When using R it's handy to load "practice" datasets using

data(iris)

or

data(mtcars)

Is there something similar for Pandas? I know I can load using any other method, just curious if there's anything builtin.

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.