I have the following Dataframe:
pd.DataFrame({'DateTime': {0: Timestamp('2022-02-08 00:00:00'),
1: Timestamp('2022-02-08 00:10:00'),
2: Timestamp('2022-02-08 00:20:00'),
3: Timestamp('2022-02-08 00:30:00'),
4: Timestamp('2022-02-08 00:40:00')},
'wind power [W]': {0: 83.9, 1: 57.2, 2: 58.2, 3: 48.0, 4: 69.5}})
DateTime wind power [W]
0 2022-02-08 00:00:00 83.9
1 2022-02-08 00:10:00 57.2
2 2022-02-08 00:20:00 58.2
3 2022-02-08 00:30:00 48.0
4 2022-02-08 00:40:00 69.5
As you can see, 83.9 is the maximum value in my second column and 48.0 the minimum value. I want to normalize these values in a range between 0.6 and 8.4, so that 83.9 would turn to 8.4 and 48.0 to 0.6. The rest of the numbers would fall somewhere in between. So far I only managed to normalize the column to a range of 0-1 with the code:
df['normalized'] = (df['wind power [W]']-df['wind power [W]'].min())/(df['wind power [W]'].max()-df['wind power [W]'].min())
I don't know how to further proceed to get these numbers in my desired range. Can someone help me, please?
We can use MinMaxScaler to perform feature scaling, MinMaxScaler supports a parameter called feature_range which allows us to specify the desired range of the transformed data
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0.6, 8.4))
df['normalized'] = scaler.fit_transform(df['wind power [W]'].values[:, None])
Alternatively if you don't want to use MinMaxScaler, here is a way scale data in pandas only:
w = df['wind power [W]'].agg(['min', 'max'])
norm = (df['wind power [W]'] - w['min']) / (w['max'] - w['min'])
df['normalized'] = norm * (8.4 - 0.6) + 0.6
print(df)
DateTime wind power [W] normalized
0 2022-02-08 00:00:00 83.9 8.400000
1 2022-02-08 00:10:00 57.2 2.598886
2 2022-02-08 00:20:00 58.2 2.816156
3 2022-02-08 00:30:00 48.0 0.600000
4 2022-02-08 00:40:00 69.5 5.271309