Introducing the set_output API

This example will demonstrate the set_output API to configure transformers to output pandas DataFrames. set_output can be configured per estimator by calling the set_output method or globally by setting set_config(transform_output="pandas"). For details, see SLEP018.

First, we load the iris dataset as a DataFrame to demonstrate the set_output API.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(as_frame=True, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
60 5.0 2.0 3.5 1.0
1 4.9 3.0 1.4 0.2
8 4.4 2.9 1.4 0.2
93 5.0 2.3 3.3 1.0
106 4.9 2.5 4.5 1.7

To configure an estimator such as preprocessing.StandardScaler to return DataFrames, call set_output. This feature requires pandas to be installed.

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler().set_output(transform="pandas")
X_test_scaled = scaler.transform(X_test)
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
39 -0.894264 0.798301 -1.271411 -1.327605
12 -1.244466 -0.086944 -1.327407 -1.459074
48 -0.660797 1.462234 -1.271411 -1.327605
23 -0.894264 0.576989 -1.159419 -0.933197
81 -0.427329 -1.414810 -0.039497 -0.275851

set_output can be called after fit to configure transform after the fact.

scaler2 = StandardScaler()
X_test_np = scaler2.transform(X_test)
print(f"Default output type: {type(X_test_np).__name__}")

X_test_df = scaler2.transform(X_test)
print(f"Configured pandas output type: {type(X_test_df).__name__}")
Default output type: ndarray
Configured pandas output type: DataFrame

In a pipeline.Pipeline, set_output configures all steps to output DataFrames.

from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import SelectPercentile

clf = make_pipeline(
    StandardScaler(), SelectPercentile(percentile=75), LogisticRegression()
clf.set_output(transform="pandas"), y_train)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('selectpercentile', SelectPercentile(percentile=75)),
                ('logisticregression', LogisticRegression())])
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Each transformer in the pipeline is configured to return DataFrames. This means that the final logistic regression step contains the feature names of the input.

array(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'],

Next we load the titanic dataset to demonstrate set_output with compose.ColumnTransformer and heterogenous data.

from sklearn.datasets import fetch_openml

X, y = fetch_openml(
    "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)

The set_output API can be configured globally by using set_config and setting transform_output to "pandas".

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.impute import SimpleImputer
from sklearn import set_config


num_pipe = make_pipeline(SimpleImputer(), StandardScaler())
num_cols = ["age", "fare"]
ct = ColumnTransformer(
        ("numerical", num_pipe, num_cols),
                sparse_output=False, drop="if_binary", handle_unknown="ignore"
            ["embarked", "sex", "pclass"],
clf = make_pipeline(ct, SelectPercentile(percentile=50), LogisticRegression()), y_train)
clf.score(X_test, y_test)

With the global configuration, all transformers output DataFrames. This allows us to easily plot the logistic regression coefficients with the corresponding feature names.

import pandas as pd

log_reg = clf[-1]
coef = pd.Series(log_reg.coef_.ravel(), index=log_reg.feature_names_in_)
_ = coef.sort_values().plot.barh()
plot set output

This resets transform_output to its default value to avoid impacting other examples when generating the scikit-learn documentation


When configuring the output type with config_context the configuration at the time when transform or fit_transform are called is what counts. Setting these only when you construct or fit the transformer has no effect.

from sklearn import config_context

scaler = StandardScaler()[num_cols])
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with config_context(transform_output="pandas"):
    # the output of transform will be a Pandas DataFrame
    X_test_scaled = scaler.transform(X_test[num_cols])
age fare
334 -0.133660 -0.438059
885 -0.894273 -0.506893
478 -2.000619 0.182778
671 -0.548540 -0.461032
817 -0.548540 -0.487001

outside of the context manager, the output will be a NumPy array

X_test_scaled = scaler.transform(X_test[num_cols])
array([[-0.13366001, -0.4380594 ],
       [-0.89427284, -0.50689261],
       [-2.00061876,  0.18277786],
       [-0.54853974, -0.46103177],
       [-0.54853974, -0.48700054]])

Total running time of the script: ( 0 minutes 0.126 seconds)

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