HW6-WeatherPy-DS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import time\n",
    "import random\n",
    "import json\n",
    "\n",
    "# Import API key\n",
    "import api_keys\n",
    "\n",
    "# Incorporated citipy to determine city based on latitude and longitude\n",
    "from citipy import citipy\n",
    "\n",
    "# Output File (CSV)\n",
    "output_data_file = \"output_data/cities.csv\"\n",
    "\n",
    "# Range of latitudes and longitudes\n",
    "lat_range = (-90, 90)\n",
    "lng_range = (-180, 180)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Cities List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-88</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-50</td>\n",
       "      <td>-42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lat  Long\n",
       "0   31    39\n",
       "1  -88    99\n",
       "2   74    96\n",
       "3  -50   -42\n",
       "4   59     3"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Random selection of latitude and longitude \n",
    "\n",
    "lat = np.random.choice(range(-90,90),2000)\n",
    "lng = np.random.choice(range(-180,180),2000)\n",
    "\n",
    "lat_lng = pd.DataFrame({\"Lat\":lat,\"Long\":lng})        \n",
    "\n",
    "#print(len(lat_lng)\n",
    "lat_lng.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:8: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the dataframe is: 2000\n"
     ]
    }
   ],
   "source": [
    "#Use citipy to merge them to nearest city\n",
    "#Create new columns for the city name: \n",
    "lat_lng[\"city_name\"] = \"\"\n",
    "lat_lng[\"country\"] = \"\"\n",
    "#LatLon.head()\n",
    "for index,row in lat_lng.iterrows():\n",
    "    city = citipy.nearest_city(row[\"Lat\"],row[\"Long\"])\n",
    "    lat_lng.set_value(index,\"city_name\",city.city_name)\n",
    "    lat_lng.set_value(index,\"country\",city.country_code)\n",
    "\n",
    "print(f\"The length of the dataframe is: {len(lat_lng)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the cleaned dataframe is:2000\n",
      "The length of the dataframe after dropping the duplicates is: 1977\n",
      "The length of the sample is: 750\n"
     ]
    },
    {
     "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>Long</th>\n",
       "      <th>city_name</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-77</td>\n",
       "      <td>-108</td>\n",
       "      <td>punta arenas</td>\n",
       "      <td>cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-47</td>\n",
       "      <td>-148</td>\n",
       "      <td>mataura</td>\n",
       "      <td>pf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>-162</td>\n",
       "      <td>bethel</td>\n",
       "      <td>us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "      <td>amderma</td>\n",
       "      <td>ru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79</td>\n",
       "      <td>-75</td>\n",
       "      <td>qaanaaq</td>\n",
       "      <td>gl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lat  Long     city_name country\n",
       "0  -77  -108  punta arenas      cl\n",
       "1  -47  -148       mataura      pf\n",
       "2   49  -162        bethel      us\n",
       "3   76    66       amderma      ru\n",
       "4   79   -75       qaanaaq      gl"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Randomly select **at least** 500 unique (non-repeat) cities based on latitude and longitude \n",
    "\n",
    "#Clean it for missing values\n",
    "nr_lat_lng = lat_lng.dropna()\n",
    "print(f\"The length of the cleaned dataframe is:{len(nr_lat_lng)}\")\n",
    "\n",
    "\n",
    "nr_lat_lng = nr_lat_lng.drop_duplicates()\n",
    "print(f\"The length of the dataframe after dropping the duplicates is: {len(nr_lat_lng)}\")\n",
    "\n",
    "# Sample for \"at least\" 500 cities \n",
    "sample = nr_lat_lng.sample(750)\n",
    "sample =sample.reset_index()\n",
    "sample = sample.iloc[:,[1,2,3,4]]\n",
    "print(f\"The length of the sample is: {len(sample)}\")\n",
    "\n",
    "sample.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform API Calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------\n",
      "Beginning Data Retrieval\n",
      "-----------------------------------------\n",
      "\n",
      "Processing Record 1 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=punta+arenas%2Ccl\n",
