from sklearn_pandas import DataFrameMapper

`DataFrameMapper` is a class from the `sklearn_pandas` library in Python. It is used to map pandas data frames into forms that can be used by scikit-learn estimators. The class takes a list of tuples, where each tuple contains a column name or a list of column names and a transformer object that specifies how to transform the data in that column. The transformer object can be a scikit-learn estimator, a function, or a list of transformers.

For example, if you have a pandas data frame with columns "age", "income", and "gender", and you want to apply different transformations to each column before feeding the data into a scikit-learn estimator, you can use `DataFrameMapper` as follows:

from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import LabelEncoder
from sklearn_pandas import DataFrameMapper

mapper = DataFrameMapper([
    ('age', StandardScaler()),
    ('income', StandardScaler()),
    (['gender'], LabelEncoder())
])
X_transformed = mapper.fit_transform(X)

In this example, the `StandardScaler` transformer is applied to the "age" and "income" columns to scale the values to have zero mean and unit variance. The `LabelEncoder` transformer is applied to the "gender" column to encode categorical values as integers. Finally, the `fit_transform` method is called to fit the transformers to the data and transform the data in one step.