How to Load the Iris Dataset in Pandas DataFrame with Python (Sklearn Example)

To lead the iris dataset in a Pandas DataFrame with Scikit-Learn and Python, use the load_iris() function from the sklearn.datasets module.

Here is how to load the Iris built-in dataset in Scikit-learn into a pandas Dataframe this way.

import pandas as pd 
from sklearn import datasets

iris = datasets.load_iris()

df = pd.DataFrame(
    iris.data, 
    columns=iris.feature_names
    )

df['target'] = iris.target

# Map targets to target names
target_names = {
    0:'setosa',
    1:'versicolor', 
    2:'virginica'
}

df['target_names'] = df['target'].map(target_names)
df.head()

There are multiple projects using the Iris dataset with Python:

• How to do PCA with Python on the Iris Dataset

What is the Iris Dataset

The Iris dataset is a dataset often used in machine learning and statistics. It contains measurements of 150 iris flowers from three different species: setosa, versicolor, and virginica. Each species is described by four features: sepal length, sepal width, petal length, and petal width in centimetres.

Jean-Christophe Chouinard

SEO Strategist at Tripadvisor, ex- Seek (Melbourne, Australia). Specialized in technical SEO. Writer in Python, Information Retrieval, SEO and machine learning. Guest author at SearchEngineJournal, SearchEngineLand and OnCrawl.