1470 lines
58 KiB
Plaintext
1470 lines
58 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "expected-payroll",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: kaggle in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.5.12)\n",
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"Requirement already satisfied: six>=1.10 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (1.15.0)\n",
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"Requirement already satisfied: certifi in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (2021.10.8)\n",
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"Requirement already satisfied: python-dateutil in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (2.8.1)\n",
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"Requirement already satisfied: requests in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (2.27.1)\n",
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"Requirement already satisfied: tqdm in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (4.59.0)\n",
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"Requirement already satisfied: python-slugify in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (6.1.1)\n",
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"Requirement already satisfied: urllib3 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from kaggle) (1.26.9)\n",
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"Requirement already satisfied: text-unidecode>=1.3 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-slugify->kaggle) (1.3)\n",
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"Requirement already satisfied: idna<4,>=2.5 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->kaggle) (3.3)\n",
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"Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests->kaggle) (2.0.12)\n",
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"Requirement already satisfied: pandas in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (1.4.1)\n",
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"Requirement already satisfied: pytz>=2020.1 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (2022.1)\n",
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"Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (2.8.1)\n",
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"Requirement already satisfied: numpy>=1.18.5 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas) (1.20.1)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.15.0)\n",
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"Requirement already satisfied: seaborn in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.11.2)\n",
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"Requirement already satisfied: pandas>=0.23 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (1.4.1)\n",
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"Requirement already satisfied: numpy>=1.15 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (1.20.1)\n",
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"Requirement already satisfied: scipy>=1.0 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (1.6.1)\n",
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"Requirement already satisfied: matplotlib>=2.2 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from seaborn) (3.5.1)\n",
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"Requirement already satisfied: packaging>=20.0 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (20.9)\n",
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"Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (4.31.1)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
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"Requirement already satisfied: pillow>=6.2.0 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (9.0.1)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.4.0)\n",
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"Requirement already satisfied: cycler>=0.10 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
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"Requirement already satisfied: pytz>=2020.1 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas>=0.23->seaborn) (2022.1)\n",
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"Requirement already satisfied: six>=1.5 in c:\\users\\cgala\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.15.0)\n"
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]
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}
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],
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"source": [
|
||
"!pip install kaggle\n",
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"!pip install pandas\n",
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"!pip install seaborn"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 1,
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"id": "genetic-plaintiff",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloading steel-industry-energy-consumption.zip to D:\\UAM zajecia\\IUM\\ium_470623\n",
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"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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" 0%| | 0.00/484k [00:00<?, ?B/s]\n",
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"100%|##########| 484k/484k [00:00<00:00, 3.32MB/s]\n",
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]
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}
