6JyaAqtSD9TSu0HEH7+PF+CKKVCGMXwhNZ6TtDkA9BaVwMvYXw3QZDtRGCIUmoj8BTQVyn1eEBkA0BrvTn8/DnGbn58QOT7FPg0vAsEeBajLIIgW4SzgLe01p+FXwdBttOBDVrrL7TWdcAc4EeZyNYSlcMbwCFKqS7hFcAwTHvSIDEXuDT886UYW3+jo5RSwCPAWq31XZa38i6fUqqDUqo0/HMx5p/jvSDIprUep7XupLUux/x9LdZaXxwE2QCUUm2UUntEfsbYplcHQT6t9f+ATUqpruGhfpjOkHmXzcKFxExKEAzZPgF6K6VKwv+3/TCO/PRly6dTJ18P4MfA+8CHwO/yLMuTGBthHWbVdAWwF8aZ+UH4uX2eZDsJY3J7B1gZfvw4CPIBRwMrwrKtBm4Kj+ddtgQ5TyPmkA6EbBi7/tvhx5rI/0CA5OsOvBn+3VYC7QIkWwnwJdDWMhYU2SZhFkirgccwrZjTlk3KZwiCIAhJtESzkiAIgpACUQ6CIAhCEqIcBEEQhCREOQiCIAhJiHIQBEEQkhDlIAgJKKW+9XHsaUqpH1leX62UuiT882VKqY5p3H+jUmpvv+cJQjZplW8BBKGJcxrwLfAqgNb6fst7l2FizoOWgS8IKRHlIAgeUEoNBsYDRZgkqOFAMXA1UB+uY3MdJjP1W0zV017AE0qpWkw14LWYkspblVK9gDu01qcppfbCJEN2AF7HUv8rfN3rw/d9DbhGa12f+08stHTErCQI3ngF6K1NMbingBu11huB+4Hp2tT5fzlysNb6WUyW7/Dwe7Uu154AvBK+9lygM4BS6nBgKKZIXnegHqOUBCHnyM5BELzRCZgVLl5WhOk5kC1OAc4B0FovUEptC4/3wzSVecOUy6GYPBc5FFoOohwEwRt3A3dprecqpU4DJqZxjV3EduutE96zq2OjgJla63Fp3EsQMkLMSoLgjbZAVfjnSy3j3wB7OJyT+N5GzE4A4FzL+H8Im4uUUmdhCs2BKZR2nlLqB+H32iulDkhTfkHwhSgHQUimRCn1qeUxCrNTeEYp9TKw1XLsPOAn4YbzJydc51Hg/vB7xZiqmX8IX8PqVJ4EnKKUegtTPvsTAK31uxgn+CKl1DvAC5hWj4KQc6QqqyAIgpCE7BwEQRCEJEQ5CIIgCEmIchAEQRCSEOUgCIIgJCHKQRAEQUhClIMgCIKQhCgHQRAEIYn/B5GcvyjxVyClAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Filtering the Data to get Northern Hemisphere\n",
    "Northern_df = Filtered_df.loc[Filtered_df['Lat']>0, :]\n",
    "\n",
    "#Plotting Latitude vs Max Temp chart for the Nothern Hemisphere \n",
    "plot_linear_regression(Northern_df['Lat'], Northern_df['Max Temp'],1,35,'Latitude', 'Max Temp')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/NorthernHem_Lat_vs_MaxTemp.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72a06486",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "There is moderate negative relationship between Latitude and Max Temp in the Northern Hemisphere. Max Temp decreases when we move away from the equator."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0f97c5",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Max Temp vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81f17ae9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.2776776455247449\n"
     ]
    },
    {
     "data": {
      "image/png": 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J1xSotPTSXo2r3zWV/RCZ8ItIH+BS4ABVbRaRmcBZwAHAPFWtF5EJwATg6qjKYRjlTDZrNlfvYFj9/AzLsaVN6da1C42Tjve8p5fF+cwbq7lp9KDYWNcQjXVc6h6EVybPziKh3SNqV08XoFZEWnAs/VXAROCoxP5pwLOY8BtGBn6s2Wy9g3zDC7OdF2Xmy3wEN+wQyjj0ILzSN/vJ6e+XyKJ6VLUJuAV4F/gAWKeqTwG7q+oHiWM+AHaLqgyGUc4UOiEo3/DCUoQl5pt11KtMyfTSQSn1JKyGxiYEGPruv5g97XLm3HcZO27eCDjuu7CITPhFpAdwKtAf6A3sICLfDXD+RSKyQEQWrF5tYWRG9VGoNZtveGExkoSl40dw3UIcw04vXbJJWB9+CBdfzKjBe7L85pN5cPrPOOjDtxj00dvUtmxCyB6SG5Qo4/iPBZar6mpVbQEeAb4OfCQivQASvz92O1lV71TVIao6pGfPnhEW0zAKJ4q460It