Asked By – canyon289
When using R it’s handy to load “practice” datasets using
Is there something similar for Pandas? I know I can load using any other method, just curious if there’s anything builtin.
Now we will see solution for issue: Sample datasets in Pandas
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.
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
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
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
Additional ways of loading the R sample data sets include
import statsmodels.api as sm iris = sm.datasets.get_rdataset('iris').data
from pydataset import data iris = data('iris')
scikit-learn returns sample data as numpy arrays rather than a pandas data
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 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.
This question is answered By – joelostblom
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