Forest Fires Data Exploration With Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90c38087",
   "metadata": {},
   "source": [
    "# Forest Fire In Brazil Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1375910a",
   "metadata": {},
   "source": [
    "Forest fire is a serious problem for the preservation of the Trophical Forest. \n",
    "\n",
    "Dataset is from Kaggle.com"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5cfead",
   "metadata": {},
   "source": [
    "#### This dataset is used for data cleaning and answering the the following questions:\n",
    "    1. Total number of fires registered\n",
    "    2. In whch month were maximum forest fire reported\n",
    "    3. In whch year were maximum forest fire reported\n",
    "    4. Which state has the maximum forest fire reported\n",
    "    5. Find the total number of fires reported in the amazonas\n",
    "    6. find the total number of fires reported in the amazonas year wise\n",
    "    7. Find the total number of fires reported in the amazonas day wise\n",
    "    8. Find total number of fires reported in 2015 and visualize each month\n",
    "    9. Find the average number of fires reported from hightest to lowest[state wise]\n",
    "    10. Find the state names of where fire were reported in dec\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "85def64e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary labraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams[\"figure.figsize\"] = (12,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b5694f8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year               int64\n",
       "state             object\n",
       "month             object\n",
       "number           float64\n",
       "date      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading in the dataset and changing the dtype of date column\n",
    "\n",
    "df = pd.read_csv(r\"C:\\Users\\Henry Oguns\\Desktop\\DATA ANALYSIS\\Portfolio Projects\\PortfolioProjects\\Project _Datasets\\amazon.csv\", \n",
    "                 parse_dates = [\"date\"])\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a3d6cf5",
   "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>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the 5 first and last rows\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0ea3460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>6449</th>\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",
       "      <th>6450</th>\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",
       "      <th>6451</th>\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",
       "      <th>6452</th>\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",
       "      <th>6453</th>\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",
       "</div>"
      ],
      "text/plain": [
       "      year      state     month  number       date\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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3d10062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6454, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the shape of the data set\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792c7650",
   "metadata": {},
   "source": [
    "We have 6,454 number of Rows and 5 number of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c1e4f9d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6454 entries, 0 to 6453\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   year    6454 non-null   int64         \n",
      " 1   state   6454 non-null   object        \n",
      " 2   month   6454 non-null   object        \n",
      " 3   number  6454 non-null   float64       \n",
      " 4   date    6454 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(2)\n",
      "memory usage: 252.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# finding information on the dataset and memory storage\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c338c9c",
   "metadata": {},
   "source": [
    "The dataset has no null value in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "207d36db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates and droping them\n",
    "\n",
    "dup = df.duplicated().any()\n",
    "dup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "87b95e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6449</th>\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",
       "      <th>6450</th>\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",
       "      <th>6451</th>\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",
       "      <th>6452</th>\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",
       "      <th>6453</th>\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>6422 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",
       "[6422 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bfc8abce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'month', 'number', 'date'], dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming Month Names to English\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "98de6204",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month_new']=df['month'].map({'Janeiro':'Jan',\n",
    "            'Fevereiro':'Feb',\n",
    "            'Mar�o':'Mar',\n",
    "            'Abril':'Apr',\n",
    "            'Maio':'May',\n",
    "            'Junho':'Jun',\n",
    "            'Julho':'Jul',\n",
    "            'Agosto':'Aug',\n",
    "            'Setembro':'Sep',\n",
    "              'Outubro':'Oct',\n",
    "              'Novembro':'Nov',\n",
    "              'Dezembro':'Dec'\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1fc46ccb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jan    541\n",
       "Nov    540\n",
       "Jul    540\n",
       "Sep    540\n",
       "Apr    540\n",
       "Oct    540\n",
       "Aug    540\n",
       "Jun    540\n",
       "Mar    540\n",
       "Feb    540\n",
       "May    540\n",
       "Dec    513\n",
       "Name: month_new, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month_new'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c8197f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing '�' in para state name to 'a', Para\n",
    "\n",
