pythonAPI_HW_sid.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This is a python script to visualize the weather of 500+ cities across the world of varying distance from the equator. The objective is to build a series of scatter plots to showcase the following relationships:\n",
    "\n",
    "* Temperature (F) vs. Latitude\n",
    "* Humidity (%) vs. Latitude\n",
    "* Cloudiness (%) vs. Latitude\n",
    "* Wind Speed (mph) vs. Latitude\n",
    "\n",
    "A minimum of 500 cities worldwide will be selected for this exercise based on latitude and longitude for weather visualization. for random selection of latitude and longitude, random module will be utilized, and then using citipy, cities nearest to the selected random latitude/longitude will be obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from citipy import citipy\n",
    "import matplotlib.pyplot as plt\n",
    "%notebook inline\n",
    "import random\n",
    "from config import api_key\n",
    "import openweathermapy.core as owm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seedify random numbers to obtain repeatable results\n",
    "random.seed(a=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yellowknife</td>\n",
       "      <td>ca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>noumea</td>\n",
       "      <td>nc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tuktoyaktuk</td>\n",
       "      <td>ca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>taman</td>\n",
       "      <td>id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>avarua</td>\n",
       "      <td>ck</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City Country\n",
       "0  yellowknife      ca\n",
       "1       noumea      nc\n",
       "2  tuktoyaktuk      ca\n",
       "3        taman      id\n",
       "4       avarua      ck"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_list=[];country_list=[]\n",
    "for i in range(0,1200):\n",
    "    lat=random.randrange(-90,90)\n",
    "    long=random.randrange(-180,180)\n",
    "    city=citipy.nearest_city(lat,long)\n",
    "    city_list.append(city.city_name)\n",
    "    country_list.append(city.country_code)\n",
    "df=pd.DataFrame({\"City\":city_list,\"Country\":country_list}) \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "After obtaining 500 cities, it is important to check that there are no repeat cities. So, we would need to count all unique combinations of city and country. This is the result I got after various iterations, by manually increasing number of selection to obtain at least 500 unique cities.\n",
    "1. 268 unique combinations of city, country out of 500 random pickings\n",
    "2. 448 unique combinations of city, country out of 1000 random pickings\n",
    "3. 510 unique combinations of city, country out of 1200 random pickings\n",
    "Seed Number was not changed in these iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "510"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting unique combinations of city and country\n",
    "grped=df.groupby([\"Country\",\"City\"])\n",
    "ds=grped.size()\n",
    "len(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>city,country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Temperature(F)</th>\n",
       "      <th>Humidity(%)</th>\n",
       "      <th>Cloudiness(%)</th>\n",
       "      <th>Wind Speed(mph)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ae</td>\n",
       "      <td>sharjah</td>\n",
       "      <td>sharjah,ae</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>af</td>\n",
       "      <td>baraki barak</td>\n",
       "      <td>baraki barak,af</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ag</td>\n",
       "      <td>codrington</td>\n",
       "      <td>codrington,ag</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ai</td>\n",
       "      <td>the valley</td>\n",
       "      <td>the valley,ai</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ao</td>\n",
       "      <td>lucapa</td>\n",
       "      <td>lucapa,ao</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country          City     city,country Lat Long Temperature(F) Humidity(%)  \\\n",
       "0      ae       sharjah       sharjah,ae                                       \n",
       "1      af  baraki barak  baraki barak,af                                       \n",