72zpHaI4rxByCa5XjwDgptGDXP3f+VjqhTzzQN+BrVvhT3+CnXcGEejVC+68c9vulXV7cNG3r2Gvq+awesddUMJ1NUXp438XGCoi3YBmYDiwANgAjAHqE78fjbAMhhE5UfmFC10cvJBByqC+/EIHRLvX1mxLH5FKUnCz9QhemHCM5xyDZHppv+Xy88zd6grk/g4sXAjjx8Mzz7jf/JprOK7L4by5KdMeD9PNAxEKv6q+JCIPAa8AW4FG4E5gR2CmiIzFaRxOj6oMhlEMooq7LmRCkFtj9JMZi7h8xiL6hBypUmjD19DYxIYtWzO213SSbaKaq0cQVnrpXM/cq67bdemU8R2o+WIdGy/7KTw3073iJ5wAN98MBx20bdM4jwyfYbvZIo3qUdXJwOS0zZtxrH/DqAii9AvnG0Xj1hh5JXArlEIbvqlzl2UkfgPYcfsu287PtQ6Bm6UO3vmLspUr2zP3qmtzSyuocvIbz3HN/Lvptf7TzJN79oRbboFzz4XOmeMSyXsn7xNlOKnN3DWMAinGUnlByTXZJ8yZoIU2fJ6ZQ1PSS+RywYw6tA8LVn7GAy++65lWOmi5/Jy7z6fvMeHZ+zjurZdcj7/3sFP47dfPYlP3XXyPkxRjsXgTfsMokHx88VFNEkpe1w9hRaoU2vD5Od+PJfzMG6tzin6Qcrmxdzc48enpXPH8A677F/TZn18cfSGv9Nm/446UhrbUE8TAhN8wCiZo9zyqwWC3FaCyEVaPpNBBaL/n57KE/TRkgf3lqvDEE3DFFfDGG8xL2725cw2/PO4HHHTdeLSmhqlzl3n2tpIL2ZR6ghiY8BtGKLiJkpdlF9VgsJ80zUnCHDAs1C8dll87V9I6AU47zIcbZeVKuPZa+POf3XefcgaXDRzNYt1xW1lPTZlJnW0hm2IkYPODCb9hREA2yy6qwWC/59fV1jBl5MBQhaZQv3QYfm2vAd4kCu5rCmzeDHfc4Vj1bW2Z+wcNcgZlj3fyG/UDGrKU4+j9emaMNSQb2suzhJ0WExN+o+IphU81m2UX1WBwLou3R7caJp8SruDHidSeQzZ3CwDPP+8I/T//6X6xX/wCLrsMunULVIaGxiYeXtjUQfRTexpXzFzsKwFb1N9ZE36joimVTzWbVX/bmYeEHqvd0NjEhs2ZsfC1NZ0jn3UbJ5I9h3R3y64b1nDZC9P5XuPf4GaXE087zRH7r3yloPt7hdEmexp+ErDZQiyGUSCl8qlms+rDjtVuaGxi/F8X09LWUVSCWPhxiDQJk/HH/hsvX3cbVz39J7pv3pB5QL9+cOutMHq0kzLBhSiyhfbx+F6kzswt+4VYDKPUFCPplptA+Ik7D+slnjJ7aYbogxOQku8s3/EPLWbK7KWsa/ZesCV2NDY6KRHmzWMUMCpt97ILfsyA226A7t1zXiqb1Q3ejXY+E83Se3vF+M7aYutGRRN1iuFcycOKkejMLcdNtu3puFmYLa3K2uaWQCmSi87atTBhgmOxi8DgwTAvJeDyuOOcxkAVVBlwz+2+RB+8re7r5izNmj46V2ZTPwnwvL6bdd3CW9fYLH6joik0xjwXuZKHxd5Kxp8lWYqQwwxU4aGHnEHZ997L3L/LLo775nvf80yJ4BevZ+K2WH36s0nN2+PmbvPq7SV7jl4D0+s3baWhsSmU/4EJvxFrCvU9R537pFSupNTy9+hW4ypIPXxaiLmigZJEnpPejf/7P8eqnzXLff+4cTBpEuwW7npOfp9JErfJWQCbWlzCQ13wM/mupU1Da3xN+I3YElZ0Q5S5T6LO0+PnGUw+ZSDjH1rcIdFZTWdh8ikDfd0jV/x7kkLq5LsB37gRbrvNmUDlxtChTkz9sGF5l8VPubx6itt16eSZPrqQQVm/k+/CanzNx2/ElrCWwYtikZQkUa9W5ecZjDq0D1O/c3AHv/HU7xwcaOZsqt+5W427LBy9X34LIuVcVnHuXDjwQMdPv8MOHUW/Sxf4zW+cSVaq8I9/hCr6XuXy8sVPGTnQ8/9dSO/Pr6CHZVCYxW/EilQLzCvhVhCrJ+qY6Li4ksKcOTusfj4bXe7rOuvVB+mNV6/PV3Plc/cz6ob57iecdx5cfz307ZvX/fItF3S00HOlZ3ZLxZFv78+PaylMg8KE34gNfpOMBbF6woqJzuaqKGdXkhthj1us/vRzxix6kmvm303XtsxJZhxwgDMoO2KEZ0x9FORbT6//dyGBBONHDOAnMxZ5GjthL55jwm/EBj9+zqBWTxgiVsqMimFFJQUZJC+ksUneZ49XX2HSf9/Dwe+/zv+5HDf1yO/xxLFnMX/SiYHqESZ+6+n32RXS+/NaTyCqmdcm/EZsyCbGgvNCHr1fT6bOXcZPZizy9WKFYTGXMqNiGK6koA1XXo3N6tW8c8lVjJpxX8bEKYC5+w7l5m+ezztf2nPb9W46ZZDvOkTB+BEDMmY8py73CMGfXSG9vxtGDWJIv12KMoPahN+IDV4i3aeulhcmHJOX5R2GxVyMkM1sFOpKCtpw+WpsWlvh/vudmPrPPgNg75RrNO3UkxuPGcvfBgzLcN+E7bbIh4bGJvcZz2mepmI3+sVYfQtM+I0YkUuk83kJw7CY47i0YhDyabhcBWjxYrjqKnjqKddz/nD4afx+6Ol8vv2OntcV4IUJx+Qsc5RkG0tqae0YK1/qRj8qTPiN2JBLpMMejPNLkF5DHJOd5d1wrVsHN98MN93kvn/4cPjlL51UCcD99fP5PMf/Ig6NZa6xpNTvU7k3+l6Y8BuxIptIl+ol9NtrKMUgsJ+GxnfDperMkL3iClixIvNm3bs70Tfnn++aEiHXRLDkPUvdOOYyFFK/T1Gn/CgVJvwxpdQvRxQUWqdSvoR+eg3F9gf7bWiyNlxvvgkTJ8LDD7vf5Ec/gsmTYffdc5Yn/T7da2sQgbUb2zN8AiVfczZbzHz69ynqeRqlQtRjYYA4MWTIEF2wYEGpi1E03HyQ5b6gRlh1inOD2H/C465x2AIsrz8p9Pt5re2aHAx3pbnZmQk7caL7/q9+FaZOhW9+s6Cyef2f8ipzyHj5+CtxhTIRWaiqQ9K3m8UfQ+KyIHOYhFWnYkU95EOxXVG+xzyeftpx3yxZknlwp06O++ZHP4LttvN131yNbynWGw5CLis+zsZFWJjwx5A4vBxhU4l1SqfYriivhuZgWQ8XXgj33ut63txDhnP90HPQfntFMiegFOsNByVbauRCXFHl0miY8MeQuLwcYVKJdUqn2P7gZEPTsmkzZy9+kmvn3812rS6Lr+y3H9x6Kw17HMTEWa+2i3Ie/nU/PbdirzccJoX0TEs5wzsoJvwxxCs6YuOW8BZiKDaVGh2RTtFcUS++yKgrr2TUCy+477/uOvjpT2HH9pj6qfXzC3a3+em5FXO94bAppGdaTi5aE/4YkvySTJm9tEPu7zUbW2JrQeQi7i98XPB0FXzyiZOx8re/dT9x5Eior4f99/e8dhjuNj89t2KuNxw2hfRMy8mdacIfU5JpXtMXfYirBeGHOL7wcfLJproKRNv42vOPc+T1o2HjusyDe/d2BmXPOMMZoM1yzWT9OonQ6hLFF8Td5qfnVs6NfCE903JyZ5rwxxivWOM4WhDlSNx8sg9Pe4I7Zv+Bo5YvdD/giivgZz9z1pb1QXr93EQ/qLvNr6jHsZH3QyGNVjm5M034Y0pDYxMCrnHhcbQgypGS+2S/+MJJeXDDDQDcn7b7H30H8YujLuTVXvvmNQ/AKzVBZxHaVPO2xOMm6mH32vKtXzn1dEz4Y8rUucs8JwPF0YIoJqkvutvs0DAHKkNFFR591LHc33knY/fGrrVcf/RYZh50HK2d2lMi9MmzofeqR5uqZ0MSJ9eXH6LutQV9HnFrFL2ITPhFZAAwI2XT3sAk4L8S2/cCVgBnqOqaqMpRrni9tEr5DeyGSfqLnjoG4vbSZ3txi+KTfecdZ5bszJnu+y+6CKZMgV69eKqxiYZHltAaUjK4oPUrpevLr8Amj2ta20xnjzGLsHptcXMFhklki62r6jJVPURVDwEOAzYCs4AJwDxV3ReYl/hspOH1cuZr/VUKuTIrpi5EnmuR70gWSt+0yXHfiDg/++zTUfQHD4b58x3rXxX++Efo1QvIXPQ8ucC3VzI4z8XLEwStX1iL2wfFb31SjwP3MYskYfTaSvU8ikGxXD3DgbdVdaWInAocldg+DXgWuLpI5SgbymmgqJj4eaGTx/hZTDt5XEGujfnzHffNokXu+2+5BcaNg+23z3mpMJPBBa1fqcIR/dbHz9KcSfLptaX3Oio5uKJYwn8WMD3x9+6q+gGAqn4gIru5nSAiFwEXAfTt27cohYwT5TRQVCwaGps8QxJTSb70foQsL5/sqlUwaRLcfbf7/rPOghtvhL33dt9fIEEEOkj9ShWO6Lc+fgU31UAK4kJKd+tUcnCFL+EXkcHAv+M8hxdU9RW/NxCRrsBIwCMdoDuqeidwJzjZOYOcWymUy0BRMUi+mLlEP/Wlz0fIXIVi0O5w112OVb9xY8Y56/v25+dHXchf9ziE3j26OefsHd3/LSqBLlUv0299slnhSVKXdWxobOqwpm7T2mbG/3UxkOmjd+tNKGSIf6X0unP6+EVkEo5L5kvArsC9InJtgHt8C3hFVT9KfP5IRHolrt0L+DhYkY1qxKubLzjpdN184kF93Kk+5INWLeNXv7uUUYP3hJoaJ3tlquhPmgSff07DK+/z1fN+z8xeh6Iinv7p9PsMq59P/wmPM6x+ftZj3YhkbAL/Ywxh47c+bselHv/rMw/hhQnHdJj5nr6mbkubMmX20ozzswVTFPt5FAM/Fv/ZwKGquglAROqBV4AbfN7jbNrdPACzgTFAfeL3o75La8SKYob+ZevmN0463nV7IHfZp5+y6ZLLeP0FjwVJTjrJSYlw4IEdNk+d+3KguQBhRIpE6QYsRS8zyKSw5HGpUT1ei7enz3rPtt2rN1HMdQKKiR/hXwFsD2xKfN4OeNvPxUWkG3AccHHK5npgpoiMBd4FTvdbWCM+FDvULV/3hqeQtbXB9OmO++YjpzN6Vsru1TvUccPRY5l9wDdBOnnGvQcdEC3ndQmibOj91ieqeofh5iqnORB+hH8zsFREnsbp+RwHPC8ivwVQ1Uu9TlTVjTguotRtn+JE+RhlTLFnvYbif166FK6+Gh5/3HX3g8NOo/6w01hbu3OH7dlCaIM2SOWUyCuVcohpTxfeHbp2ZsOWTPdgj241GdsK7UWVw/NJxY/wz0r8JHk2mqIY5UQxBSz5Qje3tObs3ndg/XpnGcGf/9x9/5FHOvsPPxyA7Rub2PzIEgjQuARtkMopkVcqJU9vkQM34a3pJHTuJLSm+PlrOguTTxnoeo1CehNxfz7p5BR+VZ1WjIIY5UWxBMwt0VhSWDNeKFWYMweuvNJZRDydbt2cjJZjxzoDtmnkY/UFPadc52fEvafiJrwtbUpdbQ07bNclcvdL3J9POjmFX0ROBq4H+iWOF0BVdeesJxoVTTEErKGxiStmLs4I4exgSS1f7mSsfPBB94uMHetY/L17+7pnPlZfkHPKdX5G3HsqXgK7rrmFRZPdB//DJO7PJx0/rp5fA6OBJao5gqiNqiFqAfOK2++6tYXzXpnDtc/c4z4z5OCDnZmyxx4bSjmioBznZ8S9p1Jq4Y3780nHj/C/B7xqom+kE6WApXbdh777L66ZfzeDPvIIJrv5ZrjkEqgN/yUvp0iNKImqoQ/r+ZZaeHM9n7h9jySXnovIV3FcPf+NE+EDgKr+KtqitTNkyBBdsGBBsW5nlJoPP+QvJ1zAOYufdN392IB/59ajzmN5XW9/g7x50tDYxPiHFtPS2nFwcOp3Dq5K8Q+b9PEbcMQ630lScRPX1HKFWc8giMhCVR2Svt2PxX8jsB4nlr9r2AUzDLZuhXvucWLq168H4JyU3Svr9uDGo8fy1L5DnYyXKUQZNnfdnKUdRB+gpVW5bs7SWAhKuZMr+2VQEY+rCy2OET9+hH8XVY1+dMQIjbhaPh1YuNCJvnn2Wdfdy8Zeyrm7fINPOuXOaBnVS7Rmo/vMT6/tUVEW/8888BqQTTbm5RITn4s4Rvz4Ef6/i8jxqvpU5KUxCqaQiSSRCsyaNU7Gyltvdd9/wglOHvtBgwAYAFzb2MR1c5b6ElqvlyjOoumnbOU2MSgIXgOynUViZyEXQqkHnt3wsxDLOOBJEdkkIp+LyBci8nnUBTPyI9/FI/wuhuEbVSfEsk8fxz2zyy4dRb9nT5g2DVpbnWOfeGKb6KeyqaXN1+3cXqJC61RXmxnrn217EPyWrZIXA/FKzuaVgTWuMfG5iCqpXiHkFH5V3UlVO6nq9qq6c+KzxfDHlHy7laEIzOuvw8iRjtB36gRnn+3krk9y6aWwerUj9B9/DOed5xwXoExueL1EQerkljFzysiB1HTqOKZQ00mYMtJ95mcQ/JYtjm6CsPDKBuqVIiOuMfG5KFXW02z4mcAlwLlAf1W9XkS+DPRS1X9GXjojMPl2K/MSmA0bHCt+8mT3/cOGOSkRjjgi6729yJV7HZy8K5NPGej6Evmtk5c75abRg5h6+sGRuIr8li2OboIw8RqQLaeYeD/EbeDZj6vn98ARtAdarAd+F1mJjILIt1vpJSQdtqs6Cc7228+x6nfcsaPob7cd/Od/wpYtzrHPP5+36Dc0NiG5D6Nb1y6eL5SvOpE76uKFCcewvP6kDrneC8WrbN3T3EhxdBNETRwt5ErDz+Du4ao6WEQaAVR1TWJVLSOG5DvRxmsCzKRBO8B3vwsPPOB+4vnnw/XXw557AokBy1ufK9hCnjp3meuyd+k0rW1mWP181/v5ndRTCnfK+BED+OmMRaSPYGzYspWGxqZtdSjXFA+FEjcLudLwI/wtItKZxApkItITMr6vRozIN98MwK8fX8LwZx7mmmfuppPbINugQU5KhOMzI3zDjEDxK7pCu0so/X5+RbNU7hS3l6ilVV0XTTcRNMLEU/hF5D5VPR/4LU5a5t1E5EbgO0CQpReNAJQk/PC55+CKKxj18suMctv/i1/AZZc52S2zEOZEFT/rq7othp1+Pz+iWYrp/tkGzSth4NaIN9ks/oMAVPUBEVmIs3iKAKNU9fViFK7aKFrM9kcfwXXXwR13uO8/7TS46SbYd99Al802IScobmLcuZPQ1qYobMvLH6QcXpTCnZKtjJUycGvEl2zC301EDoVtY2z/SPyuFZHBqvpKtEWrPiKb2t3aCvfd56REWLcuc3+/fk50zujRGSkR/NLQ2EQnDzGWxP6gSwuCs2B2co3U1AU1WlVdLX7ITziL7U7x6tEIVPTArREPsgl/H+BWcA2uUKDyViAuMdks5qDCyd/+5iwQ7sXEic4yhN27ByxlJl4plJMo5N14bd7qPZykZLp7yiXixa1HI8C5Q/uaP9+InGzC/5aqmrgXkWx+7Zwunw8/hBNPhMZG9/3HHeekRDjkkBBK2hE/E63y8Vv7ua7ihPuVW8RLtUbrGPHAT1SPUSTcrMAkGS6ftjbHT++1niw4A7K33AJdov03+xH1fNwvfq7bp66WFyaUp31i0TpGqcimCFcXrRQG0G4FXj5jkev+PV59BWS49wX694d585zfRSRXBE6+7peormsY1Y6n8Fs2ztIw6tA+TJ27jKa1zey0eQO/a6jnGys83DcAf/mLkxOnhHj5q5NumDBXVQrjuvngFWYb5+yfhuGFuXrihCr87ne8MPES72POOQfuuiuSZQbzJSp/dVz84F5htgtWfsbDC5sqMmWyUdn4WXpxe1XdlLZtV1X9JNKSpVDRSy+++qqTzyax8lQ6X3St5f/96LecNuZbJiaUZoLbsPr5nnnj3SKZynncwagsvJZe9JOk7WURGZpyodOA/w2zcFVFczOce64TLy/ipEBIF/3bb3cGb1XZafNG7v/19030iWDNAJ94DTJXWt54o3rw4+o5B7hHRJ4FegNfwmL4gzF9uuOi8eL442HmzFBi6iuZUq1dmm2lKDfxt5m3RtzxsxDLEpwF138IHA38WFXfj7pgZc2KFbD33u1WvZvoP/+849NXhblzIxV9t0VGypFSLUrilRr57MO/XHUpk43KIKfwi8jdwOU4uXsuAOaIyLiIy1VebN3qxMwnhb5/f1i+vOMxkye3LzOo6ixSUgRK5R6JAr/59cPGKz/8DaMGWd54oyzxM7j7E+DXmjhQRLoDv1LVsUUoHxDTwd25c50Fwr0YPNhZtGSPPYpXJhe8BibLcQAyPboGHAu72sW2FAPeFsZaHngN7ub08avqbWmf1wFFE/3Y8PHHcPLJ8PLL3sc89lj2/DgloJLWbI1LeGecKFpG1xLf0wgXP2vu7gvcBBwAbJ/crqp7R1iu0qMKN94I//