Asked By – SANBI samples
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() print(type(data)) data1 = pd. # Is there a Pandas method to accomplish this?
Now we will see solution for issue: How to convert a Scikit-learn dataset to a Pandas dataset
Manually, you can use
pd.DataFrame constructor, giving a numpy array (
data) and a list of the names of the columns (
To have everything in one DataFrame, you can concatenate the features and the target into one numpy array with
np.c_[...] (note the
import numpy as np import pandas as pd from sklearn.datasets import load_iris # save load_iris() sklearn dataset to iris # if you'd like to check dataset type use: type(load_iris()) # if you'd like to view list of attributes use: dir(load_iris()) iris = load_iris() # np.c_ is the numpy concatenate function # which is used to concat iris['data'] and iris['target'] arrays # for pandas column argument: concat iris['feature_names'] list # and string list (in this case one string); you can make this anything you'd like.. # the original dataset would probably call this ['Species'] data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']], columns= iris['feature_names'] + ['target'])