Projeto 1 - Análise situação Covid 19 na América do Sul.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# O Objetivo desse trabalho é:\n",
    "\n",
    "- Analisar as variantes presentes nos países da América do Sul\n",
    "  \n",
    "  Países: Brasil, Argentina, Uruguai, Paraguai, Chile, Equador, Venezuela, Peru, Colombia, Bolívia\n",
    "  \n",
    "  Obs: Alguns países estão faltando\n",
    "  \n",
    "\n",
    "- Número total de variantes\n",
    "- Avaliar quais as variantes com mais aparições totais\n",
    "- Avaliar o avanço dessas variantes conforme o tempo\n",
    "- Qual país teve mais aparições dessas variantes\n",
    "- Desafio: Se possível plotar um gráfico de mapa da América do Sul!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bibliotecas\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>variant</th>\n",
       "      <th>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
       "      <th>num_sequences_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>B.1.1.277</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>B.1.1.302</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>B.1.1.519</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-07-06</td>\n",
       "      <td>B.1.160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</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>100411</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100412</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>S:677H.Robin1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100413</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>S:677P.Pelican</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100414</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>others</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100415</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>non_who</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100416 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        location        date         variant  num_sequences  perc_sequences  \\\n",
       "0         Angola  2020-07-06           Alpha              0             0.0   \n",
       "1         Angola  2020-07-06       B.1.1.277              0             0.0   \n",
       "2         Angola  2020-07-06       B.1.1.302              0             0.0   \n",
       "3         Angola  2020-07-06       B.1.1.519              0             0.0   \n",
       "4         Angola  2020-07-06         B.1.160              0             0.0   \n",
       "...          ...         ...             ...            ...             ...   \n",
       "100411  Zimbabwe  2021-11-01         Omicron              0             0.0   \n",
       "100412  Zimbabwe  2021-11-01   S:677H.Robin1              0             0.0   \n",
       "100413  Zimbabwe  2021-11-01  S:677P.Pelican              0             0.0   \n",
       "100414  Zimbabwe  2021-11-01          others              0             0.0   \n",
       "100415  Zimbabwe  2021-11-01         non_who              0             0.0   \n",
       "\n",
       "        num_sequences_total  \n",
       "0                         3  \n",
       "1                         3  \n",
       "2                         3  \n",
       "3                         3  \n",
       "4                         3  \n",
       "...                     ...  \n",
       "100411                    6  \n",
       "100412                    6  \n",
       "100413                    6  \n",
       "100414                    6  \n",
       "100415                    6  \n",
       "\n",
       "[100416 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importando Base de Dados\n",
    "db = pd.read_csv(r'covid-variants.csv')\n",
    "display(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100416 entries, 0 to 100415\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   location             100416 non-null  object \n",
      " 1   date                 100416 non-null  object \n",
      " 2   variant              100416 non-null  object \n",
      " 3   num_sequences        100416 non-null  int64  \n",
      " 4   perc_sequences       100416 non-null  float64\n",
      " 5   num_sequences_total  100416 non-null  int64  \n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 4.6+ MB\n",
      "------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100416 entries, 0 to 100415\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count   Dtype         \n",
      "---  ------               --------------   -----         \n",
      " 0   location             100416 non-null  object        \n",
      " 1   date                 100416 non-null  datetime64[ns]\n",
      " 2   variant              100416 non-null  object        \n",
      " 3   num_sequences        100416 non-null  int64         \n",
      " 4   perc_sequences       100416 non-null  float64       \n",
      " 5   num_sequences_total  100416 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(2)\n",
      "memory usage: 4.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Tratamento de Dados\n",
    "\n",
    "# Avaliando as variáveis\n",
    "db.info()\n",
    "\n",
    "# Observe que a coluna 'date' não está sendo interpretada como data, mas sim como 'object'\n",
    "db.date = pd.to_datetime(db.date)\n",
    "print('-'*60)\n",
    "db.info()\n",
    "\n",
    "# Veja que agora todas as colunas estão sendo interpretadas corretamente\n",
    "\n",
    "# Não há colunas nulas, a partir desse ponto não é necessário fazer mais nenhuma correção"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
       "      <th>num_sequences_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Delta</th>\n",
       "      <td>3834100</td>\n",
       "      <td>122263.17</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>1132595</td>\n",
