How to convert a Scikit-learn dataset to a Pandas dataset?

Question

How do I convert data from a Scikit-learn Bunch object to a Pandas DataFrame?

from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
p         


        

Answer 1

Update: 2020

You can use the parameter as_frame=True to get pandas dataframes.

If as_frame parameter available (eg. load_iris)

from sklearn import datasets
X,y = datasets.load_iris(return_X_y=True) # numpy arrays

dic_data = datasets.load_iris(as_frame=True)
print(dic_data.keys())

df = dic_data['frame'] # pandas dataframe data + target
df_X = dic_data['data'] # pandas dataframe data only
ser_y = dic_data['target'] # pandas series target only
dic_data['target_names'] # numpy array

If as_frame parameter NOT available (eg. load_boston)

from sklearn import datasets

fnames = [ i for i in dir(datasets) if 'load_' in i]
print(fnames)

fname = 'load_boston'
loader = getattr(datasets,fname)()
df = pd.DataFrame(loader['data'],columns= loader['feature_names'])
df['target'] = loader['target']
df.head(2)