Datasets

A basic tutorial on how to list, load and visualize datasets.

In general, we recommend working with tasks, so that the results can be easily reproduced. Furthermore, the results can be compared to existing results at OpenML. However, for the purposes of this tutorial, we are going to work with the datasets directly.

In [ ]:

import openml

import openml

List datasets stored on OpenML¶

In [ ]:

datasets_df = openml.datasets.list_datasets()
print(datasets_df.head(n=10))

datasets_df = openml.datasets.list_datasets() print(datasets_df.head(n=10))

Download a dataset¶

In [ ]:

# Iris dataset https://www.openml.org/d/61
dataset = openml.datasets.get_dataset(dataset_id=61)

# Print a summary
print(
    f"This is dataset '{dataset.name}', the target feature is '{dataset.default_target_attribute}'"
)
print(f"URL: {dataset.url}")
print(dataset.description[:500])

# Iris dataset https://www.openml.org/d/61 dataset = openml.datasets.get_dataset(dataset_id=61) # Print a summary print( f"This is dataset '{dataset.name}', the target feature is '{dataset.default_target_attribute}'" ) print(f"URL: {dataset.url}") print(dataset.description[:500])

Load a dataset¶

• X - A dataframe where each row represents one example with the corresponding feature values.
• y - the classes for each example
• categorical_indicator - a list that indicates which feature is categorical
• attribute_names - the names of the features for the examples (X) and target feature (y)

In [ ]:

X, y, categorical_indicator, attribute_names = dataset.get_data(
    target=dataset.default_target_attribute
)

X, y, categorical_indicator, attribute_names = dataset.get_data( target=dataset.default_target_attribute )

Visualize the dataset

In [ ]:

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

iris_plot = sns.pairplot(pd.concat([X, y], axis=1), hue="class")
plt.show()

import matplotlib.pyplot as plt import pandas as pd import seaborn as sns iris_plot = sns.pairplot(pd.concat([X, y], axis=1), hue="class") plt.show()