Loading the iris dataset

To perform machine learning with scikit-learn, we need some data to start with. We will load the iris dataset, one of the several datasets available in scikit-learn.

Getting ready

A scikit-learn program begins with several imports. Within Python, preferably in Jupyter Notebook, load the numpy, pandas, and pyplot libraries:

import numpy as np    #Load the numpy library for fast array computations
import pandas as pd   #Load the pandas data-analysis library
import matplotlib.pyplot as plt   #Load the pyplot visualization library

If you are within a Jupyter Notebook, type the following to see a graphical output instantly:

%matplotlib inline

How to do it...

1. From the scikit-learn datasets module, access the iris dataset:

from sklearn import datasets
iris = datasets.load_iris()

How it works...

Similarly, you could have imported the diabetes dataset as follows:

from sklearn import datasets  #Import datasets module from scikit-learn
diabetes = datasets.load_diabetes()  

There! You've loaded diabetes using the load_diabetes() function of the datasets module. To check which datasets are available, type:

datasets.load_*?

Once you try that, you might observe that there is a dataset named datasets.load_digits. To access it, type the load_digits() function, analogous to the other loading functions:

digits = datasets.load_digits()

To view information about the dataset, type digits.DESCR.