Ef3seMGwe33QY1Nd7HlJhiLjJSDCvQ0hx0pBQD3qUaZDfCw09Uz73AZOA2nMHdC3DP2JmBiNQBdwEH4ky4vBBYBswA9gJWAGeo6ppgxY6Il16CoUO99/ftC/Pnwz77FK9MBVKsRUbMCiwNpejRVVIvMheV6tLyE8dfq6rzcMYDVqrqFPyHc/4GeFJV9wMOBl4HJgDzVHVfYF7ic2loboYLL2wflHUT/fvvbx+QXbmy6KJfaEROsRauzmYFGtFRigHvUg2yF5tKCoxIx4/Fv0lEOgFvisiPgSZgt1wnicjOwDeA8wFUdQuwRUROBY5KHDYNeJZiJoR76im48kpYssR9/5lnwj335FxmsBiEZUUXwz3ilUwtCiswiBVWqRZbklIsG+mVQym58H2lPONKdmn5sfgvB7oBlwKHAd8Dxvg4b29gNXCviDSKyF0isgOwu6p+AJD47dqIiMhFIrJARBasXr3ax+08eP99uOCCdqt+xIiOon/66bBoUbtV/+CDsRB9KJ0VHbSX0dDY5On7C9sKDGKFVbLFlqRYPTqve0LHxXAq6RlXskvLT1RPMoxlPY5/P8i1BwOXqOpLIvIbArh1VPVO4E5wwjkD3LedBx/MzFy5//5OjvpvfSvvZQaLRZhfPL+Wbz69jKlzl7kugRjFMoJBrLBKtthSKcWAd/KebuHClfKMixkYUWw8LX4RmZ3tx8e13wfeV9WXEp8fwmkIPhKRXol79AI+LrQSnhx4IAwc6CxWsn69Y9G/9pqzUlXMRR/C86UGsXzz6WV4NURK+AO7QRrDSrbY4kK25ULLeZY4eM/YroSZ2dks/iOA94DpwEv4jORJoqofish7IjJAVZcBw4HXEj9jgPrE70fzKbgvDjzQyX5ZpoTlvw1i+eYjlt1ra7YtiJ5KnwgsoyBWWCVbbHEh22I55R7ZVcnzRrIJ/x7AccDZOInaHgemq+rSANe/BHhARLoC7+C4ijoBM0VkLPAucHo+Ba8GwvriBRHzoGLZ0NjEhi1bM7bXdJJILKMgjWEpBj6rjWzLhUL5u30qdd5IthW4WoEngSdFZDucBuBZEfm5qt7u5+KqugjImC6MY/0bPgjjixdEzIOK5dS5y2hpzfTw77h9l0hemCCNYSVbbHEh9RkXM7LLKIysg7sJwT8JR/T3An4LPBJ9sYwwCSLmQcXS66VeuzHT9RMWQRrDOFtslRJqmm2gF8y1Fkc8hV9EpuHMuH0CuE5Vy9dZXuUEFfMgYml+9PyoxJnO5lorHzyzc4pIG7Ah8TH1IAFUVXeOuGzbiGV2TgOwjJn5UklZU1OplF5MpRA4O6eq+pncZVQ55kfPj0oNNY2za81ox0/KBsPIir3swTEXmVFKzKo3jBJQyZODjPhjFr8RGebv9cZcZEYpMeHPgYlXfuQTtVJtz9pcZEapMFdPFqohu2NUBM35Y8/aMIqHCX8WbHGR/AkatWLP2jCKhwl/Fio15K4YBM0sas/aMIqHCX8WqmWJuSgIGrViz9owiocJfxYs5C5/gq4MZc/aMIqHRfVkwULuCiNoMjWwZ20YxcAzV0+csFw9hmEYwfHK1WOuHsMwjCrDhN8wDKPKMOE3DMOoMkz4DcMwqgwTfsMwjCqjqsM5qy0pmGEYBlSx8FfimqeGYRh+qFpXjyUFMwyjWqlai9+SgpmryzCqlaq1+Ks9KZjlvzeM6qVqhb/ak4KZqyscGhqbGFY/n/4THmdY/XxrOI2yoGJdPbncGNWeFMxcXYVjAQJGuVKRwu/3hazmNU9719XS5CLy1eLqCoNsvaZq/V4Z5UFFunrMjZGband1hYH1moxypSKF317I3ARdKMXIpNoDBIzypSJdPebG8Ec1u7rCYPyIAR1cimC9JqM8qEiL39wYRjGwXpNRrkRq8YvICuALoBXYqqpDRGQXYAawF7ACOENV14R532qP2DGKh/WajHIk0qUXE8I/RFU/Sdn2S+AzVa0XkQlAD1W9Ott1bOlFwzCM4MRp6cVTgWmJv6cBo0pQBsMwjKolauFX4CkRWSgiFyW27a6qHwAkfu/mdqKIXCQiC0RkwerVqyMupmEYRvUQdVTPMFVdJSK7AU+LyBt+T1TVO4E7wXH1RFVAwzCMaiNSi19VVyV+fwzMAr4GfCQivQASvz+OsgyGYRhGRyITfhHZQUR2Sv4NHA+8CswGxiQOGwM8GlUZDMMwjEyidPXsDswSkeR9/qKqT4rIy8BMERkLvAucHmEZDMMwjDQiE35VfQc42GX7p8DwqO5rGIZhZKciZ+4ahmEY3pjwG4ZhVBkm/IZhGFWGCb9hGEaVUZFpmQ3DKD65ljs14oMJv2EYBWPrD5cX5uoxDKNgbLnT8sKE3zCMgrHlTssLE37DMArG1h8uL0z4DcMoGFvutLywwV3DMArGljstL0z4DcMIBVt/uHwwV49hGEaVYcJvGIZRZZjwG4ZhVBkm/IZhGFWGCb9hGEaVYcJvGIZRZVg4p2FUEZZB0wATfsOoGiyDppHEXD2GUSVYBk0jiQm/YVQJlkHTSGLCbxhVgmXQNJKY8BtGlWAZNI0kNrhrGFWCZdA0kpjwG0YVYRk0DTBXj2EYRtVhwm8YhlFlmPAbhmFUGSb8hmEYVYYJv2EYRpVhwm8YhlFlRB7OKSKdgQVAk6qeLCK7ADOAvYAVwBmquibqchiG4Y5l7Kw+imHxXwa8nvJ5AjBPVfcF5iU+G4ZRApIZO5vWNqO0Z+xsaGwqddGMCIlU+EVkT+Ak4K6UzacC0xJ/TwNGRVkGwzC8sYyd1UnUFv+vgauAtpRtu