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],
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"source": [
|
||
"!kaggle datasets download -d csafrit2/steel-industry-energy-consumption"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 2,
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||
"id": "compatible-following",
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||
"metadata": {},
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||
"outputs": [
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||
{
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"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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"Archive: steel-industry-energy-consumption.zip\n",
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||
" inflating: Steel_industry_data.csv \n"
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||
]
|
||
}
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||
],
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"source": [
|
||
"!unzip -o steel-industry-energy-consumption.zip"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "interstate-ethnic",
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"metadata": {},
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"outputs": [
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{
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"data": {
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|
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|
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|
||
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|
||
" <th>date</th>\n",
|
||
" <th>Usage_kWh</th>\n",
|
||
" <th>Lagging_Current_Reactive.Power_kVarh</th>\n",
|
||
" <th>Leading_Current_Reactive_Power_kVarh</th>\n",
|
||
" <th>CO2(tCO2)</th>\n",
|
||
" <th>Lagging_Current_Power_Factor</th>\n",
|
||
" <th>Leading_Current_Power_Factor</th>\n",
|
||
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|
||
" <th>WeekStatus</th>\n",
|
||
" <th>Day_of_week</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>01/01/2018 00:15</td>\n",
|
||
" <td>3.17</td>\n",
|
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||
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||
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|
||
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||
" <td>900</td>\n",
|
||
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|
||
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|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>01/01/2018 00:30</td>\n",
|
||
" <td>4.00</td>\n",
|
||
" <td>4.46</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>66.77</td>\n",
|
||
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||
" <td>1800</td>\n",
|
||
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|
||
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|
||
" <td>Light_Load</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>01/01/2018 00:45</td>\n",
|
||
" <td>3.24</td>\n",
|
||
" <td>3.28</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
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||
" <td>70.28</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>2700</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>01/01/2018 01:00</td>\n",
|
||
" <td>3.31</td>\n",
|
||
" <td>3.56</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>68.09</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>3600</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>01/01/2018 01:15</td>\n",
|
||
" <td>3.82</td>\n",
|
||
" <td>4.50</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>64.72</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>4500</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35035</th>\n",
|
||
" <td>31/12/2018 23:00</td>\n",
|
||
" <td>3.85</td>\n",
|
||
" <td>4.86</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>62.10</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>82800</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35036</th>\n",
|
||
" <td>31/12/2018 23:15</td>\n",
|
||
" <td>3.74</td>\n",
|
||
" <td>3.74</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>70.71</td>\n",
|
||
" <td>100.00</td>\n",
|
||
" <td>83700</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35037</th>\n",
|
||
" <td>31/12/2018 23:30</td>\n",
|
||
" <td>3.78</td>\n",
|
||
" <td>3.17</td>\n",
|
||
" <td>0.07</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
" <td>84600</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35038</th>\n",
|
||
" <td>31/12/2018 23:45</td>\n",
|
||
" <td>3.78</td>\n",
|
||
" <td>3.06</td>\n",
|
||
" <td>0.11</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>77.72</td>\n",
|
||
" <td>99.96</td>\n",
|
||
" <td>85500</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>35039</th>\n",
|
||
" <td>31/12/2018 00:00</td>\n",
|
||
" <td>3.67</td>\n",
|
||
" <td>3.02</td>\n",
|
||
" <td>0.07</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>77.22</td>\n",
|
||
" <td>99.98</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Weekday</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>35040 rows × 11 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" date Usage_kWh Lagging_Current_Reactive.Power_kVarh \\\n",
|
||
"0 01/01/2018 00:15 3.17 2.95 \n",
|
||
"1 01/01/2018 00:30 4.00 4.46 \n",
|
||
"2 01/01/2018 00:45 3.24 3.28 \n",
|
||
"3 01/01/2018 01:00 3.31 3.56 \n",
|
||
"4 01/01/2018 01:15 3.82 4.50 \n",
|
||
"... ... ... ... \n",
|
||
"35035 31/12/2018 23:00 3.85 4.86 \n",
|
||
"35036 31/12/2018 23:15 3.74 3.74 \n",
