Python Sklearn – sklearn.datasets.load_breast_cancer() Function

Last Updated : 10 Jun, 2022

In this article, we are going to see how to convert sklearn dataset to a pandas dataframe in Python.

Sklearn is a python library that is used widely for data science and machine learning operations. Sklearn library provides a vast list of tools and functions to train machine learning models.

The library is available via pip install.

pip install scikit-learn

There are several sample datasets present in the sklearn library to illustrate the usage of the various algorithms that can be implemented through the library. Following is the list of the sample dataset available -

load_breast_cancer
load_boston
load_iris
load_diabetes
load_digits
load_files
load_linnerud
load_sample_images
load_sample_image
load_wine

sklearn.datasets.load_breast_cancer()

It is used to load the breast_cancer dataset from Sklearn datasets.

Each of these libraries can be imported from the sklearn.datasets module. As you can see in the above datasets, the first dataset is breast cancer data. We can load this dataset using the following code.

Python3

from sklearn.datasets import load_breast_cancer data = load_breast_cancer()

The data variable is a custom data type of sklearn.Bunch which is inherited from the dict data type in python. This data variable is having attributes that define the different aspects of dataset as mentioned below.

Attribute	Type	Description
data	numpy.ndarray	A matrix form of the actual dataset values stored as NumPy's ndarray.
target	numpy.ndarray	The list of values of the target feature.
target_names	numpy.ndarray	The feature names for the target.
DESCR	str	Description of the dataset.
feature_names	numpy.ndarray	List of all the feature names included in the dataset.
filename	str	The name of the file within the sklearn dataset that is being referred to.
data_module	str	Name of the data module from where the data is being loaded.

The following code produces a sample of the data from the breast cancer dataset.

Python3

import pandas as pd data_df = pd.DataFrame(data = data.data,                        columns = data.feature_names) data_df.head().T

Output:

Sample Data Records - Breast Cancer Dataset

Python Sklearn – sklearn.datasets.load_breast_cancer() Function

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