COVID-19 - Casos a cada dia em SP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise do COVID-19 em SP (Infectados)\n",
    "\n",
    "## 1. Base de Dados Original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Estado</th>\n",
       "      <th>Sigla</th>\n",
       "      <th>26/2</th>\n",
       "      <th>27/2</th>\n",
       "      <th>28/2</th>\n",
       "      <th>29/2</th>\n",
       "      <th>1/3</th>\n",
       "      <th>2/3</th>\n",
       "      <th>3/3</th>\n",
       "      <th>4/3</th>\n",
       "      <th>...</th>\n",
       "      <th>23/3</th>\n",
       "      <th>24/3</th>\n",
       "      <th>25/3</th>\n",
       "      <th>26/3</th>\n",
       "      <th>27/3</th>\n",
       "      <th>28/3</th>\n",
       "      <th>29/3</th>\n",
       "      <th>30/3</th>\n",
       "      <th>31/3</th>\n",
       "      <th>1/4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brasil</td>\n",
       "      <td>BR</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1891</td>\n",
       "      <td>2201</td>\n",
       "      <td>2433</td>\n",
       "      <td>2915</td>\n",
       "      <td>3417</td>\n",
       "      <td>3904</td>\n",
       "      <td>4256</td>\n",
       "      <td>4579</td>\n",
       "      <td>5717</td>\n",
       "      <td>6836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acre</td>\n",
       "      <td>AC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>34</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amapá</td>\n",
       "      <td>AP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>AM</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>67</td>\n",
       "      <td>81</td>\n",
       "      <td>111</td>\n",
       "      <td>140</td>\n",
       "      <td>151</td>\n",
       "      <td>175</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pará</td>\n",
       "      <td>PA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rondônia</td>\n",
       "      <td>RO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Roraima</td>\n",
       "      <td>RR</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tocantins</td>\n",
       "      <td>TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Norte</td>\n",
       "      <td>(N)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>82</td>\n",
       "      <td>105</td>\n",
       "      <td>126</td>\n",
       "      <td>145</td>\n",
       "      <td>184</td>\n",
       "      <td>227</td>\n",
       "      <td>254</td>\n",
       "      <td>294</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alagoas</td>\n",
       "      <td>AL</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Estado Sigla  26/2  27/2  28/2  29/2  1/3  2/3  3/3  4/3  ...  23/3  \\\n",
       "0     Brasil    BR     1     1     1     2    2    2    2    3  ...  1891   \n",
       "1       Acre    AC     0     0     0     0    0    0    0    0  ...    11   \n",
       "2      Amapá    AP     0     0     0     0    0    0    0    0  ...     1   \n",
       "3   Amazonas    AM     0     0     0     0    0    0    0    0  ...    32   \n",
       "4       Pará    PA     0     0     0     0    0    0    0    0  ...     5   \n",
       "5   Rondônia    RO     0     0     0     0    0    0    0    0  ...     3   \n",
       "6    Roraima    RR     0     0     0     0    0    0    0    0  ...     2   \n",
       "7  Tocantins    TO     0     0     0     0    0    0    0    0  ...     5   \n",
       "8      Norte   (N)     0     0     0     0    0    0    0    0  ...    59   \n",
       "9    Alagoas    AL     0     0     0     0    0    0    0    0  ...     7   \n",
       "\n",
       "   24/3  25/3  26/3  27/3  28/3  29/3  30/3  31/3   1/4  \n",
       "0  2201  2433  2915  3417  3904  4256  4579  5717  6836  \n",
       "1    17    23    24    25    25    34    42    42    43  \n",
       "2     1     1     2     2     4     4     8    10    11  \n",
       "3    47    54    67    81   111   140   151   175   200  \n",
       "4     5     7    13    13    17    18    21    32    40  \n",
       "5     3     5     5     6     6     6     6     8     9  \n",
       "6     2     8     8    10    12    16    16    16    22  \n",
       "7     7     7     7     8     9     9    10    11    12  \n",
       "8    82   105   126   145   184   227   254   294   337  \n",
       "9     9    11    11    11    14    17    17    18    18  \n",
       "\n",
       "[10 rows x 38 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/elhenrico/covid19-Brazil-timeseries/master/confirmed-cases.csv\").fillna(0)\n",
