MinMaxScaler is a transformation class from scikit-learn that scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one. It is a popular scaling technique used in machine learning to normalize features before training a model.
To use MinMaxScaler in Pandas, we can first import the necessary libraries:
import pandas as pd from sklearn.preprocessing import MinMaxScaler
Once we have imported the necessary libraries, we can create a MinMaxScaler object and fit it to the data:
scaler = MinMaxScaler() scaler.fit(df)
Once the scaler has been fitted to the data, we can use it to transform the data:
scaled_df = scaler.transform(df)
The scaled data will be returned as a NumPy array. We can then convert the NumPy array back to a Pandas DataFrame if needed:
scaled_df = pd.DataFrame(scaled_df, columns=df.columns)
The following example shows how to use MinMaxScaler to scale the features in a Pandas DataFrame:
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
df = pd.DataFrame({'age': [25, 30, 35, 40], 'height': [165, 170, 175, 180]})
scaler = MinMaxScaler()
scaler.fit(df)
scaled_df = scaler.transform(df)
print(scaled_df)
The scaled data is in the range 0 to 1.
MinMaxScaler is a popular scaling technique used in machine learning to normalize features before training a model. It is easy to use and effective in normalizing features with different ranges.