netztransparenz 0.2.1

A simple client to acess data from the German Netztransparenz portal

Netztransparenz Client

This is a small python client to access the data of the german Netztransparenz portal. It handles authentication, loads the CSV data and transforms it into a pandas dataframe with some optional format changes.

This is an unofficial client, the makers of this library are not affiliated with NETZTRANSPARENZ.DE.

Installation

Install from Pypi with 'pip install netztransparenz"

Example

To use the Netztransparenz API one has to create a free account, see https://api-portal.netztransparenz.de/. With the account you get the credentials that are needed in the next step.

Basic usage

>>> import netztransparenz as nt
>>> client = nt.NetztransparenzClient("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")

#optional: check API status 
>>> client.check_health()
'"OK"'

#load data
>>> df = client.hochrechnung_solar()
>>> df
             Datum    von Zeitzone von    bis Zeitzone bis  50Hertz (MW)  Amprion (MW)  TenneT TSO (MW)  TransnetBW (MW)
0       2011-03-31  22:00          UTC  22:15          UTC          0.00         0.000            0.000            0.000
1       2011-03-31  22:15          UTC  22:30          UTC          0.00         0.000            0.000            0.000
2       2011-03-31  22:30          UTC  22:45          UTC          0.00         0.000            0.000            0.000
...            ...    ...          ...    ...          ...           ...           ...              ...              ...
504046  2025-08-15  09:30          UTC  09:45          UTC       3520.94      7021.757        11195.645         3895.891
504047  2025-08-15  09:45          UTC  10:00          UTC       3597.52      7297.191              NaN         3968.924
504048  2025-08-15  10:00          UTC  10:15          UTC           NaN           NaN              NaN              NaN

Specify timeframe

You can specify the timeframe you want data for:

>>> import datetime
>>> start = datetime.datetime(2024, 1, 1, 0, 0)
>>> end = datetime.datetime(2025, 1, 1, 0, 0)
>>> client.hochrechnung_solar(start, end)
            Datum    von Zeitzone von    bis Zeitzone bis  50Hertz (MW)  Amprion (MW)  TenneT TSO (MW)  TransnetBW (MW)
0      2024-01-01  00:00          UTC  00:15          UTC           0.0           0.0              0.0              0.0
1      2024-01-01  00:15          UTC  00:30          UTC           0.0           0.0              0.0              0.0
2      2024-01-01  00:30          UTC  00:45          UTC           0.0           0.0              0.0              0.0
...           ...    ...          ...    ...          ...           ...           ...              ...              ...
35133  2024-12-31  23:15          UTC  23:30          UTC           0.0           0.0              0.0              0.0
35134  2024-12-31  23:30          UTC  23:45          UTC           0.0           0.0              0.0              0.0
35135  2024-12-31  23:45          UTC  00:00          UTC           0.0           0.0              0.0              0.0

Transform dates

For some of the datasets, dates are split up into five columns which makes them hard to sort. If you set the transform_dates flag to true they are converted to two timestamps. Also, "von" is turned into the index.

>>> client.hochrechnung_solar(start, end, transform_dates=True)
                                                bis  50Hertz (MW)  Amprion (MW)  TenneT TSO (MW)  TransnetBW (MW)
von                                                                                                              
2024-01-01 00:00:00+00:00 2024-01-01 00:15:00+00:00           0.0           0.0              0.0              0.0
2024-01-01 00:15:00+00:00 2024-01-01 00:30:00+00:00           0.0           0.0              0.0              0.0
2024-01-01 00:30:00+00:00 2024-01-01 00:45:00+00:00           0.0           0.0              0.0              0.0
...                                             ...           ...           ...              ...              ...
2024-12-31 23:15:00+00:00 2024-12-31 23:30:00+00:00           0.0           0.0              0.0              0.0
2024-12-31 23:30:00+00:00 2024-12-31 23:45:00+00:00           0.0           0.0              0.0              0.0
2024-12-31 23:45:00+00:00 2025-01-01 00:00:00+00:00           0.0           0.0              0.0              0.0