Earth Quake Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29e4bf23",
   "metadata": {},
   "source": [
    "## AG - Port: Earth Quake Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb52c2a",
   "metadata": {},
   "source": [
    "<div><img src=\"https://www.amnh.org/var/ezflow_site/storage/images/media/amnh/images/explore/ology-images/super-article-imagery/earthquakes-tremors-from-below-imagery/earthquake-lead/4604593-3-eng-US/earthquake-lead.jpg\" width=500 /></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "003c6769",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear algebra\n",
    "import numpy as np\n",
    "#Data processing\n",
    "import pandas as pd\n",
    "#Data visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "#static images of plots embedded in this notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00339c9",
   "metadata": {},
   "source": [
    "> Let's Import the cleaned excel eata to further clean and explore in Python \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f30d73a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Data Set - Earthquakes.xlsx',  na_values = ['\\xa0'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d81d133",
   "metadata": {},
   "source": [
    "> Obsevation: The columns are not in thier appropriate Datatype, Let's Work on that."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d48899",
   "metadata": {},
   "source": [
    "> Let's Handle the missing values present in the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "649d9439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                         0\n",
       "Tsu                       1693\n",
       "Vol                       2184\n",
       "Location Name                0\n",
       "Country                      0\n",
       "Latitude                     1\n",
       "Longitude                    1\n",
       "Focal Depth (km)            19\n",
       "Mag                          6\n",
       "MMI Int                   1380\n",
       "Deaths                    1246\n",
       "Missing                   2181\n",
       "Injuries                  1122\n",
       "Damage ($Mil)             1783\n",
       "Houses Destroyed          1690\n",
       "Houses Damaged            1738\n",
       "Total Deaths              1230\n",
       "Total Missing             2179\n",
       "Total Injuries            1117\n",
       "Total Damage ($Mil)       1783\n",
       "Total Houses Destroyed    1682\n",
       "Total Houses Damaged      1771\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4efb8947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2204, 22)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4869b18a",
   "metadata": {},
   "source": [
    "#### Detecting missing data visually\n",
    "- To graphically analyse the missingness of the data, let's use a library called Missingno.it is a package for graphical analysis of missing values. To use this library, we need to import it as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abbc049e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "msno.bar(df, figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744f7828",
   "metadata": {},
   "source": [
    "> The Bar Chart Shows columns and the number of values not missing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f66462",
   "metadata": {},
   "source": [
    "- Over 50% of the data in columns (Vol, Tsu, MMI int, Deaths, Missing, Injuries, Damage, Houses destroyed, House Damaged) are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97756936",