    "df.rename({\"Unnamed: 0\": \"Estado\", \"Unnamed: 1\": \"Sigla\"}, axis='columns', inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Total de casos diários no Brasil (ordenação decrescente)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Estado</th>\n",
       "      <th>Sigla</th>\n",
       "      <th>26/2</th>\n",
       "      <th>27/2</th>\n",
       "      <th>28/2</th>\n",
       "      <th>29/2</th>\n",
       "      <th>1/3</th>\n",
       "      <th>2/3</th>\n",
       "      <th>3/3</th>\n",
       "      <th>4/3</th>\n",
       "      <th>...</th>\n",
       "      <th>24/3</th>\n",
       "      <th>25/3</th>\n",
       "      <th>26/3</th>\n",
       "      <th>27/3</th>\n",
       "      <th>28/3</th>\n",
       "      <th>29/3</th>\n",
       "      <th>30/3</th>\n",
       "      <th>31/3</th>\n",
       "      <th>1/4</th>\n",
       "      <th>Casos Confirmados</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>810</td>\n",
       "      <td>862</td>\n",
       "      <td>1052</td>\n",
       "      <td>1223</td>\n",
       "      <td>1406</td>\n",
       "      <td>1451</td>\n",
       "      <td>1517</td>\n",
       "      <td>2339</td>\n",
       "      <td>2981</td>\n",
       "      <td>2981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>RJ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>305</td>\n",
       "      <td>370</td>\n",
       "      <td>421</td>\n",
       "      <td>493</td>\n",
       "      <td>558</td>\n",
       "      <td>600</td>\n",
       "      <td>657</td>\n",
       "      <td>708</td>\n",
       "      <td>832</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ceará</td>\n",
       "      <td>CE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>182</td>\n",
       "      <td>200</td>\n",
       "      <td>235</td>\n",
       "      <td>282</td>\n",
       "      <td>314</td>\n",
       "      <td>348</td>\n",
       "      <td>372</td>\n",
       "      <td>390</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Distrito Federal</td>\n",
       "      <td>DF</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>160</td>\n",
       "      <td>160</td>\n",
       "      <td>200</td>\n",
       "      <td>230</td>\n",
       "      <td>260</td>\n",
       "      <td>289</td>\n",
       "      <td>312</td>\n",
       "      <td>332</td>\n",
       "      <td>355</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Minas Gerais</td>\n",
       "      <td>MG</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>133</td>\n",
       "      <td>153</td>\n",
       "      <td>189</td>\n",
       "      <td>205</td>\n",
       "      <td>231</td>\n",
       "      <td>261</td>\n",
       "      <td>275</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Rio Grande do Sul</td>\n",
       "      <td>RS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98</td>\n",
       "      <td>123</td>\n",
       "      <td>168</td>\n",
       "      <td>195</td>\n",
       "      <td>197</td>\n",
       "      <td>226</td>\n",
       "      <td>241</td>\n",
       "      <td>274</td>\n",
       "      <td>306</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bahia</td>\n",
       "      <td>BA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>84</td>\n",
       "      <td>104</td>\n",
       "      <td>115</td>\n",
       "      <td>128</td>\n",
       "      <td>154</td>\n",
       "      <td>176</td>\n",
       "      <td>213</td>\n",
       "      <td>246</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Santa Catarina</td>\n",
       "      <td>SC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>109</td>\n",
       "      <td>122</td>\n",
       "      <td>149</td>\n",
       "      <td>184</td>\n",
       "      <td>194</td>\n",
       "      <td>197</td>\n",
       "      <td>219</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Paraná</td>\n",
       "      <td>PR</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>81</td>\n",
       "      <td>102</td>\n",
       "      <td>119</td>\n",
       "      <td>133</td>\n",
       "      <td>148</td>\n",
       "      <td>155</td>\n",
       "      <td>179</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amazonas</td>\n",
       "      <td>AM</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>67</td>\n",
       "      <td>81</td>\n",
       "      <td>111</td>\n",
       "      <td>140</td>\n",
       "      <td>151</td>\n",
       "      <td>175</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Estado Sigla  26/2  27/2  28/2  29/2  1/3  2/3  3/3  4/3  ...  \\\n",
