examples/python-experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "65b7faf8-4e03-46b6-baed-d001e0b6c6c4",
   "metadata": {},
   "source": [
    "# Exemplo de utilização da API REST *open weather iot* em Python no Notebook Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce68718-a913-4ebb-a88b-57505c30d3b0",
   "metadata": {},
   "source": [
    "Primeiramente, instalamos o pacote necessário usando o ambiente anaconda: `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d073b396-b006-4005-ac61-eee1976e8526",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!conda install -c anaconda pandas seaborn -y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5f5e1a-470d-4de0-bc3d-44c9f948381e",
   "metadata": {},
   "source": [
    "Importamos a biblioteca necessária: `pandas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62f3a58f-c807-4ffb-8936-9832ecde0cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "febd7a26-9e40-44f6-a666-1650328dcaf9",
   "metadata": {},
   "source": [
    "Agora, fazemos uma requisição *web* para o *endpoint* `/dataseries` no servidor `owi-server.onrender.com`, obtendo uma resposta em JSON da série de dados meteorológicos brutos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8f7d6a2-ab1e-4f5a-a5f0-92991bfe0348",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_weather_data_series = pd.read_json(\"https://owi-server.onrender.com/dataseries\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3b9c03-9238-4c6e-90c4-5e27b5995580",
   "metadata": {},
   "source": [
    "Exibimos o dataframe em Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b7c0280-9ca3-43e2-b661-ce5bdeeb061b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>_id</th>\n",
       "      <th>measurements</th>\n",
       "      <th>errors</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>6399458a73a2fd0034f88431</td>\n",
       "      <td>{'SYS/power/vsys/voltage': {'raw': 32776, 'val...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 03:39:51.807347037+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6399458f73a2fd0034f88432</td>\n",
       "      <td>{'SYS/power/vsys/voltage': {'raw': 32776, 'val...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 03:39:55.893377020+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6399459373a2fd0034f88433</td>\n",
       "      <td>{'SYS/power/vsys/voltage': {'raw': 32936, 'val...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 03:39:59.969891401+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6399459773a2fd0034f88434</td>\n",
       "      <td>{'SYS/power/vsys/voltage': {'raw': 32936, 'val...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 03:40:04.043722334+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6399459b73a2fd0034f88435</td>\n",
       "      <td>{'SYS/power/vsys/voltage': {'raw': 32904, 'val...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 03:40:08.129934826+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>5756</th>\n",
       "      <td>6399c6b271d4b1003328994c</td>\n",
       "      <td>{'SYS/cpu/temperature': {'raw': 14147, 'value'...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 12:50:53.869994222+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5757</th>\n",
       "      <td>6399c6b971d4b1003328994d</td>\n",
       "      <td>{'SYS/cpu/temperature': {'raw': 14131, 'value'...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 12:50:59.481273825+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5758</th>\n",
       "      <td>6399c6bd71d4b1003328994e</td>\n",
       "      <td>{'SYS/cpu/temperature': {'raw': 14131, 'value'...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 12:51:05.083434485+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759</th>\n",
       "      <td>6399c6c371d4b1003328994f</td>\n",
       "      <td>{'SYS/cpu/temperature': {'raw': 14131, 'value'...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 12:51:10.744926991+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5760</th>\n",
       "      <td>6399c6c871d4b10033289950</td>\n",
       "      <td>{'SYS/cpu/temperature': {'raw': 14131, 'value'...</td>\n",
       "      <td>[got error `OSError: [Errno 5] EIO` while samp...</td>\n",
       "      <td>2022-12-14 12:51:16.399228407+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5761 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "                           _id  \\\n",
       "0     6399458a73a2fd0034f88431   \n",
       "1     6399458f73a2fd0034f88432   \n",
       "2     6399459373a2fd0034f88433   \n",
       "3     6399459773a2fd0034f88434   \n",
       "4     6399459b73a2fd0034f88435   \n",
       "...                        ...   \n",
       "5756  6399c6b271d4b1003328994c   \n",
       "5757  6399c6b971d4b1003328994d   \n",
       "5758  6399c6bd71d4b1003328994e   \n",
       "5759  6399c6c371d4b1003328994f   \n",
       "5760  6399c6c871d4b10033289950   \n",
       "\n",
       "                                           measurements  \\\n",
       "0     {'SYS/power/vsys/voltage': {'raw': 32776, 'val...   \n",
       "1     {'SYS/power/vsys/voltage': {'raw': 32776, 'val...   \n",
       "2     {'SYS/power/vsys/voltage': {'raw': 32936, 'val...   \n",
       "3     {'SYS/power/vsys/voltage': {'raw': 32936, 'val...   \n",
