EarthquakesNB.ipynb

{
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    "# Tremors Analysis - Project 1\n",
    "\n",
    "We will be looking at earthquake data on a global scale for the previous year (2019) to better understand where earthquakes occur most often on Earth along with how depth and magnitude correlation to related variables. We will answer any related questions as the data admits.\n",
    "\n",
    "Analysts: Sarah Zachrich, Meg Estey, Thampi Mathews, Robin Bun, Jennie Brozena\n",
    "\n",
    "A final write up of our hypothesis and analysis is included at the bottom of this notebook\n",
    "\n",
    "To run this code you will need to add your own google gkey below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
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   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "\n",
    "#dependencies - added as we found we needed\n",
    "import json\n",
    "import pandas as pd\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as st\n",
    "from scipy.stats import linregress\n",
    "import gmaps\n",
    "\n",
    "#add your gkey here!\n",
    "g_key =\"\"\n",
    "\n",
    "#just do one month to see data format\n",
    "baseurl = \"https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson\"\n",
    "janurl = \"&starttime=2019-01-01&endtime=2019-01-31\"\n",
    "url=baseurl+janurl   \n",
    "response = requests.get(url).json()\n",
    "#un-comment response to see all data output as json\n",
    "#response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-01-01&endtime=2019-01-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-02-01&endtime=2019-02-28\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-03-01&endtime=2019-03-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-04-01&endtime=2019-04-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-05-01&endtime=2019-05-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-06-01&endtime=2019-06-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-07-01&endtime=2019-07-13\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-07-14&endtime=2019-07-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-08-01&endtime=2019-08-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-09-01&endtime=2019-09-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-10-01&endtime=2019-10-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-11-01&endtime=2019-11-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-12-01&endtime=2019-12-31\n"
     ]
    }
   ],
   "source": [
    "#Created a dictionary of all months\n",
    "#We were limited by the amount of data we could do in one request so we found pulling by month worked well\n",
    "    #July had a large earthquake swarm in CA,USA which caused us to exceed our limit so we had to split that month in half\n",
    "monthurl = {\n",
    "    'janurl':\"&starttime=2019-01-01&endtime=2019-01-31\", \n",
    "    'feburl':\"&starttime=2019-02-01&endtime=2019-02-28\", \n",
    "    'marurl':\"&starttime=2019-03-01&endtime=2019-03-31\",\n",
    "    'aprurl':\"&starttime=2019-04-01&endtime=2019-04-30\",\n",
    "    'mayurl':\"&starttime=2019-05-01&endtime=2019-05-31\",\n",
    "    'junurl':\"&starttime=2019-06-01&endtime=2019-06-30\",\n",
    "    'julurl':\"&starttime=2019-07-01&endtime=2019-07-13\",\n",
    "    'julurl2':\"&starttime=2019-07-14&endtime=2019-07-31\",\n",
    "    'augurl':\"&starttime=2019-08-01&endtime=2019-08-31\",\n",
    "    'sepurl':\"&starttime=2019-09-01&endtime=2019-09-30\",\n",
    "    'octurl':\"&starttime=2019-10-01&endtime=2019-10-31\",\n",
    "    'novurl':\"&starttime=2019-11-01&endtime=2019-11-30\",\n",
    "    'decurl':\"&starttime=2019-12-01&endtime=2019-12-31\"}\n",
    "\n",
    "baseurl = \"https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson\"\n",
    "\n",
    "#create lists to hold all the variables we want\n",
    "name=[]\n",
    "felt=[]\n",
    "mag=[]\n",
    "depth=[]\n",
    "latitude=[]\n",
    "longitude=[]\n",
    "alert=[]\n",
    "sig=[]\n",
    "types=[]\n",
    "\n",
    "#loop through all months & each earthquake to get all the data we want - append into the above lists\n",
    "for urls in monthurl.items():\n",
    "    url=baseurl+urls[1]\n",
    "    print(f'Fetching {url}')\n",
    "    response = requests.get(url)\n",
    "    response_json=response.json()\n",
    "    feature_list=response_json['features']\n",
    "    \n",
    "    for each_feature in feature_list: \n",
    "        try: \n",
    "            name.append(each_feature['properties']['title'])\n",
    "            felt.append(each_feature['properties']['felt'])\n",
    "            latitude.append(each_feature['geometry']['coordinates'][1])\n",
    "            longitude.append(each_feature['geometry']['coordinates'][0])\n",
    "            depth.append(each_feature['geometry']['coordinates'][2])\n",
    "            mag.append(each_feature['properties']['mag'])\n",
    "            alert.append(each_feature['properties']['alert'])\n",
    "            sig.append(each_feature['properties']['sig'])\n",
    "            types.append(each_feature['properties']['type'])\n",
    "        except: \n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Felt</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Alert</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M 1.5 - 56km WNW of Talkeetna, Alaska</td>\n",
       "      <td>62.433300</td>\n",
       "      <td>-151.176800</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>None</td>\n",
       "      <td>35</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M 2.1 - 54km SE of Punta Cana, Dominican Republic</td>\n",
       "      <td>18.235000</td>\n",
       "      <td>-68.037300</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M 1.4 Quarry Blast - 7km ENE of Three Forks, M...</td>\n",
       "      <td>45.919833</td>\n",
       "      <td>-111.459333</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.38</td>\n",
       "      <td>None</td>\n",
       "      <td>29</td>\n",
       "      <td>quarry blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M 0.8 - 36km SW of North Nenana, Alaska</td>\n",
       "      <td>64.376300</td>\n",
       "      <td>-149.718200</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M -0.4 - 46km ESE of Beatty, Nevada</td>\n",
       "      <td>36.693800</td>\n",
       "      <td>-116.316300</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name   Latitude   Longitude  \\\n",
       "0              M 1.5 - 56km WNW of Talkeetna, Alaska  62.433300 -151.176800   \n",
       "1  M 2.1 - 54km SE of Punta Cana, Dominican Republic  18.235000  -68.037300   \n",
       "2  M 1.4 Quarry Blast - 7km ENE of Three Forks, M...  45.919833 -111.459333   \n",
       "3            M 0.8 - 36km SW of North Nenana, Alaska  64.376300 -149.718200   \n",
       "4                M -0.4 - 46km ESE of Beatty, Nevada  36.693800 -116.316300   \n",
       "\n",
       "   Depth  Felt  Magnitude Alert  Significance          Type  \n",
       "0   80.4   NaN       1.50  None            35    earthquake  \n",
       "1    8.0   NaN       2.09  None            67    earthquake  \n",
       "2   -2.0   NaN       1.38  None            29  quarry blast  \n",
       "3   15.0   NaN       0.80  None            10    earthquake  \n",
       "4    9.8   NaN      -0.40  None             0    earthquake  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dictionary to make DF\n",
    "quake_dict={\n",
    "    \"Name\":name,\n",
    "    \"Latitude\":latitude,\n",
    "    \"Longitude\":longitude,\n",
    "    \"Depth\":depth,\n",
    "    \"Felt\":felt,\n",
    "    \"Magnitude\":mag,\n",
    "    \"Alert\":alert,\n",
    "    \"Significance\":sig,\n",
    "    \"Type\":types}\n",
    "\n",
    "#convert to DF\n",
    "quakes_df=pd.DataFrame(quake_dict)\n",
    "\n",
    "#print data to csv\n",
    "quakes_df.to_csv(\"Raw_Quake_Data_2019.csv\")\n",
    "quakes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "earthquake            171402\n",
       "ice quake               1681\n",
       "quarry blast            1053\n",
       "explosion                863\n",
       "mining explosion         213\n",
       "other event              149\n",
       "chemical explosion         3\n",
       "sonic boom                 2\n",
       "mine collapse              2\n",
       "rock burst                 1\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It appears there are multiple event types other than just earthquakes\n",
    "events=quakes_df[\"Type\"].value_counts()\n",
    "events\n",
    "\n",
    "#ice quake = very cold air temperatures (negatives F) can penetrate the ground and start to rapidly freeze the goundwater\n",
    "    #expands/cracks the grounds and creates booming sounds and ice quakes\n",
    "#rock burst = spontaneous violent failure of rock that can occur in high-stress mines\n",
    "#sonic booms = usually military tests from aircrafts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Felt</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Alert</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M 1.5 - 56km WNW of Talkeetna, Alaska</td>\n",
