s = pd.DataFrame(np.random.random((3,5)), index=['foo', 'bar', 'baz'])
s
```
%% Output
0 1 2 3 4
foo 0.903758 0.597699 0.105770 0.708391 0.833426
bar 0.218514 0.784634 0.626200 0.797509 0.501407
baz 0.907245 0.841323 0.027155 0.133918 0.689129
foo 0.970723 0.416728 0.120606 0.296505 0.898589
bar 0.323891 0.369041 0.533998 0.705619 0.670083
baz 0.665230 0.203579 0.451946 0.994433 0.131078
%% Cell type:code id: tags:
``` python
s.mean()
```
%% Output
0 0.676506
1 0.741219
2 0.253041
3 0.546606
4 0.674654
0 0.653281
1 0.329783
2 0.368850
3 0.665519
4 0.566583
dtype: float64
%% Cell type:markdown id: tags:
***
# Problem I.1
The following data are made available to you on the __[coures homepage](https://nworbmot.org/courses/complex_renewable_energy_networks/)__:
`de_data.csv`, `gb_data.csv`, `eu_data.csv`
and alternatively
`wind.csv`, `solar.csv`, `load.csv`
They describe (quasi-real) time series for wind power generation $W(t)$, solar power generation $S(t)$ and load $L(t)$ in Great Britain (GB), Germany (DE) and Europe (EU). The time step is 1 h and the time series are several years long.
> Remark: In this example notebook, we only look at Germany and the EU, Great Britain works in exactly the same way.
%% Cell type:markdown id: tags:
***
**Read Data**
%% Cell type:code id: tags:
``` python
de = pd.read_csv('tutorial_data/de_data.csv', parse_dates=True, index_col=0)
eu = pd.read_csv('tutorial_data/eu_data.csv', parse_dates=True, index_col=0)