       "      <td>59642.42</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_who</th>\n",
       "      <td>931098</td>\n",
       "      <td>199595.71</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>642603</td>\n",
       "      <td>170197.70</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.177</th>\n",
       "      <td>170457</td>\n",
       "      <td>11681.60</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omicron</th>\n",
       "      <td>115538</td>\n",
       "      <td>5670.92</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gamma</th>\n",
       "      <td>115156</td>\n",
       "      <td>8793.61</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epsilon</th>\n",
       "      <td>66127</td>\n",
       "      <td>737.89</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iota</th>\n",
       "      <td>42905</td>\n",
       "      <td>908.62</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>40514</td>\n",
       "      <td>14394.47</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.160</th>\n",
       "      <td>34019</td>\n",
       "      <td>5256.85</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.258</th>\n",
       "      <td>30787</td>\n",
       "      <td>7789.39</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.1.519</th>\n",
       "      <td>22825</td>\n",
       "      <td>998.30</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.221</th>\n",
       "      <td>15377</td>\n",
       "      <td>2117.31</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mu</th>\n",
       "      <td>14248</td>\n",
       "      <td>2208.59</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lambda</th>\n",
       "      <td>9411</td>\n",
       "      <td>1930.91</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kappa</th>\n",
       "      <td>7477</td>\n",
       "      <td>546.58</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eta</th>\n",
       "      <td>6924</td>\n",
       "      <td>1707.11</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S:677H.Robin1</th>\n",
       "      <td>6547</td>\n",
       "      <td>133.46</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S:677P.Pelican</th>\n",
       "      <td>4837</td>\n",
       "      <td>43.12</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.1.277</th>\n",
       "      <td>1183</td>\n",
       "      <td>590.49</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.620</th>\n",
       "      <td>1016</td>\n",
       "      <td>235.91</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.367</th>\n",
       "      <td>961</td>\n",
       "      <td>293.37</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.1.302</th>\n",
       "      <td>486</td>\n",
       "      <td>258.21</td>\n",
       "      <td>6316093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                num_sequences  perc_sequences  num_sequences_total\n",
       "variant                                                           \n",
       "Delta                 3834100       122263.17              6316093\n",
       "Alpha                 1132595        59642.42              6316093\n",
       "non_who                931098       199595.71              6316093\n",
       "others                 642603       170197.70              6316093\n",
       "B.1.177                170457        11681.60              6316093\n",
       "Omicron                115538         5670.92              6316093\n",
       "Gamma                  115156         8793.61              6316093\n",
       "Epsilon                 66127          737.89              6316093\n",
       "Iota                    42905          908.62              6316093\n",
       "Beta                    40514        14394.47              6316093\n",
       "B.1.160                 34019         5256.85              6316093\n",
       "B.1.258                 30787         7789.39              6316093\n",
       "B.1.1.519               22825          998.30              6316093\n",
       "B.1.221                 15377         2117.31              6316093\n",
       "Mu                      14248         2208.59              6316093\n",
       "Lambda                   9411         1930.91              6316093\n",
       "Kappa                    7477          546.58              6316093\n",
       "Eta                      6924         1707.11              6316093\n",
       "S:677H.Robin1            6547          133.46              6316093\n",
       "S:677P.Pelican           4837           43.12              6316093\n",
       "B.1.1.277                1183          590.49              6316093\n",
       "B.1.620                  1016          235.91              6316093\n",
       "B.1.367                   961          293.37              6316093\n",
       "B.1.1.302                 486          258.21              6316093"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1325</td>\n",
       "      <td>3507.65</td>\n",
       "      <td>25320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>10689</td>\n",
       "      <td>6837.93</td>\n",
       "      <td>201864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>3302</td>\n",
       "      <td>3002.21</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>62668</td>\n",
       "      <td>6502.24</td>\n",
       "      <td>1132776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>14627</td>\n",
       "      <td>6356.05</td>\n",
       "      <td>301920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2420758</td>\n",
       "      <td>6738.15</td>\n",
       "      <td>49960248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>1189</td>\n",
       "      <td>4592.69</td>\n",
       "      <td>16368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>1811</td>\n",
