data preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nesse notebook preprocessamos o dataset integrado de diferentes formar e os salvamos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets gerados com os diferentes preprocessamentos:\n",
    "\n",
    "- clean_data: dataset sem valores nan e valores categoricos e padronização de dados\n",
    "- clean_data_grouped: mesmo que clean_data, mas com valores com pouca frequencia agrupados\n",
    "\n",
    "- clean_data_gender: mesmo que clean_data, mas com o valores nan do Gender removidos\n",
    "- clean_data_alignment: mesmo que clean_data, mas com o valores nan do Alignment removidos\n",
    "- clean_data_publisher: mesmo que clean_data, mas com o valores nan do Publisher removidos\n",
    "- clean_data_grouped_gender: mesmo que clean_data_grouped, mas com o valores nan do Gender removidos\n",
    "- clean_data__grouped_alignment: mesmo que clean_data_grouped, mas com o valores nan do Alignment removidos\n",
    "- clean_data_grouped_publisher: mesmo que clean_data_grouped, mas com o valores nan do Publisher removidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hero_names</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>...</th>\n",
       "      <th>Web Creation</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <th>Omnipresent</th>\n",
       "      <th>Omniscient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 177 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    hero_names Gender Eye color               Race Hair color  Height  \\\n",
       "0       A-Bomb   Male    yellow              Human    No Hair   203.0   \n",
       "1   Abe Sapien   Male      blue      Icthyo Sapien    No Hair   191.0   \n",
       "2     Abin Sur   Male      blue            Ungaran    No Hair   185.0   \n",
       "3  Abomination   Male     green  Human / Radiation    No Hair   203.0   \n",
       "4      Abraxas   Male      blue      Cosmic Entity      Black     NaN   \n",
       "\n",
       "           Publisher Skin color Alignment  Weight  ...  Web Creation  \\\n",
       "0      Marvel Comics        NaN      good   441.0  ...         False   \n",
       "1  Dark Horse Comics       blue      good    65.0  ...         False   \n",
       "2          DC Comics        red      good    90.0  ...         False   \n",
       "3      Marvel Comics        NaN       bad   441.0  ...         False   \n",
       "4      Marvel Comics        NaN       bad     NaN  ...         False   \n",
       "\n",
       "   Reality Warping  Odin Force  Symbiote Costume  Speed Force  Phoenix Force  \\\n",
       "0            False       False             False        False          False   \n",
       "1            False       False             False        False          False   \n",
       "2            False       False             False        False          False   \n",
       "3            False       False             False        False          False   \n",
       "4            False       False             False        False          False   \n",
       "\n",
       "   Molecular Dissipation  Vision - Cryo  Omnipresent  Omniscient  \n",
       "0                  False          False        False       False  \n",
       "1                  False          False        False       False  \n",
       "2                  False          False        False       False  \n",
       "3                  False          False        False       False  \n",
       "4                  False          False        False       False  \n",
       "\n",
       "[5 rows x 177 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merged_data: Dados já integrados e valores \"-99\" e \"-\" tem valor NaN agora.\n",
    "data = pd.read_csv(\"data/merged_data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([dtype('O'), dtype('float64'), dtype('bool')], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hero_names</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hero_names Gender Eye color               Race Hair color  \\\n",
       "0       A-Bomb   Male    yellow              Human    No Hair   \n",
       "1   Abe Sapien   Male      blue      Icthyo Sapien    No Hair   \n",
       "2     Abin Sur   Male      blue            Ungaran    No Hair   \n",
       "3  Abomination   Male     green  Human / Radiation    No Hair   \n",
       "4      Abraxas   Male      blue      Cosmic Entity      Black   \n",
       "\n",
       "           Publisher Skin color Alignment  \n",
       "0      Marvel Comics        NaN      good  \n",
       "1  Dark Horse Comics       blue      good  \n",
       "2          DC Comics        red      good  \n",
       "3      Marvel Comics        NaN       bad  \n",
       "4      Marvel Comics        NaN       bad  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionamos os objetos do dataset que são as caracteristicas categoricas dos personagens\n",
    "data_objects = data.select_dtypes(include='O')\n",
    "data_objects.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>hero_names</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>684</td>\n",
       "      <td>642</td>\n",
       "      <td>529</td>\n",
