mudancas_vendas_anuais.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>709.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16714</th>\n",
       "      <td>Samurai Warriors: Sanada Maru</td>\n",
       "      <td>PS3</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>Tecmo Koei</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16715</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Codemasters</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>0.01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16716</th>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Idea Factory</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16717</th>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Wanadoo</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>Tecmo Koei</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name Platform  Year_of_Release         Genre  \\\n",
       "0                         Wii Sports      Wii           2006.0        Sports   \n",
       "1                  Super Mario Bros.      NES           1985.0      Platform   \n",
       "2                     Mario Kart Wii      Wii           2008.0        Racing   \n",
       "3                  Wii Sports Resort      Wii           2009.0        Sports   \n",
       "4           Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing   \n",
       "...                              ...      ...              ...           ...   \n",
       "16714  Samurai Warriors: Sanada Maru      PS3           2016.0        Action   \n",
       "16715               LMA Manager 2007     X360           2006.0        Sports   \n",
       "16716        Haitaka no Psychedelica      PSV           2016.0     Adventure   \n",
       "16717               Spirits & Spells      GBA           2003.0      Platform   \n",
       "16718            Winning Post 8 2016      PSV           2016.0    Simulation   \n",
       "\n",
       "          Publisher  NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \\\n",
       "0          Nintendo     41.36     28.96      3.77         8.45         82.53   \n",
       "1          Nintendo     29.08      3.58      6.81         0.77         40.24   \n",
       "2          Nintendo     15.68     12.76      3.79         3.29         35.52   \n",
       "3          Nintendo     15.61     10.93      3.28         2.95         32.77   \n",
       "4          Nintendo     11.27      8.89     10.22         1.00         31.37   \n",
       "...             ...       ...       ...       ...          ...           ...   \n",
       "16714    Tecmo Koei      0.00      0.00      0.01         0.00          0.01   \n",
       "16715   Codemasters      0.00      0.01      0.00         0.00          0.01   \n",
       "16716  Idea Factory      0.00      0.00      0.01         0.00          0.01   \n",
       "16717       Wanadoo      0.01      0.00      0.00         0.00          0.01   \n",
       "16718    Tecmo Koei      0.00      0.00      0.01         0.00          0.01   \n",
       "\n",
       "       Critic_Score  Critic_Count User_Score  User_Count Developer Rating  \n",
       "0              76.0          51.0          8       322.0  Nintendo      E  \n",
       "1               NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "2              82.0          73.0        8.3       709.0  Nintendo      E  \n",
       "3              80.0          73.0          8       192.0  Nintendo      E  \n",
       "4               NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "...             ...           ...        ...         ...       ...    ...  \n",
       "16714           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16715           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16716           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16717           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16718           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "\n",
       "[16719 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\migro\\\\Desktop\\\\Programming\\\\interactive_dash\\\\Video_Games.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                object\n",
       "Platform            object\n",
       "Year_of_Release    float64\n",
       "Genre               object\n",
       "Publisher           object\n",
       "NA_Sales           float64\n",
       "EU_Sales           float64\n",
       "JP_Sales           float64\n",
       "Other_Sales        float64\n",
       "Global_Sales       float64\n",
       "Critic_Score       float64\n",
       "Critic_Count       float64\n",
       "User_Score          object\n",
       "User_Count         float64\n",
       "Developer           object\n",
       "Rating              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vendas por Ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\migro\\AppData\\Local\\Temp\\ipykernel_33500\\591548824.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  vendas_por_ano = df.groupby([\"Year_of_Release\"]).sum()[[\"Global_Sales\"]]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981.0</th>\n",
       "      <td>35.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>28.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983.0</th>\n",
       "      <td>16.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984.0</th>\n",
       "      <td>50.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985.0</th>\n",
       "      <td>53.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986.0</th>\n",
       "      <td>37.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>21.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988.0</th>\n",
       "      <td>47.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989.0</th>\n",
       "      <td>73.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>49.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991.0</th>\n",
       "      <td>32.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>76.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993.0</th>\n",
       "      <td>48.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994.0</th>\n",
       "      <td>79.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995.0</th>\n",
       "      <td>88.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996.0</th>\n",
       "      <td>199.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>200.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998.0</th>\n",
       "      <td>256.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>251.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>201.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001.0</th>\n",
       "      <td>331.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>395.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003.0</th>\n",
       "      <td>357.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>419.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>458.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>518.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>605.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>671.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>658.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>590.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>507.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>355.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>361.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>331.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>268.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>130.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.0</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Global_Sales\n",
