exemplos/temp_elipse_ecc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import epmwebapi as epm\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "credentials = os.environ.get('user_and_password').split(';')\n",
    "user = credentials[0]\n",
    "password = credentials[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cria conexao\n",
    "#para ter acesso ao ECC entre em contato com epm@elipse.com.br\n",
    "epmConn = epm.EpmConnection('http://ecc.elipse.com.br:44333', 'http://ecc.elipse.com.br:44332', user, password)\n",
    "\n",
    "\n",
    "iniTime = dt.datetime(2018, 9, 1, 0, 0, 0, 0)\n",
    "endTime = iniTime + dt.timedelta(days=60)\n",
    "\n",
    "array_dfs = []\n",
    "\n",
    "paths = ['ERSF11R4_Temp2', 'ERSF11R4_Temp2SP', 'ERSF11R4_ACCompr2', 'ERSF11R4_ACVent2']\n",
    "\n",
    "processInterval = dt.timedelta(minutes=5)\n",
    "\n",
    "\n",
    "#cria objeto queryperiod\n",
    "queryPeriod = epm.QueryPeriod(iniTime, endTime)\n",
    "\n",
    "aggInterpDetails = epm.AggregateDetails(processInterval, epm.AggregateType.Interpolative)\n",
    "\n",
    "for path in paths:\n",
    "    \n",
    "    \n",
    "    bv = epmConn.getDataObjects(path)\n",
    "\n",
    "    \n",
    "    result= bv[path].historyReadAggregate(aggInterpDetails, queryPeriod)\n",
    "    \n",
    "    #======================================================================================\n",
    "    # Transformação em DataFrame do pandas\n",
    "    #======================================================================================\n",
    "    #corrige o erro: ValueError: Big-endian buffer not supported on little-endian compiler\n",
    "    new_Quality = result[:]['Quality'].byteswap().newbyteorder()\n",
    "    new_Timestamp = result[:]['Timestamp']\n",
    "    new_Value = result[:]['Value'].byteswap().newbyteorder()\n",
    "\n",
    "    d = {'Value':new_Value, 'Timestamp':new_Timestamp, 'Quality':new_Quality}\n",
    "    df_original = pd.DataFrame(d)\n",
    "    \n",
    "    #======================================================================================\n",
    "    # Converte de UTC PARA LOCALTIME\n",
    "    #======================================================================================\n",
    "    df_original['Timestamp'] = df_original['Timestamp'].dt.tz_convert('America/Sao_Paulo')\n",
    "    \n",
    "    #array de dataframes\n",
    "    array_dfs.append(df_original)\n",
    "\n",
    "\n",
    "epmConn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17280, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_dfs[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.578434</td>\n",
       "      <td>2018-08-31 21:00:00-03:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.566795</td>\n",
       "      <td>2018-08-31 21:05:00-03:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.555159</td>\n",
       "      <td>2018-08-31 21:10:00-03:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.543522</td>\n",
       "      <td>2018-08-31 21:15:00-03:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.531885</td>\n",
       "      <td>2018-08-31 21:20:00-03:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Value                 Timestamp  Quality\n",
       "0  23.578434 2018-08-31 21:00:00-03:00        0\n",
       "1  23.566795 2018-08-31 21:05:00-03:00        0\n",
       "2  23.555159 2018-08-31 21:10:00-03:00        0\n",
       "3  23.543522 2018-08-31 21:15:00-03:00        0\n",
       "4  23.531885 2018-08-31 21:20:00-03:00        0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_dfs[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs_filtered = []\n",
    "\n",
    "for df in array_dfs:\n",
    "\n",
    "    mask = (\n",
    "        ((df.Timestamp.dt.weekday.between(0,4)) & (df.Timestamp.dt.hour.between(8,11) | (df.Timestamp.dt.hour.between(13,18) )))\n",
    "        #& (df.Quality == 0)\n",
    "    )\n",
    "\n",
    "    df_aux = df.loc[mask]\n",
    "    dfs_filtered.append(df_aux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_01 = dfs_filtered[0][['Timestamp']].copy()\n",
    "df_01['Temperatura'] = dfs_filtered[0][['Value']].copy()\n",
    "df_01['TemperaturaSP'] = dfs_filtered[1][['Value']].copy()\n",
    "df_01['Compressor'] = dfs_filtered[2][['Value']].copy()\n",
    "df_01['Ventilacao'] = dfs_filtered[3][['Value']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5040, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp        datetime64[ns, America/Sao_Paulo]\n",
       "Temperatura                                float32\n",
       "TemperaturaSP                              float32\n",
       "Compressor                                 float32\n",
       "Ventilacao                                 float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Timestamp</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>TemperaturaSP</th>\n",
       "      <th>Compressor</th>\n",
       "      <th>Ventilacao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Timestamp, Temperatura, TemperaturaSP, Compressor, Ventilacao]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01.loc[df_01.Timestamp.dt.hour==12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análises Iniciais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Timestamp</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>TemperaturaSP</th>\n",
       "      <th>Compressor</th>\n",
       "      <th>Ventilacao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5040</td>\n",
       "      <td>5040.000000</td>\n",
       "      <td>5040.000000</td>\n",
       "      <td>5040.00000</td>\n",
       "      <td>5040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2018-10-08 18:40:00-03:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.884628</td>\n",
       "      <td>24.728928</td>\n",
       "      <td>0.16627</td>\n",
       "      <td>0.375794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.700590</td>\n",
       "      <td>0.692875</td>\n",
       "      <td>0.37236</td>\n",