       "      <td>2350.00</td>\n",
       "      <td>43320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>1565</td>\n",
       "      <td>4624.63</td>\n",
       "      <td>26808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>871</td>\n",
       "      <td>3758.60</td>\n",
       "      <td>16464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               num_sequences  perc_sequences  num_sequences_total\n",
       "location                                                         \n",
       "Angola                  1325         3507.65                25320\n",
       "Argentina              10689         6837.93               201864\n",
       "Aruba                   3302         3002.21                72000\n",
       "Australia              62668         6502.24              1132776\n",
       "Austria                14627         6356.05               301920\n",
       "...                      ...             ...                  ...\n",
       "United States        2420758         6738.15             49960248\n",
       "Uruguay                 1189         4592.69                16368\n",
       "Vietnam                 1811         2350.00                43320\n",
       "Zambia                  1565         4624.63                26808\n",
       "Zimbabwe                 871         3758.60                16464\n",
       "\n",
       "[121 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Angola', 'Argentina', 'Aruba', 'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium', 'Belize', 'Benin', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Cambodia', 'Cameroon', 'Canada', 'Chile', 'Colombia', 'Costa Rica', 'Croatia', 'Curacao', 'Cyprus', 'Czechia', 'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt', 'Estonia', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Guatemala', 'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kosovo', 'Kuwait', 'Latvia', 'Lebanon', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Malta', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Nepal', 'Netherlands', 'New Zealand', 'Nigeria', 'North Macedonia', 'Norway', 'Oman', 'Pakistan', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Senegal', 'Serbia', 'Seychelles', 'Singapore', 'Sint Maarten (Dutch part)', 'Slovakia', 'Slovenia', 'South Africa', 'South Korea', 'Spain', 'Sri Lanka', 'Suriname', 'Sweden', 'Switzerland', 'Thailand', 'Togo', 'Trinidad and Tobago', 'Turkey', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Vietnam', 'Zambia', 'Zimbabwe']\n",
      "['Alpha', 'B.1.1.277', 'B.1.1.302', 'B.1.1.519', 'B.1.160', 'B.1.177', 'B.1.221', 'B.1.258', 'B.1.367', 'B.1.620', 'Beta', 'Delta', 'Epsilon', 'Eta', 'Gamma', 'Iota', 'Kappa', 'Lambda', 'Mu', 'Omicron', 'S:677H.Robin1', 'S:677P.Pelican', 'non_who', 'others']\n"
     ]
    }
   ],
   "source": [
    "# Vamos analisar quais variantes e quais países estão disponíveis\n",
    "\n",
    "df_variantes = db.groupby(['variant']).sum().sort_values(by = 'num_sequences', ascending = False)\n",
    "df_paises = db.groupby(['location']).sum()\n",
    "\n",
    "# Observe que as variantes Delta e Alpha são as mais relevantes\n",
    "\n",
    "display(df_variantes)\n",
    "display(df_paises)\n",
    "\n",
    "# Para visualisar mais facilmente os nomes dos países e variantes\n",
    "paises_lt = list(set(db['location']))\n",
    "paises_lt.sort()\n",
    "print(paises_lt)\n",
    "\n",
    "variant_lt = list(set(db['variant']))\n",
    "variant_lt.sort()\n",
    "print(variant_lt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Alpha', 'B.1.1.277', 'B.1.1.302', 'B.1.1.519', 'B.1.160',\n",
       "       'B.1.177', 'B.1.221', 'B.1.258', 'B.1.367', 'B.1.620', 'Beta',\n",
       "       'Delta', 'Epsilon', 'Eta', 'Gamma', 'Iota', 'Kappa', 'Lambda',\n",
       "       'Mu', 'Omicron', 'S:677H.Robin1', 'S:677P.Pelican', 'others',\n",
       "       'non_who'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conseguindo uma lista das variantes de uma outra forma\n",
    "db.variant.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-05-11     78\n",
       "2020-05-25     72\n",
       "2020-06-08     78\n",
       "2020-06-22     79\n",
       "2020-07-06     74\n",
       "2020-07-20     79\n",
       "2020-08-03     77\n",
       "2020-08-17     87\n",
       "2020-08-31     86\n",
       "2020-09-14     84\n",
       "2020-09-28     84\n",
       "2020-10-12     91\n",
       "2020-10-26     86\n",
       "2020-11-09     87\n",
       "2020-11-23     95\n",
       "2020-12-07     93\n",
       "2020-12-21    101\n",
       "2021-01-04    109\n",
       "2021-01-11     99\n",
       "2021-01-25    112\n",
       "2021-02-08    112\n",
       "2021-02-22    109\n",
       "2021-03-08    110\n",
       "2021-03-22    105\n",
       "2021-04-05    105\n",
       "2021-04-19    110\n",
       "2021-05-03    110\n",
       "2021-05-17    106\n",
       "2021-05-31    108\n",
       "2021-06-14    107\n",
       "2021-06-28    107\n",
       "2021-07-12    110\n",
       "2021-07-26    110\n",
       "2021-08-09    108\n",
       "2021-08-23    101\n",
       "2021-09-06    103\n",
       "2021-09-20     96\n",
       "2021-10-04     94\n",
       "2021-10-18     91\n",
       "2021-11-01     95\n",
       "2021-11-15     89\n",
       "2021-11-29     85\n",
       "2021-12-13     86\n",
       "2021-12-27     64\n",
       "2022-01-05     12\n",
       "Name: location, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-05-11    24\n",
       "2020-05-25    24\n",
       "2020-06-08    24\n",
       "2020-06-22    24\n",
       "2020-07-06    24\n",
       "2020-07-20    24\n",
       "2020-08-03    24\n",
       "2020-08-17    24\n",
       "2020-08-31    24\n",
       "2020-09-14    24\n",
       "2020-09-28    24\n",
       "2020-10-12    24\n",
       "2020-10-26    24\n",