       "      <td>62.4333</td>\n",
       "      <td>-151.1768</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>None</td>\n",
       "      <td>35</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M 2.1 - 54km SE of Punta Cana, Dominican Republic</td>\n",
       "      <td>18.2350</td>\n",
       "      <td>-68.0373</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M 0.8 - 36km SW of North Nenana, Alaska</td>\n",
       "      <td>64.3763</td>\n",
       "      <td>-149.7182</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M -0.4 - 46km ESE of Beatty, Nevada</td>\n",
       "      <td>36.6938</td>\n",
       "      <td>-116.3163</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M 2.2 - 28km SSW of Tanaga Volcano, Alaska</td>\n",
       "      <td>51.6360</td>\n",
       "      <td>-178.2355</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.20</td>\n",
       "      <td>None</td>\n",
       "      <td>74</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name  Latitude  Longitude  \\\n",
       "0              M 1.5 - 56km WNW of Talkeetna, Alaska   62.4333  -151.1768   \n",
       "1  M 2.1 - 54km SE of Punta Cana, Dominican Republic   18.2350   -68.0373   \n",
       "3            M 0.8 - 36km SW of North Nenana, Alaska   64.3763  -149.7182   \n",
       "4                M -0.4 - 46km ESE of Beatty, Nevada   36.6938  -116.3163   \n",
       "5         M 2.2 - 28km SSW of Tanaga Volcano, Alaska   51.6360  -178.2355   \n",
       "\n",
       "   Depth  Felt  Magnitude Alert  Significance        Type  \n",
       "0   80.4   NaN       1.50  None            35  earthquake  \n",
       "1    8.0   NaN       2.09  None            67  earthquake  \n",
       "3   15.0   NaN       0.80  None            10  earthquake  \n",
       "4    9.8   NaN      -0.40  None             0  earthquake  \n",
       "5   17.6   NaN       2.20  None            74  earthquake  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter data so we only have earthquake type events\n",
    "filtered_quakesdf = quakes_df.loc[quakes_df[\"Type\"]==\"earthquake\"]\n",
    "\n",
    "#print filtered df to csv\n",
    "#use this one for analysis\n",
    "filtered_quakesdf.to_csv(\"Quake_Data_2019.csv\")\n",
    "filtered_quakesdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Felt</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Significance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>171402.000000</td>\n",
       "      <td>171402.000000</td>\n",
       "      <td>171402.000000</td>\n",
       "      <td>7832.000000</td>\n",
       "      <td>171371.000000</td>\n",
       "      <td>171402.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.244129</td>\n",
       "      <td>-114.298246</td>\n",
       "      <td>20.293527</td>\n",
       "      <td>58.384448</td>\n",
       "      <td>1.526509</td>\n",
       "      <td>56.196293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.388821</td>\n",
       "      <td>60.650594</td>\n",
       "      <td>50.150678</td>\n",
       "      <td>1120.589288</td>\n",
       "      <td>1.138281</td>\n",
       "      <td>87.869358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-65.607100</td>\n",
       "      <td>-179.997500</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.690000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.645000</td>\n",
       "      <td>-148.920700</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.739050</td>\n",
       "      <td>-117.865083</td>\n",
       "      <td>7.290000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.182708</td>\n",
       "      <td>-117.382000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>86.600900</td>\n",
       "      <td>179.998200</td>\n",
       "      <td>667.390000</td>\n",
       "      <td>68916.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2061.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Latitude      Longitude          Depth          Felt  \\\n",
       "count  171402.000000  171402.000000  171402.000000   7832.000000   \n",
       "mean       40.244129    -114.298246      20.293527     58.384448   \n",
       "std        18.388821      60.650594      50.150678   1120.589288   \n",
       "min       -65.607100    -179.997500     -10.000000      0.000000   \n",
       "25%        35.645000    -148.920700       3.400000      1.000000   \n",
       "50%        36.739050    -117.865083       7.290000      2.000000   \n",
       "75%        55.182708    -117.382000      13.300000      6.000000   \n",
       "max        86.600900     179.998200     667.390000  68916.000000   \n",
       "\n",
       "           Magnitude   Significance  \n",
       "count  171371.000000  171402.000000  \n",
       "mean        1.526509      56.196293  \n",
       "std         1.138281      87.869358  \n",
       "min        -1.690000       0.000000  \n",
       "25%         0.830000      11.000000  \n",
       "50%         1.280000      25.000000  \n",
       "75%         1.900000      56.000000  \n",
       "max         8.000000    2061.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#quick overview of data count and some basic stat info\n",
    "filtered_quakesdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question: Where are earthquakes most common? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5024183036fe4a1993367b8c0b4f05cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating heat map using magnitude as weight to determine where earthquakes are most common\n",
    "#Hypothesis: Ring of fire area (around the Pacific Ocean) & and at plate boundaries\n",
    "\n",
    "gmaps.configure(api_key=g_key)\n",
    "filtered= filtered_quakesdf.loc[filtered_quakesdf[\"Magnitude\"]>=0]\n",
    "locations=filtered[[\"Latitude\", \"Longitude\"]]\n",
    "mag=filtered[\"Magnitude\"]\n",
    "fig=gmaps.figure(center=(0,10), zoom_level = 1.75)\n",
    "mag = gmaps.heatmap_layer(locations, weights=mag, dissipating=False, max_intensity=9, point_radius=1.5)\n",
    "fig.add_layer(mag)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question: Where are the biggest earthquakes occurring (& would you be able to feel them)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#measuring where are the biggest earthquakes are occurring (& where would you be able to feel them)\n",
    "#Decided to go with Northern and Southern Hemispheres of the world because it would be more logical to plot a range as opposed to a specific location since there would be so many datapoints \n",
    "#per wikipedia.org, search query \"Richter magnitude scale,\" (https://en.wikipedia.org/wiki/Richter_magnitude_scale) an earthquake could be felt at magnitude above 2.0 however will use 2.5 since that is more likely to be felt\n",
    "\n",
    "#Creating Northern and Southern Hemispheres DataFrame\n",
    "northern_hemi=filtered_quakesdf.loc[(filtered_quakesdf[\"Latitude\"]>=0)]\n",
    "southern_hemi=filtered_quakesdf.loc[(filtered_quakesdf[\"Latitude\"]<0)]\n",
    "\n",
    "#linking the Northern and Southern Hemispheres with Magnitude over 2.5\n",
    "magnitude_north=northern_hemi.loc[(northern_hemi[\"Magnitude\"]>=2.5)]\n",
    "magnitude_south=southern_hemi.loc[(southern_hemi[\"Magnitude\"]>=2.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Creating Scatterplot for the Northern Hemisphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared value is 0.10566880617657114\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 the data\n",
    "x_value=magnitude_north[\"Latitude\"]\n",
    "y_value=magnitude_north[\"Magnitude\"]\n",
    "plt.scatter(x_value,y_value)\n",
    "plt.title(\"Magnitude vs Latitude for Northern Hemisphere\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.ylabel(\"Magnitude\")\n",
    "\n",
    "#getting the linear regression data\n",
    "(slope, intercept, rvalue, pvalue, stderr)=linregress(x_value,y_value)\n",
    "regress_value=x_value*slope+intercept\n",
    "line_eq=\"y=\"+str(round(slope,2))+ \"x+\" +str(round(intercept,2))\n",
    "plt.scatter(x_value, y_value, marker=\"o\", color=\"blue\")\n",
    "plt.plot(x_value, regress_value, \"r-\")\n",
    "plt.annotate(line_eq, (55,7), fontsize=\"15\", color=\"red\")\n",
    "print(f\"The r-squared value is {rvalue**2}\")\n",
    "plt.savefig(\"Northern_latitude_vs_magnitude_linreg.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Creating Scatterplot for Southern Hemisphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-squared value is 0.006559880715560423\n"
     ]
    },
    {
     "data": {
