Are there any example data sets for Python?

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')

There are also datasets available from the Scikit-Learn library.

from sklearn import datasets

There are multiple datasets within this package. Some of the Toy Datasets are:

load_boston()          Load and return the boston house-prices dataset (regression).
load_iris()            Load and return the iris dataset (classification).
load_diabetes()        Load and return the diabetes dataset (regression).
load_digits([n_class]) Load and return the digits dataset (classification).
load_linnerud()        Load and return the linnerud dataset (multivariate regression).

I originally posted this over at the related question Sample Datasets in Pandas, but since it is relevant outside pandas I am including it here as well.

There are 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 read the seaborn sample data from its URL:

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.


Following Joran's comment, I've since found the statsmodels module, which provides its own datasets package. The online documentation shows an example of how to import datasets available in R:

import statsmodels.api as sm
duncan_prestige = sm.datasets.get_rdataset("Duncan", "car")
print duncan_prestige.__doc__

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Python

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