       "      <td>413</td>\n",
       "      <td>528</td>\n",
       "      <td>647</td>\n",
       "      <td>72</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>667</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>60</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Spider-Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Black</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>green</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>464</td>\n",
       "      <td>208</td>\n",
       "      <td>195</td>\n",
       "      <td>151</td>\n",
       "      <td>341</td>\n",
       "      <td>21</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        hero_names Gender Eye color   Race Hair color      Publisher  \\\n",
       "count          684    642       529    413        528            647   \n",
       "unique         667      2        22     60         29             24   \n",
       "top     Spider-Man   Male      blue  Human      Black  Marvel Comics   \n",
       "freq             3    464       208    195        151            341   \n",
       "\n",
       "       Skin color Alignment  \n",
       "count          72       653  \n",
       "unique         16         3  \n",
       "top         green      good  \n",
       "freq           21       435  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_objects.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 684 entries, 0 to 683\n",
      "Data columns (total 8 columns):\n",
      "hero_names    684 non-null object\n",
      "Gender        642 non-null object\n",
      "Eye color     529 non-null object\n",
      "Race          413 non-null object\n",
      "Hair color    528 non-null object\n",
      "Publisher     647 non-null object\n",
      "Skin color    72 non-null object\n",
      "Alignment     653 non-null object\n",
      "dtypes: object(8)\n",
      "memory usage: 42.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_objects.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#poucos dados em skin color, então foi removido dos dados originais\n",
    "clean_data = data.drop('Skin color', axis=1)\n",
    "'Skin color' in (clean_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analisar objetos separadamente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female', nan], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_objects.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def barplot(x, data):\n",
    "    ax = sns.countplot(x=x, data=data)\n",
    "    total = data[x].count()\n",
    "    ax.set(ylabel='Quantidade')\n",
    "    for p in ax.patches:\n",
    "        height = p.get_height()\n",
    "        ax.text(p.get_x()+p.get_width()/2.,\n",
    "                height + 3,\n",
    "                '{:1.2f}%'.format((height/total)*100),\n",
    "                ha=\"center\")\n",
    "    plt.show()\n",
    "barplot(\"Gender\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eye color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yellow', 'blue', 'green', 'brown', nan, 'red', 'violet', 'white',\n",
       "       'purple', 'black', 'silver', 'yellow / red', 'grey',\n",
       "       'yellow (without irises)', 'gold', 'blue / white', 'hazel',\n",
       "       'green / blue', 'white / red', 'indigo', 'amber', 'yellow / blue',\n",
       "       'bown'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data['Eye color'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yellow', 'blue', 'green', 'brown', nan, 'red', 'violet', 'white',\n",
       "       'purple', 'black', 'silver', 'yellow / red', 'grey',\n",
       "       'yellow (without irises)', 'gold', 'blue / white', 'hazel',\n",
       "       'green / blue', 'white / red', 'indigo', 'amber', 'yellow / blue'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# valor bown substituido por brown\n",
    "clean_data = clean_data.replace('bown', 'brown')\n",
    "clean_data['Eye color'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blue                       208\n",
       "brown                      120\n",
       "green                       71\n",
       "red                         43\n",
       "black                       21\n",
       "yellow                      19\n",
       "white                       16\n",
       "hazel                        6\n",
       "grey                         5\n",
       "purple                       4\n",
       "gold                         3\n",
       "amber                        2\n",
       "yellow (without irises)      2\n",
       "violet                       2\n",
       "silver                       1\n",
       "yellow / blue                1\n",
       "indigo                       1\n",
       "white / red                  1\n",
       "yellow / red                 1\n",
       "blue / white                 1\n",
       "green / blue                 1\n",
       "Name: Eye color, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_count = clean_data[\"Eye color\"].value_counts()\n",
    "eye_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hazel', 'grey', 'purple', 'gold', 'amber', 'yellow (without irises)',\n",