       "22          São Paulo    SP     1     1     1     2    2    2    2    3  ...   \n",
       "21     Rio de Janeiro    RJ     0     0     0     0    0    0    0    0  ...   \n",
       "11              Ceará    CE     0     0     0     0    0    0    0    0  ...   \n",
       "28   Distrito Federal    DF     0     0     0     0    0    0    0    0  ...   \n",
       "20       Minas Gerais    MG     0     0     0     0    0    0    0    0  ...   \n",
       "25  Rio Grande do Sul    RS     0     0     0     0    0    0    0    0  ...   \n",
       "10              Bahia    BA     0     0     0     0    0    0    0    0  ...   \n",
       "26     Santa Catarina    SC     0     0     0     0    0    0    0    0  ...   \n",
       "24             Paraná    PR     0     0     0     0    0    0    0    0  ...   \n",
       "3            Amazonas    AM     0     0     0     0    0    0    0    0  ...   \n",
       "\n",
       "    24/3  25/3  26/3  27/3  28/3  29/3  30/3  31/3   1/4  Casos Confirmados  \n",
       "22   810   862  1052  1223  1406  1451  1517  2339  2981               2981  \n",
       "21   305   370   421   493   558   600   657   708   832                832  \n",
       "11   182   200   235   282   314   348   372   390   444                444  \n",
       "28   160   160   200   230   260   289   312   332   355                355  \n",
       "20   130   133   153   189   205   231   261   275   314                314  \n",
       "25    98   123   168   195   197   226   241   274   306                306  \n",
       "10    76    84   104   115   128   154   176   213   246                246  \n",
       "26   107   109   122   149   184   194   197   219   235                235  \n",
       "24    65    81   102   119   133   148   155   179   224                224  \n",
       "3     47    54    67    81   111   140   151   175   200                200  \n",
       "\n",
       "[10 rows x 39 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Estado != 'Sudeste']\n",
    "df = df[df.Estado != 'Nordeste']\n",
    "df = df[df.Estado != 'Centro-Oeste']\n",
    "df = df[df.Estado != 'Sul']\n",
    "df = df[df.Estado != 'Norte']\n",
    "df = df[df.Estado != 'Brasil']\n",
    "\n",
    "confirmedCasesDf = df.copy()\n",
    "column_list = list(confirmedCasesDf)\n",
    "column_last_day = column_list[-1:]\n",
    "confirmedCasesDf[\"Casos Confirmados\"] = confirmedCasesDf[column_last_day].astype('int32')\n",
    "\n",
    "sortedConfirmedCasesGroupedDf = confirmedCasesDf.sort_values(by=['Casos Confirmados'], ascending = False)\n",
    "sortedConfirmedCasesGroupedDf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Total de casos diários somente em SP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Estado</th>\n",
       "      <th>Sigla</th>\n",
       "      <th>26/2</th>\n",
       "      <th>27/2</th>\n",
       "      <th>28/2</th>\n",
       "      <th>29/2</th>\n",
       "      <th>1/3</th>\n",
       "      <th>2/3</th>\n",
       "      <th>3/3</th>\n",
       "      <th>4/3</th>\n",
       "      <th>...</th>\n",
       "      <th>24/3</th>\n",
       "      <th>25/3</th>\n",
       "      <th>26/3</th>\n",
       "      <th>27/3</th>\n",
       "      <th>28/3</th>\n",
       "      <th>29/3</th>\n",
       "      <th>30/3</th>\n",
       "      <th>31/3</th>\n",
       "      <th>1/4</th>\n",
       "      <th>Casos Confirmados</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>810</td>\n",
       "      <td>862</td>\n",
       "      <td>1052</td>\n",
       "      <td>1223</td>\n",
       "      <td>1406</td>\n",
       "      <td>1451</td>\n",
       "      <td>1517</td>\n",
       "      <td>2339</td>\n",
       "      <td>2981</td>\n",
       "      <td>2981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estado Sigla  26/2  27/2  28/2  29/2  1/3  2/3  3/3  4/3  ...  24/3  \\\n",
       "22  São Paulo    SP     1     1     1     2    2    2    2    3  ...   810   \n",
       "\n",
       "    25/3  26/3  27/3  28/3  29/3  30/3  31/3   1/4  Casos Confirmados  \n",
       "22   862  1052  1223  1406  1451  1517  2339  2981               2981  \n",
       "\n",
       "[1 rows x 39 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmedCasesByDaySPDf = confirmedCasesDf[confirmedCasesDf.Estado == 'São Paulo']\n",
    "confirmedCasesByDaySPDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "DateParseError",
