Meteorology/Assignment2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recordes Metereológicos de 2005 a 2014 vs 2015 - Região de Ann Arbor, Michigan\n",
    "\n",
    "![chart](chart.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos construir o gráfico mostrado acima!\n",
    "\n",
    "#### Nota: essa é uma atividade do curso \"[Applied Plotting, Charting & Data Representation in Python](https://www.coursera.org/learn/python-plotting)\", da University of Michigan no Coursera.\n",
    "\n",
    "\n",
    "\n",
    "Nele, temos dados de um subset da \"The National Centers for Environmental Information\" (NCEI) [Daily Global Historical Climatology Network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt) (GHCN-Daily). Um GHCN-Daily é composto por registros climáticos diários de milhares de estações de superfície terrestre em todo o mundo.\n",
    "\n",
    "Cada linha no dataset corresponde a uma única observação.\n",
    "\n",
    "\n",
    "Temos essas variáveis:\n",
    "\n",
    "* **id** : Código de identificação da estação\n",
    "* **date** : data no formato YYYY-MM-DD (e.g. 2012-01-24 = January 24, 2012)\n",
    "* **element** : indicador do tipo do elemento\n",
    "    * TMAX : Maximum temperature (em décimos de graus Celcius)\n",
    "    * TMIN : Minimum temperature (em décimos de graus Celcius)\n",
    "* **value** : Temperatura (em décimos de graus Celcius)\n",
    "\n",
    "\n",
    "Para nossa tarefa, devemos:\n",
    "1. Ler e nos familiarizar com o dataset, e então escrever um código python que retorne um grafíco de linhas que contenha os recordes de mínima e máxima temperaturas POR DIA do ano, no período de 2005-2014. A área entre o recorde máximo e mínimo deve estar sombreada.\n",
    "2. Adicionar um scatterplot para cada ponto (máximo e minimo) em que o recorde de máxima e mínima de 2015 quebrou o recorde dos 10 anos anteriores (2005-2014).\n",
    "3. Atentar para *leap days* como o famoso 29 de fevereiro. Então devemos removê-lo.\n",
    "4. Fazer um gráfico bonito! Usando princípios de organização e não-poluição de legendas, labels, e sujeiras.\n",
    "\n",
    "Os dados recebidos são de Ann Arbor, Michigan, United States.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primeiramente vamos ler o dataset e verificar seu shape, cabecalho e fim, e tipos de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(165085, 4)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv\n",
    "df = pd.read_csv('fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Element</th>\n",
       "      <th>Data_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USW00094889</td>\n",
       "      <td>2014-11-12</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00208972</td>\n",
       "      <td>2009-04-29</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00200032</td>\n",
       "      <td>2008-05-26</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00205563</td>\n",
       "      <td>2005-11-11</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00200230</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>-106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID        Date Element  Data_Value\n",
       "0  USW00094889  2014-11-12    TMAX          22\n",
       "1  USC00208972  2009-04-29    TMIN          56\n",
       "2  USC00200032  2008-05-26    TMAX         278\n",
       "3  USC00205563  2005-11-11    TMAX         139\n",
       "4  USC00200230  2014-02-27    TMAX        -106"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Element</th>\n",
       "      <th>Data_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45691</th>\n",
       "      <td>USC00200032</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>USC00205050</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21088</th>\n",
       "      <td>USC00208202</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10253</th>\n",
       "      <td>USC00201250</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29410</th>\n",
       "      <td>USC00200230</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID        Date Element  Data_Value\n",
       "45691  USC00200032  2015-12-31    TMAX          11\n",
       "10573  USC00205050  2015-12-31    TMIN         -17\n",
       "21088  USC00208202  2015-12-31    TMAX           0\n",
       "10253  USC00201250  2015-12-31    TMIN          -6\n",
       "29410  USC00200230  2015-12-31    TMIN         -17"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Date').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Element</th>\n",
       "      <th>Data_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60995</th>\n",
       "      <td>USW00004848</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17153</th>\n",
       "      <td>USC00207320</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17155</th>\n",
       "      <td>USC00207320</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10079</th>\n",
       "      <td>USW00014833</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10073</th>\n",