       "4     {'SYS/power/vsys/voltage': {'raw': 32904, 'val...   \n",
       "...                                                 ...   \n",
       "5756  {'SYS/cpu/temperature': {'raw': 14147, 'value'...   \n",
       "5757  {'SYS/cpu/temperature': {'raw': 14131, 'value'...   \n",
       "5758  {'SYS/cpu/temperature': {'raw': 14131, 'value'...   \n",
       "5759  {'SYS/cpu/temperature': {'raw': 14131, 'value'...   \n",
       "5760  {'SYS/cpu/temperature': {'raw': 14131, 'value'...   \n",
       "\n",
       "                                                 errors  \\\n",
       "0     [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "1     [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "2     [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "3     [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "4     [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "...                                                 ...   \n",
       "5756  [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "5757  [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "5758  [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "5759  [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "5760  [got error `OSError: [Errno 5] EIO` while samp...   \n",
       "\n",
       "                               timestamp  \n",
       "0    2022-12-14 03:39:51.807347037+00:00  \n",
       "1    2022-12-14 03:39:55.893377020+00:00  \n",
       "2    2022-12-14 03:39:59.969891401+00:00  \n",
       "3    2022-12-14 03:40:04.043722334+00:00  \n",
       "4    2022-12-14 03:40:08.129934826+00:00  \n",
       "...                                  ...  \n",
       "5756 2022-12-14 12:50:53.869994222+00:00  \n",
       "5757 2022-12-14 12:50:59.481273825+00:00  \n",
       "5758 2022-12-14 12:51:05.083434485+00:00  \n",
       "5759 2022-12-14 12:51:10.744926991+00:00  \n",
       "5760 2022-12-14 12:51:16.399228407+00:00  \n",
       "\n",
       "[5761 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_weather_data_series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff00e85-c8d1-4d64-9ea3-b571c19ec705",
   "metadata": {},
   "source": [
    "Observamos que o formato da coluna `measurements` é JSON. Os sensores disponíveis nessa coluna são os seguintes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f297cd5b-c713-4f21-adc3-c0f53791c69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SYS/power/vsys/voltage', 'SYS/power/solar_panel/voltage',\n",
       "       'SYS/power/battery/voltage', 'SYS/cpu/temperature',\n",
       "       'SYS/power/battery/percentage', 'PT100/temperature',\n",
       "       'HMC5883L/1/wind_speed', 'HMC5883L/0/wind_direction', 'BME680/humidity',\n",
       "       'BME680/altitude', 'BME680/gas', 'BME680/temperature',\n",
       "       'BME680/pressure', 'HDC1080/humidity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sensors = pd.json_normalize(df_weather_data_series.measurements, max_level=0)\n",
    "sensors.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f008f8-878f-493c-a822-5675422aeec6",
   "metadata": {},
   "source": [
    "Vamos normalizar a coluna `measurements` para um dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "38ff867b-155d-45ac-96b5-8cd4f34ce082",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>SYS/power/solar_panel/voltage/raw</th>\n",
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       "      <th>SYS/cpu/temperature/raw</th>\n",
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       "      <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",
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       "      <td>V</td>\n",
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       "      <td>V</td>\n",
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       "      <td>V</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30578</td>\n",
       "      <td>ºC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>944.5737</td>\n",
       "      <td>hPa</td>\n",
       "      <td>45084.0</td>\n",
       "      <td>68.79272</td>\n",
       "      <td>% RH</td>\n",
       "      <td>2022-12-14 12:50:53.869994222+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5757</th>\n",
       "      <td>32968</td>\n",
       "      <td>4.980288</td>\n",
       "      <td>V</td>\n",
       "      <td>18500</td>\n",
       "      <td>4.005722</td>\n",
       "      <td>V</td>\n",
       "      <td>41034</td>\n",
       "      <td>4.132515</td>\n",
       "      <td>V</td>\n",
       "      <td>14131</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30598</td>\n",
       "      <td>ºC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>944.5758</td>\n",
       "      <td>hPa</td>\n",
       "      <td>45084.0</td>\n",
       "      <td>68.79272</td>\n",
       "      <td>% RH</td>\n",
       "      <td>2022-12-14 12:50:59.481273825+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5758</th>\n",
       "      <td>32856</td>\n",
       "      <td>4.963369</td>\n",
       "      <td>V</td>\n",
       "      <td>18516</td>\n",
       "      <td>4.009186</td>\n",
       "      <td>V</td>\n",
       "      <td>40953</td>\n",
       "      <td>4.124358</td>\n",
       "      <td>V</td>\n",