      "image/png": 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cujJPo6q1Sccdd5waMbz73aojI6rbt08fe+97VRuN1mPt/OQnqqD6wx9OH/vpT92x7363mEznnONSGlu2qB54oOrHP+7q/cY30vMsXqz65jdnl+ntb1fdf3/VycnseXOyYu1mlaExBZ1KMjSmK9Zu7pgMRnY6+dyqrAu4QWN0qo3gi/DNb8LAAGzc2Hp840Z3/KqryqvrmmvgjDNgv/2mj511FoyOwg9/mJzv4Q+Hpz99+tjxx8Phh7tzANddB3PmwOWXT1+zfTs86lHwspcVl/0d74CnPc2Zl6rmgAM6bqJJW8Zu1JNOPrduhTIwBV+EZcucLfxTn2o9/slPwqJF8KxnOdvz+Hhy8rGj33UXHHVU67FDD3X2+LvuypYP4PGPn853wgnwlrfAypXwm9+4Y294g5P9Qx9Kly2JW2+FT3wCLr44e97LL4e5c928wYteBPfeG33dxATs2gU//jF88IOwfLkzZXWQOrrIGdGETSUbLm6wfNVo5c+tW/Z+U/BFGByEc891Cr7pjaTqPv/t37pR8bveBUNDyenRj06va+tWGBmZeXzBAneuaL41a2DxYmfH/vrX4dOfho99zF3XZGKi9Yep+bYZPjY52VrP618Pr30tPOYx6fcY5vnPh3/9V/je9+B973M2+JNOcm8W7eyzj0snnQTPeIa73jAi6FbUx67N08TZbrqRetIG/6tfqYqofv/77vP3vufU3u23u8/33696/fXJ6dZbp8sbH1cdG5tOTVvynDmql1wys/5DDnF25zhOP131BS+YefylL1V96lNbj/3856pDQ6rDw6qvfOXMPM94hrYYEaNS2Cb/hS+oPvzh03MEGzf62+Dbue021cFB1fXrZ5678UbVH/1I9f3vd/b35cuzl2/MCoZHdkV22+GRXZXW2y0bvC10KsoRR8DJJzszxCmnuL/HHw/HHOPOH3wwHHRQchlhc8Jpp7Xa1H/wA1f+ggWwbdvMvNu3R4/QmyxYAJs2zTy+bdvMfE98Ihx9NNxyC7zmNTPzXHYZPPTQ9Oc1a9zfsCfPgQe6v2Njzuzztre5Uf22bbBjhzu3c6crZ9994+Vu5wlPcG6QN90089ySJe7viSe6+s85B978Zr83I2NW0S1TSddCGcRp/qIJOBK4OZR2ACuS8vTkCF5V9XOfU50/X/W++9zfDRumz11wQfqod/Hi6evvuqt1dL9jhzt+0kmqZ53VWu9vfuPyX3VVvGzveIfqwQfPPH7EEapvelPrsQ98wI3ejzpK9WlPU52YSL7vJC+arVuT7/nRj04uO4qjj1Z9+cuTr7nttnI8hIxSWbF2czB6ntThkV1d8zDq1gi+SujGCF5V7waOBRCRQeB+4Mqq6usqL3yhszOfdZYbrZ511vS5886D5zwnOf/w8PT/Rx4Zfc2ZZzrbcnjk+8UvQqPh7M5xnHkmXHSRm4A88UR37IYb4Ne/duea3H03rF7tFgotW+YWJa1f70bCeXjYw9zbR5g//AFe8hJ497vh1FOzlXf77U7GV70q+br/+i/39/DDs5VvVEadQgIsXzXKpWuGWjxapqM+9s7qVW/iNH+ZCfgL4L/SruvZEbyq6mtf64YCL3lJNeVv2eJG4qef7kanl12mus8+qqtXt1736EervuIVrcfOOEP18MNVv/IV1SuvVH3c41RPPHH6/Pi46lOe4mzyzVH7e96jOm+e6p13xsvk6wffJM4G/4pXtI7or77ava189rNubuPDH3ZzDYcf3urzf8YZqu97n+q3vqX67W+rvvOdrk1e/GJ/mYzKqduouS5vE2VBwgi+Uwr+cuB1MefOA24Abjj00EOrbYkq+e53qzcN3HGH6imnOMV78MGq55/vlHOYxYtnKt2tW1XPPddNQO67r/sR2rRp+vy73+1MS//zP9PHxsdVTzhB9fjjZ9bRpCwFf845rWaqW25RPfVUtzhqzhw3UXvOOW7COsz556sec4xT6vvvr/rkJ6t+8IOqe/f6y2RUDkzGWOo6txitn0lS8JUHGxORucDvgGNU9Y9J1/Z0sLG3vtWZTJqLnAzDAOoV1GvluvrFbC9Kt4ONnQnclKbce5a774Yrr4QNG5zPtyl3w2ihLrsddcsHvpt0YgR/BfBtVf1E2rU9OYI/+WT46U/hec+Dz3zGrbw0DKOFOoyc6/QmUSZdG8GLyHzgmcBXq6ynq1x7rYsH88UvmnLvUWw3purpViiH8LPtlA98nfpTpQudVHUXcECVdRhGEerkwmeUS/uzjcOFCyhnBF+3/mQ7Ohmzmn59bTfin20YGRovNYJkN/pTkonGQhUYs5rZsKvPbCX+GbpBbRVzAXXrT+byYVROnWyS7bIMNMYirysS5a9O9zubSYrgWNVcQN129zIFb1RKnVzTomSZ3DsI0hqPv4gLX53ud7bTDffMuriEThG3AqobqadDFRiR1GmZepwsg409pS1dr9P9Gt0JS9DpOunmStYs2CRr/yGiQNTuSopqZ3dd6oQsdbpfY3bQ7ZWsfYPZVrNTJ5tkJ2Sp0/36ktavV67bwpz5exFRRJTB+XtL7fv2vaqQuKF9N1KdTTSd3IG9n6hTu3VCljrdrw9p8q5Yu1kZHJ9pdpLxUu6p19qrjtDtaJK+qc4K3myr+alTeNZOyFKn+00jrV/HnS+r79v3qjhJCt5s8J6YbdXoR9L6dfz56WuqrN9Ix2zwJZBmWzU7olFXkvpmWr9OmjsoY16hF+csoHe+76bgPUnybzXfZ6OupPXNNL/t5atGYXCivViQiVJ8u2vnN+5BT33f42w33Uh1tsGrxttWzY5o1BWfvpk2Z7Bi7WYdbOwJdmaa1IHGntInpntlzkK1ft93bJK1WvJuSdZrHduIf2bh48hEbZ5pke3yrH9GU7ctCE3BV0yeX3RzD+s94p7ZkmWbZhyvyzPNO9q0/hmPe5uZ2aaDjT1dkSdJwZsNvgTy2BE3XNyYEadax+aw4WILUVtX4p7ZTd9eGBtzvNvPNK+N2/pnPHF+h/XxR5zGwgWXgItIl21LsrqFFTXSiX02Ke583XymefomWP9MYnJ0KNPxbmIj+JJo35IMSHSjqtI9rFdcuHqNvM+m2y5/zb65ZNlm9mwf5pLzFyADkxx35oOxeeJkHmyMefWtqvtg0fLb8x935oPe5cW1zUBE23T9uxhnu+lG6lUbfDs+9suqbJxmO62OqLaNn3AL0mA5S/qLsmTZpghZJ3XJsk2R10fe6+C4IuOpfavqPli0fJ/nmFReZH4ZnxnSIeJYFd9FbJK1s/hObFXhpVA3F65+Y6a3TIJyR3WgSxNv7cTKKhOxedr750DM5GJ736q6DxYtPyn8gm957W0TN/Haie9ikoK3UAUV0M3l17b0u3MkL+NvUo92L6Nf+JZRdR8sWr7fc/MvL1uZ2cr1q9tCFXSUbi6/7tWl372IT5vWpt0lfiDna0f33d6w6j5YtPzBmPvIel24fZLat51O9glT8BXQzeXXvbj0u1eJXcYfUKd2X3LGFqId+SRymX3k9oa750SUoRxzws6WI1X3wSLlr1y3hYnRKOfBmW0zsXsw8ccv3D7owMwyZGJG/+h4n4iz3XQj9YsNXrW7qwBtBWLniLNLIxO1a/foidZsdnRfm3LVfTBv+VnuK8leHltO2yrmTnwXsUnW+mLKuJW6t0e7fEnL1suUv6x28V1mn+odlJC3zmS7r/h7S2rHTvdZU/A1xVwaW6l7e+Rxk6yb62vcG0e7t0/REXxdqXwE34U+awq+pphLYyt1b4+syqEs+ctsF984KkX84OvMirWbZ9xD0ug9ky98l/pskoI3N8kuYi6NrdS9PbK5woUpJn+Z7ZKlrJXrZoY4ADKHPagbK9dt4YPrHtYWWiB7+65ct4VLzl+QK2+ZJLlJVjoiB0aALwN3AXcC/zvp+jqN4Ku0Bbcslqn41z9c10Bjz1Rc7zrat7s9gk975lWP4LPuN5DlOS5ZtmlqAtBHxtbrJ4IVmdniwbtJ3YmpfDJ3TA86akuo3EnvMtvbZsmyTYG5yeUfzBmj3o3ooxeBNds2qU/EvRHVZQTvq6gFeBnwzuDzocDxHvk+Bbwy+H8uMJJ0fV0UfJW24LRXuzKXtqfVVbfX627a4POGl4gyW+SRP6n+os8xyXum+UMRDlmQfr0qktxP48tIKDemTO+5j4zfnaR2jQsDHW7rWHNPh8NTlKHgNwD/CtwZfF4AXJ+SZz9gIzgzkE+qi4KvciSZNgosc2m7z4izLvbtJt3yoikSXqKMt6S0+ou89fmEVAjn97m+jDp9y6xqwjfJ1XG6vePriDvf6fAUSQreywYvIjep6hIR+bmqPjk4douqPikhz7HAR4FfAE8CbgTeqKo72647DzgP4NBDDz3u3nvvTZWnaqq0Bafbccuz3fXSUvpu0237f5VhALL2gzKW8pc5X1FVGIC0tix6vlOUEapgTEQGwS3VEpFFwGRKnjnAEmBD8KOwE/iH9otU9aOqulRVly5atMhTnGqpcql1Whlx5/OEHc26lL7roU0rJO3e8j7zleu2MGf+XkQUEWVw/t5cYWJ9688lp88y+kD+eQsyrLIUjb+vDEv3w0TdR5bvXVnXSoL8zZANPREWJG5oH07A2cBVwH3AOuBu4K9T8hwM3BP6fBLwzaQ8dTHRdMsGH1dHXnmy2G7r7oNehKrCN69Yu3lmiFhUpycls5XlU38eOX1s8O0mCt8FQXF1H3TUFu8ypuvtrA3ea64hpr1WrN2cOQRzVVCGHzxwFPBa4HXA4z3z/Ag4Mvj/QuB9SdfXRcGrdtCLxmOD5iJzAr724W57sFRJVeGby1o0k7X+PH2z3StG5o6F+l+04o72ovG7L7+26a4XTV6vqOY91+U7k1vBAwuTUlLeIP+xwA3ArcDXgAVJ1xdV8HVf5p6XTuziXred4vMQ9/zLujf/MAXxCi2P/EWJK3fF2s2JbdNev287Jpdbft/K2275Ru/tP07R55Ys29QysJr6Qc3oaup3H/kV/Ebg18HfCeBBYHPw/8akvHlSEQXfzyaGTowU6jIayUvS8y/j3vKEKchSX1X9N67cKBfAGanN5OHTjqluwCX3rSLtVtUI3rtvpLia+lLYRAN8BHhW6POZwPt98mZJRRR8ryuoJDrx49XrP5BJz7+Me0tabDTzWHYbfFX9N8kVMKsS9mlHf6VZTlCuoubLXKN4mZxyjy32FlCOfipDwd8YcSy20LypiILvBxNDEp0wP/WyiSvt+Re9t6TyB0O24Oard9b6quq/SeX6KuIwafeVt9wq7q94+wTzD+FVt21vNUUVfBntUIaC/zZwPnAYsBhYDXzbJ2+W1Esj+CIKo5cVaRzdbg9f80Ha5HbcQqb4EW+8XbW9rJZl+jLR4m3R8RF8BkU8lQbGU5+R9wg+Jl5+2kKywcaeqcnV4ZGEvVBl0qs/JbdPfP4VazeHJnnzK/i6jOAXApcCPw/SpT6TrFlTr9jgi9TV66aQKOrQHmnl+LinRl6TGIog4ssd2FX9bPbTLnVV9Yv8roDJPwJxsmWpzysUhIzHuKKGzqeYm5La0UfeKDn9I1ImpLrY4DuVesWLpshoqx/nCurSHknPP21kmT5p5q/8MpUlE17y5yX7PYVT8vX53SOjy8g/6ZktFEMeeYvLmS24WhaSFLxvqIIfwMxNC1X11NTMGeiVcMFFlijXZXlzmfRCe/iEiAiu9CwxqbwsZVX73PMu888b5iJ7iIKsdUaXkTckR56wDPnkrO45lxGqYBXwliC9A7gZ598+q2guP4/DZ9l/0jLm5jJ3XzmKhhQoq5zYewotf48ru1PLvdPLSx/oZKnLW34h8hm0h0AQmcz1nLK2Y5byBxtjM8I0NJfx55Gv0iX+MWEVZG78pulhBhtjU88pTxiG4ZHd3QkFEje0T0vAD/PmjUt1WsnaTqp/b2h2PcmemlpOil2uUzbr4m0z6VV2p+Yksvhnz7yPdhNAeshbPzvtzLKbPupJducs7ZPHBi9DY9o4aEdyvsEY27dMRLSXX5+IW/rv59KZfo9+9cXda/vz8G9Tn9DDRaCkSdZmOhA4A7jbJ2+WVGcFn2Z3C4cITbMrJ3tlJNufy7JZlz0XMNNDxb/sTs2dtMjo+aDS10UAACAASURBVOUEt6lD2r1F2VVjPTyaCiLuBydjeN8k8tq1p7x+Yrxo4vZ2dfJHt68MjSU+5zhZw+3v2jRfKOL2dktvZ4979chf1mK7OMpQ8BuZXtH6/4DvACf65M2S6qjg/ZXCtD9rkm/tdFnJwZ/ilFxev992JVrluoGsZVep4FtisASuidm9SiZ1Wrmn+U1Px0VJe8bFfNQnW9wF43zvi3jQNJ9NOOaLzB1TZCylXP/n3/r9Sm7b4vc0/d3yK2cypT979JvUPlcPP/h5EceGffJmSXVT8HmWXSd3nGyvdVGKLs9IwM9lL70cX7LIWKWJJu6V38UFya8gvK4bHM8/8suzWUaEO6EMjcW/JaSkqR+pPO6ACW85qW6RHt+L/CPqZprwNvsk9edMfSbmPusygr/J51jRVDcF7/tgwwqpeGdIfvh5FKLvYpcqQyKX+YPlS/yXeCKnPT663eLSYGNPZgU5ZYMvuIBmKsWYS9LSwJRZJGt944nyl+EWOdjY47XGoGgbytBYbH/O8xxqZ4PHxXQ/Drdh9pNxG3gsAU4G7krKmyfVTcH7vMK1mxRK+2IG5UdR5jL4qkwjvjJ2y1RU1Hzhe127iSPtWTTbqbx+VOQes+RtnYPwea5FZPNZJVy8DadlzTt/kyRzJ/zgIw9OnYRzgB8ADwV/m+kq4IVJefOkuin49BFG9gU1WdJAaHIpb4dImtAta3GVTwiAOOLbK72ccL1Rse6TTAVFv6y+14XlT5Spbel+ef2omP3Y+/rQgi1VjTWjtE465pNt0GPPU/cDnn8ytv2ZFA9N4Bc6IQ9lmGj+yue6oqluCt731cxrp/XMnSvappqlc/gsz6+yjXx3nUprL9+djdrzxO8qlG1k2v7ZleuvPHzD8/qGVohTRolL+nMnvwnfcFydFWtjdroKzDfJ9+VRn4crcXKf8rXBh0xmZXynI55zGRQZwb8s+Ptm4E3tKSlvnlQ3Ba8aPTqNemjh0XD66GGy5XUyagQa52KXZdQdOwqMCfSUB58QAGkkuxNGl+Mzwh0e2VXcdtp8XqFXf1+Z29vc57r2fjSz70X3v4GpSdG8o9YCXj2BrTrt2Qwm2vWnd2rK+n3L2if9vJyyP7ssqczQJEkKPjFUgYi8SlUvE5ELYhZJrYlfQpWdXghV4Lu0Pnk5c+u1Reqpuoz8dfjXlWdZvG+e4OqU69KYeQ95luSXEbog7ZkWW+5PbNlZnnGSjEl1ZAslEN9Ovn0yS5iC4n0oWoYyyB2qQFUvC/6uiUqlSNdDrFy3JX6Zctuy/KRl1+3nopYwpy3h91n2XEYYgOPOfBAZcMvkZWCS4858MFNZPkvXfeRpv8Y/FED8AMaboQlkQKfDBswd917iPi2H32WDKe0VHxaC3MvoCzM00RKygBxhH7L21YGIMAnN70Bqn5uSs3tUGpYhTNzQPpyARcDbgY8ClzeTT94sqY4mmibJNtGZ7oaxdru2DQPiXAqj7cfO1unrhljUx9xn1/gsIRzytW0+G3zS88lml48zE2SdbPO0kfvYl1PLKdOTK0/7ZpMhezjfqPAROu2imdg+VU+s+5UV/g4VhRImWX8CvBf4G+Cvmsknb5ZUZwWfJwxqu4tc1M7vSXbyuHKzLiTK64mT5PERV4evXElytm/qkNWLJvcXr21eZHhkV24/8qgU9opK6ztJFF/o45M68SNRZEOOpGdYZZu0p3xldcoGH3lwxkVws891RVMnFXx5vuTJnaFZdtSS+eRy05bER58rk6z1lCGXz3NJuqaYYpq5LqDskVv8G8HMZyxDY8GqW/d5IOvkYGFZq68j6jmuWLu5ZRvE+sgd9qAqupCqvFF8GQp+LaFNt6tKnVLwWU0Xxb9UUV/qyQQ3vvg0/YWfea7MUYGqeo/gmxRdlerzXJKuKb64ZaaprRqFUeQto0AIhNqlGNNmIXfPqn+Y6meqKUPBPwRMAqPAjuDzDp+8WVKnFHxWReSz4KlzncUvzkcZ+NjgwxS1+fs8l6RrkvdNzfclrE7J50/RS/V7LcW0aSETS52ek78LZlEKK/hOpU4p+DJND51ZEemTp1zzTJM401IchWz+HveW13RV5BmVGhumlDSZOu/RbfmK9OPkPFU842raIGsU2rwkKXivHZ1EZElEerSIzCnqxdMNsrpkpbmupZ0vlRj3rjgZ2t0pjzvzQQbDOwUNaMu5dtfLG685EJ0cQFXQyQFuvObAxDo2XNxg+apRVqzdCsAl5y9gcP7eKZe2vLs7rVy3hcH5e2ObJfkZaCH3wZv+/YDceatgeGQ361cv5JgTdnZbFKME2l2PSyVO84cTcB2wF7gxSHuB63Ex4v/CpwyfVEcbvFdMC4+d3UtJgwluYBHudVnCBEed8w01MKOOyB1w/No66rmkLxVPipviG1Olk6O/AqPN4DnX762irHbptgzduJditnhKsMFfARwT+nw08AngCEr0sKmjF42/m5Z/AKo8nSXsYpkUyCmf7PEpbYI0bx1x5UY9l/z3UTT2ex3StEteS7TGTgwoOn6P3Zaji6mALT5JwSeGKmgiIjer6rFRx6LO5aWOoQrKX87se11rHt+l4NmX82eru50iS+OLh1xIryMoIUfeuhDdTuU827rQD8+pKP7fh3ZyhyoIcbeIbBCRZwTpw8D/iMgwEGv8FJF7ROQ2EblZROqluT3Jukt8Kjn7cDhMQJqtusju71FE2c2b9eQlfA9pYRdyL+sW7ez8SAXE33s5z9aoCVWFTogb2ocT0MBFlLwS+BqwCpiP+4F4WEK+e4ADfepQrd9KVr9l4VmTz0KX+FfZJcs2xbovHnTUFs+dbuLLjzuXPZRt/rLabfR+9uY4t7uZYZd7J0XbZkuJdd7Fe6rm2l5O7rubF7rlJtnrCr7MzTtKSzKRObxBeOm9U5b5lEPTbp5tXiJ6M460Ns63tVt8KN1etVlHzVXUsl92LPWnvb7IIsUkBe9rg38s8B7c5Oq80Oj/iJR8G4GtgAKXqepHI645DzgP4NBDDz3u3nvvTZWnU9TTztl8XlnCupZlm3flZJmXKCMscvF5kKQ2qzsz27Ce/bJT9PKzTKK7NvhPABuAceAU4NPAZzzyPU1VlwBnAq8Vkae3X6CqH1XVpaq6dNGiRZ7i+OETUjfpmlrab0WRoWyhamXuBCKToXCuBerOZNufvi7KHz/Jhj88sjsIVZxF3oRruxwetghTaxZqEObWqIbKwgfHDe3DCbgx+Htb6NiPfPKGrr8QWJV0TZkmmqJxTbpv54x6FZ1UhnfHvKLGbUOWxeafdG2aLT86ZOxBR23J7o8v4x5xeuJC1KbspNW151lF/+i2DJbKepZV2eB9TTT/BZwEfBn4PnA/8E+qemRCnn2AAVV9KPj/u8C7VPXf4/KU6SY5b8Eoe7Y1ZhwfHhll99ZG6jVA5Lnuo2Q1z/i+zg409nLsM3Zw07cXQvh1URQ04mVPJkGF4ZHd7Nk2L7Z+d96/LQcbe5nYPSe6zjREGd4/SR7DqCEyiU7m6O8km2h8Qw2swHnNvAG4CDgVOCclz8OBK0WkWc/nk5R72bgvePJxn2tmE5OjQ5GhCCR250HB2Q0biaaDrO05MTqU6fpWmWbv8zN6mJK272vH6ydDVa9X1T+p6n2q+neq+kJVvS4lz69V9UlBOkZV15Ujsh9JNq2mnT3Jn7xjW2plptpRaZ6t/+Jiy6Tlj6PY2gMJJcPoESqaW0lU8CJyVVKqRKKSWL5qFCIn3YQNFzemrpGh8dazQ+MsXzWakD+Kah5ONoS4+/WXb7ptwiS1ExDkiVGog5OR+ePRoCTfDbUNo9dRlpyRPEjKX3SMcT6wzW8CbgLeAjwdeEY4JeXNk8r2g/cJP1vO7kB1mfDKE4I1vm3C5G+nyan8/mFkk0PCTvnze8dqr8vzsWSpPRXf9IMC4YIPxm22/QTgUuCZwIOq+kNV/WE1PznlsHLdlsTXnubS//WrF7J7awNVmZp8bYa2nY20m1Ocq+Ikl5y/gD3bhznoqG2ACwPcdCtNMsEMNsZYuW5L5JtBXvZsm+dC+I4NllamYfQlcZq/PQHDwLm4Uf3rffNlSWWN4P2W0s/89awmNEEnU5EwCKoMjnuECJgZUjjRpVEmUkL9+qZ8IRYsWap/qi5ccKqbZBBQ7NnAS4DDgKuAy1X1/rJ/bEpxk9y7l4MO2sK27QsZY4hUW27IPSnObbKnGBqH8QHcrHy2icaBxl4mds2d+iwDk3i5KorndS1oZvnSy6PkMg2jQ3TDTVJEPoUzz1wDrFHV23NJ0EmGh3kgy/UhPVPEb2YPc9nF/Kk0SoNRGqnHslw7SoM9DJOoxMYGmQ4nkI3JdvdEX9etily8DGPWUNF3KM0P/m+BncDjgDfItEO0AKqq+1UiVQG++uz3cNo330ODUebGRzIunWH2MsxeFrCtY3XGIuT3MQn1s73M8fwxmseox3Wtx+exi4exi/nsxhYlGbOciuILJSp41TxLCbvHynVbuPQ7q1D+wTOHsmTZ5qnFPSvXbeGSd4zkMjfMZW+bKtsVqLLRXMfijg8Tvy9p2Qwxzv7sYH92dKzOqhlnMPLN6MO8hjGGZqRx5ngfbz9mP1qGHwoDE/iHBvPHK1RBpyhqg/e3oSuI8z1tX7k5Z/5eJkbnxuTrBUJRGAe0wKufMsQYjTkPMX98N/MZZT47eeTiB5h4cJLBnRPsP387zz1zG2c/c4Arr9rMz75FcF36D9d82cWCebsYGB2lUcg4Vl/GGSz0A5H12k7kT3e8M/JRTTRJ31AFPUHWJeo3/fsBLbbqgcbYTDt0DzLllqhFluwLY8xlbHwhOxtjHLB4J7+++whuv1fcj+OyLRx14gD/5+IGL/vKPGRoAmUQ71GrKitWb+WS8xd45ilnEnUOY5E/PJtYxBzGZ6g332NZrk3LP59dmeoaZLJQm2RhgoGO/ZgsZAtn87kZb1vhv2XMb43T+9/5OGbvCD5WUVRjC+scLpywjpX9293eLhofhMy3PJkE9fVlNy+ZOITJyn6MyvzhynJsDtlCYvcKo8yb+pF5C+/jCl4SnLERfCrLV41y6ZqhFOWWpsB7WYEooBUod5jZLuJn/hmcgImBmPxZFir18nOpFmUgmOIf7rYopSFMMpe97MtDheet0o4N4RtGozgNdk+ZJD/H2S0KvuOTrL3G+tULAWee2LNtXjDClKm/02Ft+5minaTMjqasWLM9MMMYhj/KAHuYxx765/sqTDLMnqkfmQc5sOVsJXX2k4nGB+/FOz2IDI0zd5+xgou1SlTwweKNvlhAZhgVEt6nIitlbNnXN7iobUk/avX5wcuKjg2y/8G7M0RunFGCizUjUfbP9nYJbOhJZR25HYiORsngBHRwctAw6svkVGTWspl1Cv7Gaw5kybLNgXLSljTQ2MuSZZuDHZ2UwcZet/R/6pq6Izxw9/688YIdU/fgj1sT8Mc7F7Diou0MNPbSvO/Bxt5A8QdtJpMsWbaZFRdtS1DywvY/uNfr9asXtsg0PDLKijXbGWx0zvZpGPVEWbF2W2BeLp9ZZ6LJS+/sZB/yg88kc75Z/OQ6ksvsnTY1jKrI7z3TxEw0Hqxct4V5C0YR0akwuM3jc+Z3bvVoYQK//rRwyXlYuW4Lg0EoZRFlYLj5dhPN8MjuGXnCyTAMkHl7qis8LsxkN1LZG374EhVeWIbGXLjcngof7HZn9w+XPJ1vcN+dqW2UKeyvTLj2KyVUsCVL/ZwmleHdufUXRcIFd5JumWhivTxyhcEFurgox9m5yea14hGqNKsnzGBjL3OGJ8x7xjC8sIVOlRHrG9+DYXBz+fl73GfWcidGh5jog7APhtHLmA2emdvUTZHbTpx9s42yGB7ZnbiFXhzNOYes5+LpvR9Hw+g3TMET7actQ+POZ36wh2JiyATLV41G+50nZ0zcM9Wdy6Ows+Spj6nQMPoFU/BE+2m/8YId3HjNgYG/9rRPeDRJ5/JcF77e/9oVF21n/eqFU/eTJX+SCaZT4R2m27kKmm3fXP9gGLOAuNnXbqQqvGhWrN2swyO7FCZ1eGRXy6bSWXHlzJwFHx7Z5TaX9pkt97oudL3ntcMju7zl9c2fp5zcSSZCz6oaT4WDjtoy1Rcqvx9LljKlydx6iQQvmr4ewa9ct4VL1+wXeHIIe7Y1uHTNfjltyvGmnOWrRj1CIICLoDjgcR0uXIDnHEBThih5vUxMgxOJS6WXrxqNCV+QJthESjiDEDrApWv245gTdhYItZAoDA/cNTLVF4y64dfX+xOlcdCfKio6RvN3I5U9gk8acecl6Y3A+X1PqBshTqjMHdPso0VX7mBjj/e1SW8lA7HlTCpM6mBjj9dbzYq1m4OyXD7nZx93b9Ny+d1H67MJt7GNtmdDsudcRCcxW/3g45fCF18eXFyGOJxsfvnS76PKNvApO+/9p9dh9A9NHTSbn3M1fvCVm2hEZFBEfi4iV1ddF7SGHIgzceRxI8xL1rqa1/vkK3JNFrniwjj4lJ3n/lufYabshtGTVKWTOmGDfyNwZwfqmWFzj7J3x9mrqyLSZVF0hlwO5ZgTdsbnCxfheR9J8wY+JM1j+JQdOw8gkzPs+jI0zjEn7Gx7hmVq+Pq8rUZTd/mqonvrRurB9Pe+/KJjbDdlJOCRwPeAU4Gr064vaoOP9cAI7OJFvWjy0m63j7eLt9riwvkGG3umbOBZ76OIJ1HaPIZP2dO2eGdrHQjs/lF5s3vRTKqfDbcuNt7J6JTJu8pSv6WetMGLyJeB9wD7AqtU9TkR15wHnAdw6KGHHnfvvfcWqE/pts3dhyIhdjtNp9u0ujmLrOVWRXS72VzDbKfHbPAi8hzgAVW9Mek6Vf2oqi5V1aWLFi0qVGcZ9uZOkCRPr8halZyVzVnUJDxxr/RRo7MMNsYqKbdKG/zTgOeJyD3AFcCpIvLZCusrbG/uFLF+5Sn+6N2g022aJcxCWI6kfFNhJ7zWH1S30jWp3bKHl+g0OdtkcCJhDUXedq7Hj7U/6fJWdkdxtpsyE3AyHbDBq5a7crVK2v3Kff3Ru0Gn2zRu7mGgsWfKlh8lR4v/fMS8S5LffnhuIO26TGnAf/4nLH/4Xt3fiQS52mUsahNuzgtMTsneOGhHyr2OT8+ztLV93HNZsmzTjOPt9x3+jjSvC69Inl6P4ZMmMlxbRr5w3vQ2zwsJNvi+U/DG7MTnR8h34Vv8ZP1ki1Kq6scu7l68JqBLmKwtMuFnzMTnuVU1ydoRBe+bTMEbeYjbkStqhO97XfxOXpOp+au6l7Sdupq7kM24Rsa9dyYr+36M6GdaZpubgjf6miwhKXzNTUmurFWOeLO4pca5zkbdY5L5J6/7reFPWS7PUSQp+L4OVWDMDqpw5czmtpi/Hv96y6vD6C+6GqrAMHyIC4fgkyeOJNfDtPqyuC2W6eJYxI3Stw3ztLXRSpY27Gp7xw3tu5HMRDM78bWNp+Xxze9T34q1m53tOsU8U7bNesmyTTPs/OAmd5Moex7CiCdLG3aivTEbvFFn8oR1jvdMSLdr+tbX6soaUVewSUmZ5A1xXdRDyDxn/MnShp1o7yQFbyYao+vEbQm4Z9u82NfZpG0Ed29tsH71wlz1hVm/eiETu+bGloPKjHqKvo77ytZej28+3+uMeLK0Ybfb2xS80XXi7csSuwNXEVt11ry+x8vYQcynrqh6fMuzUAnFydKG3W5vU/BG10lapq9jc9hw8UwFViSEQta8vtdvuLiBjs3xkr+IbFH1OM8bTcyX5V6MeLK0YdfbO852041kNvjZS1qIgLg8eVeVZs3rc31W+fPWlVSPzz31SjiPOpOlDatub8wP3ugFnC155mh3eGSU3Vv9R8HdolPy56ln5botbLi4wZ5t8xge2c3yVaOJ8xRG72B+8EZP0PXX2YJ0Sv6s9ZQxN2D0KHFD+24kM9EYvW4+6JT8Weox18j+BjPRGMbsxcIf9DdmojG6QpVLtDu5/Dtc1+D8vcyZv7enlvl321XP6CJxQ/tuJDPR9A9VLtHu5HL7qkO9dgILT9DfYCYao9NU6VHSSW+buLqqrrdszIumf0ky0ZiCNyqhSrtvJ23KfmGDzZZtdA+zwRsdp0q7bydtykVCHxhGtzEFb1TCMSfspH3pPGhwvBid9JdPCqNQZb2GUQbtAS0MoxTuuG4fZpo2JDheDGc77oxNub2ugcYYAkyMDpkt26g9ZoM3KsF8rw2jM5gN3ug45nttGN3HFLxRCb0eV8Yw+gGzwRuV0Ek7uWEY0ZgN3jAMo4cxG7xhGMYsxBS8YRhGn1KZgheReSLyMxG5RUTuEJE1VdVlGIZhzKTKSdY9wKmq+icRGQJ+LCLXqOp1FdZpGIZhBFSm4IMwln8KPg4FqT4zuoZhGH1OpTZ4ERkUkZuBB4DvqupPq6zPMAzDmKZSBa+qE6p6LPBI4HgReUL7NSJynojcICI3bNq0qUpxjFlEJ3d8Moy60hEvGlXdBlwLLIs491FVXaqqSxctWtQJcYw+Z+W6LVy6Zr9gow5hz7YGl67Zz5S8Meuo0otmkYiMBP83gNOBu6qqzzCabLi4gY61Ti/p2Bw2XFzvXZcMo2yq9KL5X8CnRGQQ90PyJVW9usL6DAOAPdvmZTpuGP1KZSN4Vb1VVZ+sqk9U1Seo6ruqqsswwiRFrDR7vDGbsJWsRt8RvwuT2eON2YUpeKPvWL96IW+8YAfDI6NELb0we7wxWzAFb/Ql61cvZPfWeCVu9nhjNmAK3uhrbGcpYzZjCt7oa2xnKWM2Yzs6GX2N7SxlzGZsRyfDMIwexnZ0MgzDmIWYgjcMw+hTTMEbhmH0KabgDcMw+hRT8IZhGH2KKXjDMIw+xRS8YRhGn2IK3jAMo0+p1UInEdkE3NttOTw5EHiw20LkoBfl7kWZoTfl7kWZoTflLkvmxaoaud9prRR8LyEiN8StHqszvSh3L8oMvSl3L8oMvSl3J2Q2E41hGEafYgreMAyjTzEFn5+PdluAnPSi3L0oM/Sm3L0oM/Sm3JXLbDZ4wzCMPsVG8IZhGH2KKXjDMIw+xRR8DkTk9SJyt4jcISL/HDr+jyLyy+DcGd2UMYyIXCgi94vIzUF6VuhcLWUOIyKrRERF5MDgs4jIBwO5bxWRJd2WsYmIXBTIdLOIfEdEDgmO11ZmABF5n4jcFch2pYiMhM7Vso+IyF8H38FJEVnadq6WMjcRkWWBbL8UkX+orCJVtZQhAacA/wEMB58PCv4eDdwCDAOHA78CBrstbyDbhcCqiOO1lTkk46OAb+MWwB0YHHsWcA0gwAnAT7stZ0je/UL/vwH4SN1lDuT7C2BO8P97gffWvY8AjweOBK4FloaO11bmQL7BQKYjgLmBrEdXUZeN4LOzHPgnVd0DoKoPBMefD1yhqntUdSPwS+D4LsnoSy/IvB54KxD2Bng+8Gl1XAeMiMj/6op0bajqjtDHfZiWu7YyA6jqd1S1uTv5dcAjg/9r20dU9U5VvTviVG1lDjge+KWq/lpV9wJX4GQuHVPw2XkccJKI/FREfigifx4cfwTw29B19wXH6sLrgtfvy0VkQXCs1jKLyPOA+1X1lrZTdZd7nYj8FjgbeGdwuNYyt/EK3NsG9JbcTeouc8fkm1NFob2OiPwHcHDEqdW4NluAe83+c+BLInIE7tW7nY75oKbIvAG4KJDnIuD9uC9xV2WGVLnfjjMdzMgWcawWba2qX1fV1cBqEflH4HXABdS8rVX168E1q4Fx4HPNbBHX16at47JFHKuTP3jH5DMFH4Gqnh53TkSWA19VZ0z7mYhM4oIG3YezFzd5JPC7SgUNkSRzGBH5GHB18LGrMkO83CLyZzj76S0iAk62m0TkeHqkrYHPA9/EKfjatnUTETkHeA5wWtC/oXfaOkzX2zqFjslnJprsfA04FUBEHoebJHkQuAo4S0SGReRw4LHAz7omZYg2W+9fArcH/9dWZlW9TVUPUtXDVPUw3Jdiiar+ASf3ywPPlBOA7ar6+27K20REHhv6+DzgruD/2soMzqsDeBvwPFXdFTpV2z6SQN1lvh54rIgcLiJzgbNwMpeOjeCzczlwuYjcDuwFzglGO3eIyJeAX+BecV+rqhNdlDPMP4vIsbjXwHuAVwGoap1lTuJbOK+UXwK7gL/rrjgt/JOIHAlM4jx/Xh0cr7PMAP+C8zr5bvDGdJ2qvrrOfURE/hL4ELAI+KaI3KyqZ9RZZgBVHReR1+G8wwaBy1X1jirqslAFhmEYfYqZaAzDMPoUU/CGYRh9iil4wzCMPsUUvGEYRp9iCt4wDKNPMQVv9CUi8qcM154sIk8NfX61iLw8+P/cZkTIjPXf04x+aRjdwvzgDQNOBv4E/ARAVT8SOncubmFYnVZCGoYXpuCNWYOIPBc4H7f6eDMuGFgDtxhpQkReBrweOA2n8O8BlgKfE5FR4H8Dd+JC0z4YxCC/WFVPFpEDgC/gFt38jFC8kaDcNwT1/hR4TZ0W3hj9i5lojNnEj4ETVPXJuBCtb1XVe4CPAOtV9VhV/VHzYlX9MnADcHZwbjSh7AuAHwdlXwUcCiAijwdeDDxNVY8FJnA/LIZROTaCN2YTjwS+GMTmmQtsLLHspwMvBFDVb4rI1uD4acBxwPVBCIAG8EBkCYZRMqbgjdnEh4APqOpVInIybqerrIwz/eY7r+1cVNwPAT6lqv+Yoy7DKISZaIzZxP7A/cH/54SOPwTsG5On/dw9uBE5wF+Fjv8ngelFRM7E7RkA8D3gRSJyUHBuoYgszim/YWTCFLzRr8wXkftC6U24Efu/iciPcCGem3wD+Mtgo+yT2sr5JPCR4FwDWANcGpQRnihdAzxdRG7CbVLyGwBV/QVuYvc7InIr8F2gNlv1Gf2NRZM0DMPoU2wEbxiG0aeYgjcMw+hTTMEbhmH0KabgDcMw+hRT8IZhGH2KKXjDMIw+xRS8M50xSgAAAAtJREFUYRhGn/L/AdBHxVeoZAZ6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the data\n",
    "x_value=magnitude_south[\"Latitude\"]\n",
    "y_value=magnitude_south[\"Magnitude\"]\n",
    "plt.scatter(x_value,y_value)\n",
    "plt.title(\"Magnitude vs Latitude for Southern Hemisphere\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.ylabel(\"Magnitude\")\n",
    "\n",
    "#getting the linear regression data\n",
    "(slope, intercept, rvalue, pvalue, stderr)=linregress(x_value,y_value)\n",
    "regress_value=x_value*slope+intercept\n",
    "line_eq=\"y=\"+str(round(slope,2))+ \"x+\" +str(round(intercept,2))\n",
    "plt.scatter(x_value, y_value, marker=\"o\", color=\"blue\")\n",
    "plt.plot(x_value, regress_value, \"r-\")\n",
    "plt.annotate(line_eq, (-60,7), fontsize=\"15\", color=\"red\")\n",
    "print(f\"The r-squared value is {rvalue**2}\")\n",
    "plt.savefig(\"Southern_latitude_vs_magnitude_linreg.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the the two data sets are similar, let's see what it looks like for magnitude above 6.5 and above 7.0 in a map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Creating a Geomap with magnitude 6.5 and above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79c4a42a9cc54864b3d34fe6acd2b792",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(border='1px solid black', height='300px', margin='0 auto 0 auto', padding='1px', wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a dataframe for earthquakes greater than 6.0\n",
    "strong_earthdf=filtered_quakesdf.loc[(filtered_quakesdf[\"Magnitude\"]>=6.5)]\n",
    "\n",
    "#access gmaps with API key\n",
    "gmaps.configure(api_key=g_key)\n",
    "\n",