       "      <td>USW00014833</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID        Date Element  Data_Value\n",
       "60995  USW00004848  2005-01-01    TMIN           0\n",
       "17153  USC00207320  2005-01-01    TMAX         150\n",
       "17155  USC00207320  2005-01-01    TMIN         -11\n",
       "10079  USW00014833  2005-01-01    TMIN         -44\n",
       "10073  USW00014833  2005-01-01    TMAX          33"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Date').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            object\n",
       "Date          object\n",
       "Element       object\n",
       "Data_Value     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleansing\n",
    "\n",
    "Vamos primeiramente remover a coluna ID que não sera utilizada.\n",
    "\n",
    "Converteremos o campo Date para datetime, ja q ele esta em object.\n",
    "\n",
    "Logo, iremos separar o mes e dia, para podermos agrupar apenas pela data (dia-mes)\n",
    "\n",
    "Por fim vamos converter as temperaturas de Décimo de Celcius para Celcius e remover 29 de fevereiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remover id\n",
    "df.drop('ID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Element</th>\n",
       "      <th>Data_Value</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-12</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2014</td>\n",
       "      <td>11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-04-29</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2009</td>\n",
       "      <td>04-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-05-26</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>27.8</td>\n",
       "      <td>2008</td>\n",
       "      <td>05-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-11-11</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>13.9</td>\n",
       "      <td>2005</td>\n",
       "      <td>11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>-10.6</td>\n",
       "      <td>2014</td>\n",
       "      <td>02-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Element  Data_Value  Year Month_date\n",
       "0 2014-11-12    TMAX         2.2  2014      11-12\n",
       "1 2009-04-29    TMIN         5.6  2009      04-29\n",
       "2 2008-05-26    TMAX        27.8  2008      05-26\n",
       "3 2005-11-11    TMAX        13.9  2005      11-11\n",
       "4 2014-02-27    TMAX       -10.6  2014      02-27"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "# decimo celcius para celcius\n",
    "df['Data_Value'] = df['Data_Value'] / 10\n",
    "\n",
    "# separar ano do mes e dia, para podermos agrupar apenas pela data dia-mes\n",
    "df['Year'], df['Month_date'] = df['Date'].dt.year, df['Date'].dt.strftime('%m-%d')\n",
    "\n",
    "# remover feb\n",
    "df = df[~df['Month_date'].str.contains(\"02-29\")]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não podemos esquecer de antes separar o ano de 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Element</th>\n",
       "      <th>Data_Value</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>2015</td>\n",
       "      <td>01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2015-03-17</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>18.9</td>\n",
       "      <td>2015</td>\n",
       "      <td>03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2015-06-06</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>12.8</td>\n",
       "      <td>2015</td>\n",
       "      <td>06-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>17.8</td>\n",
       "      <td>2015</td>\n",
       "      <td>08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2015</td>\n",
       "      <td>08-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Element  Data_Value  Year Month_date\n",
       "19 2015-01-03    TMIN        -3.9  2015      01-03\n",
       "30 2015-03-17    TMAX        18.9  2015      03-17\n",
       "34 2015-06-06    TMIN        12.8  2015      06-06\n",
       "46 2015-08-30    TMIN        17.8  2015      08-30\n",
       "50 2015-08-30    TMIN        15.6  2015      08-30"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe de 2015\n",
    "df_15 = df[df['Date'] >= '2015']\n",
    "df_15.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OK, agora vamos procurar pelos recordes metereológicos, por dia no periodo de 2005 a 2014\n",
    "\n",
    "Existem vários registros sobre o mesmo dia, mes e ano, mas já que são registros de varias regiões isso é perfeitamente normal. Mas devemos atentar que devemos pegar o máximo ou mínimo daquele determinado dia, em 10 anos. \n",
    "\n",
    "Para um agrupamento disso, pois precisamos de uma classificação diária, precisamos extrair apenas os meses e dias do campo 'Date'.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Dataframe de 2005 a 2014\n",
    "df_ten_years = df[df['Year'] < 2015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Element</th>\n",
       "      <th>Data_Value</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month_date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47170</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>2014</td>\n",