      "Processing Record 2 of 750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:17: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:18: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:19: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:20: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:21: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:22: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:23: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "/anaconda3/envs/PythonData/lib/python3.6/site-packages/ipykernel_launcher.py:24: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=mataura%2Cpf\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=butaritari%2Cki\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=taolanaro%2Cmg\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=san+miguel%2Cbo\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=grand+river+south+east%2Cmu\n",
      "Processing Record 707 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=gurskoye%2Cru\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=albany%2Cau\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=port+alfred%2Cza\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=georgetown%2Csh\n",
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      "Processing Record 712 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=ushuaia%2Car\n",
      "Processing Record 713 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=yellowknife%2Cca\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=thompson%2Cca\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=puri%2Cin\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=taolanaro%2Cmg\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=cape+town%2Cza\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=cape+town%2Cza\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=esperance%2Cau\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=ushuaia%2Car\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=stepnogorsk%2Ckz\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=port+alfred%2Cza\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=lloydminster%2Cca\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=barrow%2Cus\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=mataura%2Cpf\n",
      "Processing Record 732 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=bargal%2Cso\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=tasiilaq%2Cgl\n",
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      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=huron%2Cus\n",
      "Processing Record 735 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=arraial+do+cabo%2Cbr\n",
      "Processing Record 736 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=cape+town%2Cza\n",
      "Processing Record 737 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=taolanaro%2Cmg\n",
      "Processing Record 738 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=busselton%2Cau\n",
      "Processing Record 739 of 750\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=lagunas%2Cpe\n",
      "Processing Record 740 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=cape+town%2Cza\n",
      "Processing Record 741 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=lubango%2Cao\n",
      "Processing Record 742 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=kaitangata%2Cnz\n",
      "Processing Record 743 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=castro%2Ccl\n",
      "Processing Record 744 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=henties+bay%2Cna\n",
      "Processing Record 745 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=fitionesti%2Cro\n",
      "Processing Record 746 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=hobart%2Cau\n",
      "Processing Record 747 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=luderitz%2Cna\n",
      "Processing Record 748 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=mataura%2Cpf\n",
      "Processing Record 749 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=hermanus%2Cza\n",
      "Processing Record 750 of 750\n",
      "http://api.openweathermap.org/data/2.5/weather?appid=7bbe5eb80fc0e9d5e7535b057e554fd5&units=imperial&q=attawapiskat%2Cca\n",
      "-----------------------------------------\n",
      "Data Retrieval Complete\n",
      "-----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# OpenWeatherMap API Key\n",
    "url = \"http://api.openweathermap.org/data/2.5/weather?\"\n",
    "params = { \"appid\":\"7bbe5eb80fc0e9d5e7535b057e554fd5\",\"units\":\"imperial\" }\n",
    "\n",