   "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>Total</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vol</th>\n",
       "      <td>2184</td>\n",
       "      <td>99.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing</th>\n",
       "      <td>2181</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Missing</th>\n",
       "      <td>2179</td>\n",
       "      <td>98.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damage ($Mil)</th>\n",
       "      <td>1783</td>\n",
       "      <td>80.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Damage ($Mil)</th>\n",
       "      <td>1783</td>\n",
       "      <td>80.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Houses Damaged</th>\n",
       "      <td>1771</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houses Damaged</th>\n",
       "      <td>1738</td>\n",
       "      <td>78.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tsu</th>\n",
       "      <td>1693</td>\n",
       "      <td>76.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houses Destroyed</th>\n",
       "      <td>1690</td>\n",
       "      <td>76.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Houses Destroyed</th>\n",
       "      <td>1682</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MMI Int</th>\n",
       "      <td>1380</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>1246</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Deaths</th>\n",
       "      <td>1230</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Injuries</th>\n",
       "      <td>1122</td>\n",
       "      <td>50.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Injuries</th>\n",
       "      <td>1117</td>\n",
       "      <td>50.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Focal Depth (km)</th>\n",
       "      <td>19</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mag</th>\n",
       "      <td>6</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Total     %\n",
       "Vol                      2184  99.1\n",
       "Missing                  2181  99.0\n",
       "Total Missing            2179  98.9\n",
       "Damage ($Mil)            1783  80.9\n",
       "Total Damage ($Mil)      1783  80.9\n",
       "Total Houses Damaged     1771  80.4\n",
       "Houses Damaged           1738  78.9\n",
       "Tsu                      1693  76.8\n",
       "Houses Destroyed         1690  76.7\n",
       "Total Houses Destroyed   1682  76.3\n",
       "MMI Int                  1380  62.6\n",
       "Deaths                   1246  56.5\n",
       "Total Deaths             1230  55.8\n",
       "Injuries                 1122  50.9\n",
       "Total Injuries           1117  50.7\n",
       "Focal Depth (km)           19   0.9\n",
       "Mag                         6   0.3\n",
       "Longitude                   1   0.0\n",
       "Latitude                    1   0.0\n",
       "Country                     0   0.0\n",
       "Location Name               0   0.0\n",
       "Date                        0   0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = df.isnull().sum().sort_values(ascending=False)\n",
    "percent_1 = df.isnull().sum()/df.isnull().count()*100\n",
    "percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n",
    "\n",
    "#Concat total and percentage using pandas 'pd.concat'\n",
    "missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5bd513",
   "metadata": {},