       "      <td>12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62752</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>-8.8</td>\n",
       "      <td>2014</td>\n",
       "      <td>12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46718</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>2014</td>\n",
       "      <td>12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62784</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>2014</td>\n",
       "      <td>12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46383</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>2014</td>\n",
       "      <td>12-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Element  Data_Value  Year Month_date\n",
       "47170 2014-12-31    TMIN       -12.2  2014      12-31\n",
       "62752 2014-12-31    TMIN        -8.8  2014      12-31\n",
       "46718 2014-12-31    TMAX        -3.9  2014      12-31\n",
       "62784 2014-12-31    TMAX        -2.8  2014      12-31\n",
       "46383 2014-12-31    TMAX        -5.6  2014      12-31"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificar o final dos df dos 10 anos\n",
    "df_ten_years.sort_values(by='Date').tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O campo 'Element' nos diz se é um valor mínimo (TMIN) ou máximo (TMAX). Se o valor for 'TMIN', devemos compará-lo com o menor mínimo, se for 'TMAX' devemos compará-lo com o maior máximo.\n",
    "\n",
    "Podemos utilizar o método `group_by` para agrupar por 'Date' (e 'Element'), e verificar rapidamente os valores de 'Data_Value' com os métodos `min` e `max` para vermos os menores valores mínimos e maiores valores máximos de cada data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Data_Value</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_date</th>\n",
       "      <th>Element</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-01</th>\n",
       "      <th>TMAX</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>-9.3</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-02</th>\n",
       "      <th>TMAX</th>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>2005-01-02</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-03</th>\n",
       "      <th>TMAX</th>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>-11.7</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>-26.7</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Date  Data_Value  Year\n",
       "Month_date Element                             \n",
       "01-01      TMAX    2005-01-01        -9.3  2005\n",
       "           TMIN    2005-01-01       -16.0  2005\n",
       "01-02      TMAX    2005-01-02       -10.0  2005\n",
       "           TMIN    2005-01-02       -26.7  2005\n",
       "01-03      TMAX    2005-01-03       -11.7  2005\n",
       "           TMIN    2005-01-03       -26.7  2005"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimo das baixas temperaturas\n",
    "(df_ten_years.groupby(['Month_date', 'Element']).min()).head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Data_Value</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_date</th>\n",
       "      <th>Element</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-01</th>\n",
       "      <th>TMAX</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-02</th>\n",
       "      <th>TMAX</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>13.9</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01-03</th>\n",
       "      <th>TMAX</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>13.3</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMIN</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Date  Data_Value  Year\n",
       "Month_date Element                             \n",
       "01-01      TMAX    2014-01-01        15.6  2014\n",
       "           TMIN    2014-01-01         7.2  2014\n",
       "01-02      TMAX    2014-01-02        13.9  2014\n",
       "           TMIN    2014-01-02         2.8  2014\n",
       "01-03      TMAX    2014-01-03        13.3  2014\n",
       "           TMIN    2014-01-03         2.8  2014"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximo das altas temperaturas\n",
    "(df_ten_years.groupby(['Month_date', 'Element']).max()).head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pegar os recordes\n",
    "\n",
    "Ok, por ultimo, podemos agrupar por 'Date', selecionar apenas 'Data_Value'. Logo após, utilizamos o método de agregação ``agg`` utilizando os parametros min e max."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recordes de 2005 a 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Record Low</th>\n",
       "      <th>Record High</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-01</th>\n",
       "      <td>-16.0</td>\n",
       "      <td>15.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-02</th>\n",
       "      <td>-26.7</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-03</th>\n",
