How to Convert Sklearn Dataset to Dataframe

In this post, you will learn how to convert Sklearn.datasets to Pandas Dataframe. It will be useful to know this technique (code example) if you are comfortable working with Pandas Dataframe. You will be able to perform several operations faster with the dataframe.

Sklearn datasets class comprises of several different types of datasets including some of the following:

  • Iris
  • Breast cancer
  • Diabetes
  • Boston
  • Linnerud
  • Images

The code sample below is demonstrated with IRIS data set. Before looking into the code sample, recall that IRIS dataset when loaded has data in form of “data” and labels present as “target”.

import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets

# Load the IRIS dataset
iris = datasets.load_iris()
X =
y =

# Create dataframe using
df = pd.DataFrame(, columns=["sepal_length", "sepal_width", "petal_length", "petal_width"])

# Append class / label data
df["class"] =

# Print the data and check for yourself

Executing the above code will print the following dataframe.

Iris dataset as pandas dataframe
Fig 1. IRIS dataset represented as Pandas dataframe

In case, you don’t want to explicitly assign column name, you could use the following commands:

# Create dataframe using
df = pd.DataFrame(

# Append class / label data
df["class"] =

# Print the data and check for yourself


In this post, you learned about how to convert the SKLearn dataset to Pandas DataFrame.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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