    "#getting coordinates\n",
    "coord=pd.DataFrame(strong_earthdf, columns=[\"Latitude\",\"Longitude\"])\n",
    "\n",
    "#creating coordinate list\n",
    "coord_list=coord.values.tolist()\n",
    "\n",
    "#figure layout\n",
    "figure_layout={\n",
    "    \"width\":\"400px\",\n",
    "    \"height\":\"300px\",\n",
    "    \"border\":\"1px solid black\",\n",
    "    \"padding\":\"1px\",\n",
    "    \"margin\":\"0 auto 0 auto\"}\n",
    "\n",
    "#Creating map\n",
    "fig=gmaps.figure(layout=figure_layout)\n",
    "markers=gmaps.marker_layer(coord)\n",
    "\n",
    "fig.add_layer(markers)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Creating a Geomap with magnitude 7.0 and above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "550ea6b002b04225b95408af593a646d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(border='1px solid black', height='300px', margin='0 auto 0 auto', padding='1px', wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating a dataframe for earthquakes greater than 7.0\n",
    "strong_earthdf2=filtered_quakesdf.loc[(filtered_quakesdf[\"Magnitude\"]>=7.0)]\n",
    "\n",
    "#access gmaps with API key\n",
    "gmaps.configure(api_key=g_key)\n",
    "\n",
    "#getting coordinates\n",
    "coord=pd.DataFrame(strong_earthdf2, columns=[\"Latitude\",\"Longitude\"])\n",
    "\n",
    "#creating coordinate list\n",
    "coord_list=coord.values.tolist()\n",
    "\n",
    "#figure layout\n",
    "figure_layout={\n",
    "    \"width\":\"400px\",\n",
    "    \"height\":\"300px\",\n",
    "    \"border\":\"1px solid black\",\n",
    "    \"padding\":\"1px\",\n",
    "    \"margin\":\"0 auto 0 auto\"}\n",
    "\n",
    "#Creating map\n",
    "fig=gmaps.figure(layout=figure_layout)\n",
    "markers=gmaps.marker_layer(coord)\n",
    "\n",
    "fig.add_layer(markers)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question: Does earthquake depth have a correlation to strength?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is 0.37.\n",
      "The r-squared value is 0.1396.\n",
      "The equation of the regression line is y = 0.01x + 1.42.\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": [
    "#started by plotting depth vs mag\n",
    "\n",
    "#had to drop some NaN values and filter to only include positive numbers\n",
    "filterdf=filtered_quakesdf.dropna(subset=[\"Magnitude\", \"Depth\"])\n",
    "filterdf=filterdf.loc[(filterdf[\"Magnitude\"]>=0) & (filterdf[\"Depth\"]>=0)]\n",
    "\n",
    "x_values = filterdf[\"Depth\"]\n",
    "y_values = filterdf[\"Magnitude\"]\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "cod =round(rvalue**2, 4)\n",
    "corr_coeff = round(st.pearsonr(x_values,y_values)[0], 2)\n",
    "\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.grid(which=\"both\")\n",
    "plt.plot(x_values, regress_values, \"r--\")\n",
    "plt.annotate(line_eq, (300,1), fontsize=15, color ='r')\n",
    "plt.title(\"Earthquake Depth vs Magnitude\")\n",
    "plt.xlabel(\"Depth (km)\")\n",
    "plt.ylabel(\"Magnitude\")\n",
    "\n",
    "plt.savefig(\"Images/earthquakedepvsmag.png\")\n",
    "\n",
    "print(f'The correlation coefficient is {corr_coeff}.')\n",
    "print(f'The r-squared value is {cod}.')\n",
    "print(f'The equation of the regression line is {line_eq}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both the correlation coefficient and the r-squared value suggest there is not much of a relation ship between these two variables. It can be noted that the deepest earthquakes seem to have a magnitude between 4-7. Shallow depths (0-200km) seem to be able to produce any magnitude of earthquake. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is 0.37.\n",
      "The r-squared value is 0.1367.\n",
      "The equation of the regression line is y = 0.65x + 43.05.\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": [
    "#This let me to ask since shallow depth can produce any magnitude quake is there a correlation between depth and significance?\n",
    "\n",
    "x_values = filtered_quakesdf[\"Depth\"]\n",
    "y_values = filtered_quakesdf[\"Significance\"]\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "cod =round(rvalue**2, 4)\n",
    "corr_coeff = round(st.pearsonr(x_values,y_values)[0], 2)\n",
    "\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.grid(which=\"both\")\n",
    "plt.plot(x_values, regress_values, \"r--\")\n",
    "plt.annotate(line_eq, (200,1500), fontsize=15, color ='r')\n",
    "plt.title(\"Earthquake Depth vs Significance\")\n",
    "plt.xlabel(\"Depth (km)\")\n",
    "plt.ylabel(\"Significance\")\n",
    "\n",
    "plt.savefig(\"Images/earthquakedepvssig.png\")\n",
    "\n",
    "print(f'The correlation coefficient is {corr_coeff}.')\n",
    "print(f'The r-squared value is {cod}.')\n",
    "print(f'The equation of the regression line is {line_eq}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This correlation coefficient and r-squared value are very similar to the chart above suggesting there is likely not much of a correlation. However, I would note that the lower depth earthquakes (< 200km deep) do have the highest significance values (likely related to magnitude)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is 0.94.\n",
      "The r-squared value is 0.8908.\n",
      "The equation of the regression line is y = 75.72x + -62.04.\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": [
    "#This then led me to plot significance vs magnitude to see if there is a correlation\n",
    "\n",
    "filterdf=filtered_quakesdf.dropna(subset=[\"Magnitude\", \"Significance\"])\n",
    "filterdf=filterdf.loc[(filterdf[\"Magnitude\"]>=0)]\n",
    "\n",
    "x_values = filterdf[\"Magnitude\"]\n",
    "y_values = filterdf[\"Significance\"]\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "cod =round(rvalue**2, 4)\n",
    "corr_coeff = round(st.pearsonr(x_values,y_values)[0], 2)\n",
    "\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.grid(which=\"both\")\n",
    "plt.plot(x_values, regress_values, \"r--\")\n",
    "plt.annotate(line_eq, (0,1500), fontsize=15, color ='r')\n",
    "plt.title(\"Earthquake Magnitude vs Significance\")\n",
    "plt.xlabel(\"Magnitude\")\n",
    "plt.ylabel(\"Significance\")\n",
    "\n",
    "plt.savefig(\"Images/earthquakemagvssig.png\")\n",
    "\n",
    "print(f'The correlation coefficient is {corr_coeff}.')\n",
    "print(f'The r-squared value is {cod}.')\n",
    "print(f'The equation of the regression line is {line_eq}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation coefficient and r-squared value are both high for this graph suggesting there is a positive correlation. From looking at the graph itself, I would suggest the correlation is non linear, becoming increasingly more significant as magnitude increases. This makes sense as the Richter Scale (magnitude) increases exponentially. See Richter Scale image in slideshow or Images folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is 0.94.\n",
      "The r-squared value is 0.8918.\n",
      "The equation of the regression line is y = 75.92x + -62.28.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I decided to add a depth component to this plot to compare all 3 variables at once\n",
    "\n",
    "filterdf=filtered_quakesdf.dropna(subset=[\"Magnitude\", \"Significance\", \"Depth\"])\n",
    "filterdf=filterdf.loc[(filterdf[\"Magnitude\"]>=0) & (filterdf[\"Depth\"]>=0)]\n",
    "\n",
    "x_values = filterdf[\"Magnitude\"]\n",
    "y_values = filterdf[\"Significance\"]\n",
    "z_values = filterdf[\"Depth\"]\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "cod =round(rvalue**2, 4)\n",
    "corr_coeff = round(st.pearsonr(x_values,y_values)[0], 2)\n",
    "\n",
    "plt.scatter(x_values, y_values, s=z_values/2, c=z_values, cmap=plt.get_cmap('seismic'))\n",
    "plt.grid(which=\"both\")\n",
    "plt.title(\"Earthquake Magnitude vs Significance level\")\n",
    "plt.xlabel(\"Magnitude\")\n",
    "plt.ylabel(\"Significance\")\n",
    "cb=plt.colorbar()\n",
    "cb.set_label(\"Depth (km)\")\n",
    "\n",
    "plt.plot(x_values, regress_values, \"y--\")\n",
    "plt.annotate(line_eq, (3,-100), fontsize=15, color ='y')\n",
    "\n",
    "plt.savefig(\"Images/earthquakemagvssigvsdep.png\")\n",
    "\n",
    "print(f'The correlation coefficient is {corr_coeff}.')\n",
    "print(f'The r-squared value is {cod}.')\n",
    "print(f'The equation of the regression line is {line_eq}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question: How deep are most earthquakes and does location play a part in this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is -0.21.\n",
      "The r-squared value is 0.0445.\n",
      "The equation of the regression line is y = -0.58x + 44.32.\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": [
    "#Depth vs Latitude correlation\n",
    "\n",
    "filterdf=filtered_quakesdf.dropna(subset=[\"Depth\", \"Latitude\"])\n",
    "filterdf=filterdf.loc[(filterdf[\"Magnitude\"]>=0) & (filterdf[\"Depth\"]>=0)]\n",
    "\n",
    "x_values = filterdf[\"Latitude\"]\n",
    "y_values = filterdf[\"Depth\"]\n",
    "\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "\n",