       "      <td>14131</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30578</td>\n",
       "      <td>ºC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>944.5703</td>\n",
       "      <td>hPa</td>\n",
       "      <td>45152.0</td>\n",
       "      <td>68.89648</td>\n",
       "      <td>% RH</td>\n",
       "      <td>2022-12-14 12:51:05.083434485+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759</th>\n",
       "      <td>32968</td>\n",
       "      <td>4.980288</td>\n",
       "      <td>V</td>\n",
       "      <td>17332</td>\n",
       "      <td>3.752820</td>\n",
       "      <td>V</td>\n",
       "      <td>41098</td>\n",
       "      <td>4.138961</td>\n",
       "      <td>V</td>\n",
       "      <td>14131</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30578</td>\n",
       "      <td>ºC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>944.5652</td>\n",
       "      <td>hPa</td>\n",
       "      <td>45084.0</td>\n",
       "      <td>68.79272</td>\n",
       "      <td>% RH</td>\n",
       "      <td>2022-12-14 12:51:10.744926991+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5760</th>\n",
       "      <td>32984</td>\n",
       "      <td>4.982705</td>\n",
       "      <td>V</td>\n",
       "      <td>18084</td>\n",
       "      <td>3.915647</td>\n",
       "      <td>V</td>\n",
       "      <td>41018</td>\n",
       "      <td>4.130904</td>\n",
       "      <td>V</td>\n",
       "      <td>14131</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30480</td>\n",
       "      <td>ºC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>944.5637</td>\n",
       "      <td>hPa</td>\n",
       "      <td>44760.0</td>\n",
       "      <td>68.29834</td>\n",
       "      <td>% RH</td>\n",
       "      <td>2022-12-14 12:51:16.399228407+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5761 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SYS/power/vsys/voltage/raw  SYS/power/vsys/voltage/value  \\\n",
       "0                          32776                      4.951283   \n",
       "1                          32776                      4.951283   \n",
       "2                          32936                      4.975454   \n",
       "3                          32936                      4.975454   \n",
       "4                          32904                      4.970620   \n",
       "...                          ...                           ...   \n",
       "5756                       33000                      4.985122   \n",
       "5757                       32968                      4.980288   \n",
       "5758                       32856                      4.963369   \n",
       "5759                       32968                      4.980288   \n",
       "5760                       32984                      4.982705   \n",
       "\n",
       "     SYS/power/vsys/voltage/unit  SYS/power/solar_panel/voltage/raw  \\\n",
       "0                              V                              11874   \n",
       "1                              V                              11842   \n",
       "2                              V                              12098   \n",
       "3                              V                              12098   \n",
       "4                              V                              12066   \n",
       "...                          ...                                ...   \n",
       "5756                           V                              17092   \n",
       "5757                           V                              18500   \n",
       "5758                           V                              18516   \n",
       "5759                           V                              17332   \n",
       "5760                           V                              18084   \n",
       "\n",
       "      SYS/power/solar_panel/voltage/value SYS/power/solar_panel/voltage/unit  \\\n",
       "0                                2.571024                                  V   \n",
       "1                                2.564095                                  V   \n",
       "2                                2.619525                                  V   \n",
       "3                                2.619525                                  V   \n",
       "4                                2.612597                                  V   \n",
       "...                                   ...                                ...   \n",
       "5756                             3.700854                                  V   \n",
       "5757                             4.005722                                  V   \n",
       "5758                             4.009186                                  V   \n",
       "5759                             3.752820                                  V   \n",
       "5760                             3.915647                                  V   \n",
       "\n",
       "      SYS/power/battery/voltage/raw  SYS/power/battery/voltage/value  \\\n",
       "0                             16484                         1.660096   \n",
       "1                             15795                         1.590707   \n",