6vqBwCJ37tFXAbDMDywjJ3VSWTCLyInAx+r6sI8z79IRBaIyILVq1eHXDrDMMAydlYrUVr8w4CRIrICeBA4RkT+DHwkIr0AEr8/djtZVe9U1SGqOqRnz54RFtMwqhfL2FmdRCb8qjpRVfdU1b2As4D5qvpdYDYwJnHYGODRqMpgGEZ2Rh3ah5tGD6JPXS0C9Kmr5abRgyyqp8IpRXbOemCmiIwF3gVOL0EZDMNIYBk7q4+iCL+qPgs8m/j7U2B4Me5rGIZhZGIzdw3DMKoME37DMIwqw4TfMAyjyjDhNwzDqDJEVUtdhpyIyGpgZanL4YNdgU9KXYgIsfqVP5VeR6tfR/qpasZEqLIQ/nJBRBao6pBSlyMqrH7lT6XX0ernD3P1GIZhVBkm/IZhGFWGCX+43FnqAkSM1a/8qfQ6Wv18YD5+wzCMKsMsfsMwjCrDhD8ERGSKiDSJyKLEz4kp+yaKyFsiskxERpSynIUiIleKiIrIrinbyr5+InK9iPwr8b97SkR6p+yrhPpNFZE3EnWcJSJ1KfsqoX6ni8hSEWkTkSFp+8q+fgAickKiDm+JSOHL1aqq/RT4A0wBrnTZfgCwGNgO6A+8DXQudXnzrOOXgbk48yl2raT6ATun/H0p8IcKq9/xQJfE3zcDN1dY/fYHBuAkghySsr1S6tc5Ufa9ga6JOh1QyDXN4o+WU4EHVXWzqi4H3gK+VuIy5cttOMtopg4KVUT9VPXzlI870F7HSqnfU6q6NfHxRWDPxN+VUr/XVdVtkeCKqB9Omd9S1XdUdQvOwlanFnJBE/7w+HGiK32PiPRIbOsDvJdyzPuJbWWFiIwEmlR1cdquiqgfgIjcKCLvAecCkxKbK6Z+KVwIPJH4uxLrl0ql1C/0epRiIZayRET+Duzhsusa4A7gehxL8XrgVpwXTFyOj2UYVY76/QzHXZBxmsu2squfqj6qqtcA14jIRODHwGQqqH6JY64BtgIPJE9zOb5s6+d2msu2WNYvB6HXw4TfJ6p6rJ/jRORPwGOJj+/j+MaT7AmsCrlooeBVPxEZhOMfXSwi4NThFRH5GhVQPxf+AjyOI/wVUz8RGQOcDAzXhOOYCqqfB2VTvxyEXg9z9YRAcvH4BN8GXk38PRs4S0S2E5H+wL7AP4tdvkJQ1SWqupuq7qXO+snvA4NV9UMqoH4AIrJvyseRwBuJvyulficAVwMjVXVjyq6KqF8WKqV+LwP7ikh/EemKs4b57EIuaBZ/OPxSRA7B6X6tAC4GUNWlIjITeA2niz1OVVtLVciwqaD61YvIAKANJ2rph1BR9ftPnMiWpxO9thdV9YeVUj8R+TZwO9ATeFxEFqnqiEqpn6puFZEf40TVdQbuUdWlhVzTZu4ahmFUGebqMQzDqDJM+A3DMKoME37DMIwqw4TfMAyjyjDhNwzDqDJM+I2qQ0TWBzj2KBH5esrnH4rIeYm/z0/N5BngmitSM5waRrGxOH7DyM5RwHrgfwFU9Q8p+87HmaxXjrNBjSrGhN8wABE5BbgWJ+3tpzjJ2mpxJnO1ish3gUuA4TgNwQpgCPCAiDQDRwCv46QF/iSRF/4WVT1KRL4ETMeZYPRPUnKvJK57aeK+LwH/rxwnGRnlhbl6DMPheWCoqh6Kk/b2KlVdAfwBuE1VD1HV55IHq+pDwALg3MS+5izXngw8n7j2bKAvgIjsD5wJDFPVQ4BWnAbHMCLFLH7DcNgTmJHIu9QVWB7itb8BjAZQ1cdFZE1i+3DgMODlRCqFWuDjEO9rGK6Y8BuGw+3Ar1R1togchbOqWlC20t6L3j5tn1tuFAGmqerEPO5lGHljrh7DcOgONCX+HpOy/QtgJ49z0vetwLHgAU5L2f4/JFw4IvItILlQzzzgOyKyW2LfLiLSL8/yG4ZvTPiNaqSbiLyf8vNTHAv/ryLyHPBJyrFzgG8nFmI/Mu069wF/SOyrBa4DfpO4RuoA7XXAN0TkFZwFbd4FUNXXcAaUnxKRfwFPA6kpvg0jEiw7p2EYRpVhFr9hGEaVYcJvGIZRZZjwG4ZhVBkm/IZhGFWGCb9hGEaVYcJvGIZRZZjwG4ZhVBkm/IZhGFXG/wfSa3/x/kruRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Filtering the Data to get Southern Hemisphere\n",
    "Southern_df = Filtered_df.loc[Filtered_df['Lat']<0, :]\n",
    "\n",
    "#Plotting Latitude vs Max Temp chart for the Souhern Hemisphere \n",
    "plot_linear_regression(Southern_df['Lat'], Southern_df['Max Temp'],-55,90,'Latitude', 'Max Temp')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/SouthernHem_Lat_vs_MaxTemp.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d652ca88",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "There is moderate positive relationship between Latitude and Max Temp in the Southern Hemisphere. As we get closer to equator, temperature increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8be5a706",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Humidity (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a304edb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.07718512413796076\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Latitude vs Humidity chart for the Nothern Hemisphere \n",