       "2020-11-09    24\n",
       "2020-11-23    24\n",
       "2020-12-07    24\n",
       "2020-12-21    24\n",
       "2021-01-04    24\n",
       "2021-01-11    24\n",
       "2021-01-25    24\n",
       "2021-02-08    24\n",
       "2021-02-22    24\n",
       "2021-03-08    24\n",
       "2021-03-22    24\n",
       "2021-04-05    24\n",
       "2021-04-19    24\n",
       "2021-05-03    24\n",
       "2021-05-17    24\n",
       "2021-05-31    24\n",
       "2021-06-14    24\n",
       "2021-06-28    24\n",
       "2021-07-12    24\n",
       "2021-07-26    24\n",
       "2021-08-09    24\n",
       "2021-08-23    24\n",
       "2021-09-06    24\n",
       "2021-09-20    24\n",
       "2021-10-04    24\n",
       "2021-10-18    24\n",
       "2021-11-01    24\n",
       "2021-11-15    24\n",
       "2021-11-29    24\n",
       "2021-12-13    24\n",
       "2021-12-27    24\n",
       "2022-01-05    24\n",
       "Name: variant, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existem informações de 24 variantes de vírus\n",
      "Existem informações de 121 países diferentes\n"
     ]
    }
   ],
   "source": [
    "# Descobrindo o número de países disponível e o número de variantes disponível\n",
    "\n",
    "dados_paises = db.groupby('date')['location'].nunique()\n",
    "dados_variantes = db.groupby('date')['variant'].nunique()\n",
    "\n",
    "display(dados_paises, dados_variantes)\n",
    "\n",
    "# Observe que não há dados de todos os países em todas as datas!\n",
    "# Isso se deve talvez a não terem sido registrados nenhum caso nessa data específica nesses países!\n",
    "# Por outro lado existem dados de todas as variantes em todas as datas!\n",
    "\n",
    "# Número exato de variantes e países\n",
    "\n",
    "print(f'Existem informações de {len(df_variantes)} variantes de vírus')\n",
    "print(f'Existem informações de {len(df_paises)} países diferentes')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avaliar quais países estão disponíveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brazil está na lista!\n",
      "Argentina está na lista!\n",
      "Uruguay está na lista!\n",
      "Paraguay está na lista!\n",
      "Chile está na lista!\n",
      "Ecuador está na lista!\n",
      "Venezuela não está na lista!\n",
      "Colombia está na lista!\n",
      "Bolívia não está na lista!\n",
      "['Brazil', 'Argentina', 'Uruguay', 'Paraguay', 'Chile', 'Ecuador', 'Peru', 'Colombia']\n"
     ]
    }
   ],
   "source": [
    "paises_amesul_lt = ['Brazil', 'Argentina', 'Uruguay', 'Paraguay', 'Chile', 'Ecuador', 'Venezuela', 'Peru', 'Colombia', 'Bolívia']\n",
    "\n",
    "for pais in paises_amesul_lt:\n",
    "    if pais in paises_lt:\n",
    "        print(f'{pais} está na lista!')\n",
    "    else:\n",
    "        print(f'{pais} não está na lista!')\n",
    "        paises_amesul_lt.remove(pais)\n",
    "        \n",
    "print(paises_amesul_lt)\n",
    "\n",
    "# Não existem dados apenas de 2 países, Venezuela e Bolívia "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtrar nossa base de dados para avaliar apenas os países de interesse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>variant</th>\n",
       "      <th>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
       "      <th>num_sequences_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>Alpha</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>B.1.1.277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>B.1.1.302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>B.1.1.519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2020-05-11</td>\n",
       "      <td>B.1.160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>311.0</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>7843</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>Omicron</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.58</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7844</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>S:677H.Robin1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7845</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>S:677P.Pelican</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7846</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>others</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7847</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2021-12-27</td>\n",
       "      <td>non_who</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7848 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      location       date         variant  num_sequences  perc_sequences  \\\n",
       "0       Brazil 2020-05-11           Alpha            0.0            0.00   \n",
       "1       Brazil 2020-05-11       B.1.1.277            0.0            0.00   \n",
       "2       Brazil 2020-05-11       B.1.1.302            0.0            0.00   \n",
       "3       Brazil 2020-05-11       B.1.1.519            0.0            0.00   \n",
       "4       Brazil 2020-05-11         B.1.160            0.0            0.00   \n",
       "...        ...        ...             ...            ...             ...   \n",
       "7843  Colombia 2021-12-27         Omicron           37.0           34.58   \n",
       "7844  Colombia 2021-12-27   S:677H.Robin1            0.0            0.00   \n",
       "7845  Colombia 2021-12-27  S:677P.Pelican            0.0            0.00   \n",
       "7846  Colombia 2021-12-27          others            0.0            0.00   \n",
       "7847  Colombia 2021-12-27         non_who            0.0            0.00   \n",
       "\n",
       "      num_sequences_total  \n",
       "0                   311.0  \n",
       "1                   311.0  \n",
       "2                   311.0  \n",
       "3                   311.0  \n",
       "4                   311.0  \n",