|
||
"35037 31/12/2018 23:30 3.78 3.17 \n",
|
||
"35038 31/12/2018 23:45 3.78 3.06 \n",
|
||
"35039 31/12/2018 00:00 3.67 3.02 \n",
|
||
"\n",
|
||
" Leading_Current_Reactive_Power_kVarh CO2(tCO2) \\\n",
|
||
"0 0.00 0.0 \n",
|
||
"1 0.00 0.0 \n",
|
||
"2 0.00 0.0 \n",
|
||
"3 0.00 0.0 \n",
|
||
"4 0.00 0.0 \n",
|
||
"... ... ... \n",
|
||
"35035 0.00 0.0 \n",
|
||
"35036 0.00 0.0 \n",
|
||
"35037 0.07 0.0 \n",
|
||
"35038 0.11 0.0 \n",
|
||
"35039 0.07 0.0 \n",
|
||
"\n",
|
||
" Lagging_Current_Power_Factor Leading_Current_Power_Factor NSM \\\n",
|
||
"0 73.21 100.00 900 \n",
|
||
"1 66.77 100.00 1800 \n",
|
||
"2 70.28 100.00 2700 \n",
|
||
"3 68.09 100.00 3600 \n",
|
||
"4 64.72 100.00 4500 \n",
|
||
"... ... ... ... \n",
|
||
"35035 62.10 100.00 82800 \n",
|
||
"35036 70.71 100.00 83700 \n",
|
||
"35037 76.62 99.98 84600 \n",
|
||
"35038 77.72 99.96 85500 \n",
|
||
"35039 77.22 99.98 0 \n",
|
||
"\n",
|
||
" WeekStatus Day_of_week Load_Type \n",
|
||
"0 Weekday Monday Light_Load \n",
|
||
"1 Weekday Monday Light_Load \n",
|
||
"2 Weekday Monday Light_Load \n",
|
||
"3 Weekday Monday Light_Load \n",
|
||
"4 Weekday Monday Light_Load \n",
|
||
"... ... ... ... \n",
|
||
"35035 Weekday Monday Light_Load \n",
|
||
"35036 Weekday Monday Light_Load \n",
|
||
"35037 Weekday Monday Light_Load \n",
|
||
"35038 Weekday Monday Light_Load \n",
|
||
"35039 Weekday Monday Light_Load \n",
|
||
"\n",
|
||
"[35040 rows x 11 columns]"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"energy_data=pd.read_csv('Steel_industry_data.csv')\n",
|
||
"energy_data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "heated-spectacular",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
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"text/html": [
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||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
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|
||
" <th>date</th>\n",
|
||
" <th>Usage_kWh</th>\n",
|
||
" <th>Lagging_Current_Reactive.Power_kVarh</th>\n",
|
||
" <th>Leading_Current_Reactive_Power_kVarh</th>\n",
|
||
" <th>CO2(tCO2)</th>\n",
|
||
" <th>Lagging_Current_Power_Factor</th>\n",
|
||
" <th>Leading_Current_Power_Factor</th>\n",
|
||
" <th>NSM</th>\n",
|
||
" <th>WeekStatus</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
" <td>35040.000000</td>\n",
|
||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>Light_Load</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>freq</th>\n",
|
||
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|
||
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|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25056</td>\n",
|
||
" <td>5088</td>\n",
|
||
" <td>18072</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>27.386892</td>\n",
|
||
" <td>13.035384</td>\n",
|
||
" <td>3.870949</td>\n",
|
||
" <td>0.011524</td>\n",
|
||
" <td>80.578056</td>\n",
|
||
" <td>84.367870</td>\n",
|
||
" <td>42750.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>33.444380</td>\n",
|
||
" <td>16.306000</td>\n",
|
||
" <td>7.424463</td>\n",
|
||
" <td>0.016151</td>\n",
|
||
" <td>18.921322</td>\n",
|
||
" <td>30.456535</td>\n",
|
||
" <td>24940.534317</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>NaN</td>\n",
|
||
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|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
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|
||
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|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.200000</td>\n",
|
||
" <td>2.300000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>63.320000</td>\n",
|
||
" <td>99.700000</td>\n",
|
||
" <td>21375.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4.570000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>87.960000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>42750.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>51.237500</td>\n",
|
||
" <td>22.640000</td>\n",
|
||
" <td>2.090000</td>\n",
|
||
" <td>0.020000</td>\n",
|
||
" <td>99.022500</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>64125.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>157.180000</td>\n",
|
||
" <td>96.910000</td>\n",
|
||
" <td>27.760000</td>\n",
|
||
" <td>0.070000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>85500.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" date Usage_kWh Lagging_Current_Reactive.Power_kVarh \\\n",
|
||
"count 35040 35040.000000 35040.000000 \n",
|
||
"unique 35040 NaN NaN \n",
|
||
"top 01/01/2018 00:15 NaN NaN \n",
|
||
"freq 1 NaN NaN \n",
|
||
"mean NaN 27.386892 13.035384 \n",
|
||
"std NaN 33.444380 16.306000 \n",
|
||
"min NaN 0.000000 0.000000 \n",
|
||
"25% NaN 3.200000 2.300000 \n",
|
||
"50% NaN 4.570000 5.000000 \n",
|
||
"75% NaN 51.237500 22.640000 \n",
|
||
"max NaN 157.180000 96.910000 \n",
|
||
"\n",
|
||
" Leading_Current_Reactive_Power_kVarh CO2(tCO2) \\\n",
|
||
"count 35040.000000 35040.000000 \n",
|
||
"unique NaN NaN \n",
|
||
"top NaN NaN \n",
|
||
"freq NaN NaN \n",
|
||
"mean 3.870949 0.011524 \n",
|
||
"std 7.424463 0.016151 \n",
|
||
"min 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 \n",
|
||
"75% 2.090000 0.020000 \n",
|
||
"max 27.760000 0.070000 \n",
|
||
"\n",
|
||
" Lagging_Current_Power_Factor Leading_Current_Power_Factor \\\n",
|
||
"count 35040.000000 35040.000000 \n",
|
||
"unique NaN NaN \n",
|
||
"top NaN NaN \n",
|
||
"freq NaN NaN \n",
|
||
"mean 80.578056 84.367870 \n",
|
||
"std 18.921322 30.456535 \n",
|
||
"min 0.000000 0.000000 \n",