       "Year_of_Release              \n",
       "1980.0                  11.38\n",
       "1981.0                  35.77\n",
       "1982.0                  28.86\n",
       "1983.0                  16.79\n",
       "1984.0                  50.36\n",
       "1985.0                  53.94\n",
       "1986.0                  37.07\n",
       "1987.0                  21.74\n",
       "1988.0                  47.22\n",
       "1989.0                  73.45\n",
       "1990.0                  49.39\n",
       "1991.0                  32.23\n",
       "1992.0                  76.17\n",
       "1993.0                  48.40\n",
       "1994.0                  79.18\n",
       "1995.0                  88.11\n",
       "1996.0                 199.15\n",
       "1997.0                 200.98\n",
       "1998.0                 256.45\n",
       "1999.0                 251.25\n",
       "2000.0                 201.58\n",
       "2001.0                 331.47\n",
       "2002.0                 395.51\n",
       "2003.0                 357.80\n",
       "2004.0                 419.05\n",
       "2005.0                 458.31\n",
       "2006.0                 518.22\n",
       "2007.0                 605.37\n",
       "2008.0                 671.79\n",
       "2009.0                 658.88\n",
       "2010.0                 590.59\n",
       "2011.0                 507.79\n",
       "2012.0                 355.84\n",
       "2013.0                 361.24\n",
       "2014.0                 331.51\n",
       "2015.0                 268.05\n",
       "2016.0                 130.10\n",
       "2017.0                   0.06\n",
       "2020.0                   0.29"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendas_por_ano = df.groupby([\"Year_of_Release\"]).sum()[[\"Global_Sales\"]]\n",
    "vendas_por_ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238.40513513513514"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendas_por_ano.values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Global_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980.0</th>\n",
       "      <td>11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981.0</th>\n",
       "      <td>35.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982.0</th>\n",
       "      <td>28.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983.0</th>\n",
       "      <td>16.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984.0</th>\n",
       "      <td>50.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985.0</th>\n",
       "      <td>53.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986.0</th>\n",
       "      <td>37.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987.0</th>\n",
       "      <td>21.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988.0</th>\n",
       "      <td>47.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989.0</th>\n",
       "      <td>73.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990.0</th>\n",
       "      <td>49.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991.0</th>\n",
       "      <td>32.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>76.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993.0</th>\n",
       "      <td>48.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994.0</th>\n",
       "      <td>79.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995.0</th>\n",
       "      <td>88.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996.0</th>\n",
       "      <td>199.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>200.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998.0</th>\n",
       "      <td>256.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>251.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>201.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001.0</th>\n",
       "      <td>331.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>395.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003.0</th>\n",
       "      <td>357.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>419.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>458.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>518.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>605.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>671.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>658.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>590.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>507.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>355.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>361.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>331.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>268.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>130.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Global_Sales\n",
       "Year_of_Release              \n",
       "1980.0                  11.38\n",
       "1981.0                  35.77\n",
       "1982.0                  28.86\n",
       "1983.0                  16.79\n",
       "1984.0                  50.36\n",
       "1985.0                  53.94\n",
       "1986.0                  37.07\n",
       "1987.0                  21.74\n",
       "1988.0                  47.22\n",
       "1989.0                  73.45\n",
       "1990.0                  49.39\n",
       "1991.0                  32.23\n",
       "1992.0                  76.17\n",
       "1993.0                  48.40\n",
       "1994.0                  79.18\n",
       "1995.0                  88.11\n",
       "1996.0                 199.15\n",
       "1997.0                 200.98\n",
       "1998.0                 256.45\n",
       "1999.0                 251.25\n",
       "2000.0                 201.58\n",
       "2001.0                 331.47\n",
       "2002.0                 395.51\n",
       "2003.0                 357.80\n",
       "2004.0                 419.05\n",
       "2005.0                 458.31\n",
       "2006.0                 518.22\n",
       "2007.0                 605.37\n",
       "2008.0                 671.79\n",
       "2009.0                 658.88\n",
       "2010.0                 590.59\n",
       "2011.0                 507.79\n",
       "2012.0                 355.84\n",
       "2013.0                 361.24\n",
       "2014.0                 331.51\n",
       "2015.0                 268.05\n",
       "2016.0                 130.10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendas_por_ano = vendas_por_ano.drop(vendas_por_ano.tail(2).index,axis=0)\n",
    "vendas_por_ano\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Year_of_Release'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vendas_por_ano.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9 (tags/v3.10.9:1dd9be6, Dec  6 2022, 20:01:21) [MSC v.1934 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "002ba6258b52fd8cc0ed2b312f9af0c993d49e1b36d6c9838f250730576004ff"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}