    "cod =round(rvalue**2, 4)\n",
    "corr_coeff = round(st.pearsonr(x_values,y_values)[0], 2)\n",
    "\n",
    "plt.scatter(x_values, y_values)\n",
    "plt.grid(which=\"both\")\n",
    "plt.plot(x_values, regress_values, \"r--\")\n",
    "plt.annotate(line_eq, (20,650), fontsize=15, color ='r')\n",
    "plt.title(\"Earthquake Latitude vs Depth\")\n",
    "plt.ylabel(\"Depth (KM)\")\n",
    "plt.xlabel(\"Latitude\")\n",
    "\n",
    "plt.savefig(\"Images/earthquake_LatvDep.png\")\n",
    "\n",
    "print(f'The correlation coefficient is {corr_coeff}.')\n",
    "print(f'The r-squared value is {cod}.')\n",
    "print(f'The equation of the regression line is {line_eq}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e694c446a39b40ecaa2a8252d9a32595",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create global map with weight as depth to see if location plays a part\n",
    "g_key = gmaps.configure(api_key=g_key)\n",
    "filtered= filtered_quakesdf.loc[filtered_quakesdf[\"Depth\"]>=0]\n",
    "locations=filtered[[\"Latitude\", \"Longitude\"]]\n",
    "depth=filtered[\"Depth\"]\n",
    "fig=gmaps.figure(center=(0,10), zoom_level = 1.8)\n",
    "depth = gmaps.heatmap_layer(locations, weights=depth, dissipating=False, max_intensity=9, point_radius=2)\n",
    "fig.add_layer(depth)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ring of Fire area (ring around the Pacific) seems to be the area with the most and deepest earthquakes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question: Is there a certain time of years earthquakes occur more often or is there a difference between hemispheres?\n",
    "\n",
    "Due to how the USGS data is set up we did a new pull loop to group the data by month - they did not have an easy way to filter date by day or month as the time was also included and made it much more complicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-01-01&endtime=2019-01-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-02-01&endtime=2019-02-28\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-03-01&endtime=2019-03-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-04-01&endtime=2019-04-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-05-01&endtime=2019-05-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-06-01&endtime=2019-06-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-07-01&endtime=2019-07-13\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-07-14&endtime=2019-07-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-08-01&endtime=2019-08-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-09-01&endtime=2019-09-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-10-01&endtime=2019-10-31\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-11-01&endtime=2019-11-30\n",
      "Fetching https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=2019-12-01&endtime=2019-12-31\n"
     ]
    }
   ],
   "source": [
    "#set up new pull loop to group dataframe by month\n",
    "#copy url dictionary from initial pull\n",
    "monthurl={\n",
    "    'janurl':\"&starttime=2019-01-01&endtime=2019-01-31\", \n",
    "    'feburl':\"&starttime=2019-02-01&endtime=2019-02-28\", \n",
    "    'marurl':\"&starttime=2019-03-01&endtime=2019-03-31\", \n",
    "    'aprurl':\"&starttime=2019-04-01&endtime=2019-04-30\", \n",
    "    'mayurl':\"&starttime=2019-05-01&endtime=2019-05-31\", \n",
    "    'junurl':\"&starttime=2019-06-01&endtime=2019-06-30\", \n",
    "    'julurl':\"&starttime=2019-07-01&endtime=2019-07-13\", \n",
    "    'julurl2':\"&starttime=2019-07-14&endtime=2019-07-31\", \n",
    "    'augurl':\"&starttime=2019-08-01&endtime=2019-08-31\", \n",
    "    'sepurl':\"&starttime=2019-09-01&endtime=2019-09-30\", \n",
    "    'octurl':\"&starttime=2019-10-01&endtime=2019-10-31\", \n",
    "    'novurl':\"&starttime=2019-11-01&endtime=2019-11-30\", \n",
    "    'decurl':\"&starttime=2019-12-01&endtime=2019-12-31\"}\n",
    "\n",
    "baseurl = \"https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson\"\n",
    "\n",
    "#create lists to hold all the variables we want - including new month variable\n",
    "month=[]\n",
    "name=[]\n",
    "felt=[]\n",
    "mag=[]\n",
    "depth=[]\n",
    "latitude=[]\n",
    "longitude=[]\n",
    "alert=[]\n",
    "sig=[]\n",
    "types=[]\n",
    "\n",
    "#loop through all months & each earthquake to get all the data we want - same as above\n",
    "for urls in monthurl.items():\n",
    "    url=baseurl+urls[1]\n",
    "    print(f'Fetching {url}')\n",
    "    response = requests.get(url)\n",
    "    response_json=response.json()\n",
    "    feature_list=response_json['features']\n",
    "\n",
    "    for each_feature in feature_list: \n",
    "        try: \n",
    "            month.append(urls[0])\n",
    "            name.append(each_feature['properties']['title'])\n",
    "            felt.append(each_feature['properties']['felt'])\n",
    "            latitude.append(each_feature['geometry']['coordinates'][1])\n",
    "            longitude.append(each_feature['geometry']['coordinates'][0])\n",
    "            depth.append(each_feature['geometry']['coordinates'][2])\n",
    "            mag.append(each_feature['properties']['mag'])\n",
    "            alert.append(each_feature['properties']['alert'])\n",
    "            sig.append(each_feature['properties']['sig'])\n",
    "            types.append(each_feature['properties']['type'])\n",
    "        except: \n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Felt</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Alert</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>janurl</td>\n",
       "      <td>M 1.5 - 56km WNW of Talkeetna, Alaska</td>\n",
       "      <td>62.433300</td>\n",
       "      <td>-151.176800</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>None</td>\n",
       "      <td>35</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>janurl</td>\n",
       "      <td>M 2.1 - 54km SE of Punta Cana, Dominican Republic</td>\n",
       "      <td>18.235000</td>\n",
       "      <td>-68.037300</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>janurl</td>\n",
       "      <td>M 1.4 Quarry Blast - 7km ENE of Three Forks, M...</td>\n",
       "      <td>45.919833</td>\n",
       "      <td>-111.459333</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.38</td>\n",
       "      <td>None</td>\n",
       "      <td>29</td>\n",
       "      <td>quarry blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>janurl</td>\n",
       "      <td>M 0.8 - 36km SW of North Nenana, Alaska</td>\n",
       "      <td>64.376300</td>\n",
       "      <td>-149.718200</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>janurl</td>\n",
       "      <td>M -0.4 - 46km ESE of Beatty, Nevada</td>\n",
       "      <td>36.693800</td>\n",
       "      <td>-116.316300</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month                                               Name   Latitude  \\\n",
       "0  janurl              M 1.5 - 56km WNW of Talkeetna, Alaska  62.433300   \n",
       "1  janurl  M 2.1 - 54km SE of Punta Cana, Dominican Republic  18.235000   \n",
       "2  janurl  M 1.4 Quarry Blast - 7km ENE of Three Forks, M...  45.919833   \n",
       "3  janurl            M 0.8 - 36km SW of North Nenana, Alaska  64.376300   \n",
       "4  janurl                M -0.4 - 46km ESE of Beatty, Nevada  36.693800   \n",
       "\n",
       "    Longitude  Depth  Felt  Magnitude Alert  Significance          Type  \n",
       "0 -151.176800   80.4   NaN       1.50  None            35    earthquake  \n",
       "1  -68.037300    8.0   NaN       2.09  None            67    earthquake  \n",
       "2 -111.459333   -2.0   NaN       1.38  None            29  quarry blast  \n",
       "3 -149.718200   15.0   NaN       0.80  None            10    earthquake  \n",
       "4 -116.316300    9.8   NaN      -0.40  None             0    earthquake  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create dictionary to make DF this time including month\n",
    "quake_dict={\n",
    "    \"Month\":month,\n",
    "    \"Name\":name,\n",
    "    \"Latitude\":latitude,\n",
    "    \"Longitude\":longitude,\n",
    "    \"Depth\":depth,\n",
    "    \"Felt\":felt,\n",
    "    \"Magnitude\":mag,\n",
    "    \"Alert\":alert,\n",
    "    \"Significance\":sig,\n",
    "    \"Type\":types}\n",
    "\n",
    "#convert to DF\n",
    "quakes_df=pd.DataFrame(quake_dict)\n",
    "\n",
    "#print data to csv\n",
    "quakes_df.to_csv(\"Raw_Quake_Data_2019_with_month.csv\")\n",
    "quakes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Felt</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Alert</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>M 1.5 - 56km WNW of Talkeetna, Alaska</td>\n",
       "      <td>62.433300</td>\n",
       "      <td>-151.176800</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>None</td>\n",
       "      <td>35</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>M 2.1 - 54km SE of Punta Cana, Dominican Republic</td>\n",
       "      <td>18.235000</td>\n",
