# OMIEData:
[](https://www.python.org/)
[](https://pypi.org/project/OMIEData/)
[](https://www.python.org/)
Python package to import data from OMIE (Iberian Peninsula's Electricity Market Operator): https://www.omie.es/
Concretely, you can easily access to data for the following markets:
- Daily market: hourly prices in Spain and Portugal, total hourly energy after auction (with/without billateral contracts), breakdown of the total hourly energy by technology and bid/ask curves.
- Intra-day market: hourly prices for the different sessions and total hourly energy.
- Additional data in next releases.
## Installation
The package is uploaded at https://pypi.org/project/OMIEData/, so
```python
python -m pip install OMIEData
```
from the command line will install the last version uploaded to pypi.
Aternatively, to install it from GitHub repository, type:
```python
python -m pip install git+https://github.com/acruzgarcia/OMIEData
```
in the command line. You can also install the .whl or .tar.gz files within [dist](https://github.com/acruzgarcia/OMIEData/tree/dev/dist) as:
```python
python -m pip install OMIEData-VERSION-py3-none-any.whl
```
or
```python
python -m pip install OMIEData-VERSION.tar.gz
```
or to install a previous version from [dist_old](https://github.com/acruzgarcia/OMIEData/tree/dev/dist_old).
## Examples:
A very simple example to download hourly electricity prices and demand:
```python
import datetime as dt
import matplotlib.pyplot as plt
from OMIEData.DataImport.omie_marginalprice_importer import OMIEMarginalPriceFileImporter
from OMIEData.Enums.all_enums import DataTypeInMarginalPriceFile
dateIni = dt.datetime(2020, 1, 1)
dateEnd = dt.datetime(2022, 3, 22)
# This can take time, it is downloading the files from the website..
df = OMIEMarginalPriceFileImporter(date_ini=dateIni, date_end=dateEnd).read_to_dataframe(verbose=True)
df.sort_values(by='DATE', axis=0, inplace=True)
print(df)
```
The code will generate a data-frame like the following one:
```python
DATE CONCEPT H1 ... H22 H23 H24
0 2020-01-01 PRICE_SP 41.88 ... 45.60 42.90 37.55
1 2020-01-01 PRICE_PT 41.88 ... 45.60 42.90 37.55
2 2020-01-01 ENER_IB 18132.30 ... 22492.60 21800.90 19946.30
3 2020-01-01 ENER_IB_BILLAT 26488.50 ... 32611.70 31523.70 29088.30
4 2020-01-02 PRICE_SP 35.40 ... 42.00 38.60 33.39
... ... ... ... ... ... ...
3241 2022-03-21 PRICE_PT 218.69 ... 261.44 240.29 228.88
3245 2022-03-22 PRICE_PT 223.00 ... 256.00 242.18 212.99
3246 2022-03-22 ENER_IB 20652.20 ... 27113.50 24167.60 21841.50
3244 2022-03-22 PRICE_SP 223.00 ... 256.00 242.18 212.99
3247 2022-03-22 ENER_IB_BILLAT 29840.30 ... 38281.20 34781.90 31872.50
[3248 rows x 26 columns]
```
You can filter the data-frame to have only the prices in spain, and then plot
```python
# Just prices in spain
str_price_spain = str(DataTypeInMarginalPriceFile.PRICE_SPAIN)
dfPrices = df[df.CONCEPT == str_price_spain]
# Plotting
plt.figure()
plt.plot(dfPrices.DATE, dfPrices.H12, label='H12')
plt.plot(dfPrices.DATE, dfPrices.H23, label='H23')
plt.legend()
plt.show()
```
which will produce the following plot:
Another example to download hourly demand resulting of the daily market auction, breakdown by technologies:
```python
import datetime as dt
from OMIEData.Enums.all_enums import SystemType
from OMIEData.DataImport.omie_energy_by_technology_importer import OMIEEnergyByTechnologyImporter
dateIni = dt.datetime(2020, 6, 1)
dateEnd = dt.datetime(2020, 7, 30)
system_type = SystemType.SPAIN
# This can take time, it is downloading the files from the website..
df = OMIEEnergyByTechnologyImporter(date_ini=dateIni,
date_end=dateEnd,
system_type=system_type).read_to_dataframe(verbose=True)
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```
Another example to download supply/demand curves:
```python
import datetime as dt
from OMIEData.DataImport.omie_supply_demand_curve_importer import OMIESupplyDemandCurvesImporter
dateIni = dt.datetime(2020, 6, 1)
dateEnd = dt.datetime(2020, 6, 1)
hour = 1
# This can take time, it is downloading the files from the website..
df = OMIESupplyDemandCurvesImporter(date_ini=dateIni, date_end=dateEnd, hour=hour).read_to_dataframe(verbose=True)
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```
Other examples that illustrate the use of the package in here:
- [examples folder](https://github.com/acruzgarcia/OMIEData/tree/dev/examples)
Enjoy!.
