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" DATE_TIME PLANT_ID SOURCE_KEY DC_POWER \\\n",
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"source": [
"# statystyki dla pełnego zbioru\n",
"\n",
"import pandas as pd\n",
"plant_all = pd.read_csv('data/Plant_1_Generation_Data.csv')\n",
"plant_all.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 3,
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" DATE_TIME PLANT_ID SOURCE_KEY DC_POWER \\\n",
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"source": [
"# statystyki dla zbioru dev\n",
"\n",
"plant_dev = pd.read_csv('data/Plant_1_Generation_Data.csv.dev')\n",
"plant_dev.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
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" DATE_TIME PLANT_ID SOURCE_KEY DC_POWER \\\n",
"count 6877 6877.0 6877 6877.000000 \n",
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"metadata": {},
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}
],
"source": [
"# statystyki dla zbioru test\n",
"\n",
"plant_test = pd.read_csv('data/Plant_1_Generation_Data.csv.test')\n",
"plant_test.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>4135001.0</td>\n",
" <td>NaN</td>\n",
" <td>3132.873631</td>\n",
" <td>306.377514</td>\n",
" <td>3292.894721</td>\n",
" <td>6.978859e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>4034.254455</td>\n",
" <td>394.177510</td>\n",
" <td>3146.231920</td>\n",
" <td>4.157218e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>4135001.0</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.183645e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>4135001.0</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.514911e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>4135001.0</td>\n",
" <td>NaN</td>\n",
" <td>400.500000</td>\n",
" <td>38.720536</td>\n",
" <td>2658.062500</td>\n",
" <td>7.146685e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>4135001.0</td>\n",
" <td>NaN</td>\n",
" <td>6337.535714</td>\n",
" <td>620.728125</td>\n",
" <td>6273.616072</td>\n",
" <td>7.268792e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>4135001.0</td>\n",
" <td>NaN</td>\n",
" <td>14471.125000</td>\n",
" <td>1410.950000</td>\n",
" <td>9163.000000</td>\n",
" <td>7.846821e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DATE_TIME PLANT_ID SOURCE_KEY DC_POWER \\\n",
"count 55024 55024.0 55024 55024.000000 \n",
"unique 3158 NaN 22 NaN \n",
"top 15-06-2020 09:30 NaN iCRJl6heRkivqQ3 NaN \n",
"freq 22 NaN 2561 NaN \n",
"mean NaN 4135001.0 NaN 3132.873631 \n",
"std NaN 0.0 NaN 4034.254455 \n",
"min NaN 4135001.0 NaN 0.000000 \n",
"25% NaN 4135001.0 NaN 0.000000 \n",
"50% NaN 4135001.0 NaN 400.500000 \n",
"75% NaN 4135001.0 NaN 6337.535714 \n",
"max NaN 4135001.0 NaN 14471.125000 \n",
"\n",
" AC_POWER DAILY_YIELD TOTAL_YIELD \n",
"count 55024.000000 55024.000000 5.502400e+04 \n",
"unique NaN NaN NaN \n",
"top NaN NaN NaN \n",
"freq NaN NaN NaN \n",
"mean 306.377514 3292.894721 6.978859e+06 \n",
"std 394.177510 3146.231920 4.157218e+05 \n",
"min 0.000000 0.000000 6.183645e+06 \n",
"25% 0.000000 0.000000 6.514911e+06 \n",
"50% 38.720536 2658.062500 7.146685e+06 \n",
"75% 620.728125 6273.616072 7.268792e+06 \n",
"max 1410.950000 9163.000000 7.846821e+06 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# statystyki dla zbioru train\n",
"\n",
"plant_train = pd.read_csv('data/Plant_1_Generation_Data.csv.train')\n",
"plant_train.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>DATE_TIME</th>\n",
" <th>PLANT_ID</th>\n",
" <th>SOURCE_KEY</th>\n",
" <th>DC_POWER</th>\n",
" <th>AC_POWER</th>\n",
" <th>DAILY_YIELD</th>\n",
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" <td>25-05-2020 07:15</td>\n",
" <td>4135001</td>\n",
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" <td>0.166544</td>\n",
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" <td>7111973.750</td>\n",
" </tr>\n",
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" <td>0.000000</td>\n",
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" <td>7115304.000</td>\n",
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" <th>6875</th>\n",
" <td>11-06-2020 18:45</td>\n",
" <td>4135001</td>\n",
" <td>ih0vzX44oOqAx2f</td>\n",
" <td>0.000000</td>\n",
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],
"text/plain": [
" DATE_TIME PLANT_ID SOURCE_KEY DC_POWER AC_POWER \\\n",
"0 10-06-2020 22:45 4135001 rGa61gmuvPhdLxV 0.000000 0.000000 \n",
"1 25-05-2020 07:15 4135001 uHbuxQJl8lW7ozc 0.166544 236.262500 \n",
"2 23-05-2020 17:45 4135001 1IF53ai7Xc0U56Y 0.109156 154.485714 \n",
"3 15-05-2020 04:45 4135001 3PZuoBAID5Wc2HD 0.000000 0.000000 \n",
"4 12-06-2020 16:30 4135001 iCRJl6heRkivqQ3 0.191808 272.157143 \n",
"... ... ... ... ... ... \n",
"6872 01-06-2020 10:00 4135001 zBIq5rxdHJRwDNY 0.539282 763.628571 \n",
"6873 27-05-2020 02:00 4135001 VHMLBKoKgIrUVDU 0.000000 0.000000 \n",
"6874 31-05-2020 21:30 4135001 3PZuoBAID5Wc2HD 0.000000 0.000000 \n",
"6875 11-06-2020 18:45 4135001 ih0vzX44oOqAx2f 0.000000 0.000000 \n",
"6876 16-06-2020 05:45 4135001 3PZuoBAID5Wc2HD 0.000000 0.000000 \n",
"\n",
" DAILY_YIELD TOTAL_YIELD \n",
"0 6565.000000 7310769.000 \n",
"1 121.750000 7111973.750 \n",
"2 8607.000000 6249141.000 \n",
"3 0.000000 6987759.000 \n",
"4 5567.428571 7391038.429 \n",
"... ... ... \n",
"6872 1779.285714 6465018.286 \n",
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"\n",
"[6877 rows x 7 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# normalizacja\n",
"\n",
"\n",
"plant_normalized = plant_test.copy()\n",
"column = 'DC_POWER'\n",
"\n",
"plant_normalized[column] = plant_normalized[column] / plant_normalized[column].abs().max()\n",
"\n",
"plant_normalized"
]
}
],
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Reference in New Issue View Git Blame Copy Permalink