       "      <td>-68.037300</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>January</td>\n",
       "      <td>M 1.4 Quarry Blast - 7km ENE of Three Forks, M...</td>\n",
       "      <td>45.919833</td>\n",
       "      <td>-111.459333</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.38</td>\n",
       "      <td>None</td>\n",
       "      <td>29</td>\n",
       "      <td>quarry blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>January</td>\n",
       "      <td>M 0.8 - 36km SW of North Nenana, Alaska</td>\n",
       "      <td>64.376300</td>\n",
       "      <td>-149.718200</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>M -0.4 - 46km ESE of Beatty, Nevada</td>\n",
       "      <td>36.693800</td>\n",
       "      <td>-116.316300</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month                                               Name   Latitude  \\\n",
       "0  January              M 1.5 - 56km WNW of Talkeetna, Alaska  62.433300   \n",
       "1  January  M 2.1 - 54km SE of Punta Cana, Dominican Republic  18.235000   \n",
       "2  January  M 1.4 Quarry Blast - 7km ENE of Three Forks, M...  45.919833   \n",
       "3  January            M 0.8 - 36km SW of North Nenana, Alaska  64.376300   \n",
       "4  January                M -0.4 - 46km ESE of Beatty, Nevada  36.693800   \n",
       "\n",
       "    Longitude  Depth  Felt  Magnitude Alert  Significance          Type  \n",
       "0 -151.176800   80.4   NaN       1.50  None            35    earthquake  \n",
       "1  -68.037300    8.0   NaN       2.09  None            67    earthquake  \n",
       "2 -111.459333   -2.0   NaN       1.38  None            29  quarry blast  \n",
       "3 -149.718200   15.0   NaN       0.80  None            10    earthquake  \n",
       "4 -116.316300    9.8   NaN      -0.40  None             0    earthquake  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename url to each month inorder to filter\n",
    "renamed_df = quakes_df.replace({'janurl':\"January\",\n",
    "                                'feburl':'February',\n",
    "                                'marurl': \"March\",\n",
    "                                'aprurl': \"April\",\n",
    "                                'mayurl': \"May\",\n",
    "                                'junurl': \"June\",\n",
    "                                'julurl': \"July\",\n",
    "                                'julurl2': \"July\",\n",
    "                                'augurl': \"August\",\n",
    "                                'sepurl': \"September\",\n",
    "                                'octurl': \"October\",\n",
    "                                'novurl': \"November\",\n",
    "                                'decurl':\"December\"})\n",
    "renamed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Felt</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Alert</th>\n",
       "      <th>Significance</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>January</td>\n",
       "      <td>M 1.5 - 56km WNW of Talkeetna, Alaska</td>\n",
       "      <td>62.4333</td>\n",
       "      <td>-151.1768</td>\n",
       "      <td>80.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>None</td>\n",
       "      <td>35</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>January</td>\n",
       "      <td>M 2.1 - 54km SE of Punta Cana, Dominican Republic</td>\n",
       "      <td>18.2350</td>\n",
       "      <td>-68.0373</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.09</td>\n",
       "      <td>None</td>\n",
       "      <td>67</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>January</td>\n",
       "      <td>M 0.8 - 36km SW of North Nenana, Alaska</td>\n",
       "      <td>64.3763</td>\n",
       "      <td>-149.7182</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>January</td>\n",
       "      <td>M -0.4 - 46km ESE of Beatty, Nevada</td>\n",
       "      <td>36.6938</td>\n",
       "      <td>-116.3163</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>January</td>\n",
       "      <td>M 2.2 - 28km SSW of Tanaga Volcano, Alaska</td>\n",
       "      <td>51.6360</td>\n",
       "      <td>-178.2355</td>\n",
       "      <td>17.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.20</td>\n",
       "      <td>None</td>\n",
       "      <td>74</td>\n",
       "      <td>earthquake</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month                                               Name  Latitude  \\\n",
       "0  January              M 1.5 - 56km WNW of Talkeetna, Alaska   62.4333   \n",
       "1  January  M 2.1 - 54km SE of Punta Cana, Dominican Republic   18.2350   \n",
       "3  January            M 0.8 - 36km SW of North Nenana, Alaska   64.3763   \n",
       "4  January                M -0.4 - 46km ESE of Beatty, Nevada   36.6938   \n",
       "5  January         M 2.2 - 28km SSW of Tanaga Volcano, Alaska   51.6360   \n",
       "\n",
       "   Longitude  Depth  Felt  Magnitude Alert  Significance        Type  \n",
       "0  -151.1768   80.4   NaN       1.50  None            35  earthquake  \n",
       "1   -68.0373    8.0   NaN       2.09  None            67  earthquake  \n",
       "3  -149.7182   15.0   NaN       0.80  None            10  earthquake  \n",
       "4  -116.3163    9.8   NaN      -0.40  None             0  earthquake  \n",
       "5  -178.2355   17.6   NaN       2.20  None            74  earthquake  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter data so we only have earthquake type events\n",
    "filtered_quakesdf = renamed_df.loc[renamed_df[\"Type\"]==\"earthquake\"]\n",
    "#print filtered df to csv\n",
    "filtered_quakesdf.to_csv(\"Quake_Data_2019_with_month_filtered.csv\")\n",
    "filtered_quakesdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": [
    "#Create Bar plt\n",
    "months=['January', \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
    "counts=[10595,\n",
    " 9015,\n",
    " 11864,\n",
    " 11441,\n",
    " 11158,\n",
    " 12308,\n",
    " 35641,\n",
    " 19028,\n",
    " 15783,\n",
    " 14486,\n",
    " 12364,\n",
    " 11687]\n",
    "\n",
    "plt.bar(months, counts, color='r', alpha=0.5, align=\"center\")\n",
    "\n",
    "tick_locations = [value for value in months]\n",
    "plt.xticks(tick_locations, months,rotation=45)\n",
    "plt.title(\"Earthquakes by Month 2019\")\n",
    "plt.xlabel(\"Month '19\")\n",
    "plt.ylabel(\"Earthquake Counts\")\n",
    "\n",
    "plt.savefig(\"Earthquakes_by_month.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Labels for the sections of our pie chart\n",
    "labels =['January', \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
    "\n",
    "# The values of each section of the pie chart\n",
    "counts = [10595,\n",
    " 9015,\n",
    " 11864,\n",
    " 11441,\n",
    " 11158,\n",
    " 12308,\n",
    " 35641,\n",
    " 19028,\n",
    " 15783,\n",
    " 14486,\n",
    " 12364,\n",
    " 11687]\n",
    "\n",
    "# The colors of each section of the pie chart\n",
    "colors = [\"olive\",\"pink\", \"orange\", \"lightcoral\", \"lightskyblue\", \"green\", \"yellowgreen\", \"purple\", \"blue\",\"orange\", \"green\", \"red\"]\n",
    "\n",
    "# Tells matplotlib to seperate the section from the others\n",
    "explode = (0, 0, 0, 0,0,0,0.1,0.1,0.1,0.1,0,0)\n",
    "          \n",
    "# Creates the pie chart based upon the values above\n",
    "# Automatically finds the percentages of each part of the pie chart\n",
    "plt.pie(counts, explode=explode, labels=labels, colors=colors,\n",
    "        autopct=\"%1.1f%%\", shadow=True, startangle=40)       \n",
    "plt.axis(\"equal\")\n",
    "plt.title(\"Earthquakes 2019 by Month\")\n",
    "plt.savefig(\"Earthquakes_by_month_pie.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Northern and Southern Hemisphere DataFrames\n",
    "northern_df=filtered_quakesdf.loc[filtered_quakesdf[\"Latitude\"]>0]\n",
    "southern_df=filtered_quakesdf.loc[filtered_quakesdf[\"Latitude\"]<0]\n",
    "\n",
    "# Create Easter and Western Hemisphere DataFrames\n",
    "#It includes Europe, Asia, Australia, and Africa. Longitudes 20° W and 160° E are often considered its boundaries.\n",
    "# This calculation is made so that Africa and Europe are not split.\n",
    "eastern_df=filtered_quakesdf.loc[filtered_quakesdf[\"Longitude\"]>20]\n",
    "western_df=filtered_quakesdf.loc[filtered_quakesdf[\"Longitude\"]<20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Name', 'Latitude', 'Longitude', 'Depth', 'Felt', 'Magnitude',\n",
       "       'Alert', 'Significance', 'Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#refresh columns layout\n",
    "filtered_quakesdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Northern Hemispheres Earthquake count- bar graph\n",
    "\n",
    "counts=[]\n",
    "for each_month in months: \n",
    "    counts.append((northern_df['Month']==each_month).sum())\n",
    "plt.bar(months, counts)\n",
    "tick_locations = [value for value in months]\n",
    "plt.xticks(tick_locations, months,rotation=45)\n",
    "plt.xlim(-0.75, len(months)-0.25)\n",
