sklearn.datasets.load_boston

sklearn.datasets.load_boston(*, return_X_y=False)[source]

DEPRECATED: load_boston is deprecated in 1.0 and will be removed in 1.2.

The Boston housing prices dataset has an ethical problem. You can refer to the documentation of this function for further details.

The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning.

In this case special case, you can fetch the dataset from the original source:

import pandas as pd
import numpy as np

data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]

Alternative datasets include the California housing dataset (i.e. func:~sklearn.datasets.fetch_california_housing) and the Ames housing dataset. You can load the datasets as follows:

from sklearn.datasets import fetch_california_housing housing = fetch_california_housing()

for the California housing dataset and:

from sklearn.datasets import fetch_openml housing = fetch_openml(name=”house_prices”, as_frame=True)

for the Ames housing dataset.

Load and return the boston house-prices dataset (regression).

Samples total

506

Dimensionality

13

Features

real, positive

Targets

real 5. - 50.

Read more in the User Guide.

Deprecated since version 1.0: This function is deprecated in 1.0 and will be removed in 1.2. See the warning message below for further details regarding the alternative datasets.

Warning

The Boston housing prices dataset has an ethical problem: as investigated in [1], the authors of this dataset engineered a non-invertible variable “B” assuming that racial self-segregation had a positive impact on house prices [2]. Furthermore the goal of the research that led to the creation of this dataset was to study the impact of air quality but it did not give adequate demonstration of the validity of this assumption.

The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning.

In this case special case, you can fetch the dataset from the original source:

import pandas as pd  # doctest: +SKIP
import numpy as np

data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]

Alternative datasets include the California housing dataset [3] (i.e. func:~sklearn.datasets.fetch_california_housing) and Ames housing dataset [4]. You can load the datasets as follows:

from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()

for the California housing dataset and:

from sklearn.datasets import fetch_openml
housing = fetch_openml(name="house_prices", as_frame=True)  # noqa

for the Ames housing dataset.

Parameters
return_X_ybool, default=False

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

New in version 0.18.

Returns
dataBunch

Dictionary-like object, with the following attributes.

datandarray of shape (506, 13)

The data matrix.

targetndarray of shape (506,)

The regression target.

filenamestr

The physical location of boston csv dataset.

New in version 0.20.

DESCRstr

The full description of the dataset.

feature_namesndarray

The names of features

(data, target)tuple if return_X_y is True

New in version 0.18.

Notes

Changed in version 0.20: Fixed a wrong data point at [445, 0].

References

1

Racist data destruction? M Carlisle,

2

Harrison Jr, David, and Daniel L. Rubinfeld. “Hedonic housing prices and the demand for clean air.” Journal of environmental economics and management 5.1 (1978): 81-102.

3

California housing dataset

4

Ames housing dataset

Examples

>>> import warnings
>>> from sklearn.datasets import load_boston
>>> with warnings.catch_warnings():
...     # You should probably not use this dataset.
...     warnings.filterwarnings("ignore")
...     X, y = load_boston(return_X_y=True)
>>> print(X.shape)
(506, 13)