min max scaler sklearn
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit_transform(X_train)
scaler.transform(X_test)min max scaler sklearn
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit_transform(X_train)
scaler.transform(X_test)Python MinMaxScaler()
from sklearn.preprocessing import MinMaxScaler
#Normal minmaxscaler function to standarise data.
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
scaler = MinMaxScaler()
scaler.fit(data)
#get data for Max and Min from scaler functions,
#best to store it in a dictionary and use it later make data normal again
print(scaler.transform([[2, 2]]))
Out>>> [[ 1.5 0. ]]
# Inverse transform the the 0-1 dataframe.
print(scaler.inverse_transform([[ 1.5 0. ]]))
Out>>> [[ 2.0 2.0]]minmax python
# 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)
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