TOMDLt's solution is not generic enough for all the datasets in scikit-learn. For example it does not work for the boston housing dataset. I propose a different solution which is more universal. No need to use numpy as well.
from sklearn import datasets
import pandas as pd
boston_data = datasets.load_boston()
df_boston = pd.DataFrame(boston_data.data,columns=boston_data.feature_names)
df_boston['target'] = pd.Series(boston_data.target)
df_boston.head()
As a general function:
def sklearn_to_df(sklearn_dataset):
df = pd.DataFrame(sklearn_dataset.data, columns=sklearn_dataset.feature_names)
df['target'] = pd.Series(sklearn_dataset.target)
return df
df_boston = sklearn_to_df(datasets.load_boston())