    "#Print the base url \n",
    "\n",
    "print(\"-----------------------------------------\")\n",
    "print(\"Beginning Data Retrieval\")\n",
    "print(\"-----------------------------------------\\n\")\n",
    "\n",
    "for index,row in sample.iterrows():\n",
    "    params[\"q\"] =f'{row[\"city_name\"]},{row[\"country\"]}'\n",
    "    print(f\"Processing Record {index+1} of {len(sample)}\")\n",
    "    response = requests.get(url,params)\n",
    "    print(response.url)\n",
    "    formatted_response = response.json()\n",
    "    sample.set_value(index,\"Latitude\",formatted_response.get(\"coord\",{}).get(\"lat\"))\n",
    "    sample.set_value(index,\"Longitude\",formatted_response.get(\"coord\",{}).get(\"lon\"))\n",
    "    sample.set_value(index,\"Temperature\",formatted_response.get(\"main\",{}).get(\"temp\"))\n",
    "    sample.set_value(index,\"Max Temperature\",formatted_response.get(\"main\",{}).get(\"temp_max\"))\n",
    "    sample.set_value(index,\"Humidity\",formatted_response.get(\"main\",{}).get(\"humidity\"))\n",
    "    sample.set_value(index,\"Wind speed\",formatted_response.get(\"wind\",{}).get(\"speed\"))\n",
    "    sample.set_value(index,\"Cloudiness\",formatted_response.get(\"clouds\",{}).get(\"all\"))\n",
    "    sample.set_value(index,\"Date\",formatted_response.get(\"dt\"))\n",
    "\n",
    "print(\"-----------------------------------------\")\n",
    "print(\"Data Retrieval Complete\")\n",
    "print(\"-----------------------------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city_name      651\n",
       "country        651\n",
       "cloudiness     651\n",
       "humidity       651\n",
       "lat            651\n",
       "lng            651\n",
       "temperature    651\n",
       "max_temp       651\n",
       "wind speed     651\n",
       "date           651\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean the retrieved data\n",
    "sample = sample.dropna()\n",
    "#Create a dataframe\n",
    "weatherPy = pd.DataFrame({\"city_name\":sample[\"city_name\"],\n",
    "                          \"country\":sample[\"country\"],\n",
    "                         \"cloudiness\":sample[\"Cloudiness\"],\n",
    "                         \"humidity\":sample[\"Humidity\"],\n",
    "                         \"lat\":sample[\"Latitude\"],\n",
    "                         \"lng\":sample[\"Longitude\"],\n",
    "                        \"temperature\":sample[\"Temperature\"],\n",
    "                          \"max_temp\":sample[\"Max Temperature\"],\n",
    "                         \"wind speed\":sample[\"Wind speed\"],\n",
    "                          \"date\":sample[\"Date\"]})\n",
    "\n",
    "weatherPy[\"date\"]=weatherPy[\"date\"].astype('int')\n",
    "# Save as CSV\n",
    "weatherPy.to_csv(\"weatherPy.csv\")\n",
    "weatherPy.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>cloudiness</th>\n",
       "      <th>humidity</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>temperature</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>651.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>6.510000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.327189</td>\n",
       "      <td>81.016897</td>\n",
       "      <td>4.448710</td>\n",
       "      <td>-8.659462</td>\n",
       "      <td>61.724009</td>\n",
       "      <td>62.051674</td>\n",
       "      <td>9.057527</td>\n",
       "      <td>1.533670e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.592603</td>\n",
       "      <td>18.116580</td>\n",
       "      <td>41.178256</td>\n",
       "      <td>102.851998</td>\n",
       "      <td>15.881617</td>\n",
       "      <td>16.252271</td>\n",
       "      <td>6.154549</td>\n",
       "      <td>1.498153e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>-54.810000</td>\n",
       "      <td>-175.200000</td>\n",
       "      <td>32.050000</td>\n",
       "      <td>32.050000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.533667e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>-33.640000</td>\n",
       "      <td>-90.350000</td>\n",
       "      <td>48.790000</td>\n",
       "      <td>48.790000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.533668e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>-2.760000</td>\n",
       "      <td>-17.340000</td>\n",
       "      <td>60.940000</td>\n",
       "      <td>61.120000</td>\n",
       "      <td>6.980000</td>\n",
       "      <td>1.533671e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>96.500000</td>\n",