    "df.state = df.state.str.replace('�', 'a', regex =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "910a4a2d",
   "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",
       "      <th>month_new</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3268</th>\n",
       "      <td>1998</td>\n",
       "      <td>Para</td>\n",
       "      <td>Setembro</td>\n",
       "      <td>6.237</td>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>1999</td>\n",
       "      <td>Espirito Santo</td>\n",
       "      <td>Julho</td>\n",
       "      <td>9.000</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>2012</td>\n",
       "      <td>Alagoas</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>41.000</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3186</th>\n",
       "      <td>2016</td>\n",
       "      <td>Para</td>\n",
       "      <td>Abril</td>\n",
       "      <td>66.000</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3090</th>\n",
       "      <td>1999</td>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>32.000</td>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>Dec</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6159</th>\n",
       "      <td>2001</td>\n",
       "      <td>Sergipe</td>\n",
       "      <td>Outubro</td>\n",
       "      <td>5.000</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6243</th>\n",
       "      <td>2006</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>14.000</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>2007</td>\n",
       "      <td>Paraiba</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>7.000</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>2001</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Julho</td>\n",
       "      <td>830.000</td>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>2017</td>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>482.000</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>323 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year           state      month   number       date month_new\n",
       "3268  1998            Para   Setembro    6.237 1998-01-01       Sep\n",
       "1795  1999  Espirito Santo      Julho    9.000 1999-01-01       Jul\n",
       "274   2012         Alagoas  Fevereiro   41.000 2012-01-01       Feb\n",
       "3186  2016            Para      Abril   66.000 2016-01-01       Apr\n",
       "3090  1999    Minas Gerais   Dezembro   32.000 1999-01-01       Dec\n",
       "...    ...             ...        ...      ...        ...       ...\n",
       "6159  2001         Sergipe    Outubro    5.000 2001-01-01       Oct\n",
       "6243  2006       Tocantins  Fevereiro   14.000 2006-01-01       Feb\n",
       "3376  2007         Paraiba  Fevereiro    7.000 2007-01-01       Feb\n",
       "6338  2001       Tocantins      Julho  830.000 2001-01-01       Jul\n",
       "2410  2017     Mato Grosso    Janeiro  482.000 2017-01-01       Jan\n",
       "\n",
       "[323 rows x 6 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= df.sample(frac = 0.05)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3b627ad1",
   "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",
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       "    }\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>year</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6454.000000</td>\n",
       "      <td>6454.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.461729</td>\n",
       "      <td>108.293163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.746654</td>\n",
       "      <td>190.812242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year       number\n",
       "count  6454.000000  6454.000000\n",
       "mean   2007.461729   108.293163\n",
       "std       5.746654   190.812242\n",
       "min    1998.000000     0.000000\n",
       "25%    2002.000000     3.000000\n",
       "50%    2007.000000    24.000000\n",
       "75%    2012.000000   113.000000\n",
       "max    2017.000000   998.000000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the overall statistics of the dataset\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f352ac95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4eb9f254",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66e84b94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'month', 'number', 'date', 'month_new'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Total number of fires registered\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ded5b95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "698924.073"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['number'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88617aec",
   "metadata": {},
   "source": [
    "We have 698,924.073 fire registered in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c37b3522",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7874fdae",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_new</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jul</td>\n",
       "      <td>92326.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oct</td>\n",
       "      <td>88681.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aug</td>\n",
       "      <td>88050.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nov</td>\n",
       "      <td>85508.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sep</td>\n",
       "      <td>58578.305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dec</td>\n",
       "      <td>57535.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jun</td>\n",
       "      <td>56010.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jan</td>\n",
       "      <td>47747.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>34731.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Feb</td>\n",
       "      <td>30848.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mar</td>\n",
       "      <td>30717.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Apr</td>\n",
       "      <td>28188.770</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month_new     number\n",
       "0        Jul  92326.113\n",
       "1        Oct  88681.579\n",
       "2        Aug  88050.435\n",
       "3        Nov  85508.054\n",
       "4        Sep  58578.305\n",
       "5        Dec  57535.480\n",