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"description": "# OMIEData: \r\n\r\n[](https://www.python.org/)\r\n[](https://pypi.org/project/OMIEData/)\r\n[](https://www.python.org/)\r\n\r\nPython package to import data from OMIE (Iberian Peninsula's Electricity Market Operator): https://www.omie.es/\r\n\r\nConcretely, you can easily access to data for the following markets:\r\n\r\n- Daily market: hourly prices in Spain and Portugal, total hourly energy after auction (with/without billateral contracts), breakdown of the total hourly energy by technology and bid/ask curves.\r\n- Intra-day market: hourly prices for the different sessions and total hourly energy.\r\n- Additional data in next releases.\r\n\r\n\r\n## Installation \r\n\r\nThe package is uploaded at https://pypi.org/project/OMIEData/, so\r\n\r\n```python\r\npython -m pip install OMIEData\r\n\r\n```\r\nfrom the command line will install the last version uploaded to pypi. \r\n\r\nAternatively, to install it from GitHub repository, type:\r\n\r\n```python\r\npython -m pip install git+https://github.com/acruzgarcia/OMIEData\r\n\r\n```\r\n\r\nin the command line. You can also install the .whl or .tar.gz files within [dist](https://github.com/acruzgarcia/OMIEData/tree/dev/dist) as:\r\n\r\n```python\r\npython -m pip install OMIEData-VERSION-py3-none-any.whl\r\n\r\n```\r\nor\r\n\r\n```python\r\npython -m pip install OMIEData-VERSION.tar.gz\r\n\r\n```\r\n\r\nor to install a previous version from [dist_old](https://github.com/acruzgarcia/OMIEData/tree/dev/dist_old).\r\n\r\n## Examples:\r\n\r\nA very simple example to download hourly electricity prices and demand:\r\n\r\n```python\r\nimport datetime as dt\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom OMIEData.DataImport.omie_marginalprice_importer import OMIEMarginalPriceFileImporter\r\nfrom OMIEData.Enums.all_enums import DataTypeInMarginalPriceFile\r\n\r\ndateIni = dt.datetime(2020, 1, 1)\r\ndateEnd = dt.datetime(2022, 3, 22)\r\n\r\n# This can take time, it is downloading the files from the website..\r\ndf = OMIEMarginalPriceFileImporter(date_ini=dateIni, date_end=dateEnd).read_to_dataframe(verbose=True)\r\ndf.sort_values(by='DATE', axis=0, inplace=True)\r\nprint(df)\r\n```\r\nThe code will generate a data-frame like the following one:\r\n\r\n```python\r\n DATE CONCEPT H1 ... H22 H23 H24\r\n0 2020-01-01 PRICE_SP 41.88 ... 45.60 42.90 37.55\r\n1 2020-01-01 PRICE_PT 41.88 ... 45.60 42.90 37.55\r\n2 2020-01-01 ENER_IB 18132.30 ... 22492.60 21800.90 19946.30\r\n3 2020-01-01 ENER_IB_BILLAT 26488.50 ... 32611.70 31523.70 29088.30\r\n4 2020-01-02 PRICE_SP 35.40 ... 42.00 38.60 33.39\r\n ... ... ... ... ... ... ...\r\n3241 2022-03-21 PRICE_PT 218.69 ... 261.44 240.29 228.88\r\n3245 2022-03-22 PRICE_PT 223.00 ... 256.00 242.18 212.99\r\n3246 2022-03-22 ENER_IB 20652.20 ... 27113.50 24167.60 21841.50\r\n3244 2022-03-22 PRICE_SP 223.00 ... 256.00 242.18 212.99\r\n3247 2022-03-22 ENER_IB_BILLAT 29840.30 ... 38281.20 34781.90 31872.50\r\n[3248 rows x 26 columns]\r\n```\r\n\r\nYou can filter the data-frame to have only the prices in spain, and then plot\r\n\r\n```python\r\n# Just prices in spain\r\nstr_price_spain = str(DataTypeInMarginalPriceFile.PRICE_SPAIN)\r\ndfPrices = df[df.CONCEPT == str_price_spain]\r\n\r\n# Plotting\r\nplt.figure()\r\nplt.plot(dfPrices.DATE, dfPrices.H12, label='H12')\r\nplt.plot(dfPrices.DATE, dfPrices.H23, label='H23')\r\nplt.legend()\r\nplt.show()\r\n```\r\n\r\nwhich will produce the following plot:\r\n\r\n\r\n\r\nAnother example to download hourly demand resulting of the daily market auction, breakdown by technologies:\r\n\r\n```python\r\nimport datetime as dt\r\nfrom OMIEData.Enums.all_enums import SystemType\r\nfrom OMIEData.DataImport.omie_energy_by_technology_importer import OMIEEnergyByTechnologyImporter\r\n\r\ndateIni = dt.datetime(2020, 6, 1)\r\ndateEnd = dt.datetime(2020, 7, 30)\r\nsystem_type = SystemType.SPAIN\r\n\r\n# This can take time, it is downloading the files from the website..\r\ndf = OMIEEnergyByTechnologyImporter(date_ini=dateIni,\r\n date_end=dateEnd,\r\n system_type=system_type).read_to_dataframe(verbose=True)\r\ndf.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)\r\nprint(df)\r\n```\r\n\r\nAnother example to download supply/demand curves:\r\n\r\n```python\r\nimport datetime as dt\r\nfrom OMIEData.DataImport.omie_supply_demand_curve_importer import OMIESupplyDemandCurvesImporter\r\n\r\ndateIni = dt.datetime(2020, 6, 1)\r\ndateEnd = dt.datetime(2020, 6, 1)\r\nhour = 1\r\n\r\n# This can take time, it is downloading the files from the website..\r\ndf = OMIESupplyDemandCurvesImporter(date_ini=dateIni, date_end=dateEnd, hour=hour).read_to_dataframe(verbose=True)\r\ndf.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)\r\nprint(df)\r\n```\r\n\r\nOther examples that illustrate the use of the package in here:\r\n\r\n- [examples folder](https://github.com/acruzgarcia/OMIEData/tree/dev/examples)\r\n\r\nEnjoy!.\r\n",
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