   "source": [
    "- Over 15 out of 22 columns in the dataset has over 50% missing values. we choose to drop all missing values, To aviod skewing our results\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09fe7fc",
   "metadata": {},
   "source": [
    "> Drop columns with over 50% missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a67c6aeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2204 entries, 0 to 2203\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Date              2204 non-null   object \n",
      " 1   Location Name     2204 non-null   object \n",
      " 2   Country           2204 non-null   object \n",
      " 3   Latitude          2203 non-null   float64\n",
      " 4   Longitude         2203 non-null   float64\n",
      " 5   Focal Depth (km)  2185 non-null   float64\n",
      " 6   Mag               2198 non-null   float64\n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 120.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(thresh = df.shape[0]*0.5, axis = 1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5ca1c3",
   "metadata": {},
   "source": [
    "> Change Date to Datetime DataType"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02067887",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01678a2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2204 entries, 0 to 2203\n",
      "Data columns (total 7 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Date              2204 non-null   datetime64[ns]\n",
      " 1   Location Name     2204 non-null   object        \n",
      " 2   Country           2204 non-null   object        \n",
      " 3   Latitude          2203 non-null   float64       \n",
      " 4   Longitude         2203 non-null   float64       \n",
      " 5   Focal Depth (km)  2185 non-null   float64       \n",
      " 6   Mag               2198 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4), object(2)\n",
      "memory usage: 120.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1c1b1d",
   "metadata": {},
   "source": [
    "- The Datatypes look better now"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecdf2930",
   "metadata": {},
   "source": [
    "## Top 10 Countries with the Highest Earthquake occurence since 1970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2404fbb6",
   "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>Number of Occurences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHINA</th>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDONESIA</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRAN</th>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JAPAN</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TURKEY</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJIBOUTI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JORDAN</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NORTH CAROLINA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANTIGUA AND BARBUDA; ST KITTS</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUINEA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Number of Occurences\n",
       "CHINA                                           211\n",
       "INDONESIA                                       199\n",
       "IRAN                                            153\n",
       "JAPAN                                           124\n",
       "TURKEY                                           82\n",
       "...                                             ...\n",
