Suppose you have a dataset that has float values and all values in the range 0 to 1.

You want to change all values to integer with a range between 10 to 20.

In this post we will learn how to do this using MinMaxScaler

Now let us scale the data as below

# convert the data to a given range
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 10))
dfx[0] = scaler.fit_transform(dfx[0].values.reshape(-1, 1))
scaler = MinMaxScaler(feature_range=(20, 50))
dfx[1] = scaler.fit_transform(dfx[1].values.reshape(-1, 1))
dfx.head()

Data after Scaling is as below

You can also round all the float data above to nearest integer as below

dfx = dfx.round(0) # rounds to nearest integer
import numpy as np
dfx = dfx.applymap(np.int64)