       "       'violet', 'silver', 'yellow / blue', 'indigo', 'white / red',\n",
       "       'yellow / red', 'blue / white', 'green / blue'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_count[eye_count < 10].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yellow', 'blue', 'green', 'brown', nan, 'red', 'outros', 'white',\n",
       "       'black'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Valores que aparecem menos de 10 vezes na coluna Eye Color são agrupados em um valor unico chamado outros\n",
    "clean_data_grouped = clean_data.copy()\n",
    "clean_data_grouped = clean_data_grouped.replace(eye_count[eye_count < 10].index, \"outros\")\n",
    "clean_data_grouped['Eye color'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Valores nan são substituidos pela moda da coluna\n",
    "clean_data['Eye color'].fillna(clean_data['Eye color'].mode()[0], inplace=True)\n",
    "clean_data_grouped['Eye color'].fillna(clean_data_grouped['Eye color'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Human                 195\n",
       "Mutant                 62\n",
       "God / Eternal          14\n",
       "Cyborg                 11\n",
       "Human / Radiation      11\n",
       "Android                 9\n",
       "Symbiote                8\n",
       "Alien                   7\n",
       "Kryptonian              7\n",
       "Demon                   6\n",
       "Atlantean               5\n",
       "Asgardian               5\n",
       "Alpha                   5\n",
       "Inhuman                 4\n",
       "Cosmic Entity           4\n",
       "New God                 3\n",
       "Animal                  3\n",
       "Human / Altered         3\n",
       "Frost Giant             2\n",
       "Eternal                 2\n",
       "Metahuman               2\n",
       "Human / Cosmic          2\n",
       "Amazon                  2\n",
       "Saiyan                  2\n",
       "Demi-God                2\n",
       "Human-Kree              2\n",
       "Vampire                 2\n",
       "Martian                 1\n",
       "Maiar                   1\n",
       "Talokite                1\n",
       "Dathomirian Zabrak      1\n",
       "Luphomoid               1\n",
       "Gungan                  1\n",
       "Tamaranean              1\n",
       "Bolovaxian              1\n",
       "Kaiju                   1\n",
       "Ungaran                 1\n",
       "Human-Vulcan            1\n",
       "Spartoi                 1\n",
       "Kakarantharaian         1\n",
       "Neyaphem                1\n",
       "Xenomorph XX121         1\n",
       "Human / Clone           1\n",
       "Clone                   1\n",
       "Yautja                  1\n",
       "Zombie                  1\n",
       "Yoda's species          1\n",
       "Mutant / Clone          1\n",
       "Human-Vuldarian         1\n",
       "Rodian                  1\n",
       "Gorilla                 1\n",
       "Strontian               1\n",
       "Bizarro                 1\n",
       "Korugaran               1\n",
       "Human-Spartoi           1\n",
       "Zen-Whoberian           1\n",
       "Icthyo Sapien           1\n",
       "Czarnian                1\n",
       "Flora Colossus          1\n",
       "Parademon               1\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_count = clean_data[\"Race\"].value_counts()\n",
    "race_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Human', 'outros', nan, 'Mutant'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Valores que aparecem menos de 15 vezes na coluna Race são agrupados em um valor unico chamado outros\n",
    "clean_data_grouped = clean_data_grouped.replace(race_count[race_count < 15].index, \"outros\")\n",
    "clean_data_grouped['Race'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Race', clean_data_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Valores nan são substituidos pela moda da coluna\n",
    "clean_data['Race'].fillna(clean_data['Race'].mode()[0], inplace=True)\n",
    "clean_data_grouped['Race'].fillna(clean_data_grouped['Race'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hair color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Black               151\n",
       "Blond                88\n",
       "Brown                82\n",
       "No Hair              69\n",
       "Red                  49\n",
       "White                22\n",
       "Auburn               13\n",
       "Green                 8\n",
       "Strawberry Blond      6\n",
       "Purple                5\n",
       "Brown / White         4\n",
       "Grey                  4\n",
       "Silver                4\n",
       "Blue                  3\n",
       "black                 3\n",
       "blond                 3\n",
       "Orange                2\n",
       "Red / Orange          1\n",
       "Red / Grey            1\n",
       "Yellow                1\n",
       "Red / White           1\n",
       "Pink                  1\n",
       "Brownn                1\n",
       "Black / Blue          1\n",
       "Magenta               1\n",
       "Orange / White        1\n",
       "Brown / Black         1\n",