       "2                             16836                         1.695546   \n",
       "3                             16099                         1.621323   \n",
       "4                             16484                         1.660096   \n",
       "...                             ...                              ...   \n",
       "5756                          41050                         4.134127   \n",
       "5757                          41034                         4.132515   \n",
       "5758                          40953                         4.124358   \n",
       "5759                          41098                         4.138961   \n",
       "5760                          41018                         4.130904   \n",
       "\n",
       "     SYS/power/battery/voltage/unit  SYS/cpu/temperature/raw  ...  \\\n",
       "0                                 V                    14099  ...   \n",
       "1                                 V                    14083  ...   \n",
       "2                                 V                    14099  ...   \n",
       "3                                 V                    14099  ...   \n",
       "4                                 V                    14099  ...   \n",
       "...                             ...                      ...  ...   \n",
       "5756                              V                    14147  ...   \n",
       "5757                              V                    14131  ...   \n",
       "5758                              V                    14131  ...   \n",
       "5759                              V                    14131  ...   \n",
       "5760                              V                    14131  ...   \n",
       "\n",
       "      BME680/temperature/raw BME680/temperature/value  \\\n",
       "0                        NaN                      NaN   \n",
       "1                        NaN                      NaN   \n",
       "2                        NaN                      NaN   \n",
       "3                        NaN                      NaN   \n",
       "4                        NaN                      NaN   \n",
       "...                      ...                      ...   \n",
       "5756                     NaN                 23.30578   \n",
       "5757                     NaN                 23.30598   \n",
       "5758                     NaN                 23.30578   \n",
       "5759                     NaN                 23.30578   \n",
       "5760                     NaN                 23.30480   \n",
       "\n",
       "      BME680/temperature/unit  BME680/pressure/raw BME680/pressure/value  \\\n",
       "0                         NaN                  NaN                   NaN   \n",
       "1                         NaN                  NaN                   NaN   \n",
       "2                         NaN                  NaN                   NaN   \n",
       "3                         NaN                  NaN                   NaN   \n",
       "4                         NaN                  NaN                   NaN   \n",
       "...                       ...                  ...                   ...   \n",
       "5756                       ºC                  NaN              944.5737   \n",
       "5757                       ºC                  NaN              944.5758   \n",
       "5758                       ºC                  NaN              944.5703   \n",
       "5759                       ºC                  NaN              944.5652   \n",
       "5760                       ºC                  NaN              944.5637   \n",
       "\n",
       "      BME680/pressure/unit  HDC1080/humidity/raw  HDC1080/humidity/value  \\\n",
       "0                      NaN                   NaN                     NaN   \n",
       "1                      NaN                   NaN                     NaN   \n",
       "2                      NaN                   NaN                     NaN   \n",
       "3                      NaN                   NaN                     NaN   \n",
       "4                      NaN                   NaN                     NaN   \n",
       "...                    ...                   ...                     ...   \n",
       "5756                   hPa               45084.0                68.79272   \n",
       "5757                   hPa               45084.0                68.79272   \n",
       "5758                   hPa               45152.0                68.89648   \n",
       "5759                   hPa               45084.0                68.79272   \n",
       "5760                   hPa               44760.0                68.29834   \n",
       "\n",
       "     HDC1080/humidity/unit                           timestamp  \n",
       "0                      NaN 2022-12-14 03:39:51.807347037+00:00  \n",
       "1                      NaN 2022-12-14 03:39:55.893377020+00:00  \n",
       "2                      NaN 2022-12-14 03:39:59.969891401+00:00  \n",
       "3                      NaN 2022-12-14 03:40:04.043722334+00:00  \n",
       "4                      NaN 2022-12-14 03:40:08.129934826+00:00  \n",
       "...                    ...                                 ...  \n",
       "5756                  % RH 2022-12-14 12:50:53.869994222+00:00  \n",