       "      <td>42.865000</td>\n",
       "      <td>73.965000</td>\n",
       "      <td>74.935000</td>\n",
       "      <td>75.160000</td>\n",
       "      <td>12.910000</td>\n",
       "      <td>1.533672e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>78.220000</td>\n",
       "      <td>179.320000</td>\n",
       "      <td>96.310000</td>\n",
       "      <td>107.600000</td>\n",
       "      <td>31.140000</td>\n",
       "      <td>1.533672e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cloudiness    humidity         lat         lng  temperature  \\\n",
       "count  651.000000  651.000000  651.000000  651.000000   651.000000   \n",
       "mean    44.327189   81.016897    4.448710   -8.659462    61.724009   \n",
       "std     34.592603   18.116580   41.178256  102.851998    15.881617   \n",
       "min      0.000000   14.000000  -54.810000 -175.200000    32.050000   \n",
       "25%     12.000000   69.000000  -33.640000  -90.350000    48.790000   \n",
       "50%     40.000000   87.000000   -2.760000  -17.340000    60.940000   \n",
       "75%     75.000000   96.500000   42.865000   73.965000    74.935000   \n",
       "max    100.000000  100.000000   78.220000  179.320000    96.310000   \n",
       "\n",
       "         max_temp  wind speed          date  \n",
       "count  651.000000  651.000000  6.510000e+02  \n",
       "mean    62.051674    9.057527  1.533670e+09  \n",
       "std     16.252271    6.154549  1.498153e+03  \n",
       "min     32.050000    0.040000  1.533667e+09  \n",
       "25%     48.790000    4.700000  1.533668e+09  \n",
       "50%     61.120000    6.980000  1.533671e+09  \n",
       "75%     75.160000   12.910000  1.533672e+09  \n",
       "max    107.600000   31.140000  1.533672e+09  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weatherPy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Unnamed: 0</th>\n",
       "      <th>city_name</th>\n",
       "      <th>country</th>\n",
       "      <th>cloudiness</th>\n",
       "      <th>humidity</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>temperature</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>wind speed</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>punta arenas</td>\n",
       "      <td>cl</td>\n",
       "      <td>90.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-53.16</td>\n",
       "      <td>-70.91</td>\n",
       "      <td>35.60</td>\n",
       "      <td>35.60</td>\n",
       "      <td>5.82</td>\n",
       "      <td>1533668400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>bethel</td>\n",
       "      <td>us</td>\n",
       "      <td>90.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>60.79</td>\n",
       "      <td>-161.76</td>\n",
       "      <td>51.80</td>\n",
       "      <td>51.80</td>\n",
       "      <td>6.93</td>\n",
       "      <td>1533668460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>qaanaaq</td>\n",
       "      <td>gl</td>\n",
       "      <td>64.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>77.48</td>\n",
       "      <td>-69.36</td>\n",
       "      <td>39.25</td>\n",
       "      <td>39.25</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1533671478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>tuatapere</td>\n",
       "      <td>nz</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>-46.13</td>\n",
       "      <td>167.69</td>\n",
       "      <td>42.58</td>\n",
       "      <td>42.58</td>\n",
       "      <td>4.63</td>\n",
       "      <td>1533671478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>bethel</td>\n",
       "      <td>us</td>\n",
       "      <td>90.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>60.79</td>\n",
       "      <td>-161.76</td>\n",
       "      <td>51.80</td>\n",
       "      <td>51.80</td>\n",
       "      <td>6.93</td>\n",
       "      <td>1533668460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     city_name country  cloudiness  humidity    lat     lng  \\\n",
       "0           0  punta arenas      cl        90.0      93.0 -53.16  -70.91   \n",
       "1           2        bethel      us        90.0      93.0  60.79 -161.76   \n",
       "2           4       qaanaaq      gl        64.0      87.0  77.48  -69.36   \n",
       "3           5     tuatapere      nz        88.0     100.0 -46.13  167.69   \n",
       "4           6        bethel      us        90.0      93.0  60.79 -161.76   \n",
       "\n",