       "DJIBOUTI                                          1\n",
       "JORDAN                                            1\n",
       "NORTH CAROLINA                                    1\n",
       "ANTIGUA AND BARBUDA; ST KITTS                     1\n",
       "GUINEA                                            1\n",
       "\n",
       "[199 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.value_counts().to_frame(\"Number of Occurences\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "461adf5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Country.value_counts().head(10).sort_values().plot(kind='barh', \n",
    "                                     figsize =(10,6),\n",
    "                                     title=\"TOP 10 MOST PLACES ON EARTH WITH HIGHEST EARTH QUAKE OCCURENCE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51aafead",
   "metadata": {},
   "source": [
    "> The Top 10 Countries with Earthquakes from 1970 to date are:\n",
    "(China, Indonesia, Iran, Japan, Turkey, Philippines, India, Peru, Russia, Chile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "693b537d",
   "metadata": {},
   "source": [
    "## Top 10 Time period (Year) with the highest earthquakes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427e0eee",
   "metadata": {},
   "source": [
    "### Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "076187d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = pd.DatetimeIndex(df['Date']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "3d089b3b",
   "metadata": {
    "scrolled": true
   },
   "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>Date</th>\n",
       "      <th>Location Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Focal Depth (km)</th>\n",
       "      <th>Mag</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-04-01</td>\n",
       "      <td>CHINA:  YUNNAN PROVINCE; VIETNAM:  HANOI</td>\n",
       "      <td>CHINA</td>\n",
       "      <td>24.1</td>\n",
       "      <td>102.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-10-01</td>\n",
       "      <td>PHILIPPINES:  MINDANAO</td>\n",
       "      <td>PHILIPPINES</td>\n",
       "      <td>6.8</td>\n",
       "      <td>126.7</td>\n",
       "      <td>73.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date                             Location Name      Country  Latitude  \\\n",
       "0 1970-04-01  CHINA:  YUNNAN PROVINCE; VIETNAM:  HANOI        CHINA      24.1   \n",
       "1 1970-10-01                    PHILIPPINES:  MINDANAO  PHILIPPINES       6.8   \n",
       "\n",
       "   Longitude  Focal Depth (km)  Mag  year  \n",
       "0      102.5              31.0  7.8  1970  \n",
       "1      126.7              73.0  7.6  1970  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "7b9d35c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2004    78\n",
       "2008    77\n",
       "2003    72\n",
       "2018    69\n",
       "2007    67\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.year.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "cf5602aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.year.value_counts().head(10).sort_values().plot(kind='barh', \n",
    "                                     figsize =(10,6),\n",
    "                                     title=\"Top 10 Time period (Year) with the highest earthquakes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087a11e6",
   "metadata": {},
   "source": [
    "> The Top 10 time periods with Earthquakes from 1970 to date are:\n",
