Earthquakes Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Earthquake Visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "# Create figure\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [16, 5]\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>I_D</th>\n",
       "      <th>FLAG_TSUNAMI</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>MINUTE</th>\n",
       "      <th>SECOND</th>\n",
       "      <th>FOCAL_DEPTH</th>\n",
       "      <th>EQ_PRIMARY</th>\n",
       "      <th>...</th>\n",
       "      <th>TOTAL_MISSING</th>\n",
       "      <th>TOTAL_MISSING_DESCRIPTION</th>\n",
       "      <th>TOTAL_INJURIES</th>\n",
       "      <th>TOTAL_INJURIES_DESCRIPTION</th>\n",
       "      <th>TOTAL_DAMAGE_MILLIONS_DOLLARS</th>\n",
       "      <th>TOTAL_DAMAGE_DESCRIPTION</th>\n",
       "      <th>TOTAL_HOUSES_DESTROYED</th>\n",
       "      <th>TOTAL_HOUSES_DESTROYED_DESCRIPTION</th>\n",
       "      <th>TOTAL_HOUSES_DAMAGED</th>\n",
       "      <th>TOTAL_HOUSES_DAMAGED_DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>5367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>5368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>6487</td>\n",
       "      <td>Tsu</td>\n",
       "      <td>1993</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>5369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>5370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3285</th>\n",
       "      <td>10487</td>\n",
       "      <td>Tsu</td>\n",
       "      <td>2020</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>10488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>790.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>10490</td>\n",
       "      <td>Tsu</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3288</th>\n",
       "      <td>10491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3289</th>\n",
       "      <td>10494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        I_D FLAG_TSUNAMI  YEAR  MONTH   DAY  HOUR  MINUTE SECOND  FOCAL_DEPTH  \\\n",
       "1925   5367          NaN  1993   10.0  20.0  16.0    15.0   59.6         37.0   \n",
       "1926   5368          NaN  1993   11.0  12.0  13.0    27.0   28.1         10.0   \n",
       "1927   6487          Tsu  1993   11.0  13.0   1.0    18.0    4.1         34.0   \n",
       "1928   5369          NaN  1993   11.0  30.0  20.0    37.0   12.8         18.0   \n",
       "1929   5370          NaN  1994    1.0   3.0   5.0    52.0   27.6          8.0   \n",
       "...     ...          ...   ...    ...   ...   ...     ...    ...          ...   \n",
       "3285  10487          Tsu  2020    3.0  25.0   2.0    49.0   21.0         57.0   \n",
       "3286  10488          NaN  2020    4.0   1.0  12.0    23.0   27.0         10.0   \n",
       "3287  10490          Tsu  2020    5.0   2.0  12.0    51.0    6.0         17.0   \n",
       "3288  10491          NaN  2020    5.0   7.0  20.0    18.0   22.0         10.0   \n",
       "3289  10494          NaN  2020    5.0  18.0  13.0    48.0    3.0         10.0   \n",
       "\n",
       "      EQ_PRIMARY  ...  TOTAL_MISSING  TOTAL_MISSING_DESCRIPTION  \\\n",
       "1925         5.1  ...            NaN                        NaN   \n",
       "1926         4.6  ...            NaN                        NaN   \n",
       "1927         7.0  ...            NaN                        NaN   \n",
       "1928         5.6  ...            NaN                        NaN   \n",
       "1929         5.7  ...            NaN                        NaN   \n",
       "...          ...  ...            ...                        ...   \n",
       "3285         7.5  ...            NaN                        NaN   \n",
       "3286         5.3  ...            NaN                        NaN   \n",
       "3287         6.6  ...            NaN                        NaN   \n",
       "3288         4.6  ...            NaN                        NaN   \n",
       "3289         5.2  ...            NaN                        NaN   \n",
       "\n",
       "      TOTAL_INJURIES  TOTAL_INJURIES_DESCRIPTION  \\\n",
       "1925             NaN                         NaN   \n",
       "1926            25.0                         1.0   \n",
       "1927             NaN                         NaN   \n",
       "1928             4.0                         1.0   \n",
       "1929             5.0                         1.0   \n",
       "...              ...                         ...   \n",
       "3285             NaN                         NaN   \n",
       "3286             NaN                         NaN   \n",
       "3287             NaN                         NaN   \n",
       "3288            22.0                         1.0   \n",
       "3289            24.0                         1.0   \n",
       "\n",
       "      TOTAL_DAMAGE_MILLIONS_DOLLARS  TOTAL_DAMAGE_DESCRIPTION  \\\n",
       "1925                            NaN                       NaN   \n",
       "1926                            NaN                       1.0   \n",
       "1927                            NaN                       1.0   \n",
       "1928                            NaN                       1.0   \n",
       "1929                            NaN                       1.0   \n",
       "...                             ...                       ...   \n",
       "3285                            NaN                       NaN   \n",
       "3286                          11.28                       3.0   \n",
       "3287                            NaN                       NaN   \n",
       "3288                            NaN                       NaN   \n",