    "plot_linear_regression(Northern_df['Lat'], Northern_df['Humidity'],0,15,'Latitude', 'Humidity (%)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/NorthernHem_Lat_vs_Humidity.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64e03ec",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Our r-value is very close to 0. It is fair to say there is no linear relationship (if r < 0.25, it is consiedered as no relationship) between Latitude vs Humidity in the Nothern Hemisphere. Humidity level isn't affected by Latitude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26565241",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Humidity (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91fb4bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.061189886794328914\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Latitude vs Humidity chart for the Southern Hemisphere \n",
    "plot_linear_regression(Southern_df['Lat'], Southern_df['Humidity'],-55,20,'Latitude', 'Humidity (%)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/SouthernHem_Lat_vs_Humidity.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7fc584",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Our r-value is very close to 0. It is fair to say there is no linear relationship (if r < 0.25, it is consiedered as no relationship) between Latitude vs Humidity in the Southern Hemisphere. Humidity level isn't affected by Latitude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea80a3ca",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "42c08425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.016001455839304794\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Latitude vs Cloudiness chart for the Nothern Hemisphere \n",
    "plot_linear_regression(Northern_df['Lat'], Northern_df['Cloudiness'],0,33,'Latitude', 'Cloudiness (%)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/NorthernHem_Lat_vs_Cloudiness.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cfe4e5",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Our r-value is very close to 0. It is fair to say there no linear relationship (if r < 0.25, it is consiedered as no relationship) between Latitude vs Cloudiness in the Nothern Hemisphere. Level of Cloudiness isn't affected by Latitude. The interesting thing here - there are bands of cities at certain cloudiness levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49b19e5",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e69708a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.07568559656192633\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Latitude vs Cloudiness chart for the Sothern Hemisphere \n",