       "      <td>0.484375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.191805</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.385906</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.841507</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.394644</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.785048</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Timestamp  Temperatura  TemperaturaSP  Compressor  \\\n",
       "count                        5040  5040.000000    5040.000000  5040.00000   \n",
       "unique                       5040          NaN            NaN         NaN   \n",
       "top     2018-10-08 18:40:00-03:00          NaN            NaN         NaN   \n",
       "freq                            1          NaN            NaN         NaN   \n",
       "mean                          NaN    24.884628      24.728928     0.16627   \n",
       "std                           NaN     0.700590       0.692875     0.37236   \n",
       "min                           NaN    22.191805      21.000000     0.00000   \n",
       "25%                           NaN    24.385906      24.500000     0.00000   \n",
       "50%                           NaN    24.841507      25.000000     0.00000   \n",
       "75%                           NaN    25.394644      25.000000     0.00000   \n",
       "max                           NaN    26.785048      26.100000     1.00000   \n",
       "\n",
       "         Ventilacao  \n",
       "count   5040.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "mean       0.375794  \n",
       "std        0.484375  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa68534cc0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_01.Temperatura.plot(kind='hist', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa68826470>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_01.TemperaturaSP.plot(kind='hist', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conforto(a):\n",
    "    if a>=24.5 and a<=25.5:\n",
    "        return 'dentro'\n",
    "    if a<24.5:\n",
    "        return 'abaixo'\n",
    "    else:\n",
    "        return 'acima'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_01['conforto_termico'] = df_01.Temperatura.apply(conforto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Timestamp</th>\n",
       "      <th>Temperatura</th>\n",
       "      <th>TemperaturaSP</th>\n",
       "      <th>Compressor</th>\n",
       "      <th>Ventilacao</th>\n",
       "      <th>conforto_termico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2018-09-03 08:00:00-03:00</td>\n",
       "      <td>23.792610</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abaixo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>2018-09-03 08:05:00-03:00</td>\n",
       "      <td>23.794382</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abaixo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>2018-09-03 08:10:00-03:00</td>\n",
       "      <td>23.796154</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abaixo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>2018-09-03 08:15:00-03:00</td>\n",
       "      <td>23.797928</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abaixo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>2018-09-03 08:20:00-03:00</td>\n",
       "      <td>23.799700</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abaixo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Timestamp  Temperatura  TemperaturaSP  Compressor  \\\n",
       "708 2018-09-03 08:00:00-03:00    23.792610           25.0         0.0   \n",
       "709 2018-09-03 08:05:00-03:00    23.794382           25.0         0.0   \n",
       "710 2018-09-03 08:10:00-03:00    23.796154           25.0         0.0   \n",
       "711 2018-09-03 08:15:00-03:00    23.797928           25.0         0.0   \n",
       "712 2018-09-03 08:20:00-03:00    23.799700           25.0         0.0   \n",
       "\n",
       "     Ventilacao conforto_termico  \n",
       "708         0.0           abaixo  \n",
       "709         0.0           abaixo  \n",
       "710         0.0           abaixo  \n",
       "711         0.0           abaixo  \n",
       "712         0.0           abaixo  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>conforto_termico</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abaixo</td>\n",
       "      <td>1665</td>\n",
       "      <td>24.499552</td>\n",
       "      <td>22.191805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>acima</td>\n",
       "      <td>1104</td>\n",
       "      <td>26.785048</td>\n",
       "      <td>25.500530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dentro</td>\n",
       "      <td>2271</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  conforto_termico  count        max        min\n",
       "0           abaixo   1665  24.499552  22.191805\n",
       "1            acima   1104  26.785048  25.500530\n",
       "2           dentro   2271  25.500000  24.500000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_temp = df_01.groupby(['conforto_termico']).Temperatura.agg(['count', 'max', 'min']).reset_index()\n",
    "grouped_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_autopct(values):\n",
    "    #values -> valores enviados\n",
    "    def my_autopct(pct):\n",
    "        #pct -> %\n",
    "        total = sum(values)\n",
    "        val = int(round(pct*total/100.0))\n",
    "        return '{p:.2f}%\\n({v:d})'.format(p=pct,v=val)\n",
    "    return my_autopct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.25, 1.25, -1.25, 1.25)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_01.groupby(['conforto_termico']).Temperatura.count().plot(kind='pie', subplots=True, autopct=make_autopct(grouped_temp['count']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Compressor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Compressor  count\n",
       "0         0.0   4202\n",
       "1         1.0    838"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_comp = df_01.groupby(['Compressor']).Compressor.agg(['count']).reset_index()\n",
    "grouped_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.25, 1.25, -1.25, 1.25)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_01.groupby(['Compressor']).Compressor.count().plot(kind='pie', subplots=True, autopct=make_autopct(grouped_comp['count']))\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}