DATA VISUALIZATION for BRAZIL FOREST FIRES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('amazon.csv', encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>month</th>\n",
       "      <th>number</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6449</td>\n",
       "      <td>2012</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6450</td>\n",
       "      <td>2013</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6451</td>\n",
       "      <td>2014</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6452</td>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>373.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6453</td>\n",
       "      <td>2016</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6454 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year      state     month  number        date\n",
       "0     1998       Acre   Janeiro     0.0  1998-01-01\n",
       "1     1999       Acre   Janeiro     0.0  1999-01-01\n",
       "2     2000       Acre   Janeiro     0.0  2000-01-01\n",
       "3     2001       Acre   Janeiro     0.0  2001-01-01\n",
       "4     2002       Acre   Janeiro     0.0  2002-01-01\n",
       "...    ...        ...       ...     ...         ...\n",
       "6449  2012  Tocantins  Dezembro   128.0  2012-01-01\n",
       "6450  2013  Tocantins  Dezembro    85.0  2013-01-01\n",
       "6451  2014  Tocantins  Dezembro   223.0  2014-01-01\n",
       "6452  2015  Tocantins  Dezembro   373.0  2015-01-01\n",
       "6453  2016  Tocantins  Dezembro   119.0  2016-01-01\n",
       "\n",
       "[6454 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Coverting the months from Portugese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_portugese=list(pd.unique(df['month']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'january': 'january',\n",
       " 'feburary': 'feburary',\n",
       " 'march': 'march',\n",
       " 'april': 'april',\n",
       " 'may': 'may',\n",
       " 'june': 'june',\n",
       " 'july': 'july',\n",
       " 'august': 'august',\n",
       " 'september': 'september',\n",
       " 'october': 'october',\n",
       " 'november': 'november',\n",
       " 'december': 'december'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_english=['january','feburary','march','april','may','june','july','august','september','october','november','december']\n",
    "dict_month=dict(zip(months_portugese,months_english))\n",
    "dict_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.month=df['month'].map(dict_month)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1998</td>\n",
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       "      <td>1998-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000-01-01</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002-01-01</td>\n",
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       "    <tr>\n",
       "      <td>6449</td>\n",
       "      <td>2012</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>december</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6450</td>\n",
       "      <td>2013</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>december</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6451</td>\n",
       "      <td>2014</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>december</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6452</td>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>december</td>\n",
       "      <td>373.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6453</td>\n",
       "      <td>2016</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>december</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6454 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year      state     month  number        date\n",
       "0     1998       Acre   january     0.0  1998-01-01\n",
       "1     1999       Acre   january     0.0  1999-01-01\n",
       "2     2000       Acre   january     0.0  2000-01-01\n",
       "3     2001       Acre   january     0.0  2001-01-01\n",
       "4     2002       Acre   january     0.0  2002-01-01\n",
       "...    ...        ...       ...     ...         ...\n",
       "6449  2012  Tocantins  december   128.0  2012-01-01\n",
       "6450  2013  Tocantins  december    85.0  2013-01-01\n",
       "6451  2014  Tocantins  december   223.0  2014-01-01\n",
       "6452  2015  Tocantins  december   373.0  2015-01-01\n",
       "6453  2016  Tocantins  december   119.0  2016-01-01\n",
       "\n",
       "[6454 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>state</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998-01-01</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999-01-01</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2003-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2004</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2005</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2005-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2006</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2007</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2008</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2012</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2013</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2014</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2017</td>\n",
       "      <td>Acre</td>\n",
       "      <td>january</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2002-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2003</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2004</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2004-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>2005</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2006</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>2007</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2008</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2008-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>2009</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2009-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2010</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2011</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2012</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2013</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2014</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2016</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>2017</td>\n",