       "3289                            NaN                       2.0   \n",
       "\n",
       "      TOTAL_HOUSES_DESTROYED TOTAL_HOUSES_DESTROYED_DESCRIPTION  \\\n",
       "1925                     NaN                                NaN   \n",
       "1926                     NaN                                NaN   \n",
       "1927                     NaN                                NaN   \n",
       "1928                   100.0                                2.0   \n",
       "1929                    56.0                                2.0   \n",
       "...                      ...                                ...   \n",
       "3285                     NaN                                NaN   \n",
       "3286                     NaN                                NaN   \n",
       "3287                     NaN                                NaN   \n",
       "3288                     NaN                                NaN   \n",
       "3289                     NaN                                1.0   \n",
       "\n",
       "     TOTAL_HOUSES_DAMAGED TOTAL_HOUSES_DAMAGED_DESCRIPTION  \n",
       "1925                  NaN                              NaN  \n",
       "1926                100.0                              2.0  \n",
       "1927                  NaN                              NaN  \n",
       "1928                  NaN                              NaN  \n",
       "1929                  NaN                              NaN  \n",
       "...                   ...                              ...  \n",
       "3285                  NaN                              NaN  \n",
       "3286                790.0                              3.0  \n",
       "3287                  NaN                              NaN  \n",
       "3288                  NaN                              NaN  \n",
       "3289                  NaN                              NaN  \n",
       "\n",
       "[1365 rows x 47 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"data/data.tsv\"\n",
    "data = pd.read_csv(filename, sep='\\t', error_bad_lines=False)\n",
    "df = data.iloc[1925:3290,[i for i in range(47)]]\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>I_D</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>MINUTE</th>\n",
       "      <th>SECOND</th>\n",
       "      <th>FOCAL_DEPTH</th>\n",
       "      <th>EQ_MAG_MW</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>TOTAL_DEATHS</th>\n",
       "      <th>TOTAL_INJURIES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>5367</td>\n",
       "      <td>1993</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEPAL</td>\n",
       "      <td>28.723</td>\n",
       "      <td>82.280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>5368</td>\n",
       "      <td>1993</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INDIA</td>\n",
       "      <td>18.120</td>\n",
       "      <td>76.533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>6487</td>\n",
       "      <td>1993</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>RUSSIA</td>\n",
       "      <td>51.934</td>\n",
       "      <td>158.647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>5369</td>\n",
       "      <td>1993</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>39.263</td>\n",
       "      <td>75.533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>5370</td>\n",
       "      <td>1994</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>36.028</td>\n",
       "      <td>100.104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3285</th>\n",
       "      <td>10487</td>\n",
       "      <td>2020</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>RUSSIA</td>\n",
       "      <td>48.986</td>\n",
       "      <td>157.693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>10488</td>\n",
       "      <td>2020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>33.124</td>\n",
       "      <td>98.916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>10490</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>GREECE</td>\n",
       "      <td>34.205</td>\n",
       "      <td>25.712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3288</th>\n",
       "      <td>10491</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IRAN</td>\n",
       "      <td>35.725</td>\n",
       "      <td>52.044</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3289</th>\n",
       "      <td>10494</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>27.296</td>\n",
       "      <td>103.281</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        I_D  YEAR  MONTH   DAY  HOUR  MINUTE SECOND  FOCAL_DEPTH  EQ_MAG_MW  \\\n",
       "1925   5367  1993   10.0  20.0  16.0    15.0   59.6         37.0        NaN   \n",
       "1926   5368  1993   11.0  12.0  13.0    27.0   28.1         10.0        NaN   \n",
       "1927   6487  1993   11.0  13.0   1.0    18.0    4.1         34.0        7.0   \n",
       "1928   5369  1993   11.0  30.0  20.0    37.0   12.8         18.0        5.6   \n",
       "1929   5370  1994    1.0   3.0   5.0    52.0   27.6          8.0        5.7   \n",
       "...     ...   ...    ...   ...   ...     ...    ...          ...        ...   \n",
       "3285  10487  2020    3.0  25.0   2.0    49.0   21.0         57.0        7.5   \n",
       "3286  10488  2020    4.0   1.0  12.0    23.0   27.0         10.0        5.3   \n",
       "3287  10490  2020    5.0   2.0  12.0    51.0    6.0         17.0        6.6   \n",
       "3288  10491  2020    5.0   7.0  20.0    18.0   22.0         10.0        NaN   \n",
       "3289  10494  2020    5.0  18.0  13.0    48.0    3.0         10.0        5.2   \n",
       "\n",
