Since I originally wrote this answer, I have updated it with the many ways that are now available for accessing sample data sets in Python. Personally, I tend to stick with whatever package I am
already using (usually seaborn or pandas). If you need offline access,
installing the data set with Quilt seems to be the only option.

Seaborn

The brilliant plotting package seaborn has several built-in sample data sets.

import seaborn as sns

iris = sns.load_dataset('iris')
iris.head()

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa

Pandas

If you do not want to import seaborn, but still want to access its sample
data sets, you can use @andrewwowens’s approach for the seaborn sample
data:

iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

Note that the sample data sets containing categorical columns have their column
type modified by sns.load_dataset() and the result might not be the same
by getting it from the url directly. The iris and tips sample data sets are also
available in the pandas github repo here.

R sample datasets

Since any dataset can be read via pd.read_csv(), it is possible to access all
R’s sample data sets by copying the URLs from this R data set
repository.

Additional ways of loading the R sample data sets include
statsmodel

import statsmodels.api as sm

iris = sm.datasets.get_rdataset('iris').data

and PyDataset

from pydataset import data

iris = data('iris')

scikit-learn

scikit-learn returns sample data as numpy arrays rather than a pandas data
frame.

from sklearn.datasets import load_iris

iris = load_iris()
# `iris.data` holds the numerical values
# `iris.feature_names` holds the numerical column names
# `iris.target` holds the categorical (species) values (as ints)
# `iris.target_names` holds the unique categorical names

Quilt

Quilt is a dataset manager created to facilitate
dataset management. It includes many common sample datasets, such as
several from the uciml sample
repository. The quick start
page shows how to install
and import the iris data set:

# In your terminal
$ pip install quilt
$ quilt install uciml/iris

After installing a dataset, it is accessible locally, so this is the best option if you want to work with the data offline.

import quilt.data.uciml.iris as ir

iris = ir.tables.iris()

   sepal_length  sepal_width  petal_length  petal_width        class
0           5.1          3.5           1.4          0.2  Iris-setosa
1           4.9          3.0           1.4          0.2  Iris-setosa
2           4.7          3.2           1.3          0.2  Iris-setosa
3           4.6          3.1           1.5          0.2  Iris-setosa
4           5.0          3.6           1.4          0.2  Iris-setosa

Quilt also support dataset versioning and include a short
description of each dataset.