    "plt.ylim(0, max(counts)+1000)\n",
    "plt.title(\"Earthquakes by Month 2019 in the Nothern Hemisphere\")\n",
    "plt.xlabel(\"Month '19\")\n",
    "plt.ylabel(\"Earthquake Counts\")\n",
    "\n",
    "plt.savefig(\"Earthquakes_by_month_NH.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Southern Hemispheres Earthquake count-bar graph\n",
    "counts=[]\n",
    "for each_month in months: \n",
    "    counts.append((southern_df['Month']==each_month).sum())\n",
    "plt.bar(months, counts)\n",
    "tick_locations = [value for value in months]\n",
    "plt.xticks(tick_locations, months,rotation=45)\n",
    "plt.xlim(-0.75, len(months)-0.25)\n",
    "plt.ylim(0, max(counts)+100)\n",
    "plt.title(\"Earthquakes by Month 2019 in the Southern Hemisphere\")\n",
    "plt.xlabel(\"Month '19\")\n",
    "plt.ylabel(\"Earthquake Counts\")\n",
    "\n",
    "plt.savefig(\"Earthquakes_by_month_SH.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eastern Hemispheres Earthquake count-bar graph\n",
    "counts=[]\n",
    "for each_month in months: \n",
    "    counts.append((eastern_df['Month']==each_month).sum())\n",
    "plt.bar(months, counts)\n",
    "tick_locations = [value for value in months]\n",
    "plt.xticks(tick_locations, months,rotation=45)\n",
    "plt.xlim(-0.75, len(months)-0.25)\n",
    "plt.ylim(0, max(counts)+100)\n",
    "plt.title(\"Earthquakes by Month 2019 in the Eastern Hemisphere\")\n",
    "plt.xlabel(\"Month '19\")\n",
    "plt.ylabel(\"Earthquake Counts\")\n",
    "plt.savefig(\"Earthquakes_by_month_EH.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Western Hemispheres Earthquake count-bar graph\n",
    "counts=[]\n",
    "for each_month in months: \n",
    "    counts.append((western_df['Month']==each_month).sum())\n",
    "plt.bar(months, counts)\n",
    "tick_locations = [value for value in months]\n",
    "plt.xticks(tick_locations, months,rotation=45)\n",
    "plt.xlim(-0.75, len(months)-0.25)\n",
    "plt.ylim(0, max(counts)+500)\n",
    "plt.title(\"Earthquakes by Month 2019 in the Western Hemisphere\")\n",
    "plt.xlabel(\"Month '19\")\n",
    "plt.ylabel(\"Earthquake Counts\")\n",
    "plt.savefig(\"Earthquakes_by_month_WH.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month           164848\n",
       "Name            164848\n",
       "Latitude        164848\n",
       "Longitude       164848\n",
       "Depth           164848\n",
       "Felt              7219\n",
       "Magnitude       164817\n",
       "Alert              486\n",
       "Significance    164848\n",
       "Type            164848\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "northern_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month           6555\n",
       "Name            6555\n",
       "Latitude        6555\n",
       "Longitude       6555\n",
       "Depth           6555\n",
       "Felt             613\n",
       "Magnitude       6555\n",
       "Alert            338\n",
       "Significance    6555\n",
       "Type            6555\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "southern_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month           162738\n",
       "Name            162738\n",
       "Latitude        162738\n",
       "Longitude       162738\n",
       "Depth           162738\n",
       "Felt              6831\n",
       "Magnitude       162707\n",
       "Alert              477\n",
       "Significance    162738\n",
       "Type            162738\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "western_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month           8665\n",
       "Name            8665\n",
       "Latitude        8665\n",
       "Longitude       8665\n",
       "Depth           8665\n",
       "Felt            1001\n",
       "Magnitude       8665\n",
       "Alert            347\n",
       "Significance    8665\n",
       "Type            8665\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eastern_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Labels for the sections of our pie chart\n",
    "labels =['Northern',\"Southern\"]\n",
    "\n",
    "# The values of each section of the pie chart\n",
    "counts = [164847, 6555]\n",
    "\n",
    "# The colors of each section of the pie chart\n",
    "colors = [\"lightskyblue\", \"lightpink\"]\n",
    "\n",
    "# Tells matplotlib to seperate the section from the others\n",
    "explode = (0, 0)\n",
    "          \n",
    "# Creates the pie chart based upon the values above\n",
    "# Automatically finds the percentages of each part of the pie chart\n",
    "plt.pie(counts, explode=explode, labels=labels, colors=colors,\n",
    "        autopct=\"%1.1f%%\", shadow=True, startangle=40)       \n",
    "plt.axis(\"equal\")\n",
    "plt.title(\"Earthquakes 2019 by Northern and Southern Hemisphere\")\n",
    "plt.savefig(\"Earthquakes_by_northvssouth_pie.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Analysis of Data\n",
    "\n",
    "\n",
    "## Where are earthquakes most common? \n",
    "##### Hypothesis: Most likely around the Ring of Fire\n",
    "Findings: Hypothesis Correct\\\n",
    "Earthquakes do appear to be most coommon around the ring of fire. Including the entire globe, they are most common at geologic tectonic plate boundaries.\n",
    "\n",
    "## Where are the biggest earthquakes occurring (& would you be able to feel them)?\n",
    "##### Hypothesis: The biggest earthquake will be around the Ring of Fire & yes you could feel them.\n",
    "Findings: Hypothesis correct\\\n",
    "The largest magnitude earthquakes appears to match where most earthquakes are occuring - the ring of fire area around the Pacific Ocean. There does not appear to be a specific set of latitudes that earthquakes appear over as the are common across all latitudes around the ring of fire. Our cut off for feeling earthquakes was magnitude 2.5 and most earthquakes in this area were much greater than that so yes you would be able to feel them.\n",
    "\n",
    "\n",
    "## Does earthquake depth have a correlation to strength? \n",
    "##### Hypothesis: Shallow earthquakes are stronger (because they are closer)\n",
    "Findings: Hypothesis somewhat correct\\\n",
    "Earthquake depth and magnitude did not appear to have a very strong correlation as evidenced by the correlation coefficient and r-squared value. However, looking at the graph it does appear that the deepest earthquakes are limited to magnitudes 4-6 while shallower earthquakes (<300km) can produce any magnitude earthquake. So in a very general sense the shallower earthquakes are indeed stronger. Analysis of significance versus depth provided similar results with poor correlation values. The graph did appear to show the shallower earthquakes (<200km were the most significant). A final analysis of magnitude versus significance did have a strong correlation. The correlation appears to be non-linear and looks similar to the Richter Scale that earthquake magnitudes are measured by.\n",
    "\n",
    "\n",
    "## How deep are most earthquakes and does location play a part in this?\n",
    "##### Hypothesis: Most earthquakes are shallow and location does not play a part\n",
    "Findings: Hypothesis correct\\\n",
    "The average depth of earthquake for this data set is less than 100km deep so most earthquakes are indeed shallow occurances. Plotting the earthquakes on a map it appears that location does not play a part as shallow earthquakes are spread pretty evenly across the globe. The deepest earthquakes appear to be around the ring of fire as well with the poles having some of the shallowest earthquakes.\n",
    "\n",
    "\n",
    "## Is there a certain time of year earthquakes occur more often or is there a difference between hemispheres?\n",
    "##### Hypothesis: Earthquakes occur all year long, all over the earth.\n",
    "Findings: Hypothesis somewhat correct - incomplete dataset\\\n",
    "Dataset note: As we only pulled the data from 2019 this is not a large enough set of data to fully answer this question. With more time and additional data over multiple years we would have been able to have gotten a much more accurate answer of this question.\\\n",
    "For the year 2019 earthquake occurance was pretty average across all months and over the entire globe. We did see an outlier earthquake swarm in July that skewed our data to show July as having many more earthquakes than average and also skewed the data to show as more numerous in the northern hemisphere. This earth quake swarm occured in early July in California and is referred to as the 2019 Ridgecreast Earthquake Sequence. This event consisted of two large main quakes (M 6.4 & M 7.1) accompanied by numerous foreshocks and aftershocks that created more than 7000 records over the course of a few days. This is also the event that caused us to have to two separate data pulls for July. Further study with a dataset containing multiple years is needed to answer this question with more accuracy and decrease the effect outliers such as the one in July have on the results.\n"
   ]
  },
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