Scikit-Learn offers several datasets to play around with - most of them being toy datasets to learn from and test things out.
Some beginners find the comfort of a tabular Pandas
DataFrame format more intuitive than NumPy arrays. Thankfully, you can import a dataset as a
Bunch object containing a
DataFrame by setting
import pandas as pd import numpy as np from sklearn.datasets import fetch_california_housing data = fetch_california_housing(as_frame=True)
Bunch object contains
target our "X" and "y", but they're separate! The
data field is a
While our target is a
0 4.526 1 3.585 2 3.521 3 3.413 4 3.422 ... 20635 0.781 20636 0.771 20637 0.923 20638 0.847 20639 0.894 Name: MedHouseVal, Length: 20640, dtype: float64
The easiest way to combine them is to simply assign the series to a
df = data.data.assign(MedHouseVal=data.target) df
This results in:
Or, you can create a new frame, with the
feature_names, adding the target by simply assigning it to a new column:
df = pd.DataFrame(data=data.data, columns=data.feature_names) df['MedHouseVal'] = data.target df
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