from sklearn import preprocessing

def sci_minmax(X):
    minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True)
    return minmax_scale.fit_transform(X)

data_normalized = sci_minmax(data)
data_variance=data_normalized.var()
data_variance.head(10)