FacVocMesmo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Faça Você Mesmo\n",
    "*Limpeza e Tratamento de Dados*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as srn \n",
    "import statistics as sts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aparencia</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Umidade</th>\n",
       "      <th>Vento</th>\n",
       "      <th>Jogar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sol</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sol</td>\n",
       "      <td>80</td>\n",
       "      <td>90.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nublado</td>\n",
       "      <td>83</td>\n",
       "      <td>86.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chuva</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chuva</td>\n",
       "      <td>68</td>\n",
       "      <td>80.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Aparencia  Temperatura  Umidade       Vento Jogar\n",
       "0       sol           85     85.0       FALSO   nao\n",
       "1       sol           80     90.0  VERDADEIRO   nao\n",
       "2   nublado           83     86.0       FALSO   sim\n",
       "3     chuva           70      NaN       FALSO   sim\n",
       "4     chuva           68     80.0       FALSO   sim"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = pd.read_csv('tempo.csv', sep=';')\n",
    "#\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aparencia\n",
       "chuva      5\n",
       "menos      1\n",
       "nublado    3\n",
       "sol        5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checando o grupo de Aparencia\n",
    "agrupado = dados.groupby(['Aparencia']).size()\n",
    "agrupado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aparencia'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agrupado.plot.bar(color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# substituido ocorrencia de Aparencia para a moda\n",
    "dados.loc[dados['Aparencia'] == 'menos', 'Aparencia'] = 'sol'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Aparencia\n",
       "chuva      5\n",
       "nublado    3\n",
       "sol        6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando se ainda existe ocorrencia\n",
    "agrupado = dados.groupby(['Aparencia']).size()\n",
    "agrupado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      14.000000\n",
       "mean      155.571429\n",
       "std       306.433759\n",
       "min        64.000000\n",
       "25%        69.250000\n",
       "50%        73.500000\n",
       "75%        80.750000\n",
       "max      1220.000000\n",
       "Name: Temperatura, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando os dados de Temperatura\n",
    "dados['Temperatura'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a mediana\n",
    "mediana = sts.median(dados['Temperatura'])  \n",
    "mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituir valores exagerado pela mediana\n",
    "dados.loc[(dados['Temperatura'] < -135) | (dados['Temperatura'] > 130), 'Temperatura'] = mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14.000000\n",
       "mean     73.678571\n",
       "std       6.556286\n",
       "min      64.000000\n",
       "25%      69.250000\n",
       "50%      72.750000\n",
       "75%      78.750000\n",
       "max      85.000000\n",
       "Name: Temperatura, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados['Temperatura'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando se exite NAs \n",
    "dados['Umidade'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Umidade\n",
       "65.0     1\n",
       "70.0     3\n",
       "75.0     1\n",
       "80.0     1\n",
       "85.0     1\n",
       "86.0     1\n",
       "90.0     2\n",
       "91.0     1\n",
       "95.0     1\n",
       "200.0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "agrupado = dados.groupby(['Umidade']).size()\n",
    "agrupado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.25"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a mediana de Umidade\n",
    "mediana = sts.median(dados['Umidade'])\n",
    "mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituir valores exagerado pela mediana\n",
    "dados['Umidade'].fillna(mediana, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando se ainda exite NAs \n",
    "dados['Umidade'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     14.000000\n",
       "mean      89.464286\n",
       "std       33.204763\n",
       "min       65.000000\n",
       "25%       71.250000\n",
       "50%       85.250000\n",
       "75%       90.000000\n",
       "max      200.000000\n",
       "Name: Umidade, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando a mediana \n",
    "dados['Umidade'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Substituir valores exagerado pela mediana\n",
    "dados.loc[(dados['Umidade'] < 0) | (dados['Umidade'] > 100), 'Umidade'] = mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14.000000\n",
       "mean     81.267857\n",
       "std       9.576847\n",
       "min      65.000000\n",
       "25%      71.250000\n",
       "50%      85.125000\n",
       "75%      89.000000\n",
       "max      95.000000\n",
       "Name: Umidade, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vericando\n",
    "dados['Umidade'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aparencia</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Umidade</th>\n",
       "      <th>Vento</th>\n",
       "      <th>Jogar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sol</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sol</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nublado</td>\n",
       "      <td>83.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chuva</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.5</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chuva</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Aparencia  Temperatura  Umidade       Vento Jogar\n",