       "     COUNTRY  LATITUDE  LONGITUDE  TOTAL_DEATHS  TOTAL_INJURIES  \n",
       "1925   NEPAL    28.723     82.280           NaN             NaN  \n",
       "1926   INDIA    18.120     76.533           NaN            25.0  \n",
       "1927  RUSSIA    51.934    158.647           NaN             NaN  \n",
       "1928   CHINA    39.263     75.533           NaN             4.0  \n",
       "1929   CHINA    36.028    100.104           NaN             5.0  \n",
       "...      ...       ...        ...           ...             ...  \n",
       "3285  RUSSIA    48.986    157.693           NaN             NaN  \n",
       "3286   CHINA    33.124     98.916           NaN             NaN  \n",
       "3287  GREECE    34.205     25.712           NaN             NaN  \n",
       "3288    IRAN    35.725     52.044           2.0            22.0  \n",
       "3289   CHINA    27.296    103.281           4.0            24.0  \n",
       "\n",
       "[1365 rows x 14 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values\n",
    "df1 = df[['I_D',\n",
    "          'YEAR', \n",
    "          'MONTH', \n",
    "          'DAY', \n",
    "          'HOUR',\n",
    "          'MINUTE',\n",
    "          'SECOND',\n",
    "          'FOCAL_DEPTH', \n",
    "          'EQ_MAG_MW', 'COUNTRY', \n",
    "          'LATITUDE', 'LONGITUDE',\n",
    "          'TOTAL_DEATHS', \n",
    "          'TOTAL_INJURIES']]\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "df1['DateTime'] = df1.apply(lambda row: datetime(\n",
    "    int(row['YEAR']),\n",
    "    int(row['MONTH']), \n",
    "    int(row['DAY']), \n",
    "    int(row['HOUR']), \n",
    "    int(row['MINUTE']),), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df1.drop(columns={'YEAR', 'MONTH', 'DAY', 'HOUR', 'MINUTE', 'SECOND'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>EventID</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Magnitude</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Injuries</th>\n",
       "      <th>DateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>5367</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NEPAL</td>\n",
       "      <td>28.723</td>\n",
       "      <td>82.280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1993-10-20 16:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>5368</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>INDIA</td>\n",
       "      <td>18.120</td>\n",
       "      <td>76.533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1993-11-12 13:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>6487</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>RUSSIA</td>\n",
       "      <td>51.934</td>\n",
       "      <td>158.647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1993-11-13 01:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>5369</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>39.263</td>\n",
       "      <td>75.533</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1993-11-30 20:37:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>5370</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>36.028</td>\n",
       "      <td>100.104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1994-01-03 05:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3285</th>\n",
       "      <td>10487</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>RUSSIA</td>\n",
       "      <td>48.986</td>\n",
       "      <td>157.693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-03-25 02:49:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>10488</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>33.124</td>\n",
       "      <td>98.916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-04-01 12:23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>10490</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>GREECE</td>\n",
       "      <td>34.205</td>\n",
       "      <td>25.712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-05-02 12:51:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3288</th>\n",
       "      <td>10491</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IRAN</td>\n",
       "      <td>35.725</td>\n",
       "      <td>52.044</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2020-05-07 20:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3289</th>\n",
       "      <td>10494</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>27.296</td>\n",
       "      <td>103.281</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2020-05-18 13:48:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1365 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EventID  Depth  Magnitude Country  Latitude  Longitude  Deaths  \\\n",
       "1925     5367   37.0        0.0   NEPAL    28.723     82.280     0.0   \n",
       "1926     5368   10.0        0.0   INDIA    18.120     76.533     0.0   \n",
       "1927     6487   34.0        7.0  RUSSIA    51.934    158.647     0.0   \n",
       "1928     5369   18.0        5.6   CHINA    39.263     75.533     0.0   \n",
       "1929     5370    8.0        5.7   CHINA    36.028    100.104     0.0   \n",
       "...       ...    ...        ...     ...       ...        ...     ...   \n",
       "3285    10487   57.0        7.5  RUSSIA    48.986    157.693     0.0   \n",
       "3286    10488   10.0        5.3   CHINA    33.124     98.916     0.0   \n",
       "3287    10490   17.0        6.6  GREECE    34.205     25.712     0.0   \n",
       "3288    10491   10.0        0.0    IRAN    35.725     52.044     2.0   \n",
       "3289    10494   10.0        5.2   CHINA    27.296    103.281     4.0   \n",
       "\n",
       "      Injuries            DateTime  \n",