       "2      ag    codrington    codrington,ag                                       \n",
       "3      ai    the valley    the valley,ai                                       \n",
       "4      ao        lucapa        lucapa,ao                                       \n",
       "\n",
       "  Cloudiness(%) Wind Speed(mph)  \n",
       "0                                \n",
       "1                                \n",
       "2                                \n",
       "3                                \n",
       "4                                "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_=ds.to_frame().reset_index()\n",
    "df_unique_cities=df_[[\"Country\",\"City\"]]\n",
    "# Creating column with city,country in the format required by openweathermapy\n",
    "df_unique_cities['city,country']=\"\"\n",
    "for index,row in df_unique_cities.iterrows():\n",
    "    row['city,country']=f\"{row['City']},{row['Country']}\"\n",
    "df_unique_cities['Lat']=\"\";df_unique_cities['Long']=\"\";df_unique_cities['Temperature(F)']=\"\";df_unique_cities['Humidity(%)']=\"\"\n",
    "df_unique_cities['Cloudiness(%)']=\"\";df_unique_cities['Wind Speed(mph)']=\"\"\n",
    "df_unique_cities.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Pulling data from openweathermapI pulled the latitude and longitude of cities to get the exact values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pulling Data From openweathermap api"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/x-python"
   },
   "source": [
    "In this step, I pull data weather data for selected cities, using openweathermap api. I also pulled exact latitude and longitude for the final plots. In this step, I realized that all cities were not available in openweathermap database. Hence, I am using try-except block to handle httperrors while printing the cities not found in the database. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP Error 404: Not Found\n",
      "codrington,ag\n",
      "HTTP Error 404: Not Found\n",
      "ngukurr,au\n",
      "HTTP Error 404: Not Found\n",
      "acarau,br\n",
      "HTTP Error 404: Not Found\n",
      "igarape-miri,br\n",
      "HTTP Error 404: Not Found\n",
      "laguna,br\n",
      "HTTP Error 404: Not Found\n",
      "bokspits,bw\n",
      "HTTP Error 404: Not Found\n",
      "longlac,ca\n",
      "HTTP Error 404: Not Found\n",
      "louisbourg,ca\n",
      "HTTP Error 404: Not Found\n",
      "saint anthony,ca\n",
      "HTTP Error 404: Not Found\n",
      "lasa,cn\n",
      "HTTP Error 404: Not Found\n",
      "warqla,dz\n",
      "HTTP Error 404: Not Found\n",
      "kemijarvi,fi\n",
      "HTTP Error 404: Not Found\n",
      "illoqqortoormiut,gl\n",
      "HTTP Error 404: Not Found\n",
      "marathopolis,gr\n",
      "HTTP Error 404: Not Found\n",
      "bolungarvik,is\n",
      "HTTP Error 404: Not Found\n",
      "skagastrond,is\n",
      "HTTP Error 404: Not Found\n",
      "yomitan,jp\n",
      "HTTP Error 404: Not Found\n",
      "chardara,kz\n",
      "HTTP Error 404: Not Found\n",
      "karaton,kz\n",
      "HTTP Error 404: Not Found\n",
      "asfi,ma\n",
      "HTTP Error 404: Not Found\n",
      "mananara,mg\n",
      "HTTP Error 404: Not Found\n",
      "taolanaro,mg\n",
      "HTTP Error 404: Not Found\n",
      "tsihombe,mg\n",
      "HTTP Error 404: Not Found\n",
      "grand river south east,mu\n",
      "HTTP Error 404: Not Found\n",
      "canitas,mx\n",
      "HTTP Error 404: Not Found\n",
      "san quintin,mx\n",
      "HTTP Error 404: Not Found\n",
      "camana,pe\n",
      "HTTP Error 404: Not Found\n",
      "marcona,pe\n",
      "HTTP Error 404: Not Found\n",
      "mataura,pf\n",
      "HTTP Error 404: Not Found\n",
      "airai,pw\n",
      "HTTP Error 404: Not Found\n",
      "amderma,ru\n",
      "HTTP Error 404: Not Found\n",
      "belushya guba,ru\n",
      "HTTP Error 404: Not Found\n",
      "chagda,ru\n",
      "HTTP Error 404: Not Found\n",
      "ishlei,ru\n",
      "HTTP Error 404: Not Found\n",
      "kadykchan,ru\n",
      "HTTP Error 404: Not Found\n",
      "kamenskoye,ru\n",
      "HTTP Error 404: Not Found\n",
      "karaul,ru\n",
      "HTTP Error 404: Not Found\n",
      "khonuu,ru\n",
      "HTTP Error 404: Not Found\n",
      "krasnoselkup,ru\n",
      "HTTP Error 404: Not Found\n",
      "ksenyevka,ru\n",