       "      <td>-26.7</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-04</th>\n",
       "      <td>-26.1</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-05</th>\n",
       "      <td>-15.0</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Record Low  Record High\n",
       "Month_date                         \n",
       "01-01            -16.0         15.6\n",
       "01-02            -26.7         13.9\n",
       "01-03            -26.7         13.3\n",
       "01-04            -26.1         10.6\n",
       "01-05            -15.0         12.8"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agg por minimo e maximo\n",
    "df_records = (df_ten_years.groupby(['Month_date'])['Data_Value']).agg([('Record Low', 'min'), ('Record High', 'max')])\n",
    "df_records.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assim temos a maior temperatura na regiao, de cada dia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 2)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_records.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recordes de 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Record Low</th>\n",
       "      <th>Record High</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-01</th>\n",
       "      <td>-13.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-02</th>\n",
       "      <td>-12.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2015-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-03</th>\n",
       "      <td>-6.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2015-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-04</th>\n",
       "      <td>-8.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2015-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-05</th>\n",
       "      <td>-15.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2015-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Record Low  Record High       Date\n",
       "Month_date                                    \n",
       "01-01            -13.3          1.1 2015-01-01\n",
       "01-02            -12.2          3.9 2015-01-02\n",
       "01-03             -6.7          3.9 2015-01-03\n",
       "01-04             -8.8          4.4 2015-01-04\n",
       "01-05            -15.5          2.8 2015-01-05"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_records_2015 = (df_15.groupby(['Month_date'])['Data_Value']).agg([('Record Low', 'min'), ('Record High', 'max')])\n",
    "df_records_2015['Date'] = df_15['Date'].sort_values().unique() # mantendo a data pra utilizar como index posteriormente\n",
    "df_records_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 3)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_records_2015.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recordes de 10 anos (2005 - 2014) que foram quebrados em 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Record Low</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-05</th>\n",
       "      <td>-15.5</td>\n",
       "      <td>2015-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-11</th>\n",
       "      <td>-20.0</td>\n",
       "      <td>2015-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02-03</th>\n",
       "      <td>-23.8</td>\n",
       "      <td>2015-02-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02-14</th>\n",
       "      <td>-23.9</td>\n",
       "      <td>2015-02-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02-15</th>\n",
       "      <td>-26.0</td>\n",
       "      <td>2015-02-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Record Low       Date\n",
       "Month_date                       \n",
       "01-05            -15.5 2015-01-05\n",
       "01-11            -20.0 2015-01-11\n",
       "02-03            -23.8 2015-02-03\n",
       "02-14            -23.9 2015-02-14\n",
       "02-15            -26.0 2015-02-15"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# menores temperaturas registradas em 10 anos (2005 - 2014) quebradas em 2015\n",
    "df_low_broken = df_records_2015[df_records_2015['Record Low'] < df_records['Record Low']].filter(['Record Low', 'Date'])\n",
    "df_low_broken.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Record High</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-09</th>\n",
       "      <td>8.3</td>\n",
       "      <td>2015-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-17</th>\n",
       "      <td>27.8</td>\n",
       "      <td>2015-04-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-07</th>\n",
       "      <td>30.6</td>\n",
       "      <td>2015-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-08</th>\n",
       "      <td>33.3</td>\n",
       "      <td>2015-05-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-11</th>\n",
       "      <td>30.6</td>\n",
       "      <td>2015-05-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Record High       Date\n",
       "Month_date                        \n",
       "02-09               8.3 2015-02-09\n",
       "04-17              27.8 2015-04-17\n",
