Aula02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3           3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "4           4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
       "\n",
       "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3   72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "4   75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
       "\n",
       "   year  region  \n",
       "0  2015  Albany  \n",
       "1  2015  Albany  \n",
       "2  2015  Albany  \n",
       "3  2015  Albany  \n",
       "4  2015  Albany  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/avocado.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 331., 2632., 4824., 4506., 3412., 1672.,  598.,  177.,   86.,\n",
       "          11.]),\n",
       " array([0.44 , 0.721, 1.002, 1.283, 1.564, 1.845, 2.126, 2.407, 2.688,\n",
       "        2.969, 3.25 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.hist(df.AveragePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "## agrupando os dados necessários\n",
    "\n",
    "data = (\n",
    "    \n",
    "    df.loc[:,['year','AveragePrice']]\n",
    "    .groupby('year')\n",
    "    .mean()\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>AveragePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1.375590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.338640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.515128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.347531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AveragePrice\n",
       "year              \n",
       "2015      1.375590\n",
       "2016      1.338640\n",
       "2017      1.515128\n",
       "2018      1.347531"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.37559038],\n",
       "       [1.3386396 ],\n",
       "       [1.51512758],\n",
       "       [1.34753086]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## criando o array de valores necessarios para o tamanho do plt.bar\n",
    "\n",
    "data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tamanho = data.values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "bacon = list(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "plt.bar(x = bacon, height= tamanho)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2015, 2016, 2017, 2018]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## criando a lista de valores necessarios para os indices do plt.bar\n",
    "\n",
    "x = list(data.index)\n",
    "x"
   ]
  },
  {
   "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": [
    "## criando um plt.bar com tamanho e x\n",
    "\n",
    "plt.figure()\n",
    "plt.bar(x = x, height= tamanho)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa4cb702490>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "plt.scatter(x=df['AveragePrice'],y=df['Total Volume'])"
   ]
  },
  {
   "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": [
    "## criando um scatterplot com AvaragePrice\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(x=df['AveragePrice'],y=df['Total Volume'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa4cb7c77d0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## adicionando dois dados num só grafico, adicionando cor, adicionando marcadores\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "\n",
    "\n",
    "plt.scatter(x = df[df.year == 2016].AveragePrice, \n",
    "            y = df[df.year == 2016]['Total Volume'], \n",
    "            c = 'tab:blue' , \n",
    "            marker = 's' ,\n",
    "            label='2016')\n",
    "\n",
    "plt.scatter(x = df[df.year == 2015].AveragePrice, \n",
    "            y = df[df.year == 2015]['Total Volume'], \n",
    "            c = 'tab:orange' , \n",
    "            marker = 'X',\n",
    "            label='2015')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## adicionando legendas\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "plt.scatter(x = df[df.year == 2016].AveragePrice, y = df[df.year == 2016]['Total Volume'], \n",
    "            c = 'tab:blue' , marker = 'd' , label='Dados 2016')\n",
    "plt.scatter(x = df[df.year == 2015].AveragePrice, y = df[df.year == 2015]['Total Volume'], \n",
    "            c = 'tab:orange' , marker = 'X', label='Dados 2015')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPp3koiQnV9VYkg9l/O6VxyW5d7zN8settV9vrT1cVXckeSTjl19e01p7eXip9+Wvv5rgC/nrz9kBAABwmA4Zc621d04yfNNB1t+UZNMk49uSnHVYswMAAGBSs3E3SwAAAOaYmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOjQglFPAOjIk/eNegajc/oFo54BAMCPcGYOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ4eMuaq6uap2V9VDE8ZeV1X3VtU3h9+vnbBsY1XtqKrHqurSCePnVdX2YdnvVFXN/p8DAAAwP0zlzNwtSS7bb2xDkq2ttRVJtg7PU1VnJFmb5Mxhm49X1THDNp9Isj7JiuFn/9cEAABgig4Zc621Lyd5Zr/hNUm2DI+3JHn7hPHbW2svtNYeT7IjyflVtTjJia21r7bWWpJPTdgGAACAwzTdz8yd2lrblSTD71OG8SVJdk5Yb2wYWzI83n98UlW1vqq2VdW2PXv2THOKAAAAR6/ZvgHKZJ+DawcZn1Rr7cbW2qrW2qpFixbN2uQAAACOFtONuaeHSycz/N49jI8lOW3CekuTPDWML51kHAAAgGmYbszdk2Td8HhdkrsnjK+tquOqannGb3Ry/3Ap5nNV9ebhLpbvmbANAAAAh2nBoVaoqk8nuSjJyVU1luRDSTYnuaOqrk7yrSRXJUlr7eGquiPJI0n2Jbmmtfby8FLvy/idMX86yReGHwAAAKbhkDHXWnvnARZdcoD1NyXZNMn4tiRnHdbsAAAAmNRs3wAFAACAOSDmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOiTmAAAAOrRg1BMA6MKT9416Bj8Zn/vk1NZ72w0/2XkAAIfNmTkAAIAOiTkAAIAOiTkAAIAOiTkAAIAOuQEKAIf2uWtHPYPRcfMXAI5QzswBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0SMwBAAB0aEYxV1X/rKoerqqHqurTVbWwql5XVfdW1TeH36+dsP7GqtpRVY9V1aUznz4AAMD8NO2Yq6olSf5pklWttbOSHJNkbZINSba21lYk2To8T1WdMSw/M8llST5eVcfMbPoAAADz00wvs1yQ5KerakGSVyd5KsmaJFuG5VuSvH14vCbJ7a21F1prjyfZkeT8Gb4/AADAvDTtmGutfTvJf0jyrSS7kvxVa+2LSU5tre0a1tmV5JRhkyVJdk54ibFhDAAAgMM0k8ssX5vxs23Lk/zNJK+pql892CaTjLUDvPb6qtpWVdv27Nkz3SkCAAActWZymeVbkzzeWtvTWnspyZ1J/l6Sp6tqcZIMv3cP648lOW3C9kszflnmj2mt3dhaW9VaW7Vo0aIZTBEAAODoNJOY+1aSN1fVq6uqklyS5NEk9yRZN6yzLsndw+N7kqytquOqanmSFUnun8H7AwAAzFsLprtha+1rVfX7SR5Isi/Jg0luTHJ8kjuq6uqMB99Vw/oPV9UdSR4Z1r+mtfbyDOcPAAAwL0075pKktfahJB/ab/iFjJ+lm2z9TUk2zeQ9AQAAmPlXEwAAADACYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDYg4AAKBDC0Y9AQBGZ+OOlaOewpHvzu2vPPzIFWePcCIA8KOcmQMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOiQmAMAAOjQjGKuqn6mqn6/qr5RVY9W1Vuq6nVVdW9VfXP4/doJ62+sqh1V9VhVXTrz6QMAAMxPMz0zd0OSP2yt/VySv5Pk0SQbkmxtra1IsnV4nqo6I8naJGcmuSzJx6vqmBm+PwAAwLw07ZirqhOT/IMkNyVJa+3F1tr3kqxJsmVYbUuStw+P1yS5vbX2Qmvt8SQ7kpw/3fcHAACYz2ZyZu5nk+xJ8ntV9WBVfbKqXpPk1NbariQZfp8yrL8kyc4J248NYwAAABymmcTcgiTnJvlEa+3nk/yfDJdUHkBNMtYmXbFqfVVtq6pte/bsmcEUAQAAjk4zibmxJGOtta8Nz38/43H3dFUtTpLh9+4J6582YfulSZ6a7IVbaze21la11lYtWrRoBlMEAAA4Ok075lpr/zvJzqp64zB0SZJHktyTZN0wti7J3cPje5Ksrarjqmp5khVJ7p/u+wMAAMxnC2a4/W8kua2qfirJXyZ5b8YD8Y6qujrJt5JclSSttYer6o6MB9++JNe01l6e4fsDAADMSzOKudbanyZZNcmiSw6w/qYkm2byngAAAMz8e+YAAAAYATEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQITEHAADQoQWjngAAHNGevO+vH3/uk6Obx6i87YZRzwCAA3BmDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoENiDgAAoEMLRj0BOvS5a2fndZ5cOTuvAwAA85AzcwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB2accxV1TFV9WBVfX54/rqqureqvjn8fu2EdTdW1Y6qeqyqLp3pewMAAMxXs3Fm7tokj054viHJ1tbaiiRbh+epqjOSrE1yZpLLkny8qo6ZhfcHAACYd2YUc1W1NMk/SvLJCcNrkmwZHm9J8vYJ47e31l5orT2eZEeS82fy/gAAAPPVTM/MfSzJv0jygwljp7bWdiXJ8PuUYXxJkp0T1hsbxgAAADhM0465qvqlJLtba1+f6iaTjLUDvPb6qtpWVdv27Nkz3SkCAAActWZyZu6CJJdX1RNJbk/yC1X1X5I8XVWLk2T4vXtYfyzJaRO2X5rkqcleuLV2Y2ttVWtt1aJFi2YwRQAAgKPTtGOutbaxtba0tbYs4zc2+W+ttV9Nck+SdcNq65LcPTy+J8naqjquqpYnWZHk/mnPHAAAYB5b8BN4zc1J7qiqq5N8K8lVSdJae7iq7kjySJJ9Sa5prb38E3h/AACAo96sxFxr7UtJvjQ83pvkkgOstynJptl4TwAAgPlsNr5nDgAAgDkm5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADok5gAAADq0YNQTAIBebNyxctRTmHt3bp/WZh+54uxZnggA+3NmDgAAoENiDgAAoENiDgAAoENiDgAAoENugDKLNk7zQ+LdeXIe3gAAAACOMM7MAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdEjMAQAAdGjaMVdVp1XVf6+qR6vq4aq6dhh/XVXdW1XfHH6/dsI2G6tqR1U9VlWXzsYfAAAAMB/N5MzcviT/vLW2Msmbk1xTVWck2ZBka2ttRZKtw/MMy9YmOTPJZUk+XlXHzGTyAAAA89W0Y661tqu19sDw+LkkjyZZkmRNki3DaluSvH14vCbJ7a21F1prjyfZkeT86b4/AADAfDYrn5mrqmVJfj7J15Kc2lrblYwHX5JThtWWJNk5YbOxYQwAAIDDNOOYq6rjk3w2yQdaa88ebNVJxtoBXnN9VW2rqm179uyZ6RQBAACOOjOKuao6NuMhd1tr7c5h+OmqWjwsX5xk9zA+luS0CZsvTfLUZK/bWruxtbaqtbZq0aJFM5kiAADAUWkmd7OsJDclebS19tsTFt2TZN3weF2SuyeMr62q46pqeZIVSe6f7vsDAADMZwtmsO0FSd6dZHtV/ekw9q+SbE5yR1VdneRbSa5Kktbaw1V1R5JHMn4nzGtaay/P4P0BAADmrWnHXGvtf2byz8ElySUH2GZTkk3TfU8AAADGzeTMHADApDbeuX3UU+jKR644e9RTADo0K19NAAAAwNwScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB0ScwAAAB1aMOoJAABHsCfvG/UMRuf0C0Y9A4CDcmYOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQ2IOAACgQwtGPYFufe7aHx97cuXczwMAAJiXnJkDAADokJgDAADokJgDAADokJgDAADokJgDAADokJgDAADokJgDAADokJgDAADokJgDAADo0IJRTwAAYL7beOf2UU+hKx+54uxRTwGOCGIOAGAyT9436hmMzukXjHoGwBS4zBIAAKBDzswBANAVl6VOnUtSj25zHnNVdVmSG5Ick+STrbXNcz0HAAAOYj5fYpq4zJRuzOllllV1TJLfTfKLSc5I8s6qOmMu5wAAAHA0mOvPzJ2fZEdr7S9bay8muT3JmjmeAwAAQPfm+jLLJUl2Tng+luRNczwHAACYF3y+8PD09hnDuY65mmSs/dhKVeuTrB+ePl9Vj/1EZzVzJyf5zqgnwRHNPsJU2E+YCvsJU2E/YSrsJ/s5gm/mcfpkg3Mdc2NJTpvwfGmSp/ZfqbV2Y5Ib52pSM1VV21prq0Y9D45c9hGmwn7CVNhPmAr7CVNhP+nfXH9m7k+SrKiq5VX1U0nWJrlnjucAAADQvTk9M9da21dV70/yRxn/aoKbW2sPz+UcAAAAjgZz/j1zrbU/SPIHc/2+P2HdXBLKyNhHmAr7CVNhP2Eq7CdMhf2kc9Xaj91/BAAAgCPcXH9mDgAAgFkg5qaoqi6rqseqakdVbZhk+UVV9VdV9afDz78ZxTwZnaq6uap2V9VDB1heVfU7wz70v6rq3LmeI6M3hf3EsYRU1WlV9d+r6tGqeriqrp1kHceUeW6K+wHy4gUAAAL/SURBVIljyjxWVQur6v6q+rNhH/m3k6zjWNKxOf/MXI+q6pgkv5tkdca/XuFPquqe1toj+636ldbaL835BDlS3JLkPyX51AGW/2KSFcPPm5J8YvjN/HJLDr6fJI4lJPuS/PPW2gNVdUKSr1fVvfv9/45jClPZTxLHlPnshSS/0Fp7vqqOTfI/q+oLrbU/nrCOY0nHnJmbmvOT7Git/WVr7cUktydZM+I5cYRprX05yTMHWWVNkk+1cX+c5GeqavHczI4jxRT2E0hrbVdr7YHh8XNJHk2yZL/VHFPmuSnuJ8xjw/Hh+eHpscPP/jfMcCzpmJibmiVJdk54PpbJD5ZvGU5jf6GqzpybqdGRqe5H4FjCK6pqWZKfT/K1/RY5pvCKg+wniWPKvFZVx1TVnybZneTe1ppjyVHEZZZTU5OM7f9fNR5IcvpwGvsfJrkr46er4Yemsh+BYwmvqKrjk3w2yQdaa8/uv3iSTRxT5qFD7CeOKfNca+3lJOdU1c8k+a9VdVZrbeLnth1LOubM3NSMJTltwvOlSZ6auEJr7dkfnsYevkvv2Ko6ee6mSAcOuR+BYwk/NHy+5bNJbmut3TnJKo4pHHI/cUzhh1pr30vypSSX7bfIsaRjYm5q/iTJiqpaXlU/lWRtknsmrlBVf6Oqanh8fsb/2e6d85lyJLsnyXuGu0a9OclftdZ2jXpSHFkcS0jG7y6X5KYkj7bWfvsAqzmmzHNT2U8cU+a3qlo0nJFLVf10krcm+cZ+qzmWdMxlllPQWttXVe9P8kdJjklyc2vt4ar69WH5f07yj5O8r6r2Jfl/SdY238g+r1TVp5NclOTkqhpL8qGMf9D4h/vIHyT5h0l2JPm/Sd47mpkySlPYTxxLSJILkrw7yfbhsy5J8q+SvD5xTOEVU9lPHFPmt8VJtgx3Zn9Vkjtaa5/f799hHUs6Vv73DAAA0B+XWQIAAHRIzAEAAHRIzAEAAHRIzAEAAHRIzAEAAHRIzAEAAHRIzAEAAHRIzAEAAHTo/wNWMsCdqwNh6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## criando um histograma\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "plt.hist(x = df[df['year'] == 2015].AveragePrice, color='tab:orange',\n",
    "        alpha=0.6,\n",
    "        label='2015')\n",
    "\n",
    "plt.hist(x = df[df['year'] == 2016].AveragePrice, color='tab:blue',\n",
    "        alpha=0.6,\n",
    "        label='2016')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3755903829029386"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media = df[df['year'] == 2015].AveragePrice.mean()\n",
    "media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Meu gráfico bonito')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.hist(x = df[df['year'] == 2015].AveragePrice, color='tab:orange',\n",
    "        alpha=0.6,\n",
    "        label='2015')\n",
    "\n",
    "plt.axvline(x = media,color='red', lw=5)\n",
    "\n",
    "plt.text(x = media+0.02, y = 1600, s='Média')\n",
    "\n",
    "plt.title('Meu gráfico bonito',size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## encontrado a media dos valores\n",
    "\n",
    "media = df[df['year'] == 2015]['AveragePrice'].mean()\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "plt.hist(x = df[df['year'] == 2015]['AveragePrice'],color='tab:orange',alpha=0.6,label='2015')\n",
    "plt.axvline(x = media ,color='tab:red')\n",
    "plt.text(x =  media + 0.01, y = 1600, s = f'Média {round(media,2)}', fontdict = {'color':'tab:red'})\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## criando mais de um histograma na mesma figura\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "plt.hist(x = df[df['year'] == 2015]['AveragePrice'],color='tab:orange',alpha=0.6,label='2015')\n",
    "plt.hist(x = df[df['year'] == 2016]['AveragePrice'],color='tab:blue',alpha=0.6,label='2016')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## gerando o gráfico acima com título\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "plt.hist(x = df[df['year'] == 2015]['AveragePrice'],color='tab:orange',alpha=0.6,label='2015')\n",
    "plt.hist(x = df[df['year'] == 2016]['AveragePrice'],color='tab:blue',alpha=0.6,label='2016')\n",
    "plt.title('AveragePrice 2015 vs. 2016',fontdict={'color':'black'})\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(104, 2)\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>Total Bags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1143</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>58638.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10269</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>457.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10268</th>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>37144.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>2015-01-18</td>\n",
       "      <td>33175.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10220</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>509.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>54103.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10219</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>407.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10218</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1015.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>58612.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Total Bags\n",
       "1143  2015-01-04    58638.68\n",
       "10269 2015-01-04      457.12\n",
       "10268 2015-01-11       33.33\n",
       "1142  2015-01-11    37144.36\n",
       "1141  2015-01-18    33175.55\n",
       "...          ...         ...\n",
       "10220 2015-12-13      509.14\n",
       "1093  2015-12-20    54103.94\n",
       "10219 2015-12-20      407.88\n",
       "10218 2015-12-27     1015.50\n",
       "1092  2015-12-27    58612.10\n",
       "\n",
       "[104 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## gerando os dados necessários para o próximo gráfico\n",
    "\n",
    "dados15 = (\n",
    "\n",
    "\n",
    "  df[['Date','Total Bags','year','region']]\n",
    "  .query('year == 2015')\n",
    "  .query(\"region == 'LasVegas'\")\n",
    "  .drop(['year','region'],axis=1)\n",
    "  .assign(Date = lambda x: pd.to_datetime(x.Date,infer_datetime_format=True))\n",
    "  .sort_values('Date')\n",
    "\n",
    ")\n",
    "\n",
    "print(dados15.shape)\n",
    "dados15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(104, 2)\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>Total Bags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3951</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>75612.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13076</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>2458.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13075</th>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>2991.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>2016-01-10</td>\n",
       "      <td>73575.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3949</th>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>70185.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13027</th>\n",
       "      <td>2016-12-11</td>\n",
       "      <td>2617.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901</th>\n",
       "      <td>2016-12-18</td>\n",
       "      <td>152694.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13026</th>\n",
       "      <td>2016-12-18</td>\n",
       "      <td>4099.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13025</th>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>5921.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3900</th>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>133812.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Total Bags\n",
       "3951  2016-01-03    75612.72\n",
       "13076 2016-01-03     2458.12\n",
       "13075 2016-01-10     2991.43\n",
       "3950  2016-01-10    73575.08\n",
       "3949  2016-01-17    70185.24\n",
       "...          ...         ...\n",
       "13027 2016-12-11     2617.12\n",
       "3901  2016-12-18   152694.40\n",
       "13026 2016-12-18     4099.95\n",
       "13025 2016-12-25     5921.68\n",
       "3900  2016-12-25   133812.44\n",
       "\n",
       "[104 rows x 2 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## gerando os dados necessários para o próximo gráfico\n",
    "\n",
    "dados16 = (\n",
    "\n",
    "\n",
    "  df[['Date','Total Bags','year','region']]\n",
    "  .query('year == 2016')\n",
    "  .query(\"region == 'LasVegas'\")\n",
    "  .drop(['year','region'],axis=1)\n",
    "  .assign(Date = lambda x: pd.to_datetime(x.Date,infer_datetime_format=True))\n",
    "  .sort_values('Date')\n",
    "\n",
    ")\n",
    "\n",
    "print(dados16.shape)\n",
    "dados16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "## gerando o gráfico de linhas com a soma cumulativa\n",
    "\n",
    "soma_cumulativa15 = dados15['Total Bags'].cumsum()\n",
    "soma_cumulativa16 = dados16['Total Bags'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "linha = soma_cumulativa15.values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "plt.plot(range(104),soma_cumulativa15,label='LasVegas 2015')\n",
    "plt.plot(range(104),soma_cumulativa16,label='LasVegas 2016')\n",
    "plt.axhline(y = linha, color= 'red')\n",
    "plt.title('Total Bags 2015 vs. 2016')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": [
    "## criando um subplot vazio\n",
    "\n",
    "plt.subplots()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## criando um subplot com 4 axes (2 linhas e 2 colunas)\n",
    "\n",
    "plt.subplots(2,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## guardando os retornos em varíaveis que podemos acessar e controlar\n",
    "\n",
    "f, ax = plt.subplots(4,2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'matplotlib.figure.Figure'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "## verificando o tipo de f e ax\n",
    "\n",
    "print(type(f))\n",
    "print(type(ax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## acessando os gráficos e adicionando os textos\n",
    "\n",
    "f, ax = plt.subplots(3,2,figsize=(12,9),sharey=True)\n",
    "\n",
    "\n",
    "for i in range(3):\n",
    "    for j in range(2):\n",
    "        ax[i,j].text(0.5, 0.5, str([i, j]),fontsize=18, ha='center')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>AveragePrice</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.33</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.35</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.93</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.08</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.28</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18244</th>\n",
       "      <td>1.63</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18245</th>\n",
       "      <td>1.71</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>1.87</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18247</th>\n",
       "      <td>1.93</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18248</th>\n",
       "      <td>1.62</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18249 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AveragePrice  year\n",
       "0              1.33  2015\n",
       "1              1.35  2015\n",
       "2              0.93  2015\n",
       "3              1.08  2015\n",
       "4              1.28  2015\n",
       "...             ...   ...\n",
       "18244          1.63  2018\n",
       "18245          1.71  2018\n",
       "18246          1.87  2018\n",
       "18247          1.93  2018\n",
       "18248          1.62  2018\n",
       "\n",
       "[18249 rows x 2 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## definindo os dados que vamos usar\n",
    "subplot_data = df[['AveragePrice','year']]\n",
