Concat & transform RTE data#
Ce notebook concat et transforme les données RAW RTE
[1]:
import pandas as pd
from energy_forecast.energy import ECO2MixDownloader
Fetching data#
[2]:
list_years = list(range(2014, 2025))
list_years
[2]:
[2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]
[3]:
list_of_dataframes = []
for year in list_years:
downloader = ECO2MixDownloader(year)
downloader.download()
df = downloader.read_file()
list_of_dataframes.append(df)
df = pd.concat(list_of_dataframes)
[5]:
# Only keep hourly data
df = df[df.index.minute == 0]
df = df.dropna()
df
[5]:
| Périmètre | Nature | Date | Heures | Consommation | Prévision J-1 | Prévision J | Fioul | Charbon | Gaz | ... | Gaz - TAC | Gaz - Cogén. | Gaz - CCG | Gaz - Autres | Hydraulique - Fil de l?eau + éclusée | Hydraulique - Lacs | Hydraulique - STEP turbinage | Bioénergies - Déchets | Bioénergies - Biomasse | Bioénergies - Biogaz | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| time | |||||||||||||||||||||
| 2014-01-01 00:00:00 | France | Données définitives | 2014-01-01 | 0 days 00:00:00 | 64660.0 | 63200.0 | 63200.0 | 454.0 | 0.0 | 2303.0 | ... | 0.0 | 1809.0 | 422.0 | 72.0 | 5662.0 | 2125.0 | 693.0 | 460.0 | 165.0 | 179.0 |
| 2014-01-01 01:00:00 | France | Données définitives | 2014-01-01 | 0 days 01:00:00 | 61362.0 | 59900.0 | 59900.0 | 281.0 | 0.0 | 2188.0 | ... | 0.0 | 1848.0 | 258.0 | 83.0 | 5457.0 | 2040.0 | 76.0 | 591.0 | 173.0 | 181.0 |
| 2014-01-01 02:00:00 | France | Données définitives | 2014-01-01 | 0 days 02:00:00 | 60748.0 | 59900.0 | 60200.0 | 281.0 | 0.0 | 2187.0 | ... | 0.0 | 1853.0 | 252.0 | 83.0 | 5201.0 | 1592.0 | 0.0 | 597.0 | 174.0 | 180.0 |
| 2014-01-01 03:00:00 | France | Données définitives | 2014-01-01 | 0 days 03:00:00 | 58061.0 | 56500.0 | 56600.0 | 281.0 | 0.0 | 2179.0 | ... | 0.0 | 1844.0 | 253.0 | 83.0 | 4947.0 | 1200.0 | 0.0 | 594.0 | 174.0 | 179.0 |
| 2014-01-01 04:00:00 | France | Données définitives | 2014-01-01 | 0 days 04:00:00 | 54475.0 | 53200.0 | 53300.0 | 280.0 | 0.0 | 2185.0 | ... | 0.0 | 1844.0 | 260.0 | 83.0 | 4662.0 | 1252.0 | 0.0 | 591.0 | 171.0 | 177.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2024-06-23 05:00:00 | France | Données temps réel | 2024-06-23 | 0 days 05:00:00 | 30263.0 | 30000 | 30400.0 | 123.0 | 0.0 | 434.0 | ... | 0 | 321 | 114.0 | 0.0 | 5353 | 1716 | 551.0 | 196 | 355.0 | 244.0 |
| 2024-06-23 06:00:00 | France | Données temps réel | 2024-06-23 | 0 days 06:00:00 | 30056.0 | 29400 | 30000.0 | 124.0 | 0.0 | 433.0 | ... | 0 | 321 | 114.0 | 0.0 | 5251 | 1838 | 589.0 | 193 | 352.0 | 244.0 |
| 2024-06-23 07:00:00 | France | Données temps réel | 2024-06-23 | 0 days 07:00:00 | 30573.0 | 30200 | 30700.0 | 124.0 | 0.0 | 437.0 | ... | 0 | 322 | 116.0 | 0.0 | 5242 | 1907 | 373.0 | 189 | 356.0 | 244.0 |
| 2024-06-23 08:00:00 | France | Données temps réel | 2024-06-23 | 0 days 08:00:00 | 32099.0 | 31700 | 32200.0 | 124.0 | 0.0 | 434.0 | ... | 0 | 321 | 115.0 | 0.0 | 5238 | 1925 | 372.0 | 189 | 352.0 | 244.0 |
| 2024-06-23 09:00:00 | France | Données temps réel | 2024-06-23 | 0 days 09:00:00 | 34821.0 | 34500 | 34900.0 | 124.0 | 0.0 | 432.0 | ... | 0 | 319 | 115.0 | 0.0 | 5177 | 1922 | 373.0 | 192 | 351.0 | 244.0 |
91834 rows × 36 columns
[ ]:
display(df.head())
| Périmètre | Nature | Date | Heures | Consommation | Prévision J-1 | Prévision J | Fioul | Charbon | Gaz | ... | Hydraulique - Fil de l?eau + éclusée | Hydraulique - Lacs | Hydraulique - STEP turbinage | Bioénergies - Déchets | Bioénergies - Biomasse | Bioénergies - Biogaz | Stockage batterie | Déstockage batterie | Eolien terrestre | Eolien offshore | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | France | Données définitives | 2014-01-01 | 00:00 | 64660.0 | 63200 | 63200.0 | 454.0 | 0.0 | 2303.0 | ... | 5662 | 2125 | 693.0 | 460 | 165.0 | 179.0 | NaN | NaN | NaN | NaN |
| 1 | France | Données définitives | 2014-01-01 | 00:30 | 63494.0 | 62100 | 61500.0 | 281.0 | 0.0 | 2367.0 | ... | 5569 | 2450 | 495.0 | 592 | 170.0 | 181.0 | NaN | NaN | NaN | NaN |
| 2 | France | Données définitives | 2014-01-01 | 01:00 | 61362.0 | 59900 | 59900.0 | 281.0 | 0.0 | 2188.0 | ... | 5457 | 2040 | 76.0 | 591 | 173.0 | 181.0 | NaN | NaN | NaN | NaN |
| 3 | France | Données définitives | 2014-01-01 | 01:30 | 61217.0 | 60600 | 60300.0 | 281.0 | 0.0 | 2190.0 | ... | 5286 | 1849 | 0.0 | 595 | 174.0 | 180.0 | NaN | NaN | NaN | NaN |
| 4 | France | Données définitives | 2014-01-01 | 02:00 | 60748.0 | 59900 | 60200.0 | 281.0 | 0.0 | 2187.0 | ... | 5201 | 1592 | 0.0 | 597 | 174.0 | 180.0 | NaN | NaN | NaN | NaN |
5 rows × 40 columns