       "Gold                  1\n",
       "Indigo                1\n",
       "Name: Hair color, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hair_count = clean_data[\"Hair color\"].value_counts()\n",
    "hair_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Erros de digitação corrigidos, 'Brownn', 'black', 'blond' agora são 'Brown', 'Black', 'Blond'.\n",
    "clean_data = clean_data.replace(['Brownn', 'black', 'blond'], ['Brown', 'Black', 'Blond'])\n",
    "clean_data_grouped = clean_data_grouped.replace(['Brownn', 'black', 'blond'], ['Brown', 'Black', 'Blond'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No Hair', 'Black', 'Blond', 'Brown', nan, 'White', 'outros',\n",
       "       'Red'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Valores que aparecem menos de 15 vezes na coluna Hair color são agrupados em um valor unico chamado outros\n",
    "clean_data_grouped = clean_data_grouped.replace(hair_count[hair_count < 15].index, \"outros\")\n",
    "clean_data_grouped['Hair color'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Valores nan são substituidos pela moda da coluna\n",
    "clean_data['Hair color'].fillna(clean_data['Hair color'].mode()[0], inplace=True)\n",
    "clean_data_grouped['Hair color'].fillna(clean_data_grouped['Hair color'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Publisher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marvel Comics        341\n",
       "DC Comics            196\n",
       "NBC - Heroes          18\n",
       "Dark Horse Comics     18\n",
       "Image Comics          14\n",
       "George Lucas          13\n",
       "Star Trek              6\n",
       "SyFy                   5\n",
       "Team Epic TV           5\n",
       "ABC Studios            4\n",
       "Shueisha               4\n",
       "IDW Publishing         4\n",
       "Icon Comics            4\n",
       "HarperCollins          3\n",
       "Wildstorm              3\n",
       "J. K. Rowling          1\n",
       "South Park             1\n",
       "Universal Studios      1\n",
       "Titan Books            1\n",
       "Rebellion              1\n",
       "Hanna-Barbera          1\n",
       "J. R. R. Tolkien       1\n",
       "Sony Pictures          1\n",
       "Microsoft              1\n",
       "Name: Publisher, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "publisher_count = clean_data[\"Publisher\"].value_counts()\n",
    "publisher_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Valores que aparecem menos de 20 vezes na coluna Publisher são agrupados em um valor unico chamado outros para clean_data e clean_data_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Marvel Comics', 'outros', 'DC Comics', nan], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data = clean_data.replace(publisher_count[publisher_count< 20].index, 'outros')\n",
    "clean_data['Publisher'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Marvel Comics', 'outros', 'DC Comics', nan], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data_grouped = clean_data_grouped.replace(publisher_count[publisher_count< 20].index, 'outros')\n",
    "clean_data_grouped['Publisher'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Publisher', clean_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "good       435\n",
       "bad        194\n",
       "neutral     24\n",
       "Name: Alignment, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alignment_count = clean_data[\"Alignment\"].value_counts()\n",
    "alignment_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Alignment', clean_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dados numericos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>203.0</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>185.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>203.0</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height  Weight\n",
       "0   203.0   441.0\n",
       "1   191.0    65.0\n",
       "2   185.0    90.0\n",
       "3   203.0   441.0\n",
       "4     NaN     NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecionar valores numeros\n",
    "data_float = clean_data.select_dtypes(include='float64')\n",
    "data_float.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>490.000000</td>\n",
       "      <td>468.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>187.237755</td>\n",
       "      <td>113.651709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>60.576189</td>\n",
       "      <td>106.505504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.200000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>173.500000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>190.250000</td>\n",
       "      <td>106.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>975.000000</td>\n",
       "      <td>900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Height      Weight\n",
       "count  490.000000  468.000000\n",
       "mean   187.237755  113.651709\n",
       "std     60.576189  106.505504\n",
       "min     15.200000    2.000000\n",
       "25%    173.500000   61.000000\n",
       "50%    183.000000   81.000000\n",