    "subplot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## criando os gráficos em cada um dos eixos\n",
    "\n",
    "f, ax = plt.subplots(2,2,figsize=(12,9),sharey=True)\n",
    "ax[0,0].hist(subplot_data.query('year == 2015').AveragePrice)\n",
    "ax[0,0].set_title('Gráfico legal')\n",
    "\n",
    "\n",
    "ax[0,1].hist(subplot_data.query('year == 2016').AveragePrice)\n",
    "ax[1,0].hist(subplot_data.query('year == 2017').AveragePrice)\n",
    "ax[1,1].hist(subplot_data.query('year == 2018').AveragePrice)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seaborn\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "dados = df[['year','AveragePrice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>year</th>\n",
       "      <th>AveragePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  AveragePrice\n",
       "0  2015          1.33\n",
       "1  2015          1.35\n",
       "2  2015          0.93\n",
       "3  2015          1.08\n",
       "4  2015          1.28"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18249, 2)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa4d0485090>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## criando um boxplot\n",
    "\n",
    "sns.catplot(x = 'year', y = 'AveragePrice', kind='box', data = dados)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "catplot == categorical plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>year</th>\n",
       "      <th>AveragePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8478</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8479</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8480</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8481</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8482</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18244</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18245</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18247</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18248</th>\n",
       "      <td>2018</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1296 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  AveragePrice\n",
       "8478   2018          1.57\n",
       "8479   2018          1.35\n",
       "8480   2018          1.12\n",
       "8481   2018          1.08\n",
       "8482   2018          1.28\n",
       "...     ...           ...\n",
       "18244  2018          1.63\n",
       "18245  2018          1.71\n",
       "18246  2018          1.87\n",
       "18247  2018          1.93\n",
       "18248  2018          1.62\n",
       "\n",
       "[1296 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados18 = dados.query(\"year == 2018\")\n",
    "dados18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": [
    "sns.distplot(dados18.AveragePrice, bins=20, kde=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Small Bags</th>\n",
       "      <th>Total Bags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8603.62</td>\n",
       "      <td>8696.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9408.07</td>\n",
       "      <td>9505.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8042.21</td>\n",
       "      <td>8145.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5677.40</td>\n",
       "      <td>5811.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5986.26</td>\n",
       "      <td>6183.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Small Bags  Total Bags\n",
       "0     8603.62     8696.87\n",
       "1     9408.07     9505.56\n",
       "2     8042.21     8145.35\n",
       "3     5677.40     5811.16\n",
       "4     5986.26     6183.95"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_data = df[['Small Bags','Total Bags']]\n",
    "scatter_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fa4cada7810>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Small Bags', y='Total Bags', data = scatter_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fa4cac78cd0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Small Bags', y='Total Bags', data = scatter_data, kind='hex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass: Identifies the type of dwelling involved in the sale.\t\n",
      "\n",
      "\n",
      "\n",
      "        20\t1-STORY 1946 & NEWER ALL STYLES\n",
      "\n",
      "        30\t1-STORY 1945 & OLDER\n",
      "\n",
      "        40\t1-STORY W/FINISHED ATTIC ALL AGES\n",
      "\n",
      "        45\t1-1/2 STORY - UNFINISHED ALL AGES\n",
      "\n",
      "        50\t1-1/2 STORY FINISHED ALL AGES\n",
      "\n",
      "        60\t2-STORY 1946 & NEWER\n",
      "\n",
      "        70\t2-STORY 1945 & OLDER\n",
      "\n",
      "        75\t2-1/2 STORY ALL AGES\n",
      "\n",
      "        80\tSPLIT OR MULTI-LEVEL\n",
      "\n",
      "        85\tSPLIT FOYER\n",
      "\n",
      "        90\tDUPLEX - ALL STYLES AND AGES\n",
      "\n",
      "       120\t1-STORY PUD (Planned Unit Development) - 1946 & NEWER\n",
      "\n",
      "       150\t1-1/2 STORY PUD - ALL AGES\n",
      "\n",
      "       160\t2-STORY PUD - 1946 & NEWER\n",
      "\n",
      "       180\tPUD - MULTILEVEL - INCL SPLIT LEV/FOYER\n",
      "\n",
      "       190\t2 FAMILY CONVERSION - ALL STYLES AND AGES\n",
      "\n",
      "\n",
      "\n",
      "MSZoning: Identifies the general zoning classification of the sale.\n",
      "\n",
      "\t\t\n",
      "\n",
      "       A\tAgriculture\n",
      "\n",
      "       C\tCommercial\n",
      "\n",
      "       FV\tFloating Village Residential\n",
      "\n",
      "       I\tIndustrial\n",
      "\n",
      "       RH\tResidential High Density\n",
      "\n",
      "       RL\tResidential Low Density\n",
      "\n",
      "       RP\tResidential Low Density Park \n",
      "\n",
      "       RM\tResidential Medium Density\n",
      "\n",
      "\t\n",
      "\n",
      "LotFrontage: Linear feet of street connected to property\n",
      "\n",
      "\n",
      "\n",
      "LotArea: Lot size in square feet\n",
      "\n",
      "\n",
      "\n",
      "Street: Type of road access to property\n",
      "\n",
      "\n",
      "\n",
      "       Grvl\tGravel\t\n",
      "\n",
      "       Pave\tPaved\n",
      "\n",
      "       \t\n",
      "\n",
      "Alley: Type of alley access to property\n",
      "\n",
      "\n",
      "\n",
      "       Grvl\tGravel\n",
      "\n",
      "       Pave\tPaved\n",
      "\n",
      "       NA \tNo alley access\n",
      "\n",
      "\t\t\n",
      "\n",
      "LotShape: General shape of property\n",
      "\n",
      "\n",
      "\n",
      "       Reg\tRegular\t\n",
      "\n",
      "       IR1\tSlightly irregular\n",
      "\n",
      "       IR2\tModerately Irregular\n",
      "\n",
      "       IR3\tIrregular\n",
      "\n",
      "       \n",
      "\n",
      "LandContour: Flatness of the property\n",
      "\n",
      "\n",
      "\n",
      "       Lvl\tNear Flat/Level\t\n",
      "\n",
      "       Bnk\tBanked - Quick and significant rise from street grade to building\n",
      "\n",
      "       HLS\tHillside - Significant slope from side to side\n",
      "\n",
      "       Low\tDepression\n",
      "\n",
      "\t\t\n",
      "\n",
      "Utilities: Type of utilities available\n",
      "\n",
      "\t\t\n",
      "\n",
      "       AllPub\tAll public Utilities (E,G,W,& S)\t\n",
      "\n",
      "       NoSewr\tElectricity, Gas, and Water (Septic Tank)\n",
      "\n",
      "       NoSeWa\tElectricity and Gas Only\n",
      "\n",
      "       ELO\tElectricity only\t\n",
      "\n",
      "\t\n",
      "\n",
      "LotConfig: Lot configuration\n",
      "\n",
      "\n",
      "\n",
      "       Inside\tInside lot\n",
      "\n",
      "       Corner\tCorner lot\n",
      "\n",
      "       CulDSac\tCul-de-sac\n",
      "\n",
      "       FR2\tFrontage on 2 sides of property\n",
      "\n",
      "       FR3\tFrontage on 3 sides of property\n",
      "\n",
      "\t\n",
      "\n",
      "LandSlope: Slope of property\n",
      "\n",
      "\t\t\n",
      "\n",
      "       Gtl\tGentle slope\n",
      "\n",
      "       Mod\tModerate Slope\t\n",
      "\n",
      "       Sev\tSevere Slope\n",
      "\n",
      "\t\n",
      "\n",
      "Neighborhood: Physical locations within Ames city limits\n",
      "\n",
      "\n",
      "\n",
      "       Blmngtn\tBloomington Heights\n",
      "\n",
      "       Blueste\tBluestem\n",
      "\n",
      "       BrDale\tBriardale\n",
      "\n",
      "       BrkSide\tBrookside\n",
      "\n",
      "       ClearCr\tClear Creek\n",
      "\n",
      "       CollgCr\tCollege Creek\n",
      "\n",
      "       Crawfor\tCrawford\n",
      "\n",
      "       Edwards\tEdwards\n",
      "\n",
      "       Gilbert\tGilbert\n",
      "\n",
      "       IDOTRR\tIowa DOT and Rail Road\n",
      "\n",
      "       MeadowV\tMeadow Village\n",
      "\n",
      "       Mitchel\tMitchell\n",
      "\n",
      "       Names\tNorth Ames\n",
      "\n",
      "       NoRidge\tNorthridge\n",
      "\n",
      "       NPkVill\tNorthpark Villa\n",
      "\n",
      "       NridgHt\tNorthridge Heights\n",
      "\n",
      "       NWAmes\tNorthwest Ames\n",
      "\n",
      "       OldTown\tOld Town\n",
      "\n",
      "       SWISU\tSouth & West of Iowa State University\n",
      "\n",
      "       Sawyer\tSawyer\n",
      "\n",
      "       SawyerW\tSawyer West\n",
      "\n",
      "       Somerst\tSomerset\n",
      "\n",
      "       StoneBr\tStone Brook\n",
      "\n",
      "       Timber\tTimberland\n",
      "\n",
      "       Veenker\tVeenker\n",
      "\n",
      "\t\t\t\n",
      "\n",
      "Condition1: Proximity to various conditions\n",
      "\n",
      "\t\n",
      "\n",
      "       Artery\tAdjacent to arterial street\n",
      "\n",
      "       Feedr\tAdjacent to feeder street\t\n",
      "\n",
      "       Norm\tNormal\t\n",
      "\n",
      "       RRNn\tWithin 200' of North-South Railroad\n",
      "\n",
      "       RRAn\tAdjacent to North-South Railroad\n",
      "\n",