       "6        Jun  56010.675\n",
       "7        Jan  47747.844\n",
       "8        May  34731.363\n",
       "9        Feb  30848.050\n",
       "10       Mar  30717.405\n",
       "11       Apr  28188.770"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Which month has the maximum forest fire reported\n",
    "\n",
    "df1 =df.groupby('month_new')['number'].sum().sort_values(ascending =False).reset_index()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "85239c26",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='month_new', y='number', data=df)\n",
    "plt.title('Total Forest Fire Reported By Months', fontsize= 15)\n",
    "plt.xlabel('Month', fontsize= 15)\n",
    "plt.ylabel('Number of Reported Fires', fontsize= 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe74e0b",
   "metadata": {},
   "source": [
    "The month July has the highest with 92,326.113 forest fire reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baefc8dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ce13bf47",
   "metadata": {},
   "source": [
    "The year 2003 has the highest report fire of 42,760.674 reports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a51d410",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "120e403c",
   "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>state</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>96246.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paraiba</td>\n",
       "      <td>52435.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sao Paulo</td>\n",
       "      <td>51121.198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rio</td>\n",
       "      <td>45160.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>44746.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Piau</td>\n",
       "      <td>37803.747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Goias</td>\n",
       "      <td>37695.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>37475.258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tocantins</td>\n",
       "      <td>33707.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>30650.129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ceara</td>\n",
       "      <td>30428.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Maranhao</td>\n",
       "      <td>25129.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Para</td>\n",
       "      <td>24512.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Pernambuco</td>\n",
       "      <td>24498.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>24385.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Santa Catarina</td>\n",
       "      <td>24359.852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amapa</td>\n",
       "      <td>21831.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rondonia</td>\n",
       "      <td>20285.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Acre</td>\n",
       "      <td>18464.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Espirito Santo</td>\n",
       "      <td>6546.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>4644.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>3561.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sergipe</td>\n",
       "      <td>3237.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               state     number\n",
       "0        Mato Grosso  96246.028\n",
       "1            Paraiba  52435.918\n",
       "2          Sao Paulo  51121.198\n",
       "3                Rio  45160.865\n",
       "4              Bahia  44746.226\n",
       "5               Piau  37803.747\n",
       "6              Goias  37695.520\n",
       "7       Minas Gerais  37475.258\n",
       "8          Tocantins  33707.885\n",
       "9           Amazonas  30650.129\n",
       "10             Ceara  30428.063\n",
       "11          Maranhao  25129.131\n",
       "12              Para  24512.144\n",
       "13        Pernambuco  24498.000\n",
       "14           Roraima  24385.074\n",
       "15    Santa Catarina  24359.852\n",
       "16             Amapa  21831.576\n",
       "17          Rondonia  20285.429\n",
       "18              Acre  18464.030\n",
       "19    Espirito Santo   6546.000\n",
       "20           Alagoas   4644.000\n",
       "21  Distrito Federal   3561.000\n",
       "22           Sergipe   3237.000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4. Which state has the maximum forest fire reported\n",
    "\n",
    "df3 =df.groupby('state')['number'].sum().sort_values(ascending =False)\n",
    "df3.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdc37df3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9fafb3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30650.129"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. find the total number of fires reported in the amazonas\n",
    "\n",
    "df[df['state']=='Amazonas']['number'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e7224d",
   "metadata": {},
   "source": [
    "Total number of 30,650.129 fire report ware reported in Amazonas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97d43ee8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "11e4aebe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2003</td>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2004</td>\n",
       "      <td>911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2005</td>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2006</td>\n",
       "      <td>721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2007</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2008</td>\n",
       "      <td>998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2009</td>\n",
       "      <td>973.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2010</td>\n",
       "      <td>889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2011</td>\n",
       "      <td>694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012</td>\n",
       "      <td>682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014</td>\n",
       "      <td>952.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016</td>\n",
       "      <td>770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  number\n",
       "0   1998   321.0\n",
       "1   1999   479.0\n",
       "2   2000   189.0\n",
       "3   2001   501.0\n",
