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My df looks like this:

Datum Zeit Temperatur[°C] Luftdruck Windgeschwindigkeit[m/s] Windrichtung[Grad] Relative Luftfeuchtigkeit[%] Globalstrahlung[W/m²]

Now i want to rename the columns like this:#

wetterdaten.rename(columns='Temperatur%': 'Temperatur', 'Luftdruck[hPa]': 'Luftdruck', inplace=True)

Where % is a wildcard.
But of course it will not work like this.

The beginning of the column name is always the same in the log data,
but the ending is temporally changing.

You can filter the columns and fetch the name:

wetterdaten.rename(columns=wetterdaten.filter(regex='Temperatur.*').columns[0]: 'Temperatur', 'wetterdaten.filter(regex='Luftdruck.*').columns[0]': 'Luftdruck', inplace=True)

You can use replace by dict, for wildcard use .* and for start of string ^:

d = '^Temperatur.*': 'Temperatur', 'Luftdruck[hPa]': 'Luftdruck'
df.columns = df.columns.to_series().replace(d, regex=True)

Sample:

cols = ['Datum', 'Zeit', 'Temperatur[°C]', 'Luftdruck' , 'Windgeschwindigkeit[m/s]',
 'Windrichtung[Grad]', 'Relative Luftfeuchtigkeit[%]', ' Globalstrahlung[W/m²]']

df = pd.DataFrame(columns=cols)
print (df)
Empty DataFrame
Columns: [Datum, Zeit, Temperatur[°C], Luftdruck, Windgeschwindigkeit[m/s], 
 Windrichtung[Grad], Relative Luftfeuchtigkeit[%], Globalstrahlung[W/m²]]
Index: 

d = '^Temperatur.*': 'Temperatur', 'Luftdruck.*': 'Luftdruck'
df.columns = df.columns.to_series().replace(d, regex=True)
print (df)

Empty DataFrame
Columns: [Datum, Zeit, Temperatur, Luftdruck, Windgeschwindigkeit[m/s], 
 Windrichtung[Grad], Relative Luftfeuchtigkeit[%], Globalstrahlung[W/m²]]
Index: 

You may prepare a function for renaming you columns:

rename_columns(old_name):
 if old_name == 'Temperatur':
 new_name = old_name + whichever_you_wants # may be another function call
 elif old_name == 'Luftdruck':
 new_name = 'Luftdruck[hPa]'
 else:
 new_name = old_name
 return new_name

and then use the .rename() method with that function as a parameter:

wetterdaten.rename(columns=rename_columns, inplace=True)