      "       PosN\tNear positive off-site feature--park, greenbelt, etc.\n",
      "\n",
      "       PosA\tAdjacent to postive off-site feature\n",
      "\n",
      "       RRNe\tWithin 200' of East-West Railroad\n",
      "\n",
      "       RRAe\tAdjacent to East-West Railroad\n",
      "\n",
      "\t\n",
      "\n",
      "Condition2: Proximity to various conditions (if more than one is present)\n",
      "\n",
      "\t\t\n",
      "\n",
      "       Artery\tAdjacent to arterial street\n",
      "\n",
      "       Feedr\tAdjacent to feeder street\t\n",
      "\n",
      "       Norm\tNormal\t\n",
      "\n",
      "       RRNn\tWithin 200' of North-South Railroad\n",
      "\n",
      "       RRAn\tAdjacent to North-South Railroad\n",
      "\n",
      "       PosN\tNear positive off-site feature--park, greenbelt, etc.\n",
      "\n",
      "       PosA\tAdjacent to postive off-site feature\n",
      "\n",
      "       RRNe\tWithin 200' of East-West Railroad\n",
      "\n",
      "       RRAe\tAdjacent to East-West Railroad\n",
      "\n",
      "\t\n",
      "\n",
      "BldgType: Type of dwelling\n",
      "\n",
      "\t\t\n",
      "\n",
      "       1Fam\tSingle-family Detached\t\n",
      "\n",
      "       2FmCon\tTwo-family Conversion; originally built as one-family dwelling\n",
      "\n",
      "       Duplx\tDuplex\n",
      "\n",
      "       TwnhsE\tTownhouse End Unit\n",
      "\n",
      "       TwnhsI\tTownhouse Inside Unit\n",
      "\n",
      "\t\n",
      "\n",
      "HouseStyle: Style of dwelling\n",
      "\n",
      "\t\n",
      "\n",
      "       1Story\tOne story\n",
      "\n",
      "       1.5Fin\tOne and one-half story: 2nd level finished\n",
      "\n",
      "       1.5Unf\tOne and one-half story: 2nd level unfinished\n",
      "\n",
      "       2Story\tTwo story\n",
      "\n",
      "       2.5Fin\tTwo and one-half story: 2nd level finished\n",
      "\n",
      "       2.5Unf\tTwo and one-half story: 2nd level unfinished\n",
      "\n",
      "       SFoyer\tSplit Foyer\n",
      "\n",
      "       SLvl\tSplit Level\n",
      "\n",
      "\t\n",
      "\n",
      "OverallQual: Rates the overall material and finish of the house\n",
      "\n",
      "\n",
      "\n",
      "       10\tVery Excellent\n",
      "\n",
      "       9\tExcellent\n",
      "\n",
      "       8\tVery Good\n",
      "\n",
      "       7\tGood\n",
      "\n",
      "       6\tAbove Average\n",
      "\n",
      "       5\tAverage\n",
      "\n",
      "       4\tBelow Average\n",
      "\n",
      "       3\tFair\n",
      "\n",
      "       2\tPoor\n",
      "\n",
      "       1\tVery Poor\n",
      "\n",
      "\t\n",
      "\n",
      "OverallCond: Rates the overall condition of the house\n",
      "\n",
      "\n",
      "\n",
      "       10\tVery Excellent\n",
      "\n",
      "       9\tExcellent\n",
      "\n",
      "       8\tVery Good\n",
      "\n",
      "       7\tGood\n",
      "\n",
      "       6\tAbove Average\t\n",
      "\n",
      "       5\tAverage\n",
      "\n",
      "       4\tBelow Average\t\n",
      "\n",
      "       3\tFair\n",
      "\n",
      "       2\tPoor\n",
      "\n",
      "       1\tVery Poor\n",
      "\n",
      "\t\t\n",
      "\n",
      "YearBuilt: Original construction date\n",
      "\n",
      "\n",
      "\n",
      "YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\n",
      "\n",
      "\n",
      "\n",
      "RoofStyle: Type of roof\n",
      "\n",
      "\n",
      "\n",
      "       Flat\tFlat\n",
      "\n",
      "       Gable\tGable\n",
      "\n",
      "       Gambrel\tGabrel (Barn)\n",
      "\n",
      "       Hip\tHip\n",
      "\n",
      "       Mansard\tMansard\n",
      "\n",
      "       Shed\tShed\n",
      "\n",
      "\t\t\n",
      "\n",
      "RoofMatl: Roof material\n",
      "\n",
      "\n",
      "\n",
      "       ClyTile\tClay or Tile\n",
      "\n",
      "       CompShg\tStandard (Composite) Shingle\n",
      "\n",
      "       Membran\tMembrane\n",
      "\n",
      "       Metal\tMetal\n",
      "\n",
      "       Roll\tRoll\n",
      "\n",
      "       Tar&Grv\tGravel & Tar\n",
      "\n",
      "       WdShake\tWood Shakes\n",
      "\n",
      "       WdShngl\tWood Shingles\n",
      "\n",
      "\t\t\n",
      "\n",
      "Exterior1st: Exterior covering on house\n",
      "\n",
      "\n",
      "\n",
      "       AsbShng\tAsbestos Shingles\n",
      "\n",
      "       AsphShn\tAsphalt Shingles\n",
      "\n",
      "       BrkComm\tBrick Common\n",
      "\n",
      "       BrkFace\tBrick Face\n",
      "\n",
      "       CBlock\tCinder Block\n",
      "\n",
      "       CemntBd\tCement Board\n",
      "\n",
      "       HdBoard\tHard Board\n",
      "\n",
      "       ImStucc\tImitation Stucco\n",
      "\n",
      "       MetalSd\tMetal Siding\n",
      "\n",
      "       Other\tOther\n",
      "\n",
      "       Plywood\tPlywood\n",
      "\n",
      "       PreCast\tPreCast\t\n",
      "\n",
      "       Stone\tStone\n",
      "\n",
      "       Stucco\tStucco\n",
      "\n",
      "       VinylSd\tVinyl Siding\n",
      "\n",
      "       Wd Sdng\tWood Siding\n",
      "\n",
      "       WdShing\tWood Shingles\n",
      "\n",
      "\t\n",
      "\n",
      "Exterior2nd: Exterior covering on house (if more than one material)\n",
      "\n",
      "\n",
      "\n",
      "       AsbShng\tAsbestos Shingles\n",
      "\n",
      "       AsphShn\tAsphalt Shingles\n",
      "\n",
      "       BrkComm\tBrick Common\n",
      "\n",
      "       BrkFace\tBrick Face\n",
      "\n",
      "       CBlock\tCinder Block\n",
      "\n",
      "       CemntBd\tCement Board\n",
      "\n",
      "       HdBoard\tHard Board\n",
      "\n",
      "       ImStucc\tImitation Stucco\n",
      "\n",
      "       MetalSd\tMetal Siding\n",
      "\n",
      "       Other\tOther\n",
      "\n",
      "       Plywood\tPlywood\n",
      "\n",
      "       PreCast\tPreCast\n",
      "\n",
      "       Stone\tStone\n",
      "\n",
      "       Stucco\tStucco\n",
      "\n",
      "       VinylSd\tVinyl Siding\n",
      "\n",
      "       Wd Sdng\tWood Siding\n",
      "\n",
      "       WdShing\tWood Shingles\n",
      "\n",
      "\t\n",
      "\n",
      "MasVnrType: Masonry veneer type\n",
      "\n",
      "\n",
      "\n",
      "       BrkCmn\tBrick Common\n",
      "\n",
      "       BrkFace\tBrick Face\n",
      "\n",
      "       CBlock\tCinder Block\n",
      "\n",
      "       None\tNone\n",
      "\n",
      "       Stone\tStone\n",
      "\n",
      "\t\n",
      "\n",
      "MasVnrArea: Masonry veneer area in square feet\n",
      "\n",
      "\n",
      "\n",
      "ExterQual: Evaluates the quality of the material on the exterior \n",
      "\n",
      "\t\t\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tAverage/Typical\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       Po\tPoor\n",
      "\n",
      "\t\t\n",
      "\n",
      "ExterCond: Evaluates the present condition of the material on the exterior\n",
      "\n",
      "\t\t\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tAverage/Typical\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       Po\tPoor\n",
      "\n",
      "\t\t\n",
      "\n",
      "Foundation: Type of foundation\n",
      "\n",
      "\t\t\n",
      "\n",
      "       BrkTil\tBrick & Tile\n",
      "\n",
      "       CBlock\tCinder Block\n",
      "\n",
      "       PConc\tPoured Contrete\t\n",
      "\n",
      "       Slab\tSlab\n",
      "\n",
      "       Stone\tStone\n",
      "\n",
      "       Wood\tWood\n",
      "\n",
      "\t\t\n",
      "\n",
      "BsmtQual: Evaluates the height of the basement\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent (100+ inches)\t\n",
      "\n",
      "       Gd\tGood (90-99 inches)\n",
      "\n",
      "       TA\tTypical (80-89 inches)\n",
      "\n",
      "       Fa\tFair (70-79 inches)\n",
      "\n",
      "       Po\tPoor (<70 inches\n",
      "\n",
      "       NA\tNo Basement\n",
      "\n",
      "\t\t\n",
      "\n",
      "BsmtCond: Evaluates the general condition of the basement\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tTypical - slight dampness allowed\n",
      "\n",
      "       Fa\tFair - dampness or some cracking or settling\n",
      "\n",
      "       Po\tPoor - Severe cracking, settling, or wetness\n",
      "\n",
      "       NA\tNo Basement\n",
      "\n",
      "\t\n",
      "\n",
      "BsmtExposure: Refers to walkout or garden level walls\n",
      "\n",
      "\n",
      "\n",
      "       Gd\tGood Exposure\n",
      "\n",
      "       Av\tAverage Exposure (split levels or foyers typically score average or above)\t\n",
      "\n",
      "       Mn\tMimimum Exposure\n",
      "\n",
      "       No\tNo Exposure\n",
      "\n",
      "       NA\tNo Basement\n",
      "\n",
      "\t\n",
      "\n",
      "BsmtFinType1: Rating of basement finished area\n",
      "\n",
      "\n",
      "\n",
      "       GLQ\tGood Living Quarters\n",
      "\n",
      "       ALQ\tAverage Living Quarters\n",
      "\n",
      "       BLQ\tBelow Average Living Quarters\t\n",
      "\n",
      "       Rec\tAverage Rec Room\n",
      "\n",
      "       LwQ\tLow Quality\n",
      "\n",
      "       Unf\tUnfinshed\n",
      "\n",
      "       NA\tNo Basement\n",
      "\n",
      "\t\t\n",
      "\n",
      "BsmtFinSF1: Type 1 finished square feet\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinType2: Rating of basement finished area (if multiple types)\n",
      "\n",
      "\n",
      "\n",
      "       GLQ\tGood Living Quarters\n",
      "\n",
      "       ALQ\tAverage Living Quarters\n",
      "\n",
      "       BLQ\tBelow Average Living Quarters\t\n",
      "\n",
      "       Rec\tAverage Rec Room\n",
      "\n",
      "       LwQ\tLow Quality\n",
      "\n",
      "       Unf\tUnfinshed\n",
      "\n",
      "       NA\tNo Basement\n",
      "\n",
      "\n",
      "\n",
      "BsmtFinSF2: Type 2 finished square feet\n",