    "(2004, 2008, 2003, 2018, 2007, 2017, 2006, 2019, 2010, 2009)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fa8f1c",
   "metadata": {},
   "source": [
    "### Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f9f25d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tYbK0EnhpRExuajsGWFFTPZIkSZKkTRgv10yuBZYALwSuLdsOA64CiIi3A68DtgceAl6bmRkRy4DFwEzgIOB44BTgEWBhZv59ua9XRMTbgJ2AszLz/LE/JEmSJEnqXuNlZBLgq8DRABGxH3ALRXB8HHAkMDsz9wSuAN7etN2VmRnAU4C3AfsDewGzImJWuc42wAHAK4CzxvxIJEmSJKnLjZeRSYDvAPMiYhLFFNfLgWOB1cBxwLERsQfwUopRzAGLyz8PphiNfKB8fyhARAB8OzMbEfEL4AljfBySJEnShNPbO63uEoY0XuvqBuMmTGbm2oi4GTgQOAR4P0WY3A34MfAvwJXAH4C9mzYdeG7Hw8CfHpoZEbsAfyzf9pef0SjDpSRJkqQWGo/Pc/Q5k6PT9JzJoZe3sZbN8VXgo8DPMrO/bHsQ+HVmngv8FHgVMHmIba8HXh4RUyNiK+AyYN821CxJkiRJE854C5MLKW6mc3lT20PApIi4HbgRuBOYMXjDzLyRYvTyx8DNwA8z86qxLliSJEmSJqKeRqMx8loTUx+wdO68RSxftW6kdSVJkqQJa+H8OeNyOqnTXEenaZrrDGDZY5a3uyBJkiRJUuczTEqSJEmSKhs3d3Mdry447bC6S5AkSZLGtfUb+kdeSV3HMDmClSvXsnGj15W2k3Pb62Pf18N+r499Xw/7vT72fT3sd3Urp7lKkiRJkiozTEqSJEmSKjNMSpIkSZIqM0xKkiRJkiozTEqSJEmSKjNMSpIkSZIqM0xKkiRJkiozTEqSJEmSKjNMSpIkSZIqM0xKkiRJkiozTEqSJEmSKutpNBp11zBe9QFL6y5CkiRJqtv6Df2sWb2u7jIq6+2dxooVa+ouo2NNmtTD9OlTAWYAywYv36rdBXWaufMWsXxV5/3gSJIkSa2ycP4cjGQazGmukiRJkqTKxnxkMiKmAh8DXgI8CKwGzsjMq4dZ//8BCzLzVSPst5GZPa2uV5IkSZI0sjEdmYyIHmAh8BDwrMx8LvBO4JKImD3MZjsAe49lXZIkSZKk0RnrkcmDgacAh2RmAyAzb4qIecCHIuL/gPOA7YD7geOBzwC7RMQ3M/NVEfEG4FSgAfwceHtmrgWIiPOB/YH7gJMz87cR8TTgc8B04I/AO8rPXFC2PQ14X2YuHONjlyRJkqSuNdbXTO4H/GwgSDb5YbnsUuAfM/M5wFeAd1GMXN5TBsnnAP8AHFyu8yDw4ab9/CAzZwLfBD5dtl1MERb3Ad5c7nfAysx8pkFSkiRJkkZnrEcmG8N8xtbAZGDnzLwCIDM/BxARfU3rHQwszMyV5fvzgYvK1+sy89Ly9SXAvPL6zP2AiyJiYB9TI2J6+XrxqI9IkiRJmoB6e6fVXcIW6dS6O8FYh8nFwDsjYkpmPtzU/nzgJ8BeAw0RsQ2wC7Cxab3BI6c9/LnmRwa1P0wRUNeXo5UD+92VYgotgM/4kCRJkrZAJz6v0edMjk7TcyaHXj6WH56Z1wO/AD4VEVMAImIWcBrwEeB/IuKwcvXXAWcC/fw5MF4HHBERO5bv3wRcW76eGhFHlK9PBq7KzAeAX0XECeVnvZhiSq0kSZIkqYXa8ZzJo4ANwG0RcTvFtY0nZOZ1wAnA6RGxBDgGeC/wv8BvI+LazLwFOBv4QUTcCTyeIogC/B9wZETcDLwY+Luy/XjgjRExsO0xQ1yzKUmSJEkahZ5Gw5w1jD5g6dx5i1i+ytmxkiRJmrgWzp/TkdNFneY6Ok3TXGcAyx6zvN0FSZIkSZI6n2FSkiRJklTZWN/NteNdcNphI68kSZIkdbH1G/rrLkHjkGFyBCtXrmXjRq8rbSfnttfHvq+H/V4f+74e9nt97Pt62O/qVk5zlSRJkiRVZpiUJEmSJFVmmJQkSZIkVWaYlCRJkiRVZpiUJEmSJFVmmJQkSZIkVWaYlCRJkiRVZpiUJEmSJFVmmJQkSZIkVWaYlCRJkiRVZpiUJEmSJFVmmJQkSZIkVdbTaDTqrmG86gOW1l2EJEmSVLf1G/pZs3pd3WVU1ts7jRUr1tRdRseaNKmH6dOnAswAlg1evlW7C+o0c+ctYvmqzvvBkSRJklpl4fw5GMk0mNNcJUmSJEmVdUSYjIi+iGhExHmD2meW7SfVVJokSZIkTUgdESZLK4GXRsTkprZjgBU11SNJkiRJE1YnXTO5FlgCvBC4tmw7DLgKICLeDrwO2B54CHgtsCtwZma+oFznJOCAzHxrOwuXJEmSpG7TSWES4KvA0cC1EbEfcAvQAzwOOAKYnZnrIuJM4O3AO4EvRMRTM/Mu4PXAB+opXZIkSepcvb3T6i5hi3Rq3Z2g08Lkd4B5ETGJYorr5cCxwGrgOODYiNgDeCmwJDMbEXExcEJEXATslJmLa6pdkiRJ6lid+IgNHw0yOk2PBhl6eRtrGbXMXAvcDBwIHEI5xRXYDfgx8HjgSmABxYgl5etjKaa9/lvbipUkSZKkLtZRYbL0VeCjwM8ys79sexD4dWaeC/wUeBUwGSAz7wb+B3grcEn7y5UkSZKk7tOJYXIhMJNiiuuAh4BJEXE7cCNwJzCjaflXgNsz8552FSlJkiRJ3awjrpnMzGVAX/l6LbBd07KTypf/MtS2EbEVcCjwxbGsUZIkSZImkk4cmdxsEdED3ANsBL5VbzWSJEmS1D16Go1G3TWMV33A0rqLkCRJkuq2fkM/a1avq7uMyryb6+g03c11BrBs8PKOmOZap5Ur17Jxo4G7nfyhr499Xw/7vT72fT3s9/rY9/Ww39WtunqaqyRJkiRpbBgmJUmSJEmVGSYlSZIkSZUZJiVJkiRJlRkmJUmSJEmVGSYlSZIkSZUZJiVJkiRJlRkmJUmSJEmVGSYlSZIkSZUZJiVJkiRJlRkmJUmSJEmVGSYlSZIkSZX1NBqNumsYr/qApXUXIUmSJNVt/YZ+1qxeV3cZlfX2TmPFijV1l9GxJk3qYfr0qQAzgGWDl2/V7oI6zdx5i1i+qvN+cCRJkqRWWTh/DkYyDeY0V0mSJElSZW0JkxFxUkQsaMdnSZIkSZLGniOTkiRJkqTK2nrNZEQcDJwFbAc8Hvi7zPx2OWr5ADALeDJwZmZeFBFnAGTmGeX2y4DZwP3ABcCuwC7AVcAbgYOBc4DJwO3AQcBhmfnLiNgeuBN4emauH/ODlSRJkqQu1u6RyXcAb8zMfSjC37ymZbtRhL8jgE+MsJ9XAEsy8/nA0ylC5D7lsj2AQzLzdcDFwAll+6uBKwySkiRJkjR67b6b6wnA4RHxGuB5wNSmZYsysxERtwE7bmonmXlZROwfEe8GnglMb9pXZuYD5euLKEYtTwdOBD7YsiORJEmSJpDe3ml1l7BFOrXuTjBmYTIiDgLuysx7gB6gH7geuBa4Drga+HLTJusBykA50Nbg0aOnU8p9vwM4GjifIizuWX4GwJ+e45GZyyLi7og4CtgpMxe38BAlSZKkCaMTn9focyZHp+k5k0MvH8PPPhk4sny9F/AbiimopwNXAnMorm3clPuAZwNExP7AzmX7i4HzMvNSYBtg5ib2dSHwGeCSLTgGSZIkSdIQxjJMng2cGBF3UIwcfo7ipjm/AO4ApgHblTfGGc5XgB0j4naK6y1vKts/BXw4Im4tX98AzBhmH9+gmDZrmJQkSZKkFulpNBp11zBmIqIHeBlwSmYeUXHzPmDp3HmLWL5q3UjrSpIkSV1r4fw5HTld1Gmuo9M0zXUGsGzw8nbfgKfdzgVeSREoJUmSJEkt0tUjk6PUByytuwhJkiSpbus39LNmdefN1nNkcnQm+sjkqK1cuZaNGw3c7eQPfX3s+3rY7/Wx7+thv9fHvq+H/a5uNZY34JEkSZIkdSnDpCRJkiSpMsOkJEmSJKkyw6QkSZIkqTLDpCRJkiSpMsOkJEmSJKkyw6QkSZIkqTLDpCRJkiSpMsOkJEmSJKkyw6QkSZIkqTLDpCRJkiSpMsOkJEmSJKmynkajUXcN41UfsLTuIiRJkqRWWL+hnzWr19VdRlv19k5jxYo1dZfRsSZN6mH69KkAM4Blg5dv1e6COs3ceYtYvmpi/dBJkiSp+yycPwdjlVrJaa6SJEmSpMo6IkxGxEkRsaDuOiRJkiRJhY4Ik5IkSZKk8aWjrpmMiOuAMzLzuojoA67LzL5y1PIBYBbwZODMzLwoIqYCnwX2BCYDH8vMy2opXpIkSZK6SDeNTO4GHAQcAXyibDsN+HlmzgJeCPxDRPxlTfVJkiRJUtfoqJHJESzKzEZE3AbsWLYdCmwXESeX77cHng38po4CJUmSpDr19k6ru4S2m4jH3C7jNkxGxEHAXZl5D9AD9AON8jXAlEGbrAcoA+VA22TghMy8sdznTsD9Y1y6JEmSNC5NtGcu+pzJ0Wl6zuTQy9tYS1UnA0eWr/eiGE28j2JkkaZlm3IN8FaAiNgZuAXYvZVFSpIkSdJENJ7D5NnAiRFxB8UNdD4HnAO8LSJuBLbdjH18BNi2nPp6DfC+zLxrrAqWJEmSpImip9Fo1F3DeNUHLJ07bxHLV62ruxZJkiRpVBbOnzPhpnw6zXV0mqa5zgCWPWZ5uwuSJEmSJHU+w6QkSZIkqbJxezfX8eKC0w6ruwRJkiRp1NZv6K+7BHUZw+QIVq5cy8aNXlfaTs5tr499Xw/7vT72fT3s9/rY9/Ww39WtnOYqSZIkSarMMClJkiRJqswwKUmSJEmqzDApSZIkSarMMClJkiRJqswwKUmSJEmqzDApSZIkSarMMClJkiRJqswwKUmSJEmqzDApSZIkSarMMClJkiRJqqyn0WjUXcN41QcsrbsISZIkTVzrN/SzZvW6usvoWL2901ixYk3dZXSsSZN6mD59KsAMYNng5Vu1u6BOM3feIpav8gdYkiRJ7bdw/hyMQhqvnOYqSZIkSaqs0shkRPQBvwRuL5u2BW4A3p+Z/9va0jZZx2zgjMyc3a7PlCRJkiT92ZaMTN6TmTMzcybwDOAPwNdbWpUkSZIkaVwb1TWTmdmIiA8D/xsRewEvB/4GmAz8J/D35Tp/B5wCPAIszMy/j4idgPOA3YCNwAcy86qIOAPYHdgD6AXOAl4EHADcDBxbfvwTIuJ7wJOBxcDfZuaGiHgpcCYwheIGOm/KzJURsaxcbyZwUGYuH82xS5IkSdJENuprJjPzIeBXFCFtFrAfsDdFyDs+IvYD3gbsD+wFzIqIWcCngQszcxZwBHBeREwrd/scYDbwZuAi4GPAnsA+5T6guKPQO8r304BTIqIX+CjwkszcmyLQfqyp3CszMwySkiRJkjQ6rbqbawN4F8VI4s/Ltm2B3wJPohiNfKBsPxQgIg4FnhERZ5btU4Cnlq+/n5n9EXE3cG9m3l5u83tgh3KdH2bmr8r2S4E3AHdRjGpeGxFQjJDe31Tn4hYdryRJktQWvb3TRl5Jw7L/xs6ow2REbA0EcC1waWZ+smx/PNAPzKUImwPr7wL8kSLoHZKZ95ftOwPLgSOBh5o+on+Yj25unwQ8XO7zR5l5RLnPbYCpTev5jA9JkiR1FJ+TuOV8zuToND1ncujlo9l5REwCPgL8N3Ah8LqImBoRWwHfAo4Grgde3tR+GbAvcA3F9Fci4lnAbcB2FT7+wIjYvazh9cBVFCOPz4+IPcp1PgR8YjTHKEmSJEl6rC0Jk7tExJKIWEJxQ5wnA6/NzIXAv1MEutuAJcDFmXkj8C/Aj8v1f5iZV1Fc7/i8iLgFuBw4ITOr/G+DX1AE2FuB3wMXZOYfgJOBr0bErRTXWJ66BccoSZIkSdqEnkajMfJaE1MfsHTuvEUsX+XsWEmSJLXfwvlznKY5Ck5zHZ2maa4zgGWPWd7ugiRJkiRJnc8wKUmSJEmqrFWPBulaF5x2WN0lSJIkaYJav2G4BxtI9TNMjmDlyrVs3Oh1pe3k3Pb62Pf1sN/rY9/Xw36vj31fD/td3cpprpIkSZKkygyTkiRJkqTKDJOSJEmSpMoMk5IkSZKkygyTkiRJkqTKDJOSJEmSpMoMk5IkSZKkygyTkiRJkqTKDJOSJEmSpMoMk5IkSZKkygyTkiRJkqTKehqNRt01jFd9wNK6i5AkSVJ3W7+hnzWr19VdRlfq7Z3GihVr6i6jY02a1MP06VMBZgDLBi/fqt0FdZq58xaxfJU/3JIkSRobC+fPwbijTuQ0V0mSJElSZbWFyYjYMyIaEfHqNn7mte36LEmSJEnqZnWOTJ4MfA14Sxs/c3YbP0uSJEmSulYt10xGxBTgeOAg4IaIeGpm3hURy4DZmbksImYDZ2Tm7IjYE1hQ1ns98LLMfFpELACuy8wF5X4bmdkTES8CzgEawCrgtcDp5TqLM/OA9h2tJEmSJHWfukYmXwHcnZm/BL4FvHmE9S8GTs/MmcBvGDkEnwackpn7At8H9snMdwIYJCVJkiRp9Oq6m+sbgMvK15cDl0bEh4ZaMSJ2BPoy8z/KpguBd42w/+8A34yIbwHfzszvj75kSZIkaWz09k6ru4SuZd+OnbaHyYh4IvAyYFZEvAvoAXYAjqKYltpTrjql/PORprbB/rR+OXUWgMw8NyIWAocD50TE1zPzrFYfiyRJktQKPgtxbPicydFpes7k0MvbWMuA1wFXZ+aumdmXmU8BzgJOAe4Dnl2uNwcgMx8A7oqIl5Xtx1GESAatf+TAB0TEYmBaZn4KOBfYp1z0SET4bE1JkiRJGqU6wuRJwL8OavsssD9