       "4   2002   928.0\n",
       "5   2003   725.0\n",
       "6   2004   911.0\n",
       "7   2005   676.0\n",
       "8   2006   721.0\n",
       "9   2007   215.0\n",
       "10  2008   998.0\n",
       "11  2009   973.0\n",
       "12  2010   889.0\n",
       "13  2011   694.0\n",
       "14  2012   682.0\n",
       "15  2013   248.0\n",
       "16  2014   952.0\n",
       "17  2015   565.0\n",
       "18  2016   770.0\n",
       "19  2017   552.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 6. find the total number of fires reported in the amazonas year wise\n",
    "\n",
    "df4= df[df['state']=='Amazonas']\n",
    "df5 = df4.groupby('year')['number'].max().reset_index()\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bb40cb5f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='year', y='number', data=df5)\n",
    "plt.title('Total Forest Fire Reported in Amazona Yearwise', fontsize= 15)\n",
    "plt.xlabel('Year', fontsize= 15)\n",
    "plt.ylabel('Number of Reported Fires', fontsize= 15)\n",
    "plt.xticks(rotation=65)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc32c207",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37e2c48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fb67f26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. find the total number of fires reported in the amazonas day wise\n",
    "df6= df[df['state']=='Amazonas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7b6e31cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.groupby(df['date'].dt.dayofweek).sum().number\n",
    "\n",
    "import calendar\n",
    "\n",
    "day = [calendar.day_name[x] for x in range (0,7)]\n",
    "day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1e20ba2d",
   "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>index</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>1886.601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>6474.217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3910.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>5754.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>5446.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>4162.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>3015.186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index    number\n",
       "0     Monday  1886.601\n",
       "1    Tuesday  6474.217\n",
       "2  Wednesday  3910.177\n",
       "3   Thursday  5754.802\n",
       "4     Friday  5446.480\n",
       "5   Saturday  4162.666\n",
       "6     Sunday  3015.186"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day = df6.groupby(df['date'].dt.dayofweek).sum().number\n",
    "\n",
    "import calendar\n",
    "\n",
    "day.index = [calendar.day_name[x] for x in range (0,7)]\n",
    "day.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b61bc56f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1d47d68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'month', 'number', 'date', 'month_new'], dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. find total number of fires reported in 2015 and visualize each month\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7c8896db",
   "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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>month</th>\n",
       "      <th>number</th>\n",
       "      <th>date</th>\n",
       "      <th>month_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Janeiro</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Fevereiro</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Mar�o</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Abril</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2015</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Maio</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>May</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6372</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Agosto</td>\n",
       "      <td>2.540</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6392</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Setembro</td>\n",
       "      <td>5.510</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6412</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Outubro</td>\n",
       "      <td>4.844</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6432</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Novembro</td>\n",
       "      <td>833.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6452</th>\n",
       "      <td>2015</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>Dezembro</td>\n",
       "      <td>373.000</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>324 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year      state      month   number       date month_new\n",
       "17    2015       Acre    Janeiro    1.000 2015-01-01       Jan\n",
       "37    2015       Acre  Fevereiro    2.000 2015-01-01       Feb\n",
       "57    2015       Acre      Mar�o    2.000 2015-01-01       Mar\n",
       "77    2015       Acre      Abril    3.000 2015-01-01       Apr\n",
       "97    2015       Acre       Maio    2.000 2015-01-01       May\n",
       "...    ...        ...        ...      ...        ...       ...\n",
       "6372  2015  Tocantins     Agosto    2.540 2015-01-01       Aug\n",
       "6392  2015  Tocantins   Setembro    5.510 2015-01-01       Sep\n",
       "6412  2015  Tocantins    Outubro    4.844 2015-01-01       Oct\n",
       "6432  2015  Tocantins   Novembro  833.000 2015-01-01       Nov\n",
       "6452  2015  Tocantins   Dezembro  373.000 2015-01-01       Dec\n",
       "\n",
       "[324 rows x 6 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = df[df['year']==2015]\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "da7272e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_new</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>4635.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Oct</td>\n",
       "      <td>4499.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jul</td>\n",
       "      <td>4364.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aug</td>\n",
       "      <td>4363.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dec</td>\n",
       "      <td>4088.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nov</td>\n",