      "\n",
      "\n",
      "\n",
      "BsmtUnfSF: Unfinished square feet of basement area\n",
      "\n",
      "\n",
      "\n",
      "TotalBsmtSF: Total square feet of basement area\n",
      "\n",
      "\n",
      "\n",
      "Heating: Type of heating\n",
      "\n",
      "\t\t\n",
      "\n",
      "       Floor\tFloor Furnace\n",
      "\n",
      "       GasA\tGas forced warm air furnace\n",
      "\n",
      "       GasW\tGas hot water or steam heat\n",
      "\n",
      "       Grav\tGravity furnace\t\n",
      "\n",
      "       OthW\tHot water or steam heat other than gas\n",
      "\n",
      "       Wall\tWall furnace\n",
      "\n",
      "\t\t\n",
      "\n",
      "HeatingQC: Heating quality and condition\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tAverage/Typical\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       Po\tPoor\n",
      "\n",
      "\t\t\n",
      "\n",
      "CentralAir: Central air conditioning\n",
      "\n",
      "\n",
      "\n",
      "       N\tNo\n",
      "\n",
      "       Y\tYes\n",
      "\n",
      "\t\t\n",
      "\n",
      "Electrical: Electrical system\n",
      "\n",
      "\n",
      "\n",
      "       SBrkr\tStandard Circuit Breakers & Romex\n",
      "\n",
      "       FuseA\tFuse Box over 60 AMP and all Romex wiring (Average)\t\n",
      "\n",
      "       FuseF\t60 AMP Fuse Box and mostly Romex wiring (Fair)\n",
      "\n",
      "       FuseP\t60 AMP Fuse Box and mostly knob & tube wiring (poor)\n",
      "\n",
      "       Mix\tMixed\n",
      "\n",
      "\t\t\n",
      "\n",
      "1stFlrSF: First Floor square feet\n",
      "\n",
      " \n",
      "\n",
      "2ndFlrSF: Second floor square feet\n",
      "\n",
      "\n",
      "\n",
      "LowQualFinSF: Low quality finished square feet (all floors)\n",
      "\n",
      "\n",
      "\n",
      "GrLivArea: Above grade (ground) living area square feet\n",
      "\n",
      "\n",
      "\n",
      "BsmtFullBath: Basement full bathrooms\n",
      "\n",
      "\n",
      "\n",
      "BsmtHalfBath: Basement half bathrooms\n",
      "\n",
      "\n",
      "\n",
      "FullBath: Full bathrooms above grade\n",
      "\n",
      "\n",
      "\n",
      "HalfBath: Half baths above grade\n",
      "\n",
      "\n",
      "\n",
      "Bedroom: Bedrooms above grade (does NOT include basement bedrooms)\n",
      "\n",
      "\n",
      "\n",
      "Kitchen: Kitchens above grade\n",
      "\n",
      "\n",
      "\n",
      "KitchenQual: Kitchen quality\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tTypical/Average\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       Po\tPoor\n",
      "\n",
      "       \t\n",
      "\n",
      "TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n",
      "\n",
      "\n",
      "\n",
      "Functional: Home functionality (Assume typical unless deductions are warranted)\n",
      "\n",
      "\n",
      "\n",
      "       Typ\tTypical Functionality\n",
      "\n",
      "       Min1\tMinor Deductions 1\n",
      "\n",
      "       Min2\tMinor Deductions 2\n",
      "\n",
      "       Mod\tModerate Deductions\n",
      "\n",
      "       Maj1\tMajor Deductions 1\n",
      "\n",
      "       Maj2\tMajor Deductions 2\n",
      "\n",
      "       Sev\tSeverely Damaged\n",
      "\n",
      "       Sal\tSalvage only\n",
      "\n",
      "\t\t\n",
      "\n",
      "Fireplaces: Number of fireplaces\n",
      "\n",
      "\n",
      "\n",
      "FireplaceQu: Fireplace quality\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent - Exceptional Masonry Fireplace\n",
      "\n",
      "       Gd\tGood - Masonry Fireplace in main level\n",
      "\n",
      "       TA\tAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement\n",
      "\n",
      "       Fa\tFair - Prefabricated Fireplace in basement\n",
      "\n",
      "       Po\tPoor - Ben Franklin Stove\n",
      "\n",
      "       NA\tNo Fireplace\n",
      "\n",
      "\t\t\n",
      "\n",
      "GarageType: Garage location\n",
      "\n",
      "\t\t\n",
      "\n",
      "       2Types\tMore than one type of garage\n",
      "\n",
      "       Attchd\tAttached to home\n",
      "\n",
      "       Basment\tBasement Garage\n",
      "\n",
      "       BuiltIn\tBuilt-In (Garage part of house - typically has room above garage)\n",
      "\n",
      "       CarPort\tCar Port\n",
      "\n",
      "       Detchd\tDetached from home\n",
      "\n",
      "       NA\tNo Garage\n",
      "\n",
      "\t\t\n",
      "\n",
      "GarageYrBlt: Year garage was built\n",
      "\n",
      "\t\t\n",
      "\n",
      "GarageFinish: Interior finish of the garage\n",
      "\n",
      "\n",
      "\n",
      "       Fin\tFinished\n",
      "\n",
      "       RFn\tRough Finished\t\n",
      "\n",
      "       Unf\tUnfinished\n",
      "\n",
      "       NA\tNo Garage\n",
      "\n",
      "\t\t\n",
      "\n",
      "GarageCars: Size of garage in car capacity\n",
      "\n",
      "\n",
      "\n",
      "GarageArea: Size of garage in square feet\n",
      "\n",
      "\n",
      "\n",
      "GarageQual: Garage quality\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tTypical/Average\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       Po\tPoor\n",
      "\n",
      "       NA\tNo Garage\n",
      "\n",
      "\t\t\n",
      "\n",
      "GarageCond: Garage condition\n",
      "\n",
      "\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tTypical/Average\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       Po\tPoor\n",
      "\n",
      "       NA\tNo Garage\n",
      "\n",
      "\t\t\n",
      "\n",
      "PavedDrive: Paved driveway\n",
      "\n",
      "\n",
      "\n",
      "       Y\tPaved \n",
      "\n",
      "       P\tPartial Pavement\n",
      "\n",
      "       N\tDirt/Gravel\n",
      "\n",
      "\t\t\n",
      "\n",
      "WoodDeckSF: Wood deck area in square feet\n",
      "\n",
      "\n",
      "\n",
      "OpenPorchSF: Open porch area in square feet\n",
      "\n",
      "\n",
      "\n",
      "EnclosedPorch: Enclosed porch area in square feet\n",
      "\n",
      "\n",
      "\n",
      "3SsnPorch: Three season porch area in square feet\n",
      "\n",
      "\n",
      "\n",
      "ScreenPorch: Screen porch area in square feet\n",
      "\n",
      "\n",
      "\n",
      "PoolArea: Pool area in square feet\n",
      "\n",
      "\n",
      "\n",
      "PoolQC: Pool quality\n",
      "\n",
      "\t\t\n",
      "\n",
      "       Ex\tExcellent\n",
      "\n",
      "       Gd\tGood\n",
      "\n",
      "       TA\tAverage/Typical\n",
      "\n",
      "       Fa\tFair\n",
      "\n",
      "       NA\tNo Pool\n",
      "\n",
      "\t\t\n",
      "\n",
      "Fence: Fence quality\n",
      "\n",
      "\t\t\n",
      "\n",
      "       GdPrv\tGood Privacy\n",
      "\n",
      "       MnPrv\tMinimum Privacy\n",
      "\n",
      "       GdWo\tGood Wood\n",
      "\n",
      "       MnWw\tMinimum Wood/Wire\n",
      "\n",
      "       NA\tNo Fence\n",
      "\n",
      "\t\n",
      "\n",
      "MiscFeature: Miscellaneous feature not covered in other categories\n",
      "\n",
      "\t\t\n",
      "\n",
      "       Elev\tElevator\n",
      "\n",
      "       Gar2\t2nd Garage (if not described in garage section)\n",
      "\n",
      "       Othr\tOther\n",
      "\n",
      "       Shed\tShed (over 100 SF)\n",
      "\n",
      "       TenC\tTennis Court\n",
      "\n",
      "       NA\tNone\n",
      "\n",
      "\t\t\n",
      "\n",
      "MiscVal: $Value of miscellaneous feature\n",
      "\n",
      "\n",
      "\n",
      "MoSold: Month Sold (MM)\n",
      "\n",
      "\n",
      "\n",
      "YrSold: Year Sold (YYYY)\n",
      "\n",
      "\n",
      "\n",
      "SaleType: Type of sale\n",
      "\n",
      "\t\t\n",
      "\n",
      "       WD \tWarranty Deed - Conventional\n",
      "\n",
      "       CWD\tWarranty Deed - Cash\n",
      "\n",
      "       VWD\tWarranty Deed - VA Loan\n",
      "\n",
      "       New\tHome just constructed and sold\n",
      "\n",
      "       COD\tCourt Officer Deed/Estate\n",
      "\n",
      "       Con\tContract 15% Down payment regular terms\n",
      "\n",
      "       ConLw\tContract Low Down payment and low interest\n",
      "\n",
      "       ConLI\tContract Low Interest\n",
      "\n",
      "       ConLD\tContract Low Down\n",
      "\n",
      "       Oth\tOther\n",
      "\n",
      "\t\t\n",
      "\n",
      "SaleCondition: Condition of sale\n",
      "\n",
      "\n",
      "\n",
      "       Normal\tNormal Sale\n",
      "\n",
      "       Abnorml\tAbnormal Sale -  trade, foreclosure, short sale\n",
      "\n",
      "       AdjLand\tAdjoining Land Purchase\n",
      "\n",
      "       Alloca\tAllocation - two linked properties with separate deeds, typically condo with a garage unit\t\n",
      "\n",
      "       Family\tSale between family members\n",
      "\n",
      "       Partial\tHome was not completed when last assessed (associated with New Homes)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data/houses_train.csv')\n",
    "\n",
    "def get_data_desc():\n",
    "    \"\"\"Essa função devolve a descrição completa do dataset\"\"\"\n",
    "    with open('data/data_description.txt','r') as file:\n",
    "        for line in file:\n",
    "            print(line)\n",
    "\n",
    "get_data_desc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_data = data[['LotArea','LotFrontage','PoolArea','GrLivArea','SalePrice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fa4cb7eb790>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(house_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>LotArea</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.426095</td>\n",
       "      <td>0.077672</td>\n",
       "      <td>0.263116</td>\n",
       "      <td>0.263843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>0.426095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.402797</td>\n",