       "0       sol         85.0     85.0       FALSO   nao\n",
       "1       sol         80.0     90.0  VERDADEIRO   nao\n",
       "2   nublado         83.0     86.0       FALSO   sim\n",
       "3     chuva         70.0     85.5       FALSO   sim\n",
       "4     chuva         68.0     80.0       FALSO   sim"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vento\n",
       "FALSO         7\n",
       "VERDADEIRO    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checando a coluna Vento\n",
    "agrupado = dados.groupby(['Vento']).size()\n",
    "agrupado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando NAs\n",
    "dados['Vento'].isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituino NAs pela moda\n",
    "dados['Vento'].fillna('FALSO', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verificando NAs\n",
    "dados['Vento'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jogar\n",
       "nao    5\n",
       "sim    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agrupado = dados.groupby(['Jogar']).size()\n",
    "agrupado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aparencia</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Umidade</th>\n",
       "      <th>Vento</th>\n",
       "      <th>Jogar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sol</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sol</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nublado</td>\n",
       "      <td>83.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chuva</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.5</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chuva</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Aparencia  Temperatura  Umidade       Vento Jogar\n",
       "0       sol         85.0     85.0       FALSO   nao\n",
       "1       sol         80.0     90.0  VERDADEIRO   nao\n",
       "2   nublado         83.0     86.0       FALSO   sim\n",
       "3     chuva         70.0     85.5       FALSO   sim\n",
       "4     chuva         68.0     80.0       FALSO   sim"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Aparencia</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>Umidade</th>\n",
       "      <th>Vento</th>\n",
       "      <th>Jogar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sol</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sol</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nublado</td>\n",
       "      <td>83.0</td>\n",
       "      <td>86.00</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chuva</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.50</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chuva</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chuva</td>\n",
       "      <td>65.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sol</td>\n",
       "      <td>64.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sol</td>\n",
       "      <td>73.5</td>\n",
       "      <td>95.00</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sol</td>\n",
       "      <td>69.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>chuva</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.25</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sol</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.00</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nublado</td>\n",
       "      <td>72.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>nublado</td>\n",
       "      <td>81.0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>FALSO</td>\n",
       "      <td>sim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>chuva</td>\n",
       "      <td>71.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>VERDADEIRO</td>\n",
       "      <td>nao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Aparencia  Temperatura  Umidade       Vento Jogar\n",
       "0        sol         85.0    85.00       FALSO   nao\n",
       "1        sol         80.0    90.00  VERDADEIRO   nao\n",
       "2    nublado         83.0    86.00       FALSO   sim\n",
       "3      chuva         70.0    85.50       FALSO   sim\n",
       "4      chuva         68.0    80.00       FALSO   sim\n",
       "5      chuva         65.0    70.00  VERDADEIRO   nao\n",
       "6        sol         64.0    65.00  VERDADEIRO   sim\n",
       "7        sol         73.5    95.00       FALSO   nao\n",
       "8        sol         69.0    70.00       FALSO   sim\n",
       "9      chuva         75.0    85.25       FALSO   sim\n",
       "10       sol         75.0    70.00  VERDADEIRO   sim\n",
       "11   nublado         72.0    90.00  VERDADEIRO   sim\n",
       "12   nublado         81.0    75.00       FALSO   sim\n",
       "13     chuva         71.0    91.00  VERDADEIRO   nao"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "agrupado = dados.groupby(['Aparencia']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Aparencia'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agrupado.plot.bar(color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\João Inácio\\dadospython\\venv\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Temperatura')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "srn.distplot(dados['Temperatura']).set_title('Temperatura')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\João Inácio\\dadospython\\venv\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Umidade')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "srn.distplot(dados['Umidade']).set_title('Umidade')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "6472c388de1e56fc3ac43c292f80bc31b4cb5c04a0aa35baa9005f87b8831804"
  },
  "kernelspec": {
   "display_name": "Python 3.9.12 ('venv': venv)",
   "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": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}