       "75%    190.250000  106.500000\n",
       "max    975.000000  900.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_float.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 684 entries, 0 to 683\n",
      "Data columns (total 2 columns):\n",
      "Height    490 non-null float64\n",
      "Weight    468 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 10.8 KB\n"
     ]
    }
   ],
   "source": [
    "data_float.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cIZThhplNlm1/wcy+Os9jPmvzfBuhme0ofKNdhX0Pmdm62nsLLA1CGStaCOFYCOFPF3GIhyQRynCDUMaKYGbXm9m3zOz78e2uuLx4NW1mnzSz75rZaTMbKLvybi35HuRvxH95dVD573EYMbORBIYFzJKavwqwbNaa2csl2xv14Z/pH1H+e3u/Y2a/pPyfqv5K2eOPSDoSQnjKzP6wbN/tkn5V0n9L+ndJd4UQvmJmX5S0M4Twbr0HAywEoQxPpkIItxU2zOwLktrjzd2Sbsl/7YIkaUP8TX2l7pT0u/H9f1D+qyYLvhdCeDs+7svKf0/ud+rZeaAeCGWsFKsk3RFC+HlpYUlIz+d/S+5fFuc+nGJNGSvFv0nqLmyY2W0V6nxX0u/F9++p8rg/k/QLi+saUD+EMlaKg5La4/+U8lVJ5WvGUv6TFF80s1ckZZT/3y7m84Skk7zRBy/4ljg0jPjzxlMhhGBm9yj/f6F9Lul+AbVgXQ2NZLukr8Zfwv5T5b/HFlhRuFIGAEdYUwYARwhlAHCEUAYARwhlAHCEUAYAR/4fmql6OxPVFWUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=clean_data[\"Height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# valores nan substituidos pela mediana\n",
    "clean_data['Height'].fillna(clean_data['Height'].median(), inplace=True)\n",
    "clean_data_grouped['Height'].fillna(clean_data_grouped['Height'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=clean_data[\"Weight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# valores nan substituidos pela mediana\n",
    "clean_data['Weight'].fillna(clean_data['Weight'].median(), inplace=True)\n",
    "clean_data_grouped['Weight'].fillna(clean_data_grouped['Weight'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discretização dos valores numericos para o clean_data_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>hero_names</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Agility</th>\n",
       "      <th>...</th>\n",
       "      <th>Web Creation</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <th>Omnipresent</th>\n",
       "      <th>Omniscient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Fin Fang Foom</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>outros</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>5</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>good</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Galactus</td>\n",
       "      <td>Male</td>\n",
       "      <td>Black</td>\n",
       "      <td>outros</td>\n",
       "      <td>Black</td>\n",
       "      <td>5</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>neutral</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Groot</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>outros</td>\n",
       "      <td>Black</td>\n",
       "      <td>5</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        hero_names Gender Eye color    Race Hair color  Height      Publisher  \\\n",
       "230  Fin Fang Foom   Male       red  outros    No Hair       5  Marvel Comics   \n",
       "245       Galactus   Male     Black  outros      Black       5  Marvel Comics   \n",
       "270          Groot   Male    yellow  outros      Black       5  Marvel Comics   \n",
       "\n",
       "    Alignment  Weight  Agility  ...  Web Creation  Reality Warping  \\\n",
       "230      good    18.0    False  ...         False            False   \n",
       "245   neutral    16.0    False  ...         False            False   \n",
       "270      good     4.0    False  ...         False            False   \n",
       "\n",
       "     Odin Force  Symbiote Costume  Speed Force  Phoenix Force  \\\n",
       "230       False             False        False          False   \n",
       "245       False             False        False          False   \n",
       "270       False             False        False          False   \n",
       "\n",
       "     Molecular Dissipation  Vision - Cryo  Omnipresent  Omniscient  \n",
       "230                  False          False        False       False  \n",
       "245                  False          False        False       False  \n",
       "270                  False          False        False       False  \n",
       "\n",
       "[3 rows x 176 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# personagens com altura maior que 600\n",
    "gigantes = clean_data_grouped.loc[clean_data_grouped[\"Height\"]>600].copy()\n",
    "# discretiza os valores dessas alturas\n",
    "gigantes['Height'] = 5\n",
    "gigantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hero_names</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Agility</th>\n",
       "      <th>...</th>\n",
       "      <th>Web Creation</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <th>Omnipresent</th>\n",
       "      <th>Omniscient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>outros</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>267.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>bad</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Giganta</td>\n",
       "      <td>Female</td>\n",
       "      <td>green</td>\n",
       "      <td>Human</td>\n",