wEfDpiPgp8H9Ny18PnB4RNwIHAOvK9s8DsyPiFuAFwL1l+weBBRHxc2Au8P6y/dvAzRGxTSsPSJIkSZImmraP0mXmc4ZoWwFsV74dHDQBXgMclZn3RsRRwLRyu7uAfZvWe3fZfjXw3CE+p23PtJQkSZKkbtYpUz5/C3w/Ih6meNTH3JrrkSRJkqQJrSPCZPkcyQU1lyFJkiRJKnVEmKzTBacdVncJkiRJ6mLrN/TXXYK0RQyTI1i5ci0bNzZGXlEt4y2c62Pf18N+r499Xw/7vT72fT3sd3WrOu7mKkmSJEnqcIZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmU9jUaj7hrGqz5gad1FSJIkqbus39DPmtXr6i5jQujtncaKFWvqLqNjTZrUw/TpUwFmAMsGL9+q3QV1mrnzFrF8lT/skiRJao2F8+dgvFE32KwwGRFHAx8o158E/Ftmfrzqh0XE4cAemfnJqtsO2k8fcF1m9o1mP5IkSZKkLTPiNZMR8WRgPnBYZj4XeD5wbEQcsQWfty/wuC3YTpIkSZI0jmzOyOQTgCnAdsDKzFwbEScC6yNiP+Dcctl9wFsyc2lEXAcsAV4IbAO8G/gf4BSAiLgb+BrwWWBPYDLwscy8LCJOAl4BTAd2Ac4DngIcAqwEXlbWtU1EfBUI4C5gbmauGqGm+4FnA8dk5pKKfSVJkiRJKo04MpmZNwPfBn4TET+JiI9RhL/fAl8EjsvMfShGL7/QtOnjyvbjgIuBXwOfBz6fmRcBpwE/z8xZFKHzHyLiL8tt9weOBF4CfBK4MjP3Kpe9pPzzicA/l6OldwGnR8TWI9R0S2aGQVKSJEmSRmezrpnMzLdGxDzgMIow99/A2cBTge9ExMCqzVNYv1BuuyQi7gX24tEOBbaLiJPL99tTjBoC/FdmrgZWl/u+umy/G9jhz2Xl9eXrSygC6x4j1LR4c45XkiRJGku9vdPqLmHCsK/HzohhMiJeAUzNzMuBi4CLIuJNFCOOv8nMmeV6k4Gdmjbtb3o9adB7KEY3T8jMG8vtd6KYhno88FDzipk5eNuh9v9wuc9N1eRtWSVJklQ7H1fRHj4aZHSaHg0y9PLN2McfgbPLO6gSET3ATIrRyR0j4qByvZOBLzdtd2y5/r4Uo4m3UgTAgQB7DfDWcp2dgVuA3TejngHPjIi9y9dvAK4C7hyhJkmSJElSC2zONZPXAh8BroiIpAhsjwBnAK8B5kfELcCJwNymTf8yIm4Ezqe44c0jwA+B4yPiHeU+t42I2yiC5fsy864Ktf+a4jrJW4Fe4J8yc8MINUmSJEmSWqCn0Wi0fKflnVPPyMzrWr7z9ukDls6dt4jlq5wdK0mSpNZYOH+OUy/bxGmuo9M0zXUGsOwxy9tdkCRJkiSp823W3VyryszZY7FfSZIkSdL4MCZhsptccNphdZcgSZKkLrJ+w1APKpA6j2FyBCtXrmXjxtZfV6rhObe9PvZ9Pez3+tj39bDf62Pf18N+V7fymklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlW9VdwHg3ffrUukuYkHp7p9VdwoRl39fDfq+PfV8P+70+9v3mWb+hnzWr19VdhjSuGSZHMHfeIpav8otEkiRpIlk4fw5r6i5CGuec5ipJkiRJqqxjwmRE7BkRjYh49RZsu6T884yIOKPVtUmSJEnSRNMxYRI4Gfga8JaqG2bmzJZXI0mSJEkTWEdcMxkRU4DjgYOAGyLiqZl5V0QsA74KvLhc9eTMvCkirgPuB54NHAPclJk97a9ckiRJkrpTp4xMvgK4OzN/CXwLeHPTsgczc2/gdODipvZbMjMyc0nbqpQkSZKkCaIjRiaBNwCXla8vBy6NiA+V788HyMyFEXFxRDyhbF/c5holSZLURVr5GBUfyVIf+37sjPswGRFPBF4GzIqIdwE9wA7AUeUq/U2rTwIeKV/7PA9JkiRtsRUrWvNwkN7eaS3bl6qx70dn0qQepk+fOvzyNtaypV4HXJ2Zu2ZmX2Y+BTgLOKVcfixARLwKuCMzV9VUpyRJkiRNGJ0QJk8C/nVQ22eB/YFtgBeUj/54D3BiWyuTJEmSpAlq3E9zzcznDNG2AtiuvJvr+zNz2aDlswe97yn/PGOs6pQkSZKkiaQTRiYlSZIkSePMuB+Z3JTM7Bvrz7jgtMPG+iMkSZI0zqzf0D/yStIE19Fhsh1WrlzLxo2NusuYULzrVn3s+3rY7/Wx7+thv9fHvpfUSk5zlSRJkiRVZpiUJEmSJFVmmJQkSZIkVWaYlCRJkiRVZpiUJEmSJFVmmJQkSZIkVWaYlCRJkiRVZpiUJEmSJFVmmJQkSZIkVWaYlCRJkiRVZpiUJEmSJFVmmJQkSZIkVbZV3QWMd9OnT627hAmpt3da3SVMWPZ9Pez3+tj39bDf6zPe+n79hn7WrF5XdxmStoBhcgRz5y1i+Sq/4CRJksbCwvlzWFN3EZK2iNNcJUmSJEmVtWxkMiL6gF8Ctw9a9MrM/N0Q658BkJlntKoGSZIkSVJ7tHqa6z2ZObPF+5QkSZIkjTNjfs1kROwEnAfsBmwEPpCZV5WL94+IxcBU4PzM/HREzAbOASYDtwFL4c8jmBGxDJgN3A9cAOwK7AJcBbwROLhp+9uBg4DDMvOXEbE9cCfw9MxcP5bHLUmSJEndrNVhcpeIWNL0/lJgFnBhZn4nInYGfhQRM8vlOwMHUgS/n0fED8r2PYCnZOYDA9Nhh/AKYElmviYitqYIjvsMsf1HgBOA04FXA1cYJCVJkiRpdMZ8mmtE3Ac8IyLOLJumAE8tX38lMx8s11tIMap4M5CZ+cCmPigzL4uI/SPi3cAzgekUI5wM2v4iilHL04ETgQ9u+eFJkiSp1cbb40rGwkQ4xvHKvh877Xg0yGTgkMy8H6AcnVwOHAn0N603CXi4fN38LI4Gj77r7JRyP+8AjgbOpwiLewI9g7fPzGURcXdEHAXslJmLW3NYkiRJaoUVK7r74SC9vdO6/hjHK/t+dCZN6mH69KnDL29DDdcAbwOIiGdRXAe5Xbns6Ij4i4jYATgcuHaI7e8Dnl1uvz/F1FiAFwPnZealwDbATIrgOpQLgc8Al4z2YCRJkiRJ7QmT7wCeFxG3AJcDJ2TmwP8euBv4L+BHwD9l5h1DbP8VYMeIuL3c101l+6eAD0fEreXrG4AZw9TwDWBHDJOSJEmS1BI9jUaj7hrGVET0AC8DTsnMIyps2gcsnTtvEctXrRtpXUmSJG2BhfPndP00RKda1se+H52maa4zgGWDl7fjmsm6nQu8kiJQSpIkSZJaoOvDZGa+G3h3zWVIkiRJUlfp+jA5WhecdljdJUiSJHWt9Rv6R15J0rhkmBzBypVr2bixu68rHW+c214f+74e9nt97Pt62O/1se8ltVI77uYqSZIkSeoyhklJkiRJUmWGSUmSJElSZV4zObzJUDxbRe1nv