       "   temperature  max_temp  wind speed        date  \n",
       "0        35.60     35.60        5.82  1533668400  \n",
       "1        51.80     51.80        6.93  1533668460  \n",
       "2        39.25     39.25        2.95  1533671478  \n",
       "3        42.58     42.58        4.63  1533671478  \n",
       "4        51.80     51.80        6.93  1533668460  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert the CSV into a dataframe for the plotting purposes\n",
    "weatherPy_plt = pd.read_csv(\"weatherPy.csv\")\n",
    "weatherPy_plt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis: Maximum Temperature (F) vs. Latitude\n",
    "fig, ax = plt.subplots(figsize=(10, 9))\n",
    "ax.scatter(weatherPy_plt['lat'], weatherPy_plt['max_temp'], c='blue',alpha=.6, linewidth=1, edgecolor='black')\n",
    "\n",
    "#Set x and y limits, title, labels\n",
    "ax.set_xlim(-90, 90)\n",
    "ax.set_ylim(min(weatherPy_plt['max_temp'])-20, max(weatherPy_plt['max_temp'])+20)\n",
    "ax.set_xlabel(\"City Latitude\")\n",
    "ax.set_ylabel(\"Maximum Temperature (F)\")\n",
    "ax.set_title(\"City Latitude vs. Maximum Temperature (8/7/2018)\")\n",
    "\n",
    "# Save png images for each scatter plot.\n",
    "plt.savefig(\"Latitude vs. Maximum_Temperature.png\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " #Analysis 2: Humidity (%) vs. Latitude\n",
    "fig, ax = plt.subplots(figsize=(10, 9))\n",
    "ax.scatter(weatherPy_plt['lat'], weatherPy_plt['humidity'], c='blue',alpha=.6, linewidth=1, edgecolor='black')\n",
    "\n",
    "#Set x and y limits, title, labels\n",
    "ax.set_xlim(-90, 90)\n",
    "ax.set_ylim(min(weatherPy_plt['humidity'])-20, max(weatherPy_plt['humidity'])+20)\n",
    "ax.set_xlabel(\"City Latitude\")\n",
    "ax.set_ylabel(\"Humidity (%) \")\n",
    "ax.set_title(\"City Latitude vs. Humidity (%) (8/7/2018)\")\n",
    "\n",
    "# Save png images for each scatter plot.\n",
    "plt.savefig(\"Latitude vs. Humidity (%).png\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis 3. Cloudiness (%) vs. Latitude\n",
    "fig, ax = plt.subplots(figsize=(10, 9))\n",
    "ax.scatter(weatherPy_plt['lat'], weatherPy_plt['cloudiness'], c='blue',alpha=.6, linewidth=1, edgecolor='black')\n",
    "\n",
    "#Set x and y limits, title, labels\n",
    "ax.set_xlim(-90, 90)\n",
    "ax.set_ylim(min(weatherPy_plt['cloudiness'])-20, max(weatherPy_plt['cloudiness'])+20)\n",
    "ax.set_xlabel(\"City Latitude\")\n",
    "ax.set_ylabel(\"Cloudiness (%) \")\n",
    "ax.set_title(\"City Latitude vs. Cloudiness (%) (8/7/2018)\")\n",
    "\n",
    "# Save png images for each scatter plot.\n",
    "plt.savefig(\"Latitude vs. Cloudiness (%).png\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis 4. Wind Speed (mph) vs. Latitude\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 9))\n",
    "ax.scatter(weatherPy_plt['lat'], weatherPy_plt['wind speed'], c='blue',alpha=.6, linewidth=1, edgecolor='black')\n",
    "\n",
    "#Set x and y limits, title, labels\n",
    "ax.set_xlim(-90, 90)\n",
    "ax.set_ylim(min(weatherPy_plt['wind speed'])-20, max(weatherPy_plt['wind speed'])+20)\n",
    "ax.set_xlabel(\"City Latitude\")\n",
    "ax.set_ylabel(\"Wind Speed (mph)\")\n",
    "ax.set_title(\"City Latitude vs. Wind Speed (mph) (8/7/2018)\")\n",
    "\n",
    "# Save png images for each scatter plot.\n",
    "plt.savefig(\"Latitude vs. Wind Speed (mph).png\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Observations: \n",
    "    \n",
    "#1. Latitude vs. Maximum_Temperature: As one moves away from the equator on either directions (northern or the southern hemisphere, the temperature as of today (8/7/2018) changes from highto low. What is striking is that the highest temperatures are NOT at the equator. The max temperature of 107.6 is recorded a little below 40 degree latitude N. The highest recorded temperatures and cools off as one moves towards the poles. \n",
    "#2. In terms of humidity, extremely hot and cooler places tend to be humid. Cooler palces below 40F seem be a very humid. This is true for places that record very temperatures too (for example between latidues of 20 to 40 degree N, the humidity can be either very high (100%) or relatively low humidity of less than 20%. \n",
    "#3. Wind speed is on an average close to 9 mph (pretty calm) compared to the highest windspeed of about 31 mph. As we move away from the equator the wind speed remains closely tied to the 0 to 10 mph."
   ]
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}