    "plot_linear_regression(Southern_df['Lat'], Southern_df['Cloudiness'],-55,20,'Latitude', 'Cloudiness (%)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/SouthernHem_Lat_vs_Cloudiness.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb9a4b2",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Our r-value is very close to 0. It is fair to say there is no linear relationship (if r < 0.25, it is consiedered as no relationship) between Latitude vs Cloudiness in the Southern Hemisphere. Level of Cloudiness isn't affected by Latitude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190faace",
   "metadata": {},
   "source": [
    "####  Northern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49c6cf2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.00898068073473699\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Latitude vs Wind Speed chart for the Nothern Hemisphere \n",
    "plot_linear_regression(Northern_df['Lat'], Northern_df['Wind Speed'],20,29,'Latitude', 'Wind Speed (mph)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/NorthernHem_Lat_vs_WindSpeed.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0759e02",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Our r-value is very close to 0. It is fair to say there is no relationship (if r < 0.25, it is consiedered as no relationship) between Latitude vs Wind Speed in the Northern Hemisphere. Wind Speed isn't affected by Latitude."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1127405",
   "metadata": {},
   "source": [
    "####  Southern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01704d13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.10140564775472584\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Latitude vs Wind Speed chart for the Sothern Hemisphere \n",
    "plot_linear_regression(Southern_df['Lat'], Southern_df['Wind Speed'],-32,20,'Latitude', 'Wind Speed (mph)')\n",
    "\n",
    "#Saving Figure to my output_data folder\n",
    "plt.savefig(\"../output_data/SouthernHem_Lat_vs_WindSpeed.png\")\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b54751",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Our r-value is very close to 0. It is fair to say there is no relationship (if r < 0.25, it is consiedered as no relationship) between Latitude vs Wind Speed in the Southern Hemisphere. Wind Speed isn't affected by Latitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b0bba2b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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
}