       "...                   ...  \n",
       "7843                107.0  \n",
       "7844                107.0  \n",
       "7845                107.0  \n",
       "7846                107.0  \n",
       "7847                107.0  \n",
       "\n",
       "[7848 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Brazil       1056\n",
       "Peru         1056\n",
       "Argentina    1056\n",
       "Ecuador      1056\n",
       "Colombia     1056\n",
       "Chile        1056\n",
       "Paraguay      936\n",
       "Uruguay       576\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pegar apenas os países da america do sul\n",
    "\n",
    "dict = {'location':[],'date': [], 'variant': [], 'num_sequences': [], 'perc_sequences':[], 'num_sequences_total':[]}\n",
    "amsul_df = pd.DataFrame.from_dict(dict)\n",
    "\n",
    "for pais in paises_amesul_lt:\n",
    "    df2 = db[db['location'] == pais]\n",
    "    amsul_df = pd.concat([amsul_df,df2], ignore_index = True)\n",
    "\n",
    "display(amsul_df)\n",
    "amsul_df['location'].value_counts()\n",
    "\n",
    "# Aparentemente todos os países foram filtrados corretamente\n",
    "# Pareguai e Uruguais possuem menos dados\n",
    "# Mas de um modo geral a base esta com quantidade de dados padronizados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Países"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "plt.title('Covid Variantes - America do Sul', fontdict={'fontweight':'bold', 'fontsize': 18})\n",
    "\n",
    "# Plot dos gráficos\n",
    "\n",
    "cor_lt = ['g','b','yellow','m','k','y','r','c']\n",
    "\n",
    "for i, pais in enumerate(paises_amesul_lt):\n",
    "    plt.plot(amsul_df[amsul_df['location'] == pais]['date'],\n",
    "             amsul_df[amsul_df['location'] == pais]['num_sequences_total'],\n",
    "             label = pais,\n",
    "             color = cor_lt[i],\n",
    "             linestyle = '-',\n",
    "             marker = '.')\n",
    "\n",
    "plt.xticks(amsul_df[amsul_df['location'] == 'Brazil']['date'][::200].tolist())\n",
    "\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Num Sequencia Total')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Observando o gráfico podemos notar que o país que:\n",
    "# O país que mais se destaca é o Brasil em termo de número total\n",
    "# Chile apresentou uma alta expressiva entre 2021/08/09 e 2021/11/29\n",
    "# Paraguai ficou com números muito baixos, provavelmente por ser um país menor ou por registros faltantes\n",
    "# A maior parte dos países começou a relatar casos em maior quantidade a partir de 2021/04/19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Variantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
       "      <th>num_sequences_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.12</td>\n",
       "      <td>768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.17</td>\n",
       "      <td>1684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.54</td>\n",
       "      <td>2372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-22</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.31</td>\n",
       "      <td>5996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-03</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>5565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-17</th>\n",
       "      <td>12.0</td>\n",
       "      <td>1.44</td>\n",
       "      <td>5075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>5467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>7415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-28</th>\n",
       "      <td>70.0</td>\n",
       "      <td>9.36</td>\n",
       "      <td>6448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-12</th>\n",
       "      <td>383.0</td>\n",
       "      <td>41.04</td>\n",
       "      <td>8124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-26</th>\n",
       "      <td>868.0</td>\n",
       "      <td>92.69</td>\n",
       "      <td>8381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-09</th>\n",
       "      <td>1980.0</td>\n",
       "      <td>190.47</td>\n",
       "      <td>8064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>3471.0</td>\n",
       "      <td>277.74</td>\n",
       "      <td>7190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>5821.0</td>\n",
       "      <td>384.44</td>\n",
       "      <td>8546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>6976.0</td>\n",
       "      <td>483.26</td>\n",
       "      <td>8623.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-04</th>\n",
       "      <td>7542.0</td>\n",
       "      <td>555.41</td>\n",
       "      <td>8554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>7221.0</td>\n",
       "      <td>593.84</td>\n",
       "      <td>7734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>5903.0</td>\n",
       "      <td>639.79</td>\n",
       "      <td>6205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>5480.0</td>\n",
       "      <td>560.21</td>\n",
       "      <td>5662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29</th>\n",
       "      <td>4330.0</td>\n",
       "      <td>574.78</td>\n",
       "      <td>4407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>2030.0</td>\n",
       "      <td>544.01</td>\n",
       "      <td>2148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>455.0</td>\n",
       "      <td>217.66</td>\n",
       "      <td>1022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            num_sequences  perc_sequences  num_sequences_total\n",
       "date                                                          \n",
       "2020-05-11            0.0            0.00                713.0\n",
       "2020-05-25            0.0            0.00                506.0\n",