       "5757                  % RH 2022-12-14 12:50:59.481273825+00:00  \n",
       "5758                  % RH 2022-12-14 12:51:05.083434485+00:00  \n",
       "5759                  % RH 2022-12-14 12:51:10.744926991+00:00  \n",
       "5760                  % RH 2022-12-14 12:51:16.399228407+00:00  \n",
       "\n",
       "[5761 rows x 53 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = pd.json_normalize(df_weather_data_series.measurements, sep='/')\n",
    "weather = pd.concat([weather, df_weather_data_series.timestamp], axis=1, join='inner', copy=False)\n",
    "weather"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad1f27c-80ae-4bf9-af89-c43b4efd0eed",
   "metadata": {},
   "source": [
    "Vamos selecionar apenas as colunas que contém o valor e unidade de medida da temperatura da CPU do sistema:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8e09da0-7f22-4de7-938c-16a701294baa",
   "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>SYS/cpu/temperature/value</th>\n",
       "      <th>SYS/cpu/temperature/unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.70368</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.17182</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.70368</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.70368</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.70368</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5756</th>\n",
       "      <td>23.29925</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5757</th>\n",
       "      <td>23.76739</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5758</th>\n",
       "      <td>23.76739</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759</th>\n",
       "      <td>23.76739</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5760</th>\n",
       "      <td>23.76739</td>\n",
       "      <td>ºC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5761 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SYS/cpu/temperature/value SYS/cpu/temperature/unit\n",
       "0                      24.70368                       ºC\n",
       "1                      25.17182                       ºC\n",
       "2                      24.70368                       ºC\n",
       "3                      24.70368                       ºC\n",
       "4                      24.70368                       ºC\n",
       "...                         ...                      ...\n",
       "5756                   23.29925                       ºC\n",
       "5757                   23.76739                       ºC\n",
       "5758                   23.76739                       ºC\n",
       "5759                   23.76739                       ºC\n",
       "5760                   23.76739                       ºC\n",
       "\n",
       "[5761 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather[['SYS/cpu/temperature/value', 'SYS/cpu/temperature/unit']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7de1b0-cc2e-43a5-8677-6ad015390415",
   "metadata": {},
   "source": [
    "A média da temperatura da CPU do sistema é:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31b1b769-c260-4ae3-9245-85f622472e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.948743130872543"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather['SYS/cpu/temperature/value'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2a3b56-6395-4663-ba26-03398d1996d0",
   "metadata": {},
   "source": [
    "A média da temperatura da CPU e da voltagem do sistema (calcular a média de diversas colunas simultaneamente) é:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e9a10de-890a-4dc5-afb8-5c12269ed715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SYS/cpu/temperature/value       23.948743\n",
       "SYS/power/vsys/voltage/value     4.977296\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather[['SYS/cpu/temperature/value', 'SYS/power/vsys/voltage/value']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558342bf-1689-4e61-a71b-cd5245faa7a0",
   "metadata": {},
   "source": [
    "Podemos também fazer um gráfico da variação da temperatura da CPU:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "714edc56-577d-4b85-8bf4-58758afb46b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f71f41e2b80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lineplot(data=weather[['SYS/cpu/temperature/value']])\n",
    "g.set_ylabel('°C')\n",
    "g.legend(loc='upper right', labels=['Temperatura da CPU'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "09541ee4-d31e-4aff-a293-db3de031cbfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f718586b220>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lineplot(data=weather[['SYS/power/vsys/voltage/value']])\n",
    "g.set_ylabel('V')\n",
    "g.legend(loc='upper right', labels=['Tensão interna'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e5a731b9-d460-4927-8672-b47abdc452c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f71f41e8a00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lineplot(data=weather[['PT100/temperature/value']])\n",
    "g.set_ylabel('°C')\n",
    "g.legend(loc='upper right', labels=['Temperatura do PT100'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}