      "HTTP Error 404: Not Found\n",
      "mys shmidta,ru\n",
      "HTTP Error 404: Not Found\n",
      "nizhneyansk,ru\n",
      "HTTP Error 404: Not Found\n",
      "primore,ru\n",
      "HTTP Error 404: Not Found\n",
      "sentyabrskiy,ru\n",
      "HTTP Error 404: Not Found\n",
      "shchelyayur,ru\n",
      "HTTP Error 404: Not Found\n",
      "tumannyy,ru\n",
      "HTTP Error 404: Not Found\n",
      "ust-kamchatsk,ru\n",
      "HTTP Error 404: Not Found\n",
      "sakakah,sa\n",
      "HTTP Error 404: Not Found\n",
      "barentsburg,sj\n",
      "HTTP Error 404: Not Found\n",
      "bargal,so\n",
      "HTTP Error 404: Not Found\n",
      "faya,td\n",
      "HTTP Error 404: Not Found\n",
      "asau,tv\n",
      "HTTP Error 404: Not Found\n",
      "karauzyak,uz\n",
      "HTTP Error 404: Not Found\n",
      "da nang,vn\n",
      "HTTP Error 404: Not Found\n",
      "halalo,wf\n",
      "HTTP Error 404: Not Found\n",
      "vaitupu,wf\n",
      "HTTP Error 404: Not Found\n",
      "saleaula,ws\n",
      "HTTP Error 404: Not Found\n",
      "samusu,ws\n",
      "HTTP Error 404: Not Found\n",
      "satitoa,ws\n",
      "HTTP Error 404: Not Found\n",
      "scottsburgh,za\n",
      "HTTP Error 404: Not Found\n",
      "umzimvubu,za\n"
     ]
    }
   ],
   "source": [
    "import urllib\n",
    "settings = {\"APPID\": api_key, \"units\": \"imperial\"}\n",
    "# keys = [\"coord.lon\",\"coord.lat\",\"clouds.all\",\"main.temp\", \"main.humidity\", \"wind.speed\"]\n",
    "for index,row in df_unique_cities.iterrows():\n",
    "    try:\n",
    "        data=owm.get_current(row['city,country'],**settings)\n",
    "        row['Lat']=data(\"coord.lat\");row['Long']=data(\"coord.lat\")\n",
    "        row['Temperature(F)']=data(\"main.temp\");row['Humidity(%)']=data(\"main.humidity\")\n",
    "        row['Cloudiness(%)']=data(\"clouds.all\");row['Wind Speed(mph)']=data(\"wind.speed\")\n",
    "        #     data.get_many(keys)\n",
    "    except urllib.error.HTTPError as err:\n",
    "        if err==404:\n",
    "            print(row['city,country'])\n",
    "            continue\n",
    "        else:\n",
    "            print(err)\n",
    "            print(row['city,country'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country            510\n",
       "City               510\n",
       "city,country       510\n",
       "Lat                449\n",
       "Long               449\n",
       "Temperature(F)     449\n",
       "Humidity(%)        449\n",
       "Cloudiness(%)      449\n",
       "Wind Speed(mph)    449\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing the datatype from str to numeric\n",
    "df_unique_cities[[\"Lat\",\"Long\",\"Temperature(F)\",\"Humidity(%)\",\"Cloudiness(%)\",\"Wind Speed(mph)\"]]=df_unique_cities[[\"Lat\",\"Long\",\"Temperature(F)\",\"Humidity(%)\",\"Cloudiness(%)\",\n",
    "                                                                                                                    \"Wind Speed(mph)\"]].apply(pd.to_numeric,errors=\"coerce\")\n",
    "df_unique_cities.count()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Hence, weather data of 449 (out of 510) was returned from openweathermap. We will utilize data of 449 cities for our final plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>city,country</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Temperature(F)</th>\n",
       "      <th>Humidity(%)</th>\n",
       "      <th>Cloudiness(%)</th>\n",
       "      <th>Wind Speed(mph)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ae</td>\n",
       "      <td>sharjah</td>\n",
       "      <td>sharjah,ae</td>\n",
       "      <td>25.36</td>\n",
       "      <td>25.36</td>\n",
       "      <td>88.20</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>af</td>\n",
       "      <td>baraki barak</td>\n",
       "      <td>baraki barak,af</td>\n",
       "      <td>33.97</td>\n",
       "      <td>33.97</td>\n",
       "      <td>62.60</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ai</td>\n",
       "      <td>the valley</td>\n",
       "      <td>the valley,ai</td>\n",
       "      <td>18.22</td>\n",
       "      <td>18.22</td>\n",
       "      <td>82.47</td>\n",
       "      <td>74.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ao</td>\n",
       "      <td>lucapa</td>\n",
