You can use rpy2 package to access all R datasets from Python.

Set up the interface:

>>> from rpy2.robjects import r, pandas2ri
>>> def data(name): 
...    return pandas2ri.ri2py(r[name])

Then call data() with any dataset’s name of the available datasets (just like in R)

>>> df = data('iris')
>>> df.describe()
       Sepal.Length  Sepal.Width  Petal.Length  Petal.Width
count    150.000000   150.000000    150.000000   150.000000
mean       5.843333     3.057333      3.758000     1.199333
std        0.828066     0.435866      1.765298     0.762238
min        4.300000     2.000000      1.000000     0.100000
25%        5.100000     2.800000      1.600000     0.300000
50%        5.800000     3.000000      4.350000     1.300000
75%        6.400000     3.300000      5.100000     1.800000
max        7.900000     4.400000      6.900000     2.500000

To see a list of the available datasets with a description for each:

>>> print(r.data())

Note: rpy2 requires R installation with setting R_HOME variable, and pandas must be installed as well.

UPDATE

I just created PyDataset, which is a simple module to make loading a dataset from Python as easy as R‘s (and it does not require R installation, only pandas).

To start using it, install the module:

$ pip install pydataset

Then just load up any dataset you wish (currently around 757 datasets available):

from pydataset import data

titanic = data('titanic')