       "2020-06-08            0.0            0.00                377.0\n",
       "2020-06-22            0.0            0.00                391.0\n",
       "2020-07-06            0.0            0.00                520.0\n",
       "2020-07-20            0.0            0.00                374.0\n",
       "2020-08-03            0.0            0.00                398.0\n",
       "2020-08-17            0.0            0.00                479.0\n",
       "2020-08-31            0.0            0.00                489.0\n",
       "2020-09-14            0.0            0.00                409.0\n",
       "2020-09-28            0.0            0.00                343.0\n",
       "2020-10-12            0.0            0.00                347.0\n",
       "2020-10-26            0.0            0.00                492.0\n",
       "2020-11-09            0.0            0.00                325.0\n",
       "2020-11-23            1.0            1.59                491.0\n",
       "2020-12-07            0.0            0.00                637.0\n",
       "2020-12-21            1.0            3.12                768.0\n",
       "2021-01-04            2.0            2.42                926.0\n",
       "2021-01-11            0.0            0.00                713.0\n",
       "2021-01-25            0.0            0.00               1292.0\n",
       "2021-02-08            0.0            0.00               1365.0\n",
       "2021-02-22            1.0            4.17               1684.0\n",
       "2021-03-08            1.0            1.54               2372.0\n",
       "2021-03-22            2.0            0.10               2692.0\n",
       "2021-04-05            1.0            0.04               3716.0\n",
       "2021-04-19            4.0            3.31               5996.0\n",
       "2021-05-03            7.0            0.46               5565.0\n",
       "2021-05-17           12.0            1.44               5075.0\n",
       "2021-05-31            5.0            0.62               5467.0\n",
       "2021-06-14           19.0            1.73               7415.0\n",
       "2021-06-28           70.0            9.36               6448.0\n",
       "2021-07-12          383.0           41.04               8124.0\n",
       "2021-07-26          868.0           92.69               8381.0\n",
       "2021-08-09         1980.0          190.47               8064.0\n",
       "2021-08-23         3471.0          277.74               7190.0\n",
       "2021-09-06         5821.0          384.44               8546.0\n",
       "2021-09-20         6976.0          483.26               8623.0\n",
       "2021-10-04         7542.0          555.41               8554.0\n",
       "2021-10-18         7221.0          593.84               7734.0\n",
       "2021-11-01         5903.0          639.79               6205.0\n",
       "2021-11-15         5480.0          560.21               5662.0\n",
       "2021-11-29         4330.0          574.78               4407.0\n",
       "2021-12-13         2030.0          544.01               2148.0\n",
       "2021-12-27          455.0          217.66               1022.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
       "      <th>num_sequences_total</th>\n",
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       "    <tr>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>713.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-05-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>377.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>391.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>489.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>768.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>17.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11</th>\n",
       "      <td>25.0</td>\n",
       "      <td>38.50</td>\n",
       "      <td>713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>37.0</td>\n",
       "      <td>18.03</td>\n",
       "      <td>1292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>21.0</td>\n",
       "      <td>10.55</td>\n",
       "      <td>1365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-22</th>\n",
       "      <td>45.0</td>\n",
       "      <td>15.52</td>\n",
       "      <td>1684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08</th>\n",
       "      <td>68.0</td>\n",
       "      <td>46.08</td>\n",
       "      <td>2372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-22</th>\n",
       "      <td>120.0</td>\n",
       "      <td>48.44</td>\n",
       "      <td>2692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-05</th>\n",
       "      <td>235.0</td>\n",
       "      <td>62.95</td>\n",
       "      <td>3716.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-19</th>\n",
       "      <td>302.0</td>\n",
       "      <td>63.46</td>\n",
       "      <td>5996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-03</th>\n",
       "      <td>190.0</td>\n",
       "      <td>54.89</td>\n",
       "      <td>5565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-17</th>\n",
       "      <td>184.0</td>\n",
       "      <td>51.75</td>\n",
       "      <td>5075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>167.0</td>\n",
       "      <td>37.11</td>\n",
       "      <td>5467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-14</th>\n",
       "      <td>151.0</td>\n",
       "      <td>27.76</td>\n",
       "      <td>7415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-28</th>\n",
       "      <td>192.0</td>\n",
       "      <td>33.51</td>\n",
       "      <td>6448.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-12</th>\n",
       "      <td>104.0</td>\n",
       "      <td>13.03</td>\n",
       "      <td>8124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-26</th>\n",