       "      <td>0.351799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>0.077672</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170205</td>\n",
       "      <td>0.092404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.263116</td>\n",
       "      <td>0.402797</td>\n",
       "      <td>0.170205</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.708624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>0.263843</td>\n",
       "      <td>0.351799</td>\n",
       "      <td>0.092404</td>\n",
       "      <td>0.708624</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              LotArea  LotFrontage  PoolArea  GrLivArea  SalePrice\n",
       "LotArea      1.000000     0.426095  0.077672   0.263116   0.263843\n",
       "LotFrontage  0.426095     1.000000  0.206167   0.402797   0.351799\n",
       "PoolArea     0.077672     0.206167  1.000000   0.170205   0.092404\n",
       "GrLivArea    0.263116     0.402797  0.170205   1.000000   0.708624\n",
       "SalePrice    0.263843     0.351799  0.092404   0.708624   1.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4ca708310>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(house_data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fa4ca303710>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = 'GrLivArea', y = 'SalePrice', data = data[['SalePrice','GrLivArea']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## verificando os dados para regressão linear simples\n",
    "\n",
    "x = [-2,-1,0,1,3,5,7,9]\n",
    "y = [10,7,5,3,2,-3,-5,-7]\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(x=x,y=y,s=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.98727131],\n",
       "       [-0.98727131,  1.        ]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## verificando a correlação\n",
    "\n",
    "np.corrcoef(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([-2,-1,0,1,3,5,7,9]).reshape(-1,1)\n",
    "y = [10,7,5,3,2,-3,-5,-7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.49771689]), 5.6187214611872145)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## conferindo o resultado do google sheets\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(x,y)\n",
    "lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-219.03881279])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## utilizando o predict\n",
    "\n",
    "lr.predict([[150]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predizendo para x=0 -- Resultado [5.61872146]\n",
      "Predizendo para x=1 -- Resultado [4.12100457]\n",
      "Predizendo para x=2 -- Resultado [2.62328767]\n",
      "Predizendo para x=3 -- Resultado [1.12557078]\n",
      "Predizendo para x=4 -- Resultado [-0.37214612]\n",
      "Predizendo para x=5 -- Resultado [-1.86986301]\n",
      "Predizendo para x=6 -- Resultado [-3.36757991]\n",
      "Predizendo para x=7 -- Resultado [-4.8652968]\n",
      "Predizendo para x=8 -- Resultado [-6.3630137]\n",
      "Predizendo para x=9 -- Resultado [-7.86073059]\n"
     ]
    }
   ],
   "source": [
    "## gerando várias predições\n",
    "\n",
    "for i in range(10):\n",
    "  print(f'Predizendo para x={i} -- Resultado {lr.predict([[i]])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## verificando o modelo montado\n",
    "\n",
    "x = [-2,-1,0,1,3,5,7,9]\n",
    "y = [10,7,5,3,2,-3,-5,-7]\n",
    "\n",
    "y_pred = [lr.predict([[x]])[0] for x in x]\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(x=x,y=y,s=100)\n",
    "sns.lineplot(x=x,y=y_pred,color='tab:orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.54973822]), 5.6187214611872145)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## separar em treino e teste\n",
    "\n",
    "x_test = np.array([0,1]).reshape(-1,1)\n",
    "y_test = [5,3]\n",
    "\n",
    "x_treino = np.array([-2,-1,3,5,7,9]).reshape(-1,1)\n",
    "y_treino = [10,7,2,-3,-5,-7]\n",
    "\n",
    "lr2 = LinearRegression()\n",
    "lr2.fit(x_treino,y_treino)\n",
    "lr2.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "## definido nosso_mean_squared_error\n",
    "\n",
    "def nosso_mean_squared_error(y_real,y_pred): \n",
    "    return ((y_real - y_pred)**2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7822275922747606"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## calculando nosso_mean_squared_error\n",
    "\n",
    "valores_reais_de_y = np.array([5,3])\n",
    "valores_preditos_de_y = np.array([6.09075044, 4.54101222])\n",
    "\n",
    "nosso_mean_squared_error(valores_reais_de_y,valores_preditos_de_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.782227582699063"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## usando a API do sklearn\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(y_test,lr2.predict(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AMES HOUSING\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sem pre-processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea',\n",
       "       'TotalBsmtSF', '1stFlrSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = data.corr().SalePrice.sort_values(ascending=False)[:10].index\n",
    "colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[colunas].drop(['SalePrice'],axis=1)\n",
    "y = data.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.25, random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>5</td>\n",
       "      <td>1440</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>540</td>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>6</td>\n",
       "      <td>832</td>\n",
       "      <td>2</td>\n",
       "      <td>528</td>\n",
       "      <td>832</td>\n",
       "      <td>832</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>8</td>\n",
       "      <td>1601</td>\n",
       "      <td>2</td>\n",
       "      <td>670</td>\n",
       "      <td>1581</td>\n",
       "      <td>1601</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>5</td>\n",
       "      <td>764</td>\n",
       "      <td>2</td>\n",
       "      <td>504</td>\n",
       "      <td>680</td>\n",
       "      <td>764</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>5</td>\n",
       "      <td>882</td>\n",
       "      <td>1</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>882</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     OverallQual  GrLivArea  GarageCars  GarageArea  TotalBsmtSF  1stFlrSF  \\\n",
       "242            5       1440           1         352          540       889   \n",
       "617            6        832           2         528          832       832   \n",
       "539            8       1601           2         670         1581      1601   \n",
       "264            5        764           2         504          680       764   \n",
       "392            5        882           1         294            0       882   \n",
       "\n",
       "     FullBath  TotRmsAbvGrd  YearBuilt  \n",
       "242         1             6       1900  \n",
       "617         1             4       1954  \n",
       "539         2             6       2001  \n",
       "264         1             4       1925  \n",
       "392         1             5       1959  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.10326861e+04,  5.13894105e+01,  1.31354386e+04,  1.25390283e+01,\n",
       "        1.74390298e+01,  1.43381887e+01, -5.11917102e+03,  1.37730220e+02,\n",
       "        3.29258651e+02])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-732009.1865895989"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>7</td>\n",
       "      <td>1426</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>6</td>\n",
       "      <td>1298</td>\n",
       "      <td>2</td>\n",
       "      <td>403</td>\n",
       "      <td>1298</td>\n",
       "      <td>1298</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>6</td>\n",
       "      <td>1694</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>1181</td>\n",
       "      <td>1390</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018</th>\n",
       "      <td>7</td>\n",
       "      <td>1472</td>\n",
       "      <td>2</td>\n",
       "      <td>402</td>\n",
       "      <td>384</td>\n",
       "      <td>802</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>5</td>\n",
       "      <td>2080</td>\n",
       "      <td>2</td>\n",
       "      <td>504</td>\n",
       "      <td>1040</td>\n",
       "      <td>1040</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OverallQual  GrLivArea  GarageCars  GarageArea  TotalBsmtSF  1stFlrSF  \\\n",
       "860             7       1426           1         216          912       912   \n",
       "1146            6       1298           2         403         1298      1298   \n",
       "1092            6       1694           2         576         1181      1390   \n",
       "1018            7       1472           2         402          384       802   \n",
       "628             5       2080           2         504         1040      1040   \n",
       "\n",
       "      FullBath  TotRmsAbvGrd  YearBuilt  \n",
       "860          1             7       1918  \n",
       "1146         2             5       1985  \n",
       "1092         2             7       1925  \n",
       "1018         2             7       1991  \n",
       "628          1             9       1969  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38012.211746459056"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-Processamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![minmax](imgs/minxmax.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>856</td>\n",
       "      <td>856</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>920</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>756</td>\n",
       "      <td>961</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>6</td>\n",
       "      <td>1647</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>953</td>\n",
       "      <td>953</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>6</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>1542</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>7</td>\n",