       "1925       0.0 1993-10-20 16:15:00  \n",
       "1926      25.0 1993-11-12 13:27:00  \n",
       "1927       0.0 1993-11-13 01:18:00  \n",
       "1928       4.0 1993-11-30 20:37:00  \n",
       "1929       5.0 1994-01-03 05:52:00  \n",
       "...        ...                 ...  \n",
       "3285       0.0 2020-03-25 02:49:00  \n",
       "3286       0.0 2020-04-01 12:23:00  \n",
       "3287       0.0 2020-05-02 12:51:00  \n",
       "3288      22.0 2020-05-07 20:18:00  \n",
       "3289      24.0 2020-05-18 13:48:00  \n",
       "\n",
       "[1365 rows x 9 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df1.rename(columns={'I_D':'EventID',\n",
    "                             'FOCAL_DEPTH':'Depth', \n",
    "                             'LATITUDE':'Latitude', \n",
    "                             'LONGITUDE':'Longitude',\n",
    "                             'EQ_MAG_MW':'Magnitude',\n",
    "                             'COUNTRY':'Country',\n",
    "                             'TOTAL_DEATHS':'Deaths',\n",
    "                             'TOTAL_INJURIES':'Injuries'\n",
    "                             })\n",
    "df_new\n",
    "#DateTime,Latitude,Longitude,Depth,Magnitude,MagType,NbStations,Gap,Distance,RMS,Source,EventID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new['Latitude'] = pd.to_numeric(df_new['Latitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df_new.to_csv('data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kepler.GL Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Guide: https://docs.kepler.gl/docs/keplergl-jupyter\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e9f7b233be942f4a2be4d6e478f1d20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "KeplerGl(height=300)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load an empty map\n",
    "from keplergl import KeplerGl \n",
    "map_1 = KeplerGl(height=300)\n",
    "map_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Map saved to index.html!\n"
     ]
    }
   ],
   "source": [
    "map_1.save_to_html(file_name='index.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "magnitude = df['EQ_MAG_MW'].dropna()\n",
    "#display(magnitude)\n",
    "#print(magnitude.shape)\n",
    "row_num = df.loc[~df['EQ_MAG_MW'].isna()].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "fill": "tozeroy",
         "name": "Earthquakes Per Year",
         "type": "scatter",
         "x": [
          1927,
          1928,
          1929,
          1931,
          1932,
          1933,
          1934,
          1935,
          1936,
          1937,
          1938,
          1939,
          1940,
          1941,
          1942,
          1943,
          1944,
          1945,
          1946,
          1947,
          1948,
          1949,
          1950,
          1951,
          1952,
          1953,
          1954,
          1955,
          1956,
          1957,
          1958,
          1959,
          1960,
          1961,
          1962,
          1963,
          1964,
          1965,
          1966,
          1967,
          1968,
          1969,
          1970,
          1971,
          1972,
          1975,
          1976,
          1977,
          1978,
          1979,
          1980,
          1981,
          1982,
          1983,
          1984,
          1985,
          1986,
          1987,
          1988,
          1989,
          1990,
          1991,
          1992,
          1993,
          1994,
          1995,
          1996,
          1997,
          1998,
          1999,
          2000,
          2001,
          2002,
          2003,
          2004,
          2005,
          2006,
          2007,
          2008,
          2009,
          2010,
          2011,
          2012,
          2014,
          2015,
          2016,
          2017,
          2018,
          2020,
          2021,
          2022,
          2023,
          2024,
          2025,
          2026,
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          2110,
          2112,
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          2117,
          2118,
          2120,
          2121,
          2122,
          2123,
          2124,
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          2171,
          2173,
          2174,
          2175,
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          2178,
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          2183,
          2184,
          2185,
          2186,
          2187,
          2188,
          2189,
          2190,
          2191,
          2192,
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          2195,
          2196,
          2197,
          2199,
          2201,
          2202,
          2203,
          2204,
          2205,
          2206,
          2208,
          2209,
          2210,
          2211,
          2212,
          2213,
          2215,
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          2218,
          2219,
          2220,
          2222,
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          2240,
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          2243,
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          2248,
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          2251,
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          2254,
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          2257,
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          2302,
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          2324,
          2325,
          2326,
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          2341,