     "evalue": "Invalid date specified (3/32)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\pichau\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[0;32m   1978\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1979\u001b[1;33m             \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1980\u001b[0m             \u001b[1;31m# If tzaware, these values represent unix timestamps, so we\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mDateParseError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-ebaaf47fc9fa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     14\u001b[0m dfForPlotSP = pd.DataFrame(\n\u001b[0;32m     15\u001b[0m     {'datas': datesSP, 'infectados': infectedSP})\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mdfForPlotSP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'datas'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfForPlotSP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'datas'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[0mdfForPlotSP\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\pichau\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\pichau\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    777\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 778\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    779\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    780\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\pichau\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, box, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    461\u001b[0m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m             \u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 463\u001b[1;33m             \u001b[0mallow_object\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    464\u001b[0m         )\n\u001b[0;32m    465\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\pichau\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[0;32m   1982\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"i8\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz_parsed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1983\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1984\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1985\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1986\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtz_parsed\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\pichau\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py\u001b[0m in \u001b[0;36mobjects_to_datetime64ns\u001b[1;34m(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)\u001b[0m\n\u001b[0;32m   1973\u001b[0m             \u001b[0mdayfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1974\u001b[0m             \u001b[0myearfirst\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1975\u001b[1;33m             \u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1976\u001b[0m         )\n\u001b[0;32m   1977\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime_object\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.array_to_datetime_object\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslibs/parsing.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.parsing._parse_delimited_date\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mDateParseError\u001b[0m: Invalid date specified (3/32)"
     ]
    }
   ],
   "source": [
    "confirmedCasesByDaySPCleanDf = confirmedCasesByDaySPDf.drop([\"Estado\", \"Sigla\", \"Casos Confirmados\"], axis=1)\n",
    "datesSP = list(confirmedCasesByDaySPCleanDf)\n",
    "\n",
    "i = 0\n",
    "for d in datesSP:\n",
    "    if i < 9:\n",
    "        datesSP[i] = '03/0' + str(i + 1) + '/2020'\n",
    "    else:\n",
    "        datesSP[i] = '03/' + str(i + 1) + '/2020'\n",
    "    i = i + 1\n",
    "\n",
    "infectedSP = confirmedCasesByDaySPCleanDf.iloc[0]\n",
    "\n",
    "dfForPlotSP = pd.DataFrame(\n",
    "    {'datas': datesSP, 'infectados': infectedSP})\n",
    "dfForPlotSP['datas'] = pd.to_datetime(dfForPlotSP['datas'])\n",
    "dfForPlotSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "left, right = plt.xlim()\n",
    "plt.xlim(0, 0.01)\n",
    "\n",
    "dfForPlotSP.set_index('datas')['infectados'].plot(legend=True, grid=True, label='Confirmed Cases');\n",
    "plt.title(\"COVID-19 in São Paulo\")\n",
    "plt.xlabel(\"Dates\")\n",
    "plt.ylabel(\"Number of cases\")\n",
    "plt.legend()\n",
    "left, right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Previsão por meio da Regressão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Dados comuns a todos os algoritmos abaixo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotGraph(XTrained, yTrained, XReal, yReal, algorithm):\n",