       "      <td>113.0</td>\n",
       "      <td>20.43</td>\n",
       "      <td>8381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-09</th>\n",
       "      <td>61.0</td>\n",
       "      <td>11.76</td>\n",
       "      <td>8064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-23</th>\n",
       "      <td>36.0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>7190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-06</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2.88</td>\n",
       "      <td>8546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>8623.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-04</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>8554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>7734.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-01</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.81</td>\n",
       "      <td>6205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>5662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            num_sequences  perc_sequences  num_sequences_total\n",
       "date                                                          \n",
       "2020-05-11            0.0            0.00                713.0\n",
       "2020-05-25            0.0            0.00                506.0\n",
       "2020-06-08            0.0            0.00                377.0\n",
       "2020-06-22            0.0            0.00                391.0\n",
       "2020-07-06            0.0            0.00                520.0\n",
       "2020-07-20            0.0            0.00                374.0\n",
       "2020-08-03            0.0            0.00                398.0\n",
       "2020-08-17            0.0            0.00                479.0\n",
       "2020-08-31            0.0            0.00                489.0\n",
       "2020-09-14            0.0            0.00                409.0\n",
       "2020-09-28            0.0            0.00                343.0\n",
       "2020-10-12            0.0            0.00                347.0\n",
       "2020-10-26            0.0            0.00                492.0\n",
       "2020-11-09            0.0            0.00                325.0\n",
       "2020-11-23            0.0            0.00                491.0\n",
       "2020-12-07            0.0            0.00                637.0\n",
       "2020-12-21            0.0            0.00                768.0\n",
       "2021-01-04           17.0           14.30                926.0\n",
       "2021-01-11           25.0           38.50                713.0\n",
       "2021-01-25           37.0           18.03               1292.0\n",
       "2021-02-08           21.0           10.55               1365.0\n",
       "2021-02-22           45.0           15.52               1684.0\n",
       "2021-03-08           68.0           46.08               2372.0\n",
       "2021-03-22          120.0           48.44               2692.0\n",
       "2021-04-05          235.0           62.95               3716.0\n",
       "2021-04-19          302.0           63.46               5996.0\n",
       "2021-05-03          190.0           54.89               5565.0\n",
       "2021-05-17          184.0           51.75               5075.0\n",
       "2021-05-31          167.0           37.11               5467.0\n",
       "2021-06-14          151.0           27.76               7415.0\n",
       "2021-06-28          192.0           33.51               6448.0\n",
       "2021-07-12          104.0           13.03               8124.0\n",
       "2021-07-26          113.0           20.43               8381.0\n",
       "2021-08-09           61.0           11.76               8064.0\n",
       "2021-08-23           36.0            6.75               7190.0\n",
       "2021-09-06           17.0            2.88               8546.0\n",
       "2021-09-20            6.0            2.90               8623.0\n",
       "2021-10-04            3.0            0.96               8554.0\n",
       "2021-10-18            3.0            0.83               7734.0\n",
       "2021-11-01           10.0            1.81               6205.0\n",
       "2021-11-15            4.0            1.20               5662.0\n",
       "2021-11-29            0.0            0.00               4407.0\n",
       "2021-12-13            0.0            0.00               2148.0\n",
       "2021-12-27            0.0            0.00               1022.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Como visto anteriormente, as variantes delta e alpha foram as mais relevantes\n",
    "# Por isso vamos focar nossa atenção nelas\n",
    "\n",
    "# Delta\n",
    "delta_df = amsul_df[amsul_df['variant']=='Delta']\n",
    "delta_df = delta_df.groupby(['date']).sum()\n",
    "\n",
    "# Alpha\n",
    "alpha_df = amsul_df[amsul_df['variant']=='Alpha']\n",
    "alpha_df = alpha_df.groupby(['date']).sum()\n",
    "\n",
    "display(delta_df, alpha_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "plt.title('Alpha x Delta - America do Sul', fontdict={'fontweight':'bold', 'fontsize': 18})\n",
    "\n",
    "plt.plot(list(delta_df.index),\n",
    "         delta_df['num_sequences'],\n",
    "         label = 'Delta',\n",
    "         color = 'y',\n",
    "         linestyle = '-',\n",
    "         marker = '.')\n",
    "\n",
    "plt.plot(list(alpha_df.index),\n",
    "         alpha_df['num_sequences'],\n",
    "         label = 'Alpha',\n",
    "         color = 'g',\n",
    "         linestyle = '-',\n",
    "         marker = '.')\n",
    "\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Num Sequencia Total')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Observando o gráfico, vemos que a variante Alpha foi muito pouco expressiva na América do Sul\n",