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          2398,
          2399,
          2400,
          2401,
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          2406,
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          2412,
          2413,
          2415,
          2416,
          2421,
          2423,
          2426,
          2427,
          2428,
          2430,
          2431,
          2434,
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          2436,
          2438,
          2439,
          2440,
          2443,
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          2445,
          2447,
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          2449,
          2450,
          2452,
          2454,
          2456,
          2458,
          2460,
          2461,
          2462,
          2463,
          2464,
          2467,
          2468,
          2472,
          2475,
          2476,
          2477,
          2478,
          2482,
          2483,
          2484,
          2485,
          2486,
          2487,
          2490,
          2495,
          2496,
          2499,
          2501,
          2502,
          2504,
          2507,
          2509,
          2511,
          2512,
          2515,
          2516,
          2517,
          2518,
          2519,
          2522,
          2524,
          2525,
          2526,
          2527,
          2528,
          2529,
          2531,
          2532,
          2533,
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          2541,
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          2559,
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          2566,
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          2579,
          2583,
          2584,
          2585,
          2589,
          2590,
          2591,
          2592,
          2593,
          2594,
          2595,
          2597,
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          2600,
          2601,
          2602,
          2603,
          2604,
          2605,
          2607,
          2608,
          2609,
          2610,
          2611,
          2612,
          2613,
          2614,
          2615,
          2616,
          2617,
          2618,
          2619,
          2620,
          2622,
          2623,
          2624,
          2626,
          2627,
          2630,
          2631,
          2633,
          2634,
          2635,
          2636,
          2637,
          2638,
          2639,
          2640,
          2642,
          2646,
          2647,
          2648,
          2649,
          2650,
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          2659,
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          2669,
          2670,
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          2677,
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          2682,
          2683,
          2684,
          2685,
          2686,
          2687,
          2689,
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          2691,
          2692,
          2693,
          2695,
          2697,
          2699,
          2700,
          2702,
          2705,
          2706,
          2707,
          2710,
          2711,
          2712,
          2714,
          2715,
          2716,
          2721,
          2722,
          2724,
          2725,
          2726,
          2727,
          2728,
          2729,
          2730,
          2731,
          2734,
          2735,
          2737,
          2739,
          2740,
          2741,
          2742,
          2744,
          2745,
          2746,
          2747,
          2748,
          2749,
          2750,
          2751,
          2752,
          2754,
          2759,
          2761,
          2762,
          2767,
          2768,
          2769,
          2772,
          2773,
          2775,
          2776,
          2778,
          2779,
          2780,
          2781,
          2782,
          2783,
          2784,
          2785,
          2786,
          2787,
          2789,
          2790,
          2791,
          2792,
          2795,
          2797,
          2799,
          2800,
          2801,
          2803,
          2804,
          2805,
          2806,
          2807,
          2808,
          2809,
          2811,
          2812,
          2814,
          2815,
          2816,
          2819,
          2820,
          2821,
          2822,
          2823,
          2824,
          2828,
          2829,
          2830,
          2831,
          2832,
          2833,
          2834,
          2835,
          2837,
          2838,
          2839,
          2840,
          2841,
          2842,
          2843,
          2844,
          2845,
          2846,
          2847,
          2848,
          2849,
          2850,
          2851,
          2853,
          2854,
          2855,
          2856,
          2857,
          2858,
          2859,
          2861,
          2862,
          2864,
          2865,
          2866,
          2867,
          2868,
          2869,
          2870,
          2871,
          2872,
          2874,
          2875,
          2876,
          2877,
          2878,
          2879,
          2880,
          2881,
          2882,
          2883,
          2884,
          2886,
          2888,
          2890,
          2891,
          2892,
          2893,
          2894,
          2895,
          2897,
          2898,
          2899,
          2900,
          2901,
          2903,
          2904,
          2905,
          2906,
          2907,