       "      <td>lucapa,ao</td>\n",
       "      <td>-8.42</td>\n",
       "      <td>-8.42</td>\n",
       "      <td>64.81</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ao</td>\n",
       "      <td>namibe</td>\n",
       "      <td>namibe,ao</td>\n",
       "      <td>-15.19</td>\n",
       "      <td>-15.19</td>\n",
       "      <td>71.83</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country          City     city,country    Lat   Long  Temperature(F)  \\\n",
       "0      ae       sharjah       sharjah,ae  25.36  25.36           88.20   \n",
       "1      af  baraki barak  baraki barak,af  33.97  33.97           62.60   \n",
       "3      ai    the valley    the valley,ai  18.22  18.22           82.47   \n",
       "4      ao        lucapa        lucapa,ao  -8.42  -8.42           64.81   \n",
       "5      ao        namibe        namibe,ao -15.19 -15.19           71.83   \n",
       "\n",
       "   Humidity(%)  Cloudiness(%)  Wind Speed(mph)  \n",
       "0         46.0            0.0             3.36  \n",
       "1         39.0            0.0             4.70  \n",
       "3         74.0           40.0            13.87  \n",
       "4         77.0            0.0             1.70  \n",
       "5        100.0            0.0             2.15  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unique_cities=df_unique_cities.dropna(axis=0,how=\"any\")\n",
    "df_unique_cities.to_csv(\"Output/city_weatherdata.csv\")\n",
    "df_unique_cities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# City Latitude Vs Max Temperature (F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_unique_cities.plot(kind=\"scatter\",x=\"Lat\",y=\"Temperature(F)\",facecolor=\"b\",edgecolor='black')\n",
    "plt.title(\"City Latitude Vs Maximum Temperature (May26, 2018)\")\n",
    "plt.xlabel(\"Latitude\");plt.ylabel(\"Max Temperature (F)\")\n",
    "plt.grid(linestyle='-.',color='black',linewidth=.5,alpha=.25)\n",
    "plt.xlim(-60,80);plt.ylim(0,120)\n",
    "plt.savefig(\"Output/latvstemp.png\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# City Latitude Vs Humidity Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_unique_cities.plot(kind=\"scatter\",x=\"Lat\",y=\"Humidity(%)\",facecolor=\"b\",edgecolor='black')\n",
    "plt.title(\"City Latitude Vs Humidity (%) (May26, 2018)\")\n",
    "plt.xlabel(\"Latitude\");plt.ylabel(\"Humidity (%)\")\n",
    "plt.grid(linestyle='-.',color='black',linewidth=.5,alpha=.25)\n",
    "plt.xlim(-60,80);plt.ylim(0,120)\n",
    "plt.savefig(\"Output/latvshumidity.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# City Latitude Vs Cloudiness Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_unique_cities.plot(kind=\"scatter\",x=\"Lat\",y=\"Cloudiness(%)\",facecolor=\"b\",edgecolor='black')\n",
    "plt.title(\"City Latitude Vs Cloudiness (%) (May26, 2018)\")\n",
    "plt.xlabel(\"Latitude\");plt.ylabel(\"Cloudiness (%)\")\n",
    "plt.grid(linestyle='-.',color='black',linewidth=.5,alpha=.25)\n",
    "plt.xlim(-60,80);plt.ylim(0,120)\n",
    "plt.savefig(\"Output/latvscloudiness.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# City Latitude Vs Wind Speed Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_unique_cities.plot(kind=\"scatter\",x=\"Lat\",y=\"Wind Speed(mph)\",facecolor=\"b\",edgecolor='black')\n",
    "plt.title(\"City Latitude Vs Wind Speed (mph) (May26, 2018)\")\n",
    "plt.xlabel(\"Latitude\");plt.ylabel(\"Wind Speed (mph)\")\n",
    "plt.grid(linestyle='-.',color='black',linewidth=.5,alpha=.25)\n",
    "plt.xlim(-60,80);plt.ylim(0,45)\n",
    "plt.savefig(\"Output/latvswind.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. The cities near equator (0 latitude) have higher maximum temperature, with temperature falling down as the cities go farther from equator.\n",
    "    2. Humidity and cloudiness show no correlation to the latitudes. Here the data is quite evenly distributed.\n",
    "    3. Wind speed in most cities are less than 15mph which shows good mild weather today for most places. However some places have wind speed in higher range (15-25mph). In general we do not see any coorelation between wind speed and the latitude."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonData]",
   "language": "python",
   "name": "conda-env-PythonData-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}