       "      <td>Red</td>\n",
       "      <td>62.5</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>bad</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Hulk</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>outros</td>\n",
       "      <td>outros</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>Juggernaut</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Red</td>\n",
       "      <td>287.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>neutral</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Red Hulk</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>outros</td>\n",
       "      <td>Black</td>\n",
       "      <td>213.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>neutral</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>Sasquatch</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>Human</td>\n",
       "      <td>outros</td>\n",
       "      <td>305.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     hero_names  Gender Eye color    Race Hair color  Height      Publisher  \\\n",
       "180    Darkseid    Male       red  outros    No Hair   267.0      DC Comics   \n",
       "254     Giganta  Female     green   Human        Red    62.5      DC Comics   \n",
       "294        Hulk    Male     green  outros     outros   244.0  Marvel Comics   \n",
       "332  Juggernaut    Male      blue   Human        Red   287.0  Marvel Comics   \n",
       "493    Red Hulk    Male    yellow  outros      Black   213.0  Marvel Comics   \n",
       "517   Sasquatch    Male       red   Human     outros   305.0  Marvel Comics   \n",
       "\n",
       "    Alignment  Weight  Agility  ...  Web Creation  Reality Warping  \\\n",
       "180       bad       6     True  ...         False            False   \n",
       "254       bad       6    False  ...         False            False   \n",
       "294      good       6     True  ...         False            False   \n",
       "332   neutral       6    False  ...         False            False   \n",
       "493   neutral       6    False  ...         False            False   \n",
       "517      good       6     True  ...         False            False   \n",
       "\n",
       "     Odin Force  Symbiote Costume  Speed Force  Phoenix Force  \\\n",
       "180       False             False        False          False   \n",
       "254       False             False        False          False   \n",
       "294       False             False        False          False   \n",
       "332       False             False        False          False   \n",
       "493       False             False        False          False   \n",
       "517       False             False        False          False   \n",
       "\n",
       "     Molecular Dissipation  Vision - Cryo  Omnipresent  Omniscient  \n",
       "180                  False          False        False       False  \n",
       "254                  False          False        False       False  \n",
       "294                  False          False        False       False  \n",
       "332                  False          False        False       False  \n",
       "493                  False          False        False       False  \n",
       "517                  False          False        False       False  \n",
       "\n",
       "[6 rows x 176 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#personagens com peso maior que 600\n",
    "obesos = clean_data_grouped.loc[clean_data_grouped[\"Weight\"]>600].copy()\n",
    "# discretiza o peso\n",
    "obesos['Weight'] = 6\n",
    "obesos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(681, 176)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# os personagens com altura maior que 600 removidos do dataset agrupado\n",
    "print(clean_data_grouped.shape)\n",
    "clean_data_grouped.drop(gigantes.index, inplace=True)\n",
    "clean_data_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684, 176)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discretização da altura dividindo a altura em 4 bins com larguras iguais\n",
    "labels = [1, 2, 3, 4]\n",
    "clean_data_grouped[\"Height\"] = pd.cut(clean_data_grouped[\"Height\"], 4, labels=labels)\n",
    "# adiciona os com altura maior que 600 e label 5\n",
    "clean_data_grouped = clean_data_grouped.append(gigantes)\n",
    "clean_data_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Height', clean_data_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(678, 176)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# os personagens com peso maior que 600 removidos do dataset agrupado\n",
    "print(clean_data_grouped.shape)\n",
    "clean_data_grouped.drop(obesos.index, inplace=True)\n",
    "clean_data_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(684, 176)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discretização da altura dividindo a altura em 5 bins com larguras iguais\n",
    "labels = [1, 2, 3, 4, 5]\n",
    "clean_data_grouped[\"Weight\"] = pd.cut(clean_data_grouped[\"Weight\"], 5, labels=labels)\n",
    "# adiciona os com peso maior que 600 e label 6\n",