       "05-07              30.6 2015-05-07\n",
       "05-08              33.3 2015-05-08\n",
       "05-11              30.6 2015-05-11"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maiores temperaturas registradas em 10 anos (2005 - 2014) quebradas em 2015\n",
    "df_high_broken = df_records_2015[df_records_2015['Record High'] > df_records['Record High']].filter(['Record High', 'Date'])\n",
    "df_high_broken.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization\n",
    "\n",
    "**Para visualizarmos os dados, devemos primeiramente definir a data de observação (ano de 2015)**\n",
    "\n",
    "`date_index = np.arange('2015-01-01','2016-01-01', dtype='datetime64[D]')`\n",
    "\n",
    "**Posteriormente criamos a figura e plotamos as linhas de máximas e mínimas**\n",
    "\n",
    "`plt.plot(date_index,df_records['Record High'], color='lightcoral', linewidth=2.5)`\n",
    "\n",
    "`plt.plot(date_index,df_records['Record Low'], color='skyblue', linewidth=2.5)`\n",
    "\n",
    "**Entao plotamos um scatterplot dos recordes que 2015 quebrou**\n",
    "\n",
    "É necessária conversar para datetime, das datas do dataframe dos recordes quebrados, para estarem na mesma dimensão do espaço de visualização criado em `date_index`\n",
    "\n",
    "Serão plotados triangulos para mostrar recordes de máxima, e triangulos invertidos para recordes de mínima.\n",
    "\n",
    "```plt.plot(list(map(pd.to_datetime, df_high_broken['Date'])), df_high_broken['Record High'], 'r^',\n",
    "        list(map(pd.to_datetime, df_low_broken['Date'])), df_low_broken['Record Low'], 'cv', ms=8, alpha=1)```\n",
    "      \n",
    "**Linha horizontal para salientar 0 graus**\n",
    "\n",
    "``plt.axhline(linewidth=0.5, color='b', alpha=0.5)``\n",
    "\n",
    "**Definimos os limites de x e y**\n",
    "\n",
    "Dessa maneira, o gráfico fica sem espaços em branco com relação as bordas.\n",
    "\n",
    "``ax.axis(['2015/01/01','2015/12/31',-50,50])``\n",
    "\n",
    "**Renomear os ticks**\n",
    "\n",
    "``ax.set_xticklabels(('jan', 'fev', 'mar', 'abr', 'maio', 'jun', 'jul', 'ago', 'set', 'out', 'nov', 'dez'),fontsize=16)\n",
    "ax.set_yticklabels(('50 $^{\\circ}$C', '40 $^{\\circ}$C', '20 $^{\\circ}$C', \n",
    "                    '0 $^{\\circ}$C', '-20 $^{\\circ}$C', '-40 $^{\\circ}$C', '-50 $^{\\circ}$C'), fontsize=16)``\n",
    "\n",
    "**Sombrear a área entre eles**\n",
    "\n",
    "``ax.fill_between(date_index, df_records['Record High'], df_records['Record Low'], facecolor='grey', alpha=0.3)``\n",
    "\n",
    "**Por fim adicionar titulo e legenda**\n",
    "\n",
    "``plt.title()\n",
    "plt.legend()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3789e09f60>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LZV9cPKYjOD9Geo+SP3aHpbTbgPVy6dsSTfO6C8s+K+X/WKF8v87OjQbH6tBc2hEp7ab8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\nCCGEEEII4VT/D8d6SzParvojAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3789fcd470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# range para visualização\n",
    "date_index = np.arange('2015-01-01','2016-01-01', dtype='datetime64[D]')\n",
    "date_index = list(map(pd.to_datetime, date_index)) # conversao para lista\n",
    "plt.figure(figsize=(20, 10)) # criacao da figura que sera desenhado o grafico\n",
    "\n",
    "# plot maximas\n",
    "plt.plot(date_index,df_records['Record High'], color='lightcoral', linewidth=2.5)\n",
    "\n",
    "# plot minimas\n",
    "plt.plot(date_index,df_records['Record Low'], color='skyblue', linewidth=2.5)\n",
    "\n",
    "#2015 recordes\n",
    "plt.plot(list(map(pd.to_datetime, df_high_broken['Date'])), df_high_broken['Record High'], 'r^',\n",
    "        list(map(pd.to_datetime, df_low_broken['Date'])), df_low_broken['Record Low'], 'cv', ms=8, alpha=1)\n",
    "\n",
    "# linha horizontal na temperatura 0\n",
    "plt.axhline(linewidth=0.5, color='b', alpha=0.5)\n",
    "\n",
    "# limites de x e y\n",
    "ax = plt.gca() # pegando os eixos atuais (get current axes)\n",
    "ax.axis(['2015/01/01','2015/12/31',-50,50])\n",
    "\n",
    "# renomeando os ticks\n",
    "ax.set_xticklabels(('jan', 'fev', 'mar', 'abr', 'maio', 'jun', 'jul', 'ago', 'set', 'out', 'nov', 'dez'),fontsize=16)\n",
    "ax.set_yticklabels(('50 $^{\\circ}$C', '40 $^{\\circ}$C', '20 $^{\\circ}$C', \n",
    "                    '0 $^{\\circ}$C', '-20 $^{\\circ}$C', '-40 $^{\\circ}$C', '-50 $^{\\circ}$C'), fontsize=16)\n",
    "\n",
    "# pintar a diferença entre as linhas\n",
    "ax.fill_between(date_index, df_records['Record High'], df_records['Record Low'], facecolor='grey', alpha=0.3)\n",
    "\n",
    "# Adicionar titulo\n",
    "plt.title(\"Recordes Metereológicos de 2005 a 2014 vs Recordes Metereológicos de 2015 na \\n Região de Ann Arbor, Michigan\", fontsize=19)\n",
    "\n",
    "# Adicionar legenda\n",
    "plt.legend(['Temperatura máxima', 'Temperatura Mínima', \"Recorde Temp. Máxima em 2015\", \"Recorde Temp. Mínima em 2015\"], loc='lower center', prop={'size':16})\n"
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