          2908,
          2909,
          2911,
          2912,
          2914,
          2915,
          2916,
          2917,
          2918,
          2919,
          2920,
          2921,
          2922,
          2923,
          2924,
          2925,
          2926,
          2927,
          2929,
          2930,
          2931,
          2932,
          2933,
          2934,
          2935,
          2936,
          2937,
          2939,
          2940,
          2941,
          2942,
          2943,
          2944,
          2945,
          2946,
          2947,
          2949,
          2950,
          2951,
          2952,
          2953,
          2954,
          2955,
          2956,
          2957,
          2958,
          2959,
          2960,
          2961,
          2962,
          2963,
          2964,
          2965,
          2966,
          2967,
          2968,
          2969,
          2970,
          2971,
          2972,
          2973,
          2974,
          2975,
          2976,
          2978,
          2979,
          2980,
          2981,
          2982,
          2983,
          2984,
          2985,
          2988,
          2989,
          2996,
          2997,
          2999,
          3000,
          3001,
          3002,
          3003,
          3004,
          3005,
          3006,
          3007,
          3008,
          3009,
          3010,
          3011,
          3012,
          3013,
          3014,
          3015,
          3016,
          3017,
          3018,
          3019,
          3020,
          3021,
          3022,
          3023,
          3024,
          3025,
          3026,
          3027,
          3028,
          3029,
          3030,
          3031,
          3032,
          3033,
          3034,
          3035,
          3036,
          3037,
          3038,
          3039,
          3040,
          3041,
          3042,
          3043,
          3044,
          3045,
          3046,
          3047,
          3048,
          3049,
          3050,
          3051,
          3052,
          3054,
          3055,
          3056,
          3057,
          3058,
          3059,
          3060,
          3061,
          3062,
          3063,
          3064,
          3066,
          3068,
          3069,
          3070,
          3071,
          3072,
          3073,
          3074,
          3075,
          3077,
          3078,
          3079,
          3080,
          3081,
          3082,
          3083,
          3084,
          3085,
          3086,
          3087,
          3088,
          3089,
          3090,
          3091,
          3092,
          3093,
          3094,
          3095,
          3096,
          3097,
          3098,
          3100,
          3102,
          3103,
          3105,
          3106,
          3107,
          3108,
          3109,
          3111,
          3113,
          3114,
          3115,
          3116,
          3118,
          3119,
          3120,
          3121,
          3122,
          3123,
          3124,
          3125,
          3126,
          3127,
          3128,
          3131,
          3132,
          3133,
          3134,
          3135,
          3136,
          3137,
          3138,
          3139,
          3140,
          3141,
          3142,
          3143,
          3144,
          3145,
          3147,
          3148,
          3152,
          3154,
          3155,
          3156,
          3157,
          3158,
          3159,
          3161,
          3162,
          3163,
          3164,
          3165,
          3166,
          3169,
          3170,
          3171,
          3173,
          3174,
          3176,
          3178,
          3179,
          3180,
          3181,
          3182,
          3183,
          3184,
          3185,
          3187,
          3188,
          3189,
          3191,
          3192,
          3193,
          3194,
          3195,
          3196,
          3197,
          3198,
          3200,
          3201,
          3202,
          3203,
          3204,
          3205,
          3206,
          3207,
          3208,
          3209,
          3210,
          3211,
          3212,
          3213,
          3214,
          3217,
          3219,
          3221,
          3223,
          3224,
          3225,
          3227,
          3228,
          3229,
          3230,
          3231,
          3232,
          3233,
          3234,
          3235,
          3238,
          3239,
          3240,
          3241,
          3242,
          3243,
          3244,
          3246,
          3247,
          3248,
          3249,
          3250,
          3251,
          3252,
          3254,
          3255,
          3256,
          3257,
          3258,
          3259,
          3260,
          3261,
          3262,
          3263,
          3264,
          3266,
          3267,
          3268,
          3271,
          3272,
          3273,
          3274,
          3275,
          3277,
          3278,
          3279,
          3280,
          3281,
          3283,
          3284,
          3285,
          3286,
          3287,
          3289
         ],
         "y": [
          7,
          5.6,
          5.7,
          6.7,
          7,
          5.8,
          6.2,
          6.9,
          6.1,
          6.1,
          5.4,
          7.6,
          6.4,
          6.1,
          6.5,
          7.8,
          6.6,
          6.5,
          6.4,
          6.1,
          6.8,
          8.2,
          6.8,
          5.9,
          5.9,
          7,
          8.3,
          6.8,
          7.3,
          7.1,
          5.5,