    "clean_data_grouped = clean_data_grouped.append(obesos)\n",
    "clean_data_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Weight', clean_data_grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 684 entries, 0 to 683\n",
      "Data columns (total 7 columns):\n",
      "hero_names    684 non-null object\n",
      "Gender        642 non-null object\n",
      "Eye color     684 non-null object\n",
      "Race          684 non-null object\n",
      "Hair color    684 non-null object\n",
      "Publisher     647 non-null object\n",
      "Alignment     653 non-null object\n",
      "dtypes: object(7)\n",
      "memory usage: 37.5+ KB\n"
     ]
    }
   ],
   "source": [
    "clean_data.select_dtypes(include='O').info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 684 entries, 0 to 517\n",
      "Data columns (total 7 columns):\n",
      "hero_names    684 non-null object\n",
      "Gender        642 non-null object\n",
      "Eye color     684 non-null object\n",
      "Race          684 non-null object\n",
      "Hair color    684 non-null object\n",
      "Publisher     647 non-null object\n",
      "Alignment     653 non-null object\n",
      "dtypes: object(7)\n",
      "memory usage: 62.8+ KB\n"
     ]
    }
   ],
   "source": [
    "clean_data_grouped.select_dtypes(include='O').info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(642, 176)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data_gender = clean_data.copy()\n",
    "clean_data_grouped_gender = clean_data_grouped.copy()\n",
    "# remover valores nan na coluna Gender do dataset\n",
    "print(clean_data_gender.shape)\n",
    "clean_data_gender = clean_data_gender.dropna(subset=['Gender'])\n",
    "clean_data_gender.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(642, 176)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remover valores nan na coluna Gender do dataset\n",
    "clean_data_grouped_gender = clean_data_grouped_gender.dropna(subset=['Gender'])\n",
    "clean_data_grouped_gender.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para os outros datasets o valor nan de Gender será a moda\n",
    "clean_data['Gender'].fillna(clean_data['Gender'].mode()[0], inplace=True)\n",
    "clean_data_grouped['Gender'].fillna(clean_data_grouped['Gender'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Publisher dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(647, 176)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data_publisher = clean_data.copy()\n",
    "clean_data_grouped_publisher = clean_data_grouped.copy()\n",
    "# remover valores nan na coluna Publisher do dataset\n",
    "print(clean_data_publisher.shape)\n",
    "clean_data_publisher = clean_data_publisher.dropna(subset=['Publisher'])\n",
    "clean_data_publisher.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(647, 176)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remover valores nan na coluna Publisher do dataset\n",
    "clean_data_grouped_publisher = clean_data_grouped_publisher.dropna(subset=['Publisher'])\n",
    "clean_data_grouped_publisher.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para os outros datasets o valor nan de Publisher será a moda\n",
    "clean_data['Publisher'].fillna(clean_data['Publisher'].mode()[0], inplace=True)\n",
    "clean_data_grouped['Publisher'].fillna(clean_data_grouped['Publisher'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para os outros datasets o valor nan de Publisher será a moda\n",
    "clean_data_gender['Publisher'].fillna(clean_data_gender['Publisher'].mode()[0], inplace=True)\n",
    "clean_data_grouped_gender['Publisher'].fillna(clean_data_grouped_gender['Publisher'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alignment dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(653, 176)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data_alignment = clean_data.copy()\n",
    "clean_data_grouped_alignment = clean_data_grouped.copy()\n",
    "# remover valores nan na coluna Alignment do dataset\n",
    "print(clean_data_alignment.shape)\n",
    "clean_data_alignment = clean_data_alignment.dropna(subset=['Alignment'])\n",
    "clean_data_alignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(653, 176)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remover valores nan na coluna Alignment do dataset\n",
    "clean_data_grouped_alignment = clean_data_grouped_alignment.dropna(subset=['Alignment'])\n",
    "clean_data_grouped_alignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para os outros datasets o valor nan de Alignment será a moda\n",
    "clean_data['Alignment'].fillna(clean_data['Alignment'].mode()[0], inplace=True)\n",
    "clean_data_grouped['Alignment'].fillna(clean_data_grouped['Alignment'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# para os outros datasets o valor nan de Alignment será a moda\n",
    "clean_data_gender['Alignment'].fillna(clean_data_gender['Alignment'].mode()[0], inplace=True)\n",
    "clean_data_grouped_gender['Alignment'].fillna(clean_data_grouped_gender['Alignment'].mode()[0], inplace=True)\n",
    "clean_data_publisher['Alignment'].fillna(clean_data_publisher['Alignment'].mode()[0], inplace=True)\n",