       "      <td>4034.518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jun</td>\n",
       "      <td>3260.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apr</td>\n",
       "      <td>2573.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sep</td>\n",
       "      <td>2494.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>May</td>\n",
       "      <td>2384.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Feb</td>\n",
       "      <td>2309.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mar</td>\n",
       "      <td>2202.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   month_new    number\n",
       "0        Jan  4635.000\n",
       "1        Oct  4499.525\n",
       "2        Jul  4364.392\n",
       "3        Aug  4363.125\n",
       "4        Dec  4088.522\n",
       "5        Nov  4034.518\n",
       "6        Jun  3260.552\n",
       "7        Apr  2573.000\n",
       "8        Sep  2494.658\n",
       "9        May  2384.000\n",
       "10       Feb  2309.000\n",
       "11       Mar  2202.000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.groupby('month_new')['number'].sum().sort_values(\n",
    "ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "5087422b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "sns.lineplot(x='month_new', y='number', data=df8)\n",
    "plt.xticks(rotation=65)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9879d03d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7879204c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'month', 'number', 'date', 'month_new'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the average number of fires reported from hightest to lowest[state wise]\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d37b6fa6",
   "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>state</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sao Paulo</td>\n",
       "      <td>213.896226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mato Grosso</td>\n",
       "      <td>201.351523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>187.222703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Piau</td>\n",
       "      <td>158.174674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Goias</td>\n",
       "      <td>157.721841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>156.800243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tocantins</td>\n",
       "      <td>141.037176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>128.243218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ceara</td>\n",
       "      <td>127.314071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Paraiba</td>\n",
       "      <td>109.698573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Maranhao</td>\n",
       "      <td>105.142808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Para</td>\n",
       "      <td>102.561272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pernambuco</td>\n",
       "      <td>102.502092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>102.029598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Santa Catarina</td>\n",
       "      <td>101.924067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Amapa</td>\n",
       "      <td>91.345506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rondonia</td>\n",
       "      <td>84.876272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Acre</td>\n",
       "      <td>77.255356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rio</td>\n",
       "      <td>62.985865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Espirito Santo</td>\n",
       "      <td>27.389121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>19.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>14.899582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Sergipe</td>\n",
       "      <td>13.543933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               state      number\n",
       "0          Sao Paulo  213.896226\n",
       "1        Mato Grosso  201.351523\n",
       "2              Bahia  187.222703\n",
       "3               Piau  158.174674\n",
       "4              Goias  157.721841\n",
       "5       Minas Gerais  156.800243\n",
       "6          Tocantins  141.037176\n",
       "7           Amazonas  128.243218\n",
       "8              Ceara  127.314071\n",
       "9            Paraiba  109.698573\n",
       "10          Maranhao  105.142808\n",
       "11              Para  102.561272\n",
       "12        Pernambuco  102.502092\n",
       "13           Roraima  102.029598\n",
       "14    Santa Catarina  101.924067\n",
       "15             Amapa   91.345506\n",
       "16          Rondonia   84.876272\n",
       "17              Acre   77.255356\n",
       "18               Rio   62.985865\n",
       "19    Espirito Santo   27.389121\n",
       "20           Alagoas   19.350000\n",
       "21  Distrito Federal   14.899582\n",
       "22           Sergipe   13.543933"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9 = df.groupby('state')['number'].mean().sort_values(\n",
    "ascending=False).reset_index()\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9ab51abc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "sns.barplot(x='state', y='number', data=df9)\n",
    "plt.xticks(rotation=65)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d457d33",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9590b7b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'state', 'month', 'number', 'date', 'month_new'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to find the state names of where fire were reported in dec\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e1c71669",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Acre', 'Alagoas', 'Amapa', 'Amazonas', 'Bahia', 'Ceara',\n",
       "       'Distrito Federal', 'Espirito Santo', 'Goias', 'Maranhao',\n",
       "       'Mato Grosso', 'Minas Gerais', 'Para', 'Paraiba', 'Pernambuco',\n",
       "       'Piau', 'Rio', 'Rondonia', 'Roraima', 'Santa Catarina',\n",
       "       'Sao Paulo', 'Sergipe', 'Tocantins'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['month_new']=='Dec']['state'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c5961ce",
   "metadata": {},
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