       "      <td>Acre</td>\n",
       "      <td>feburary</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1998</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1998-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1999</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1999-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2000</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2000-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>2001</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>2002</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2002-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2003</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2003-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2004</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>2005</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2005-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>2006</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>2007</td>\n",
       "      <td>Acre</td>\n",
       "      <td>march</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2007-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year state     month  number        date\n",
       "0   1998  Acre   january     0.0  1998-01-01\n",
       "1   1999  Acre   january     0.0  1999-01-01\n",
       "2   2000  Acre   january     0.0  2000-01-01\n",
       "3   2001  Acre   january     0.0  2001-01-01\n",
       "4   2002  Acre   january     0.0  2002-01-01\n",
       "5   2003  Acre   january    10.0  2003-01-01\n",
       "6   2004  Acre   january     0.0  2004-01-01\n",
       "7   2005  Acre   january    12.0  2005-01-01\n",
       "8   2006  Acre   january     4.0  2006-01-01\n",
       "9   2007  Acre   january     0.0  2007-01-01\n",
       "10  2008  Acre   january     0.0  2008-01-01\n",
       "11  2009  Acre   january     0.0  2009-01-01\n",
       "12  2010  Acre   january     1.0  2010-01-01\n",
       "13  2011  Acre   january     0.0  2011-01-01\n",
       "14  2012  Acre   january     0.0  2012-01-01\n",
       "15  2013  Acre   january     0.0  2013-01-01\n",
       "16  2014  Acre   january     0.0  2014-01-01\n",
       "17  2015  Acre   january     1.0  2015-01-01\n",
       "18  2016  Acre   january    12.0  2016-01-01\n",
       "19  2017  Acre   january     0.0  2017-01-01\n",
       "20  1998  Acre  feburary     0.0  1998-01-01\n",
       "21  1999  Acre  feburary     0.0  1999-01-01\n",
       "22  2000  Acre  feburary     0.0  2000-01-01\n",
       "23  2001  Acre  feburary     0.0  2001-01-01\n",
       "24  2002  Acre  feburary     1.0  2002-01-01\n",
       "25  2003  Acre  feburary     0.0  2003-01-01\n",
       "26  2004  Acre  feburary     3.0  2004-01-01\n",
       "27  2005  Acre  feburary     0.0  2005-01-01\n",
       "28  2006  Acre  feburary     0.0  2006-01-01\n",
       "29  2007  Acre  feburary     5.0  2007-01-01\n",
       "30  2008  Acre  feburary     0.0  2008-01-01\n",
       "31  2009  Acre  feburary     2.0  2009-01-01\n",
       "32  2010  Acre  feburary     0.0  2010-01-01\n",
       "33  2011  Acre  feburary     0.0  2011-01-01\n",
       "34  2012  Acre  feburary     0.0  2012-01-01\n",
       "35  2013  Acre  feburary     0.0  2013-01-01\n",
       "36  2014  Acre  feburary     0.0  2014-01-01\n",
       "37  2015  Acre  feburary     2.0  2015-01-01\n",
       "38  2016  Acre  feburary     5.0  2016-01-01\n",
       "39  2017  Acre  feburary     1.0  2017-01-01\n",
       "40  1998  Acre     march     0.0  1998-01-01\n",
       "41  1999  Acre     march     0.0  1999-01-01\n",
       "42  2000  Acre     march    11.0  2000-01-01\n",
       "43  2001  Acre     march     0.0  2001-01-01\n",
       "44  2002  Acre     march     0.0  2002-01-01\n",
       "45  2003  Acre     march     0.0  2003-01-01\n",
       "46  2004  Acre     march     0.0  2004-01-01\n",
       "47  2005  Acre     march     3.0  2005-01-01\n",
       "48  2006  Acre     march     0.0  2006-01-01\n",
       "49  2007  Acre     march     2.0  2007-01-01"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Acre', 'Alagoas', 'Amapa', 'Amazonas', 'Bahia', 'Ceara',\n",
       "       'Distrito Federal', 'Espirito Santo', 'Goias', 'Maranhao',\n",
       "       'Mato Grosso', 'Minas Gerais', 'Pará', 'Paraiba', 'Pernambuco',\n",
       "       'Piau', 'Rio', 'Rondonia', 'Roraima', 'Santa Catarina',\n",
       "       'Sao Paulo', 'Sergipe', 'Tocantins'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,\n",
       "       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_state_fires=df.groupby('state')# gropuing dataframe state wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_names=list(df.state.unique())# name of each state present in Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_states_numbers=[]# to hold the numbers for TOP 5 places that caught most fires from 1998 to 2017\n",
    "top_5_states_names=[]# to hold the names for TOP 5 places that caught most fires from 1998 to 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for state in states_names:\n",
    "    top_5_states_numbers.append(total_state_fires.get_group(state).number.sum())\n",
    "    # sum of all fires that took place in each state from 1998 to 2017\n",
    "    top_5_states_names.append(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_total_fires=pd.DataFrame(data={'States':top_5_states_names,\n",
    "                                 'Total_Fires':top_5_states_numbers},columns=['States','Total_Fires'])\n",
    "\n",
    "df_total_fires=df_total_fires.sort_values(['Total_Fires'],ascending=False).iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'HIGHEST NUMBER OF FIRES')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(df_total_fires.States,df_total_fires.Total_Fires,palette='winter')\n",
    "plt.xlabel('STATES',fontsize=20)\n",
    "plt.ylabel('HIGHEST NUMBER OF FIRES',fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}