    "clean_data_grouped_publisher['Alignment'].fillna(clean_data_grouped_publisher['Alignment'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# não tem mais nenhum nan nos datasets\n",
    "print(clean_data.isnull().values.any())\n",
    "print(clean_data_grouped.isnull().values.any())\n",
    "print(clean_data_gender.isnull().values.any())\n",
    "print(clean_data_alignment.isnull().values.any())\n",
    "print(clean_data_publisher.isnull().values.any())\n",
    "print(clean_data_grouped_gender.isnull().values.any())\n",
    "print(clean_data_grouped_alignment.isnull().values.any())\n",
    "print(clean_data_grouped_publisher.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lidando com dados categoricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(data):\n",
    "    data.drop(\"hero_names\", axis=1, inplace=True)\n",
    "    data = data.replace(['Male', 'Female'], [0,1])\n",
    "    #one hot encoding\n",
    "    data = pd.get_dummies(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(684, 283)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data.shape)\n",
    "clean_data = preprocessing(clean_data)\n",
    "clean_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(684, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(684, 194)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_grouped.shape)\n",
    "clean_data_grouped = preprocessing(clean_data_grouped)\n",
    "clean_data_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_publisher(data, label_name):\n",
    "    data.drop(\"hero_names\", axis=1, inplace=True)\n",
    "    data = data.replace(['Male', 'Female'], [0,1])\n",
    "    data = data.replace(['Marvel Comics', 'DC Comics', 'outros'], [3,2,1])\n",
    "    #one hot encoding\n",
    "    data = pd.get_dummies(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing_alignment(data, label_name):\n",
    "    data.drop(\"hero_names\", axis=1, inplace=True)\n",
    "    data = data.replace(['Male', 'Female'], [0,1])\n",
    "    data = data.replace(['good', 'neutral', 'bad'], [2,1,0])\n",
    "    # one hot encoding\n",
    "    data = pd.get_dummies(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(653, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(653, 281)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_alignment.shape)\n",
    "clean_data_alignment = preprocessing_alignment(clean_data_alignment, 'Alignment')\n",
    "clean_data_alignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(653, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(653, 192)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_grouped_alignment.shape)\n",
    "clean_data_grouped_alignment = preprocessing_alignment(clean_data_grouped_alignment, 'Alignment')\n",
    "clean_data_grouped_alignment.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(647, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(647, 280)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_publisher.shape)\n",
    "clean_data_publisher = preprocessing_publisher(clean_data_publisher, 'Publisher')\n",
    "clean_data_publisher.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(647, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(647, 192)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_grouped_publisher.shape)\n",
    "clean_data_grouped_publisher = preprocessing_publisher(clean_data_grouped_publisher, 'Publisher')\n",
    "clean_data_grouped_publisher.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(642, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(642, 278)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_gender.shape)\n",
    "clean_data_gender = preprocessing(clean_data_gender)\n",
    "clean_data_gender.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(642, 176)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(642, 194)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(clean_data_grouped_gender.shape)\n",
    "clean_data_grouped_gender = preprocessing(clean_data_grouped_gender)\n",
    "clean_data_grouped_gender.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salvar os datasets\n",
    "clean_data.to_csv('data/clean_data.csv', index=False)\n",
    "clean_data_grouped.to_csv('data/clean_data_grouped.csv', index=False)\n",
    "clean_data_gender.to_csv('data/clean_data_gender.csv', index=False)\n",
    "clean_data_alignment.to_csv('data/clean_data_alignment.csv', index=False)\n",
    "clean_data_publisher.to_csv('data/clean_data_publisher.csv', index=False)\n",
    "clean_data_grouped_gender.to_csv('data/clean_data_grouped_gender.csv', index=False)\n",
    "clean_data_grouped_alignment.to_csv('data/clean_data_grouped_alignment.csv', index=False)\n",
    "clean_data_grouped_publisher.to_csv('data/clean_data_grouped_publisher.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Barplot labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Accelerated Healing', clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Super Strength', clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Stamina', clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplot('Flight', clean_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}