          7.8,
          5.3,
          6.9,
          6.5,
          7.1,
          6.4,
          5.9,
          6.9,
          5.4,
          7.4,
          7.2,
          6.8,
          6.6,
          6.9,
          6.5,
          6.8,
          5.6,
          8,
          7.7,
          7.4,
          6.4,
          7,
          6,
          6.8,
          8,
          7.1,
          6.8,
          6.2,
          6.7,
          7.2,
          7.9,
          7.9,
          6.6,
          8.2,
          7.5,
          7.1,
          6.3,
          5.9,
          7.2,
          6,
          7.9,
          7.3,
          5.7,
          6.9,
          6,
          6.8,
          6.6,
          6.7,
          7.7,
          6.7,
          5.8,
          7.2,
          5.9,
          6.5,
          7.1,
          6.1,
          5.6,
          6.1,
          5.9,
          6,
          6.2,
          7.7,
          6.7,
          6.5,
          7.2,
          6.4,
          5.8,
          5.9,
          7,
          6,
          5.7,
          6,
          5.9,
          6.2,
          7.7,
          7.1,
          7.5,
          6.6,
          6.1,
          7,
          7.8,
          5.7,
          6.6,
          6.3,
          5.9,
          6.4,
          6.6,
          5.6,
          8.1,
          5.2,
          5.8,
          5.7,
          7.5,
          6.6,
          5.6,
          6.6,
          6.2,
          6.3,
          6.2,
          6,
          5.7,
          7,
          7.2,
          6.4,
          6.6,
          5.5,
          5.3,
          5.4,
          5.4,
          5.6,
          7.7,
          6.2,
          6,
          6.6,
          5.1,
          6.6,
          6.8,
          6.2,
          5.4,
          7,
          6.3,
          6.7,
          7.6,
          5.2,
          6,
          5.8,
          7.7,
          6.5,
          7.5,
          7.2,
          5.9,
          5.3,
          5.7,
          7.2,
          5.3,
          7.5,
          5.7,
          7.3,
          6.5,
          5.6,
          5.1,
          5.9,
          5.3,
          7.6,
          5.5,
          7.6,
          5.4,
          7.9,
          6,
          6.3,
          6.7,
          6.4,
          6.5,
          7.9,
          6.5,
          6.1,
          5.4,
          5.4,
          6.1,
          6.4,
          6.5,
          6.8,
          5,
          6.5,
          6.7,
          5.1,
          6.5,
          8,
          7.8,
          7.8,
          6.8,
          7,
          6,
          7.5,
          7.7,
          7.7,
          6.6,
          5.6,
          6.8,
          6.8,
          5.7,
          5.5,
          5,
          8.4,
          5.5,
          6.6,
          7.6,
          6.5,
          5.8,
          6.1,
          7,
          7.5,
          5.6,
          7.8,
          6.8,
          7.2,
          5.3,
          6.7,
          5.6,
          5.9,
          6.5,
          5.4,
          5.5,
          7.4,
          7.5,
          6.1,
          6.4,
          7.1,
          6.4,
          5.9,
          6.6,
          5.2,
          5.7,
          5.4,
          7.1,
          5.5,
          6,
          6.5,
          6.5,
          5.3,
          6.5,
          6.2,
          7.7,
          7.7,
          5.2,
          5.5,
          6,
          7.6,
          6.5,
          6.4,
          5.3,
          5.6,
          7.6,
          6.7,
          6.2,
          5.7,
          5.4,
          7.3,
          7.9,
          6.3,
          5.9,
          5.7,
          5.5,
          5.2,
          5.7,
          5.5,
          5.2,
          7.3,
          7.5,
          5.5,
          6.1,
          5.3,
          5.8,
          6.3,
          5.2,
          5,
          6.3,
          5.4,
          6.5,
          5.5,
          5.7,
          6.4,
          4.6,
          6.4,
          5.8,
          6.8,
          7,
          7,
          5.7,
          4,
          5.2,
          6.8,
          5.2,
          5.8,
          7.6,
          5.9,
          5.5,
          5.4,
          5.7,
          7.6,
          6,
          5.4,
          6.3,
          5.4,
          5.5,
          7.2,
          5.3,
          5.7,
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       "            Plotly.purge(gd);\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=row_num, \n",
    "    y=magnitude,\n",
    "    name='Earthquakes Per Year',\n",
    "    fill='tozeroy'\n",
    "))   \n",
    "\n",
    "# Set title\n",
    "fig.update_layout(\n",
    "    title_text=\"哈咯你好嗎\"\n",
    ")\n",
    "# Add range slider\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        rangeslider=dict(\n",
    "            visible=True\n",
    "        )\n",
    "    ),\n",
    "    showlegend=True\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{7.0: 37, 5.6: 56, 5.7: 49, 6.7: 39, 5.8: 45, 6.2: 29, 6.9: 34, 6.1: 56, 5.4: 45, 7.6: 25, 6.4: 45, 6.5: 45, 7.8: 19, 6.6: 48, 6.8: 39, 8.2: 6, 5.9: 41, 8.3: 5, 7.3: 20, 7.1: 27, 5.5: 46, 5.3: 31, 7.4: 13, 7.2: 32, 8.0: 5, 7.7: 24, 6.0: 46, 7.9: 15, 7.5: 29, 6.3: 43, 8.1: 5, 5.2: 28, 5.1: 22, 5.0: 15, 8.4: 2, 4.6: 1, 4.0: 1, 4.9: 8, 4.8: 3, 9.1: 2, 8.6: 2, 4.5: 4, 4.2: 2, 8.8: 1, 4.1: 1, 3.6: 1, 3.9: 1, 4.4: 1, 4.7: 1}\n"
     ]
    }
   ],
   "source": [
    "magnitude_count = {}\n",
    "for number in magnitude:\n",
    "    if number not in magnitude_count:\n",
    "        magnitude_count[number] = 1\n",
    "    else:\n",
    "        magnitude_count[number] += 1\n",
    "print(magnitude_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EQ_MAG_MW        2026\n",
       "EQ_MAG_MS        1338\n",
