from numpy import asarray
from sklearn.preprocessing import MinMaxScaler
# define data
data = asarray([[100, 0.001],
[8, 0.05],
[50, 0.005],
[88, 0.07],
[4, 0.1]])
print(data)
# define min max scaler
scaler = MinMaxScaler()
# transform data
scaled = scaler.fit_transform(data)
print(scaled)
# Were you looking for min max python?
# if this is the case (works also with sets and numpy array)
>>> l = [1,2,8,4,7,9,6,3]
>>> min(l)
1
>>> max(l)
9