I am trying to scale a pandas or numpy array from 0 to a unknown max value with the defined number replaced with 1.
One solution I tried is just dividing the defined number I want by the array.
test = df['Temp'] / 33
This method does not scale all the way from 0 and I'm stuck trying to figure out a better mathematical way of solving this.
First, transform the DataFrame to a numpy array
import numpy as np
T = np.array(df['Temp'])
Then scale it to a [0, 1] interval:
def scale(A):
return (A-np.min(A))/(np.max(A) - np.min(A))
T_scaled = scale(T)
Then transform it to anywhere you want, e.g. to [55..100]
T2 = 55 + 45*T_scaled
I'm sure that this can be done within Pandas too (but I'm not familiar with it). Perhaps you might study Pandas df.apply()
scaled = (df['Temp']-df['Temp'].min()) / (33 - df['Temp'].min())
Just replace 33 with the number to want your data scaled to!