       "EQ_MAG_MB        1504\n",
       "EQ_MAG_ML        3127\n",
       "EQ_MAG_MFA       3289\n",
       "EQ_MAG_UNK       2844\n",
       "INTENSITY        2020\n",
       "COUNTRY             0\n",
       "STATE            3096\n",
       "LOCATION_NAME       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0       10.0\n",
       "1        NaN\n",
       "2        NaN\n",
       "3        NaN\n",
       "4        NaN\n",
       "        ... \n",
       "3285     6.0\n",
       "3286     NaN\n",
       "3287     7.0\n",
       "3288     NaN\n",
       "3289     NaN\n",
       "Name: INTENSITY, Length: 3290, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.iloc[0:3290,[i for i in range(10,20)]].isna().sum())\n",
    "display(data.iloc[0:3290,16])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = df['COUNTRY'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'NEPAL': 6, 'INDIA': 53, 'RUSSIA': 28, 'CHINA': 163, 'USA': 61, 'INDONESIA': 140, 'UGANDA': 1, 'IRAN': 92, 'HAITI': 3, 'FIJI': 6, 'JAPAN': 80, 'AFGHANISTAN': 27, 'TAIWAN': 27, 'COLOMBIA': 14, 'BOLIVIA': 2, 'NEW ZEALAND': 28, 'ALGERIA': 16, 'PHILIPPINES': 42, 'CYPRUS': 1, 'SAMOA': 3, 'GREECE': 26, 'NEW CALEDONIA': 13, 'CHILE': 34, 'PAPUA NEW GUINEA': 21, 'MEXICO': 32, 'TURKEY': 51, 'ECUADOR': 16, 'EGYPT': 2, 'PERU': 34, 'SOLOMON ISLANDS': 19, 'CROATIA': 2, 'KYRGYZSTAN': 7, 'ALBANIA': 9, 'PAKISTAN': 25, 'TRINIDAD AND TOBAGO': 4, 'USA TERRITORY': 10, 'VENEZUELA': 9, 'SOUTH AFRICA': 6, 'ITALY': 21, 'TONGA': 3, 'GUATEMALA': 10, 'ANTARCTICA': 3, 'AUSTRIA': 1, 'AZORES (PORTUGAL)': 1, 'ARMENIA': 1, 'SERBIA': 4, 'SPAIN': 3, 'AZERBAIJAN': 6, 'BANGLADESH': 7, 'VANUATU': 20, 'ICELAND': 3, 'AUSTRALIA': 5, 'NICARAGUA': 8, 'TANZANIA': 5, 'PANAMA': 9, 'TURKMENISTAN': 1, 'EL SALVADOR': 9, 'CANADA': 5, 'TAJIKISTAN': 12, 'RWANDA': 4, 'GEORGIA': 2, 'ARGENTINA': 6, 'GERMANY': 4, 'UK': 3, 'FRANCE': 4, 'CONGO': 3, 'BHUTAN': 5, 'INDIAN OCEAN': 1, 'MYANMAR (BURMA)': 9, 'DOMINICAN REPUBLIC': 2, 'BULGARIA': 3, 'SLOVAKIA': 1, 'JORDAN': 1, 'BURUNDI': 1, 'MOROCCO': 2, 'SAUDI ARABIA': 3, 'SLOVENIA': 1, 'POLAND': 6, 'ROMANIA': 1, 'COSTA RICA': 6, 'GUADELOUPE': 2, 'VIETNAM': 1, 'ETHIOPIA': 2, 'MOZAMBIQUE': 3, 'MALAWI': 3, 'HUNGARY': 2, 'SWITZERLAND': 1, 'THAILAND': 2, 'LAOS': 1, 'MARTINIQUE': 1, 'BRAZIL': 4, 'KERMADEC ISLANDS (NEW ZEALAND)': 3, 'LEBANON': 1, 'ATLANTIC OCEAN': 1, 'CZECH REPUBLIC': 1, 'MACEDONIA': 2, 'HONDURAS': 3, 'KAZAKHSTAN': 1, 'UZBEKISTAN': 1, 'BOSNIA-HERZEGOVINA': 1, 'MALAYSIA': 1, 'SOUTH GEORGIA AND THE SOUTH SANDWICH ISLANDS': 1, 'SOUTH KOREA': 2, 'MADAGASCAR': 1, 'ZAMBIA': 1, 'MONTENEGRO': 1, 'NETHERLANDS': 2, 'COMOROS': 1, 'CUBA': 1}\n"
     ]
    }
   ],
   "source": [
    "country_count_earthquakes = {}\n",
    "for country in countries:\n",
    "    if country not in country_count_earthquakes:\n",
    "        country_count_earthquakes[country] = 1\n",
    "    else:\n",
    "        country_count_earthquakes[country] += 1\n",
    "print(country_count_earthquakes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = df['YEAR'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1993: 4, 1994: 32, 1995: 31, 1996: 19, 1997: 34, 1998: 31, 1999: 35, 2000: 37, 2001: 25, 2002: 60, 2003: 72, 2004: 78, 2005: 60, 2006: 61, 2007: 67, 2008: 77, 2009: 61, 2010: 62, 2011: 60, 2012: 49, 2013: 52, 2014: 55, 2015: 48, 2016: 52, 2017: 64, 2018: 65, 2019: 61, 2020: 13}\n"
     ]
    }
   ],
   "source": [
    "year_count = {}\n",
    "for year in years:\n",
    "    if year not in year_count:\n",
    "        year_count[year] = 1\n",
    "    else:\n",
    "        year_count[year] += 1\n",
    "print(year_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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   "source": [
    "fig = go.Figure()\n",
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    "fig.add_trace(go.Scatter(\n",
    "    x=list(year_count.keys()), \n",
    "    y=list(year_count.values()),\n",
    "    name='Earthquakes Per Year',\n",
    "    fill='tozeroy'\n",
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    "\n",
    "# Set title\n",
    "fig.update_layout(\n",
    "    title_text=\"Earthquakes per Year\"\n",
    ")\n",
    "# Add range slider\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
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    "    showlegend=True\n",
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    "fig.show()"
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  },
  {
   "cell_type": "code",
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          "MADAGASCAR",
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          "COMOROS",
          "CUBA"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=list(country_count_earthquakes.keys()), \n",
    "    y=list(country_count_earthquakes.values()),\n",
    "    name='Earthquakes Per Country from 1920-2020',\n",
    "    fill='tozeroy'\n",
    "))   \n",
    "\n",
    "# Set title\n",
    "fig.update_layout(\n",
    "    title_text=\"Earthquakes per Country\"\n",
    ")\n",
    "# Add range slider\n",
    "fig.update_layout(\n",
    "    xaxis=dict(\n",
    "        rangeslider=dict(\n",
    "            visible=True\n",
    "        )\n",
    "    ),\n",
    "    showlegend=True\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_earthquakes_by_country(country):\n",
    "    results={}\n",
    "    for items in years:\n",
    "        sub = df[\n",
    "            (df['COUNTRY'].str.contains(country)) &  \n",
    "            (df['YEAR'].str.contains(items))\n",
    "        ]\n",
    "        results[items] = sub.shape[0]\n",
    "    return results"
   ]
  }
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