|
||
"25% 63.320000 99.700000 \n",
|
||
"50% 87.960000 100.000000 \n",
|
||
"75% 99.022500 100.000000 \n",
|
||
"max 100.000000 100.000000 \n",
|
||
"\n",
|
||
" NSM WeekStatus Day_of_week Load_Type \n",
|
||
"count 35040.000000 35040 35040 35040 \n",
|
||
"unique NaN 2 7 3 \n",
|
||
"top NaN Weekday Monday Light_Load \n",
|
||
"freq NaN 25056 5088 18072 \n",
|
||
"mean 42750.000000 NaN NaN NaN \n",
|
||
"std 24940.534317 NaN NaN NaN \n",
|
||
"min 0.000000 NaN NaN NaN \n",
|
||
"25% 21375.000000 NaN NaN NaN \n",
|
||
"50% 42750.000000 NaN NaN NaN \n",
|
||
"75% 64125.000000 NaN NaN NaN \n",
|
||
"max 85500.000000 NaN NaN NaN "
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"energy_data.describe(include='all')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "loved-delight",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Training set size:\n",
|
||
"(31536, 11)\n",
|
||
"Testing set size:\n",
|
||
"(1752, 11)\n",
|
||
"Dev set size:\n",
|
||
"(1752, 11)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"train_data, test_data = train_test_split(energy_data, test_size=3504, random_state=1)\n",
|
||
"test_data, dev_data = train_test_split(test_data, test_size=1752, random_state=1)\n",
|
||
"print('Training set size:')\n",
|
||
"print(train_data.shape)\n",
|
||
"print('Testing set size:')\n",
|
||
"print(test_data.shape)\n",
|
||
"print('Dev set size:')\n",
|
||
"print(dev_data.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "formed-virginia",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
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|
||
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|
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|
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|
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|
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|
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|
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|
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>27.756787</td>\n",
|
||
" <td>13.375628</td>\n",
|
||
" <td>3.880634</td>\n",
|
||
" <td>0.011729</td>\n",
|
||
" <td>80.745548</td>\n",
|
||
" <td>84.345154</td>\n",
|
||
" <td>43186.643836</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>32.895802</td>\n",
|
||
" <td>16.482148</td>\n",
|
||
" <td>7.376468</td>\n",
|
||
" <td>0.015943</td>\n",
|
||
" <td>18.927378</td>\n",
|
||
" <td>30.475427</td>\n",
|
||
" <td>24440.888112</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.520000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>38.330000</td>\n",
|
||
" <td>14.070000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.200000</td>\n",
|
||
" <td>2.270000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>63.942500</td>\n",
|
||
" <td>99.690000</td>\n",
|
||
" <td>22500.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4.680000</td>\n",
|
||
" <td>5.110000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>87.940000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>43200.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>52.187500</td>\n",
|
||
" <td>24.050000</td>\n",
|
||
" <td>2.177500</td>\n",
|
||
" <td>0.020000</td>\n",
|
||
" <td>99.030000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>63900.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>139.030000</td>\n",
|
||
" <td>80.750000</td>\n",
|
||
" <td>27.580000</td>\n",
|
||
" <td>0.060000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>100.000000</td>\n",
|
||
" <td>85500.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" date Usage_kWh Lagging_Current_Reactive.Power_kVarh \\\n",
|
||
"count 1752 1752.000000 1752.000000 \n",
|
||
"unique 1752 NaN NaN \n",
|
||
"top 02/06/2018 02:00 NaN NaN \n",
|
||
"freq 1 NaN NaN \n",
|
||
"mean NaN 27.756787 13.375628 \n",
|
||
"std NaN 32.895802 16.482148 \n",
|
||
"min NaN 2.520000 0.000000 \n",
|
||
"25% NaN 3.200000 2.270000 \n",
|
||
"50% NaN 4.680000 5.110000 \n",
|
||
"75% NaN 52.187500 24.050000 \n",
|
||
"max NaN 139.030000 80.750000 \n",
|
||
"\n",
|
||
" Leading_Current_Reactive_Power_kVarh CO2(tCO2) \\\n",
|
||
"count 1752.000000 1752.000000 \n",
|
||
"unique NaN NaN \n",
|
||
"top NaN NaN \n",
|
||
"freq NaN NaN \n",
|
||
"mean 3.880634 0.011729 \n",
|
||
"std 7.376468 0.015943 \n",
|
||
"min 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 \n",
|
||
"75% 2.177500 0.020000 \n",
|
||
"max 27.580000 0.060000 \n",
|
||
"\n",
|
||
" Lagging_Current_Power_Factor Leading_Current_Power_Factor \\\n",
|
||
"count 1752.000000 1752.000000 \n",
|
||
"unique NaN NaN \n",
|
||
"top NaN NaN \n",
|
||
"freq NaN NaN \n",
|
||
"mean 80.745548 84.345154 \n",
|
||
"std 18.927378 30.475427 \n",
|
||
"min 38.330000 14.070000 \n",
|
||
"25% 63.942500 99.690000 \n",
|
||
"50% 87.940000 100.000000 \n",
|
||
"75% 99.030000 100.000000 \n",
|
||
"max 100.000000 100.000000 \n",
|
||
"\n",
|
||
" NSM WeekStatus Day_of_week Load_Type \n",
|
||
"count 1752.000000 1752 1752 1752 \n",
|
||
"unique NaN 2 7 3 \n",
|
||
"top NaN Weekday Monday Light_Load \n",
|
||
"freq NaN 1274 275 894 \n",
|
||
"mean 43186.643836 NaN NaN NaN \n",
|
||
"std 24440.888112 NaN NaN NaN \n",
|
||
"min 0.000000 NaN NaN NaN \n",
|
||
"25% 22500.000000 NaN NaN NaN \n",
|
||
"50% 43200.000000 NaN NaN NaN \n",
|
||
"75% 63900.000000 NaN NaN NaN \n",
|
||
"max 85500.000000 NaN NaN NaN "
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dev_data.describe(include='all')"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.9.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|