       "      <td>2340</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>1152</td>\n",
       "      <td>1188</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>5</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>1078</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>5</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "      <td>1256</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      OverallQual  GrLivArea  GarageCars  GarageArea  TotalBsmtSF  1stFlrSF  \\\n",
       "0               7       1710           2         548          856       856   \n",
       "1               6       1262           2         460         1262      1262   \n",
       "2               7       1786           2         608          920       920   \n",
       "3               7       1717           3         642          756       961   \n",
       "4               8       2198           3         836         1145      1145   \n",
       "...           ...        ...         ...         ...          ...       ...   \n",
       "1455            6       1647           2         460          953       953   \n",
       "1456            6       2073           2         500         1542      2073   \n",
       "1457            7       2340           1         252         1152      1188   \n",
       "1458            5       1078           1         240         1078      1078   \n",
       "1459            5       1256           1         276         1256      1256   \n",
       "\n",
       "      FullBath  TotRmsAbvGrd  YearBuilt  \n",
       "0            2             8       2003  \n",
       "1            2             6       1976  \n",
       "2            2             6       2001  \n",
       "3            1             7       1915  \n",
       "4            2             9       2000  \n",
       "...        ...           ...        ...  \n",
       "1455         2             7       1999  \n",
       "1456         2             7       1978  \n",
       "1457         2             9       1941  \n",
       "1458         1             5       1950  \n",
       "1459         1             6       1965  \n",
       "\n",
       "[1460 rows x 9 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max = MinMaxScaler()\n",
    "scaled_x = min_max.fit_transform(x.drop(['OverallQual'],axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
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       "<p>1460 rows × 8 columns</p>\n",
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       "      GrLivArea  GarageCars  GarageArea  TotalBsmtSF  1stFlrSF  FullBath  \\\n",
       "0      0.259231        0.50    0.386460     0.140098  0.119780  0.666667   \n",
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       "1459   0.173700        0.25    0.194640     0.205565  0.211565  0.333333   \n",
       "\n",
       "      TotRmsAbvGrd  YearBuilt  \n",
       "0         0.500000   0.949275  \n",
       "1         0.333333   0.753623  \n",
       "2         0.333333   0.934783  \n",
       "3         0.416667   0.311594  \n",
       "4         0.583333   0.927536  \n",
       "...            ...        ...  \n",
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       "1456      0.416667   0.768116  \n",
       "1457      0.583333   0.500000  \n",
       "1458      0.250000   0.565217  \n",
       "1459      0.333333   0.673913  \n",
       "\n",
       "[1460 rows x 8 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = pd.DataFrame(scaled_x,columns=x.drop(['OverallQual'],axis=1).columns)\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(categories='auto')\n",
    "qual_trans = ohe.fit_transform(x[['OverallQual']])\n",
    "qual_trans.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "qual_trans = pd.DataFrame(qual_trans.todense(),columns=ohe.categories_)\n",
    "qual_trans.columns = ['qual_'+str(x) for x in list(zip(*qual_trans.columns.values))[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in qual_trans.columns:\n",
    "    df_scaled[column] = qual_trans[column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "      GrLivArea  GarageCars  GarageArea  TotalBsmtSF  1stFlrSF  FullBath  \\\n",
       "0      0.259231        0.50    0.386460     0.140098  0.119780  0.666667   \n",
       "1      0.174830        0.50    0.324401     0.206547  0.212942  0.666667   \n",
       "2      0.273549        0.50    0.428773     0.150573  0.134465  0.666667   \n",
       "3      0.260550        0.75    0.452750     0.123732  0.143873  0.333333   \n",
       "4      0.351168        0.75    0.589563     0.187398  0.186095  0.666667   \n",
       "...         ...         ...         ...          ...       ...       ...   \n",
       "1455   0.247362        0.50    0.324401     0.155974  0.142038  0.666667   \n",
       "1456   0.327619        0.50    0.352609     0.252373  0.399036  0.666667   \n",
       "1457   0.377920        0.25    0.177715     0.188543  0.195961  0.666667   \n",
       "1458   0.140166        0.25    0.169252     0.176432  0.170721  0.333333   \n",
       "1459   0.173700        0.25    0.194640     0.205565  0.211565  0.333333   \n",
       "\n",
       "      TotRmsAbvGrd  YearBuilt  qual_1  qual_2  qual_3  qual_4  qual_5  qual_6  \\\n",
       "0         0.500000   0.949275     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "1         0.333333   0.753623     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "2         0.333333   0.934783     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3         0.416667   0.311594     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4         0.583333   0.927536     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "...            ...        ...     ...     ...     ...     ...     ...     ...   \n",
       "1455      0.416667   0.920290     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "1456      0.416667   0.768116     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "1457      0.583333   0.500000     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "1458      0.250000   0.565217     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "1459      0.333333   0.673913     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "\n",
       "      qual_7  qual_8  qual_9  qual_10  \n",
       "0        1.0     0.0     0.0      0.0  \n",
       "1        0.0     0.0     0.0      0.0  \n",
       "2        1.0     0.0     0.0      0.0  \n",
       "3        1.0     0.0     0.0      0.0  \n",
       "4        0.0     1.0     0.0      0.0  \n",
       "...      ...     ...     ...      ...  \n",
       "1455     0.0     0.0     0.0      0.0  \n",
       "1456     0.0     0.0     0.0      0.0  \n",
       "1457     1.0     0.0     0.0      0.0  \n",
       "1458     0.0     0.0     0.0      0.0  \n",
       "1459     0.0     0.0     0.0      0.0  \n",
       "\n",
       "[1460 rows x 18 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(df_scaled,y, test_size = 0.25, random_state=43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_scaled = LinearRegression()\n",
    "lr_scaled.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36789.94001804078"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr_scaled.predict(x_test)\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R2\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## demonstrando r2\n",
    "\n",
    "a = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "b = np.array([10,11,15,18,19,21,25,25.33,27,29.55])\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(x=a,y=b,s=100,label='valores reais')\n",
    "plt.axhline(b.mean(),color='tab:red',label='média')\n",
    "plt.title('Você consegue fazer uma predição melhor que essa?',size=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## valores reais\n",
    "a = np.array([1,2,3,4,5,6,7,8,9,10])\n",
    "b = np.array([10,11,15,18,19,21,25,25.33,27,29.55])\n",
    "\n",
    "## valor predito\n",
    "y_pred = np.array([10.19890909, 12.39648485, 14.59406061, 16.79163636, 18.98921212,21.18678788, 23.38436364, 25.58193939, 27.77951515, 29.97709091])\n",
    "\n",
    "## gráfico\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(x=a,y=b,s=100,label='valores reais')\n",
    "sns.lineplot(x=a,y=y_pred,label='Resultado do modelo',color='tab:orange')\n",
    "plt.title('Consigo!',size=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.dummy import DummyRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DummyRegressor().fit(a,b)\n",
    "r2_score(model.predict(a),b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9821458097110134"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression().fit(a.reshape(-1,1),b)\n",
    "r2_score(lr.predict(a.reshape(-1,1)),b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7948807032646409"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## aplicando r2 no nosso problema\n",
    "\n",
    "y_pred = lr_scaled.predict(x_test)\n",
    "\n",
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8064764553870117"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## avaliando overfitting\n",
    "\n",
    "y_pred_train = lr_scaled.predict(x_train)\n",
    "\n",
    "r2_score(y_train,y_pred_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>qual_1</th>\n",
       "      <th>qual_2</th>\n",
       "      <th>qual_3</th>\n",
       "      <th>qual_4</th>\n",
       "      <th>qual_5</th>\n",
       "      <th>qual_6</th>\n",
       "      <th>qual_7</th>\n",
       "      <th>qual_8</th>\n",
       "      <th>qual_9</th>\n",
       "      <th>qual_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.259231</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.386460</td>\n",
       "      <td>0.140098</td>\n",
       "      <td>0.119780</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.949275</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.174830</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.324401</td>\n",
       "      <td>0.206547</td>\n",