    "# Apesar de possuir um grande impacto global"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Brasil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>num_sequences</th>\n",
       "      <th>perc_sequences</th>\n",
       "      <th>num_sequences_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gamma</th>\n",
       "      <td>47252.0</td>\n",
       "      <td>1309.78</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delta</th>\n",
       "      <td>33292.0</td>\n",
       "      <td>916.59</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_who</th>\n",
       "      <td>8189.0</td>\n",
       "      <td>2069.87</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>others</th>\n",
       "      <td>8174.0</td>\n",
       "      <td>2067.92</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>1043.0</td>\n",
       "      <td>40.83</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omicron</th>\n",
       "      <td>189.0</td>\n",
       "      <td>61.34</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mu</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lambda</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.56</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.177</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.1.519</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.160</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S:677H.Robin1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eta</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.221</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.1.277</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iota</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kappa</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.620</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.367</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.258</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S:677P.Pelican</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.1.1.302</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epsilon</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90020.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                num_sequences  perc_sequences  num_sequences_total\n",
       "variant                                                           \n",
       "Gamma                 47252.0         1309.78              90020.0\n",
       "Delta                 33292.0          916.59              90020.0\n",
       "non_who                8189.0         2069.87              90020.0\n",
       "others                 8174.0         2067.92              90020.0\n",
       "Alpha                  1043.0           40.83              90020.0\n",
       "Omicron                 189.0           61.34              90020.0\n",
       "Mu                       23.0            0.52              90020.0\n",
       "Lambda                   21.0            0.56              90020.0\n",
       "Beta                     10.0            0.43              90020.0\n",
       "B.1.177                   5.0            0.71              90020.0\n",
       "B.1.1.519                 4.0            0.41              90020.0\n",
       "B.1.160                   3.0            0.60              90020.0\n",
       "S:677H.Robin1             2.0            0.12              90020.0\n",
       "Eta                       1.0            0.08              90020.0\n",
       "B.1.221                   1.0            0.11              90020.0\n",
       "B.1.1.277                 0.0            0.00              90020.0\n",
       "Iota                      0.0            0.00              90020.0\n",
       "Kappa                     0.0            0.00              90020.0\n",
       "B.1.620                   0.0            0.00              90020.0\n",
       "B.1.367                   0.0            0.00              90020.0\n",
       "B.1.258                   0.0            0.00              90020.0\n",
       "S:677P.Pelican            0.0            0.00              90020.0\n",
       "B.1.1.302                 0.0            0.00              90020.0\n",
       "Epsilon                   0.0            0.00              90020.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "brasil_df = amsul_df[amsul_df['location']=='Brazil']\n",
    "brasil_variant_df = brasil_df.groupby(['variant']).sum().sort_values(by = 'num_sequences', ascending = False)\n",
    "display(brasil_variant_df)\n",
    "\n",
    "# No Brasil as variantes mais relevantes são: Gamma, Delta, Alpha e Omicron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Brasil foi o país com maior número de casos na América do Sul\n",
    "# Vamos analisar quais variantes foram mais expressivas no país\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "plt.title('Variantes - Brasil', fontdict={'fontweight':'bold', 'fontsize': 18})\n",
    "\n",
    "variant_lt = ['Gamma', 'Delta', 'Alpha', 'Omicron']\n",
    "cor_lt = ['blue','green','yellow','red']\n",
    "\n",
    "for i, variant in enumerate(variant_lt):\n",
    "    plt.plot(brasil_df[brasil_df['variant'] == variant]['date'],\n",
    "             brasil_df[brasil_df['variant'] == variant]['num_sequences'],\n",
    "             label = variant,\n",
    "             color = cor_lt[i],\n",
    "             linestyle = '-',\n",
    "             marker = '.')\n",
    "\n",
    "\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Num Sequencia Total')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# É possível que as variantes Gamma e Delta tiveram grande impacto no Brasil\n",
    "# Também é possível verificar que elas tiveram picos em diferentes momentos\n",
    "# Gamma ficou mais ativa entre os meses 4 a 8 de 2021\n",
    "# Delta ficou mais ativa entre os meses 9 a 12 de 2021"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}