    "    df = pd.DataFrame(\n",
    "    {'datas': X, 'infectados': y.astype(int)})\n",
    "    df['datas'] = pd.to_datetime(df['datas'])\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = plt.axes()\n",
    "    redLine = plt.plot(XReal, yReal, 'r:', label='Confirmed Cases')\n",
    "    df.set_index('datas')['infectados'].plot(legend=True, grid=True, label='Predicted Cases');\n",
    "    plt.title(\"COVID-19 in São Paulo (\"+algorithm+\")\")\n",
    "    plt.xlabel(\"Datas\")\n",
    "    plt.ylabel(\"Número de casos\")\n",
    "    \n",
    "    blue_patch = mpatches.Patch(color='blue', label='Predicted Cases')\n",
    "    red_patch = mpatches.Patch(color='red', label='Confirmed Cases')\n",
    "    plt.legend(handles=[red_patch, blue_patch])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "diasASimular = 60\n",
    "dfForPlotDaysWithInfected = dfForPlotSP[dfForPlotSP['infectados'] != 0]\n",
    "\n",
    "XTrain = np.arange(0, len(dfForPlotDaysWithInfected['datas']))\n",
    "yTrain = dfForPlotDaysWithInfected['infectados']\n",
    "\n",
    "XPredict = np.arange(0, len(XTrain) + diasASimular)\n",
    "\n",
    "XRealCases = dfForPlotDaysWithInfected['datas']\n",
    "yRealCases = dfForPlotDaysWithInfected['infectados']\n",
    "\n",
    "predictedDays = pd.date_range(dfForPlotDaysWithInfected['datas'].iloc[-1] + datetime.timedelta(days=1), periods=diasASimular, freq='D')\n",
    "X = np.append(dfForPlotDaysWithInfected['datas'], predictedDays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Regressão Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#treino\n",
    "from sklearn.linear_model import LinearRegression\n",
    "LRModel = LinearRegression()\n",
    "LRModel.fit(XTrain.reshape(-1, 1), yTrain)\n",
    "\n",
    "#predição\n",
    "y = LRModel.predict(XPredict.reshape(-1, 1)).astype(int)\n",
    "y = y + y[0] * -1\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotGraph(X, y, XRealCases, yRealCases, \"Linear Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Polynomial Regression 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#treino\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "polynomialFeatures = PolynomialFeatures(degree = 2)\n",
    "XPoly = polynomialFeatures.fit_transform(XTrain.reshape(-1, 1))\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "polyLinearRegression = LinearRegression()\n",
    "polyLinearRegression.fit(XPoly, yTrain)\n",
    "\n",
    "#predição\n",
    "XPoly = polynomialFeatures.fit_transform(XPredict.reshape(-1, 1))\n",
    "y = polyLinearRegression.predict(XPoly).astype(int)\n",
    "miny = min(y)\n",
    "y = y + miny * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotGraph(X, y, XRealCases, yRealCases, \"Polynomial Linear Regression 2D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Polynomial Regression 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#treino\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "polynomialFeatures = PolynomialFeatures(degree = 3)\n",
    "XPoly = polynomialFeatures.fit_transform(XTrain.reshape(-1, 1))\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "polyLinearRegression = LinearRegression()\n",
    "polyLinearRegression.fit(XPoly, yTrain)\n",
    "\n",
    "#predição\n",
    "XPoly = polynomialFeatures.fit_transform(XPredict.reshape(-1, 1))\n",
    "y = polyLinearRegression.predict(XPoly).astype(int)\n",
    "miny = min(y)\n",
    "y = y + miny * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotGraph(X, y, XRealCases, yRealCases, \"Polynomial Linear Regression 3D\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Polynomial Regression 4D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#treino\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "polynomialFeatures = PolynomialFeatures(degree = 4)\n",
    "XPoly = polynomialFeatures.fit_transform(XTrain.reshape(-1, 1))\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "polyLinearRegression = LinearRegression()\n",
    "polyLinearRegression.fit(XPoly, yTrain)\n",
    "\n",
    "#predição\n",
    "XPoly = polynomialFeatures.fit_transform(XPredict.reshape(-1, 1))\n",
    "y = polyLinearRegression.predict(XPoly).astype(int)\n",
    "miny = min(y)\n",
    "y = y + miny * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotGraph(X, y, XRealCases, yRealCases, \"Polynomial Linear Regression 4D\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}