9fHvq+H/V4f+74e9nt97Pt62O/1se+3XFPfTR5qeU+j4c1lhnEgcH3dRUiSJElSzQ4CfjS40TA5vL8A9gPuBR6puRZJkiRJarfJwM7AT4ENgxcaJiVJkiRJlXkDHkmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZVvVXcB4FBHHAacBU4BPZeZnay6pq0XEh4G/Kd9+NzPfFxEXAQcCD5btH8nMb9ZSYJeKiGuBJwIPl01vAaYBnwS2BS7PzNNqKq9rRcQbgbc3Nc0ALgG2x3N+TETE44AbgMMzc1lEHMoQ53lEzAS+CDwO+CFwSmb211N15xui398MvBNoAD8D3pKZD5X/BpwMrCo3/YL/7o7OEH0/5L+pnvOt1dzvwLOAf2pa/GRgcWYe7jnfWsP8Hun3fJsYJgeJiCcDZwGzgA3ADRFxbWbeXm9l3an8YT8M2JviF4zvRcSrgH2BF2bmvXXW160iogfYA3jKwJdoRGwLJHAw8DvguxHxssy8sr5Ku09mfpHiHzIi4tnAt4AzgGvxnG+5iDgA+ALF+T5wnl/I0Of5l4A3ZuZ/R8QFwJuAz9VTeWcbot/3AN5L8W/rGmAB8LfAuRTf98dm5o9rKbbLDO770nD/pnrOt8jgfs/M/wD+o1z2JOC/gL8rV/ecb5Fhfo98LfAx/J5vC6e5PtahwDWZeX9mPgh8HTi65pq62b3AqZn5UGY+DNwB7F7+d2FE3BIRH4kIz9XWivLPRRFxc0S8Hdgf+FVmLi0D5peA19RW4cTwOeCDwB/xnB8rb6IILfeU74c8zyPiKcC2mfnf5XoL8PwfjcH9vgF4W2auzswGcCvFOQ/FL9YfLM/9f4mIbdpfbld5VN9HxHYM8f3iOd9yg8/5Zh8HPp+Zvyrfe863zlC/R+6B3/Nt4y8rj7ULxYk54F5g15pq6XqZ+YuBH+qIeDrFNIXvAddQTAF5HnAQMLe2IrvTDsDVwKuAFwGnUPyy4bnfJuX/Td02M78GPAnP+TGRmW/MzOubmob7jve7v4UG93tm3p2Z3weIiF6Kqd7fjoipwE0Uo5b7AI8HPtT+irvHEOf8cN8vnvMtNES/A3/63WY28Jnyved8Cw3ze+RG/J5vG6e5PtYkimHyAT0UJ6XGUDnd77vAezMzKULOwLJ/Bl5PMX1ELVBOrfnT9JpyqseZwI+aVvPcH1tvobieg8z8DZ7z7TLcd7zf/W1QXkpyJXBBZl5XNr+8afl8imnI/9D+6rrTJr5fbsdzvh3eDPxrZm4AyMy1eM63XPPvkUA/j57m7ff8GHJk8rH+B9i56f2TGHrKglokIl5AMUr2/sy8OCKeExGvblqlhz/fJEYtEBEHRsSLmpp6gGV47rdFRGxNcS3Hd8r3nvPtM9x3vN/9YywinkFxc5KLM/Mfy7bdI+LkptU891tsE98vnvPtcSTwlYE3nvOtN/j3SPyebyvD5GNdBbwoInrL6wxeTTHtUmMgInajuAHJcZk58GXbA3wqInaIiCkU/1fPu1q21uOBj0fENhExDTiR4tq9iIinRcRk4DiKEQS13l7AL8vrssFzvp0WM8R5npl3A+vLX0oAXofnf8uU3zOLgNMyc37TonXAORExo7wx2N/iud9qQ36/eM6PvYh4AsXlDEubmj3nW2iY3yP9nm8jw+Qgmfl7iqkG1wJLgC9n5k9qLaq7vQfYBvhkRCyJiCXAXwFnU9z57HZgSWZeVl+J3Sczr6CYDnIT8HPgwnLq60nAv1P0+50UN6BS6/0lxf8hBSAzb8Fzvi0ycz3Dn+fHA+dGxJ3AVMprnNQSbwR2Ak4d+K6PiDMzcwXFlO+FFHeT7gHmb2I/qmiE7xfP+bH1qO96AM/5lhvq98iT8Hu+bXoajcbIa0mSJEmS1MSRSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJlhklJkiRJUmWGSUmSJElSZYZJSZIkSVJl/z+PVnFa1e21awAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Month'] = pd.DatetimeIndex(df['Date']).month_name(locale = 'English')\n",
    "\n",
    "df.head(2)\n",
    "\n",
    "df.year.value_counts().head()\n",
    "\n",
    "df.Month.value_counts().sort_values().plot(kind='barh', \n",
    "                                     figsize =(15,6),\n",
    "                                     title=\"Months and their earthquakes\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abed867a",
   "metadata": {},
   "source": [
    "> Novermber has the highest earthquake occurence than any other month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "131caed1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2204 entries, 0 to 2203\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Date              2204 non-null   datetime64[ns]\n",
      " 1   Location Name     2204 non-null   object        \n",
      " 2   Country           2204 non-null   object        \n",
      " 3   Latitude          2203 non-null   float64       \n",
      " 4   Longitude         2203 non-null   float64       \n",
      " 5   Focal Depth (km)  2185 non-null   float64       \n",
      " 6   Mag               2198 non-null   float64       \n",
      " 7   year              2204 non-null   int64         \n",
      " 8   Month             2204 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(4), int64(1), object(3)\n",
      "memory usage: 155.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}