       "      <td>0.212942</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.753623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.273549</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.428773</td>\n",
       "      <td>0.150573</td>\n",
       "      <td>0.134465</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.934783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.260550</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.452750</td>\n",
       "      <td>0.123732</td>\n",
       "      <td>0.143873</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.311594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.351168</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.589563</td>\n",
       "      <td>0.187398</td>\n",
       "      <td>0.186095</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.927536</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>0.247362</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.324401</td>\n",
       "      <td>0.155974</td>\n",
       "      <td>0.142038</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.920290</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>0.327619</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.352609</td>\n",
       "      <td>0.252373</td>\n",
       "      <td>0.399036</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.768116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>0.377920</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.177715</td>\n",
       "      <td>0.188543</td>\n",
       "      <td>0.195961</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>0.140166</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.169252</td>\n",
       "      <td>0.176432</td>\n",
       "      <td>0.170721</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.565217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0.173700</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.194640</td>\n",
       "      <td>0.205565</td>\n",
       "      <td>0.211565</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      GrLivArea  GarageCars  GarageArea  TotalBsmtSF  1stFlrSF  FullBath  \\\n",
       "0      0.259231        0.50    0.386460     0.140098  0.119780  0.666667   \n",
       "1      0.174830        0.50    0.324401     0.206547  0.212942  0.666667   \n",
       "2      0.273549        0.50    0.428773     0.150573  0.134465  0.666667   \n",
       "3      0.260550        0.75    0.452750     0.123732  0.143873  0.333333   \n",
       "4      0.351168        0.75    0.589563     0.187398  0.186095  0.666667   \n",
       "...         ...         ...         ...          ...       ...       ...   \n",
       "1455   0.247362        0.50    0.324401     0.155974  0.142038  0.666667   \n",
       "1456   0.327619        0.50    0.352609     0.252373  0.399036  0.666667   \n",
       "1457   0.377920        0.25    0.177715     0.188543  0.195961  0.666667   \n",
       "1458   0.140166        0.25    0.169252     0.176432  0.170721  0.333333   \n",
       "1459   0.173700        0.25    0.194640     0.205565  0.211565  0.333333   \n",
       "\n",
       "      TotRmsAbvGrd  YearBuilt  qual_1  qual_2  qual_3  qual_4  qual_5  qual_6  \\\n",
       "0         0.500000   0.949275     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "1         0.333333   0.753623     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "2         0.333333   0.934783     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3         0.416667   0.311594     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4         0.583333   0.927536     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "...            ...        ...     ...     ...     ...     ...     ...     ...   \n",
       "1455      0.416667   0.920290     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "1456      0.416667   0.768116     0.0     0.0     0.0     0.0     0.0     1.0   \n",
       "1457      0.583333   0.500000     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "1458      0.250000   0.565217     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "1459      0.333333   0.673913     0.0     0.0     0.0     0.0     1.0     0.0   \n",
       "\n",
       "      qual_7  qual_8  qual_9  qual_10  \n",
       "0        1.0     0.0     0.0      0.0  \n",
       "1        0.0     0.0     0.0      0.0  \n",
       "2        1.0     0.0     0.0      0.0  \n",
       "3        1.0     0.0     0.0      0.0  \n",
       "4        0.0     1.0     0.0      0.0  \n",
       "...      ...     ...     ...      ...  \n",
       "1455     0.0     0.0     0.0      0.0  \n",
       "1456     0.0     0.0     0.0      0.0  \n",
       "1457     1.0     0.0     0.0      0.0  \n",
       "1458     0.0     0.0     0.0      0.0  \n",
       "1459     0.0     0.0     0.0      0.0  \n",
       "\n",
       "[1460 rows x 18 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       208500\n",
       "1       181500\n",
       "2       223500\n",
       "3       140000\n",
       "4       250000\n",
       "         ...  \n",
       "1455    175000\n",
       "1456    210000\n",
       "1457    266500\n",
       "1458    142125\n",
       "1459    147500\n",
       "Name: SalePrice, Length: 1460, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27528.24462914, 30092.6876499 , 26532.14580458, 44704.83650775,\n",
       "       42239.44828422, 33241.90534567, 30477.40356432, 29918.7728108 ,\n",
       "       59968.60617721, 28564.84595115])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_lr = LinearRegression()\n",
    "arr = cross_val_score(cross_lr,df_scaled,y,cv=10,scoring='neg_root_mean_squared_error')\n",
    "arr*-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35326.88967247516"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr*-1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def train_models(x,y,cv):\n",
    "\n",
    "    error = pd.DataFrame([])\n",
    "\n",
    "    ## val dummy\n",
    "    dummy = DummyRegressor(strategy='mean')\n",
    "    error['dummy'] = (cross_val_score(dummy,x,y,cv=cv,scoring='neg_root_mean_squared_error')*-1)\n",
    "\n",
    "    ## val LinearModel\n",
    "    linear = LinearRegression()\n",
    "    error['linear'] = (cross_val_score(linear,x,y,cv=cv,scoring='neg_root_mean_squared_error')*-1)\n",
    "\n",
    "    ## val DecisionTree\n",
    "    dt_reg = DecisionTreeRegressor()\n",
    "    error['tree'] = (cross_val_score(dt_reg,x,y,cv=cv,scoring='neg_root_mean_squared_error')*-1)\n",
    "    \n",
    "    ## val RandomForest\n",
    "    rf_reg = RandomForestRegressor()\n",
    "    error['forest'] = (cross_val_score(rf_reg,x,y,cv=cv,scoring='neg_root_mean_squared_error')*-1)\n",
    "    \n",
    "    error = error.transpose()\n",
    "    error = error.assign(mean = lambda x: x.mean(axis=1))\n",
    "\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dummy</th>\n",
       "      <td>68479.618481</td>\n",
       "      <td>80011.928200</td>\n",
       "      <td>79275.659589</td>\n",
       "      <td>82868.159128</td>\n",
       "      <td>96386.204909</td>\n",
       "      <td>76808.566291</td>\n",
       "      <td>72198.448617</td>\n",
       "      <td>71213.042540</td>\n",
       "      <td>90347.614676</td>\n",
       "      <td>72613.278974</td>\n",
       "      <td>79020.252141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear</th>\n",
       "      <td>27528.244629</td>\n",
       "      <td>30092.687650</td>\n",
       "      <td>26532.145805</td>\n",
       "      <td>44704.836508</td>\n",
       "      <td>42239.448284</td>\n",
       "      <td>33241.905346</td>\n",
       "      <td>30477.403564</td>\n",
       "      <td>29918.772811</td>\n",
       "      <td>59968.606177</td>\n",
       "      <td>28564.845951</td>\n",
       "      <td>35326.889672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tree</th>\n",
       "      <td>39384.204286</td>\n",
       "      <td>42630.754100</td>\n",
       "      <td>37773.962826</td>\n",
       "      <td>62414.939106</td>\n",
       "      <td>49521.324698</td>\n",
       "      <td>38336.003033</td>\n",
       "      <td>33551.236631</td>\n",
       "      <td>38884.758865</td>\n",
       "      <td>39771.321137</td>\n",
       "      <td>35929.178115</td>\n",
       "      <td>41819.768280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forest</th>\n",
       "      <td>32099.719479</td>\n",
       "      <td>31485.062403</td>\n",
       "      <td>28316.194959</td>\n",
       "      <td>52515.159464</td>\n",
       "      <td>35868.543116</td>\n",
       "      <td>26225.576497</td>\n",
       "      <td>25164.529749</td>\n",
       "      <td>25865.019593</td>\n",
       "      <td>40136.324844</td>\n",
       "      <td>28169.174482</td>\n",
       "      <td>32584.530459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0             1             2             3             4  \\\n",
       "dummy   68479.618481  80011.928200  79275.659589  82868.159128  96386.204909   \n",
       "linear  27528.244629  30092.687650  26532.145805  44704.836508  42239.448284   \n",
       "tree    39384.204286  42630.754100  37773.962826  62414.939106  49521.324698   \n",
       "forest  32099.719479  31485.062403  28316.194959  52515.159464  35868.543116   \n",
       "\n",
       "                   5             6             7             8             9  \\\n",
       "dummy   76808.566291  72198.448617  71213.042540  90347.614676  72613.278974   \n",
       "linear  33241.905346  30477.403564  29918.772811  59968.606177  28564.845951   \n",
       "tree    38336.003033  33551.236631  38884.758865  39771.321137  35929.178115   \n",
       "forest  26225.576497  25164.529749  25865.019593  40136.324844  28169.174482   \n",
       "\n",
       "                mean  \n",
       "dummy   79020.252141  \n",
       "linear  35326.889672  \n",
       "tree    41819.768280  \n",
       "forest  32584.530459  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_models(df_scaled,y,cv=10)"
   ]
  }
 ],
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